From 703df1122671d2d6ca3fdd90ee7ee7e966bbc840 Mon Sep 17 00:00:00 2001 From: Vini Salazar <17276653+vinisalazar@users.noreply.github.com> Date: Thu, 9 Oct 2025 14:29:42 +1100 Subject: [PATCH 01/14] Update CONTRIBUTORS.md - Add vinisalazar --- CONTRIBUTORS.yaml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml index 42cecf54591a0f..8133d37cad160d 100644 --- a/CONTRIBUTORS.yaml +++ b/CONTRIBUTORS.yaml @@ -3055,6 +3055,14 @@ VerenaMoo: name: Verena Moosmann joined: 2024-12 +vinisalazar: + name: Vini Salazar + joined: 2025-10 + orcid: 0000-0002-8362-3195 + affiliations: + - unimelb + - melbournebioinformatics + vivekbhr: name: Vivek Bhardwaj joined: 2017-09 From 7f4c380fd5a6e2f16a63d5581707a56cd8dc6cfe Mon Sep 17 00:00:00 2001 From: Vini Salazar <17276653+vinisalazar@users.noreply.github.com> Date: Thu, 9 Oct 2025 17:43:20 +1100 Subject: [PATCH 02/14] tutorials/metagenomic-binning: small improvements - Create images directory - Point readers to metagenomics-assembly dir as prerequisite --- .../{ => images}/binning.png | Bin .../metagenomics-binning/tutorial.md | 52 ++++++++---------- 2 files changed, 22 insertions(+), 30 deletions(-) rename topics/microbiome/tutorials/metagenomics-binning/{ => images}/binning.png (100%) diff --git a/topics/microbiome/tutorials/metagenomics-binning/binning.png b/topics/microbiome/tutorials/metagenomics-binning/images/binning.png similarity index 100% rename from topics/microbiome/tutorials/metagenomics-binning/binning.png rename to topics/microbiome/tutorials/metagenomics-binning/images/binning.png diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index d79801cc5ac05b..8792894b2b15fb 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -4,17 +4,16 @@ title: Binning of metagenomic sequencing data zenodo_link: https://zenodo.org/record/7818827 extra: zenodo_link_results: https://zenodo.org/record/7845138 -level: Introductory +level: Intermediate questions: - What is metagenomic binning refers to? -- Which tools should be used for metagenomic binning? -- How to assess the quality of metagenomic data binning? +- Which tools may be used for metagenomic binning? +- How to assess the quality of metagenomic binning? objectives: -- Describe what metagenomics binning is -- Describe common problems in metagenomics binning -- What software tools are available for metagenomics binning -- Binning of contigs into metagenome-assembled genomes (MAGs) using MetaBAT 2 software -- Evaluation of MAG quality and completeness using CheckM software +- Describe what is metagenomics binning. +- Describe common challenges in metagenomics binning. +- Perform metagenomic binning using MetaBAT 2 software. +- Evaluation of MAG quality and completeness using CheckM software. time_estimation: 2H key_points: - Metagenomics binning is a computational approach to grouping together DNA sequences @@ -56,11 +55,14 @@ recordings: --- - Metagenomics is the study of genetic material recovered directly from environmental samples, such as soil, water, or gut contents, without the need for isolation or cultivation of individual organisms. Metagenomics binning is a process used to classify DNA sequences obtained from metagenomic sequencing into discrete groups, or bins, based on their similarity to each other. The goal of metagenomics binning is to assign the DNA sequences to the organisms or taxonomic groups that they originate from, allowing for a better understanding of the diversity and functions of the microbial communities present in the sample. This is typically achieved through computational methods that include sequence similarity, composition, and other features to group the sequences into bins. +> +> Before starting this tutorial, it is recommended to do the [**Metagenomics Assembly Tutorial**]({% link topics/microbiome/tutorials/metagenomics-assembly/tutorial.md %}) +{: .comment} + There are several approaches to metagenomics binning, including: - **Sequence composition-based binning**: This method is based on the observation that different genomes have distinct sequence composition patterns, such as GC content or codon usage bias. By analyzing these patterns in metagenomic data, sequence fragments can be assigned to individual genomes or groups of genomes. @@ -76,7 +78,7 @@ There are several approaches to metagenomics binning, including: Each of these methods has its strengths and limitations, and the choice of binning method depends on the specific characteristics of the metagenomic data set and the research question being addressed. -**Metagenomics binning is a complex process that involves many steps and can be challenging due to several problems that can occur during the process**. Some of the most common problems encountered in metagenomics binning include: +**Metagenomic binning is a complex process that involves many steps and can be challenging due to several problems that can occur during the process**. Some of the most common problems encountered in metagenomic binning include: - **High complexity**: Metagenomic samples contain DNA from multiple organisms, which can lead to high complexity in the data. - **Fragmented sequences**: Metagenomic sequencing often generates fragmented sequences, which can make it difficult to assign reads to the correct bin. @@ -99,7 +101,7 @@ There are plenty of computational tools to perform metafenomics binning. Some of A benchmark study of metagenomics software can be found at {%cite Sczyrba2017%}. MetaBAT 2 outperforms previous MetaBAT and other alternatives in both accuracy and computational efficiency . All are based on default parameters ({%cite Sczyrba2017%}). -**In this tutorial, we will learn how to run metagenomic binning tools and evaluate the quality of the results**. In order to do that, we will use data from the study: [Temporal shotgun metagenomic dissection of the coffee fermentation ecosystem](https://www.ebi.ac.uk/metagenomics/studies/MGYS00005630#overview) and MetaBAT 2 algorithm. MetaBAT is a popular software tool for metagenomics binning, and there are several reasons why it is often used: +**In this tutorial, we will learn how to run metagenomic binning tools and evaluate the quality of the results**. In order to do that, we will use data from the study: [Temporal shotgun metagenomic dissection of the coffee fermentation ecosystem](https://www.ebi.ac.uk/metagenomics/studies/MGYS00005630#overview) and the MetaBAT 2 algorithm. MetaBAT is a popular software tool for metagenomics binning, and there are several reasons why it is often used: - *High accuracy*: MetaBAT uses a combination of tetranucleotide frequency, coverage depth, and read linkage information to bin contigs, which has been shown to be highly accurate and efficient. - *Easy to use*: MetaBAT has a user-friendly interface and can be run on a standard desktop computer, making it accessible to a wide range of researchers with varying levels of computational expertise. - *Flexibility*: MetaBAT can be used with a variety of sequencing technologies, including Illumina, PacBio, and Nanopore, and can be applied to both microbial and viral metagenomes. @@ -186,7 +188,7 @@ As explained before, there are many challenges to metagenomics binning. The most - Chimeric sequences. - Strain variation. -![Image show the binning process where sequences are grouped together based on genome signatures like the kmer profiles of each contig, contig coverage, or GC content](./binning.png "Binning"){:width="60%"} +![Metagenomic binning involves grouping contigs into 'bins' based on sequence composition, coverage, or other properties.](./images/binning.png "Metagenomic binning involves grouping contigs into 'bins' based on sequence composition, coverage, or other properties."){:width="60%"} In this tutorial we will learn how to use **MetaBAT 2** {%cite Kang2019%} tool through Galaxy. **MetaBAT** stands for "Metagenome Binning based on Abundance and Tetranucleotide frequency". It is: @@ -198,19 +200,9 @@ We will use the uploaded assembled fasta files as input to the algorithm (For si > Individual binning of short-reads with MetaBAT 2 > 1. {% tool [MetaBAT 2](toolshed.g2.bx.psu.edu/repos/iuc/megahit/megahit/1.2.9+galaxy0) %} with parameters: > - *"Fasta file containing contigs"*: `assembly fasta files` -> +> - In **Advanced options**, keep all as **default**. +> - In **Output options:** +> - *"Save cluster memberships as a matrix format?"*: `"Yes"` > {: .hands_on} @@ -249,15 +241,15 @@ These output files can be further analyzed and used for downstream applications > {: .hands_on} {: .comment} -> +> Binning metrics > > 1. How many bins has been for ERR2231567 sample? -> 2. How many sequences are contained in the second bin? +> 2. How many contigs are in the bin with most contigs? What about the one with the least? > > > > > -> > 1. There are 6 bins identified -> > 2. 167 sequences are classified into the second bin. +> > 1. There are 6 bins identified. +> > 2. 7170 in the one with the most contigs, and 140 in the one with the least (these numbers may differ slightly depending on the version of MetaBAT2). > > > {: .solution} > @@ -269,7 +261,7 @@ De-replication is the process of identifying sets of genomes that are the "same" A common use for genome de-replication is the case of individual assembly of metagenomic data. If metagenomic samples are collected in a series, a common way to assemble the short reads is with a “co-assembly”. That is, combining the reads from all samples and assembling them together. The problem with this is assembling similar strains together can severely fragment assemblies, precluding recovery of a good genome bin. An alternative option is to assemble each sample separately, and then “de-replicate” the bins from each assembly to make a final genome set. -![Image shows the process of individual assembly on two strains and five samples, after individual assembly of samples two samples are chosen for de-replication process. In parallel, co-assembly on all five samples is performed](./individual-assembly.png "Individual assembly followed by de-replication vs co-assembly"){:width="80%"} +![Image shows the process of individual assembly on two strains and five samples, after individual assembly of samples two samples are chosen for de-replication process. In parallel, co-assembly on all five samples is performed](./individual-assembly.png "Individual assembly followed by de-replication vs co-assembly."){:width="80%"} MetaBAT 2 does not explicitly perform dereplication in the sense of identifying groups of identical or highly similar genomes in a given dataset. Instead, MetaBAT 2 focuses on improving the accuracy of binning by leveraging various features such as read coverage, differential coverage across samples, and sequence composition. It aims to distinguish between different genomes present in the metagenomic dataset and assign contigs to the appropriate bins. From 4b9afb2b42d6af584f0d843d2e945d39701885e2 Mon Sep 17 00:00:00 2001 From: Vini Salazar <17276653+vinisalazar@users.noreply.github.com> Date: Thu, 9 Oct 2025 17:51:39 +1100 Subject: [PATCH 03/14] Add vamb to binning tools --- .../tutorials/metagenomics-binning/tutorial.bib | 11 +++++++++++ .../tutorials/metagenomics-binning/tutorial.md | 9 ++++++--- 2 files changed, 17 insertions(+), 3 deletions(-) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib index 18aac5e68c40ef..a70fc4afc6cd6c 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib @@ -1,3 +1,14 @@ +@article{nissen2021improved, + title={Improved metagenome binning and assembly using deep variational autoencoders}, + author={Nissen, Jakob Nybo and Johansen, Joachim and Alles{\o}e, Rosa Lundbye and S{\o}nderby, Casper Kaae and Armenteros, Jose Juan Almagro and Gr{\o}nbech, Christopher Heje and Jensen, Lars Juhl and Nielsen, Henrik Bj{\o}rn and Petersen, Thomas Nordahl and Winther, Ole and others}, + journal={Nature biotechnology}, + volume={39}, + number={5}, + pages={555--560}, + year={2021}, + publisher={Nature Publishing Group US New York} +} + @article{maxbin2015, author = {Wu, Yu-Wei and Simmons, Blake A. and Singer, Steven W.}, title = "{MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets}", diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 8792894b2b15fb..ba9bcbac4560d4 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -88,16 +88,19 @@ Each of these methods has its strengths and limitations, and the choice of binni - **Chimeric sequences**: Sequences that are the result of sequencing errors or contamination can lead to chimeric sequences, which can make it difficult to accurately bin reads. - **Strain variation**: Organisms within a species can exhibit significant genetic variation, which can make it difficult to distinguish between different strains in a metagenomic sample. -There are plenty of computational tools to perform metafenomics binning. Some of the most widely used include: +There are plenty of algorithms that perform metagenomic binning. Some of the most widely used include: - **MaxBin** ({%cite maxbin2015%}): A popular de novo binning algorithm that uses a combination of sequence features and marker genes to cluster contigs into genome bins. - **MetaBAT** ({%cite Kang2019%}): Another widely used de novo binning algorithm that employs a hierarchical clustering approach based on tetranucleotide frequency and coverage information. - **CONCOCT** ({%cite Alneberg2014%}): A de novo binning tool that uses a clustering algorithm based on sequence composition and coverage information to group contigs into genome bins. - **MyCC** ({%cite Lin2016%}): A reference-based binning tool that uses sequence alignment to identify contigs belonging to the same genome or taxonomic group. - **GroopM** ({%cite Imelfort2014%}): A hybrid binning tool that combines reference-based and de novo approaches to achieve high binning accuracy. -- **MetaWRAP** ({%cite Uritskiy2018%}): A comprehensive metagenomic analysis pipeline that includes various modules for quality control, assembly, binning, and annotation. -- **Anvi'o** ({%cite Eren2015%}): A platform for visualizing and analyzing metagenomic data, including features for binning, annotation, and comparative genomics. - **SemiBin** ({%cite Pan2022%}): A command tool for metagenomic binning with deep learning, handles both short and long reads. +- **Vamb** ({%cite nissen2021improved%}): An algorithm that uses variational autoencoders (VAEs) to encode sequence composition and coverage information. + +Other tools also include: +- **MetaWRAP** ({%cite Uritskiy2018%}): A comprehensive metagenomic analysis pipeline that includes various modules for quality control, assembly, binning, and annotation. +- **Anvi'o** ({%cite Eren2015%}): A platform for visualizing and analyzing metagenomic data, including features for binning, annotation, and comparative genomics. Uses CONCOCT as the default binning backend. A benchmark study of metagenomics software can be found at {%cite Sczyrba2017%}. MetaBAT 2 outperforms previous MetaBAT and other alternatives in both accuracy and computational efficiency . All are based on default parameters ({%cite Sczyrba2017%}). From 95948671063c854f29805ebe704b58be8ac53d71 Mon Sep 17 00:00:00 2001 From: Vini Salazar <17276653+vinisalazar@users.noreply.github.com> Date: Fri, 10 Oct 2025 14:32:22 +1100 Subject: [PATCH 04/14] Fix references to MetaBAT2 - References were incorrectly pointing to MEGAHIT --- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index ba9bcbac4560d4..19a72eccc7be06 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -201,7 +201,7 @@ In this tutorial we will learn how to use **MetaBAT 2** {%cite Kang2019%} tool t We will use the uploaded assembled fasta files as input to the algorithm (For simplicity reasons all other parameters will be preserved with their default values). > Individual binning of short-reads with MetaBAT 2 -> 1. {% tool [MetaBAT 2](toolshed.g2.bx.psu.edu/repos/iuc/megahit/megahit/1.2.9+galaxy0) %} with parameters: +> 1. {% tool [MetaBAT 2](https://toolshed.g2.bx.psu.edu/view/iuc/metabat2/01f02c5ddff8) %} with parameters: > - *"Fasta file containing contigs"*: `assembly fasta files` > - In **Advanced options**, keep all as **default**. > - In **Output options:** @@ -239,7 +239,7 @@ These output files can be further analyzed and used for downstream applications > > ``` > > > > -> > 2. Create a collection named `MEGAHIT Contig`, rename your pairs with the sample name +> > 2. Create a collection named `MetaBAT2 Bins` and add the zip files to it. > > > {: .hands_on} {: .comment} From 3c4c3dbaebc4ca50738678d60a533630fc296692 Mon Sep 17 00:00:00 2001 From: Vini Salazar <17276653+vinisalazar@users.noreply.github.com> Date: Wed, 22 Oct 2025 12:46:26 +1100 Subject: [PATCH 05/14] Add requirements for metagenomics binning tutorial Code review from @shiltemann --- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 19a72eccc7be06..4d82c9a82a5046 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -31,6 +31,11 @@ contributions: authorship: - npechl - fpsom +requirements: + - type: internal + topic: metagenomics + tutorials: + - metagenomics-assembly subtopic: metagenomics tags: - binning From b90fd141595b7f589aece0abc2c21ff41d05d26e Mon Sep 17 00:00:00 2001 From: paulzierep Date: Tue, 18 Nov 2025 16:08:08 +0100 Subject: [PATCH 06/14] add 4 binners, add cyoa, add mappng and bam sorting --- .../metagenomics-binning/concoct_version.md | 0 .../metagenomics-binning/maxbin2_version.md | 9 ++ .../metagenomics-binning/metabet2_version.md | 72 +++++++++ .../metagenomics-binning/semibin_version.md | 45 ++++++ .../metagenomics-binning/tutorial.md | 149 +++++++++--------- 5 files changed, 201 insertions(+), 74 deletions(-) create mode 100644 topics/microbiome/tutorials/metagenomics-binning/concoct_version.md create mode 100644 topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md create mode 100644 topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md create mode 100644 topics/microbiome/tutorials/metagenomics-binning/semibin_version.md diff --git a/topics/microbiome/tutorials/metagenomics-binning/concoct_version.md b/topics/microbiome/tutorials/metagenomics-binning/concoct_version.md new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md b/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md new file mode 100644 index 00000000000000..1b62c6d77a3f05 --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md @@ -0,0 +1,9 @@ +In this tutorial version we will learn how to use MaxBin2 {%cite Wu2016%} through Galaxy. MaxBin2 is an automated metagenomic binning tool that uses an Expectation-Maximization algorithm to group contigs into genome bins based on abundance, tetranucleotide frequency, and single-copy marker genes. + +> Individual binning of short-reads with MaxBin2 +> 1. {% tool [MetaBAT 2](https://toolshed.g2.bx.psu.edu/view/iuc/metabat2/01f02c5ddff8) %} with parameters: +> - *"Fasta file containing contigs"*: `assembly fasta files` +> - In **Advanced options**, keep all as **default**. +> - In **Output options:** +> +{: .hands_on} \ No newline at end of file diff --git a/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md b/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md new file mode 100644 index 00000000000000..a47ab7e2de47d5 --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md @@ -0,0 +1,72 @@ + +In this tutorial version we will learn how to use **MetaBAT 2** {%cite Kang2019%} tool through Galaxy. **MetaBAT** stands for "Metagenome Binning based on Abundance and Tetranucleotide frequency". It is: + +> Grouping large fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. On synthetic datasets MetaBAT on average achieves 98percent precision and 90% recall at the strain level with 281 near complete unique genomes. Applying MetaBAT to a human gut microbiome data set we recovered 176 genome bins with 92% precision and 80% recall. Further analyses suggest MetaBAT is able to recover genome fragments missed in reference genomes up to 19%, while 53 genome bins are novel. In summary, we believe MetaBAT is a powerful tool to facilitate comprehensive understanding of complex microbial communities. +{: .quote author="Kang et al, 2019" } + +MetaBAT is a popular software tool for metagenomics binning, and there are several reasons why it is often used: +- *High accuracy*: MetaBAT uses a combination of tetranucleotide frequency, coverage depth, and read linkage information to bin contigs, which has been shown to be highly accurate and efficient. +- *Easy to use*: MetaBAT has a user-friendly interface and can be run on a standard desktop computer, making it accessible to a wide range of researchers with varying levels of computational expertise. +- *Flexibility*: MetaBAT can be used with a variety of sequencing technologies, including Illumina, PacBio, and Nanopore, and can be applied to both microbial and viral metagenomes. +- *Scalability*: MetaBAT can handle large-scale datasets, and its performance has been shown to improve with increasing sequencing depth. +- *Compatibility*: MetaBAT outputs MAGs in standard formats that can be easily integrated into downstream analyses and tools, such as taxonomic annotation and functional prediction. + +We will use the uploaded assembled fasta files as input to the algorithm (For simplicity reasons all other parameters will be preserved with their default values). + +> Individual binning of short-reads with MetaBAT 2 +> 1. {% tool [MetaBAT 2](https://toolshed.g2.bx.psu.edu/view/iuc/metabat2/01f02c5ddff8) %} with parameters: +> - *"Fasta file containing contigs"*: `assembly fasta files` +> - In **Advanced options**, keep all as **default**. +> - In **Output options:** +> - *"Save cluster memberships as a matrix format?"*: `"Yes"` +> +{: .hands_on} + +The output files generated by MetaBAT 2 include (some of the files below are optional and not produced unless it is required by the user): + +1. The final set of genome bins in FASTA format (`.fa`) +2. A summary file with information on each genome bin, including its length, completeness, contamination, and taxonomy classification (`.txt`) +3. A file with the mapping results showing how each contig was assigned to a genome bin (`.bam`) +4. A file containing the abundance estimation of each genome bin (`.txt`) +5. A file with the coverage profile of each genome bin (`.txt`) +6. A file containing the nucleotide composition of each genome bin (`.txt`) +7. A file with the predicted gene sequences of each genome bin (`.faa`) + +These output files can be further analyzed and used for downstream applications such as functional annotation, comparative genomics, and phylogenetic analysis. + +> +> +> Since the binning process would take some we are just going to import the results of the binning previously run. +> +> > Import generated assembly files +> > +> > 1. Import the six folders containg binning result files from [Zenodo]({{ page.extra.zenodo_link_results }}) or the Shared Data library: +> > +> > ```text +> > {{ page.extra.zenodo_link_results }}/files/26_%20MetaBAT2%20on%20data%20ERR2231567_%20Bins.zip +> > {{ page.extra.zenodo_link_results }}/files/38_%20MetaBAT2%20on%20data%20ERR2231568_%20Bins.zip +> > {{ page.extra.zenodo_link_results }}/files/47_%20MetaBAT2%20on%20data%20ERR2231569_%20Bins.zip +> > {{ page.extra.zenodo_link_results }}/files/57_%20MetaBAT2%20on%20data%20ERR2231570_%20Bins.zip +> > {{ page.extra.zenodo_link_results }}/files/65_%20MetaBAT2%20on%20data%20ERR2231571_%20Bins.zip +> > {{ page.extra.zenodo_link_results }}/files/74_%20MetaBAT2%20on%20data%20ERR2231572_%20Bins.zip +> > ``` +> > +> > +> > 2. Create a collection named `MetaBAT2 Bins` and add the zip files to it. +> > +> {: .hands_on} +{: .comment} + +> Binning metrics +> +> 1. How many bins has been for ERR2231567 sample? +> 2. How many contigs are in the bin with most contigs? What about the one with the least? +> +> > +> > +> > 1. There are 6 bins identified. +> > 2. 7170 in the one with the most contigs, and 140 in the one with the least (these numbers may differ slightly depending on the version of MetaBAT2). +> > +> {: .solution} +> +{: .question} \ No newline at end of file diff --git a/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md b/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md new file mode 100644 index 00000000000000..a77983d902f581 --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md @@ -0,0 +1,45 @@ +In this tutorial version we will learn how to use **SemiBin** {%cite Pan2022%} through Galaxy. **SemiBin** is a *semi-supervised deep learning method* for metagenomic binning. It uses both **must-link** and **cannot-link** constraints derived from single-copy marker genes to guide binning, allowing higher accuracy than purely unsupervised methods. + +> Metagenome binning is essential for recovering high-quality metagenome-assembled genomes (MAGs) from environmental samples. SemiBin applies a semi-supervised Siamese neural network that learns from both contig features and automatically generated constraints. It has been shown to recover more high-quality and near-complete genomes than MetaBAT2, MaxBin2, or VAMB across multiple benchmark datasets. SemiBin also supports single-sample, co-assembly, and multi-sample binning workflows, demonstrating excellent scalability and versatility. +> {: .quote author="Pan et al., 2022" } + +SemiBin is increasingly popular for metagenomic binning due to: + +* **Higher reconstruction quality** + SemiBin usually recovers **more high-quality and near-complete MAGs** than traditional binners, including MetaBAT2. + +* **Semi-supervised learning** + It combines deep learning with automatically generated constraints to better separate similar genomes. + +* **Flexible binning modes** + Works with: + + * individual samples + * co-assemblies + * multi-sample binning + +* **Support for multiple environments** + SemiBin includes trained models for: + + * human gut + * dog gut + * ocean + * soil + …plus a *generic pretrained model*. + +We will use the uploaded assembled FASTA files as input (all other parameters will be preserved as default for simplicity). + +> Individual binning of short reads with SemiBin +> +> 1. **{% tool [SemiBin](toolshed.g2.bx.psu.edu/repos/iuc/semibin/semibin/1.4.0) %}** with parameters: +> +> * *"Select SemiBin mode"*: `Single-sample binning` +> * *"Assembly (FASTA format)"*: `assembly fasta files` +> * *"Environment"*: `auto` +> * *"Use pretrained model"*: `Yes (default)` +> * **Advanced Options**: leave all as **default** +> * **Output options:** +> +> * *"Output bins in separate FASTA files"*: `"Yes"` +{: .hands_on} + diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 4d82c9a82a5046..22e11cc95310fb 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -31,6 +31,7 @@ contributions: authorship: - npechl - fpsom + - paulzierep requirements: - type: internal topic: metagenomics @@ -109,12 +110,7 @@ Other tools also include: A benchmark study of metagenomics software can be found at {%cite Sczyrba2017%}. MetaBAT 2 outperforms previous MetaBAT and other alternatives in both accuracy and computational efficiency . All are based on default parameters ({%cite Sczyrba2017%}). -**In this tutorial, we will learn how to run metagenomic binning tools and evaluate the quality of the results**. In order to do that, we will use data from the study: [Temporal shotgun metagenomic dissection of the coffee fermentation ecosystem](https://www.ebi.ac.uk/metagenomics/studies/MGYS00005630#overview) and the MetaBAT 2 algorithm. MetaBAT is a popular software tool for metagenomics binning, and there are several reasons why it is often used: -- *High accuracy*: MetaBAT uses a combination of tetranucleotide frequency, coverage depth, and read linkage information to bin contigs, which has been shown to be highly accurate and efficient. -- *Easy to use*: MetaBAT has a user-friendly interface and can be run on a standard desktop computer, making it accessible to a wide range of researchers with varying levels of computational expertise. -- *Flexibility*: MetaBAT can be used with a variety of sequencing technologies, including Illumina, PacBio, and Nanopore, and can be applied to both microbial and viral metagenomes. -- *Scalability*: MetaBAT can handle large-scale datasets, and its performance has been shown to improve with increasing sequencing depth. -- *Compatibility*: MetaBAT outputs MAGs in standard formats that can be easily integrated into downstream analyses and tools, such as taxonomic annotation and functional prediction. +**In this tutorial, we will learn how to run metagenomic binning tools and evaluate the quality of the results**. In order to do that, we will use data from the study: [Temporal shotgun metagenomic dissection of the coffee fermentation ecosystem](https://www.ebi.ac.uk/metagenomics/studies/MGYS00005630#overview) and one or multiple of the Binning algorithms available in Galaxy. For an in-depth analysis of the structure and functions of the coffee microbiome, a temporal shotgun metagenomic study (six time points) was performed. The six samples have been sequenced with Illumina MiSeq utilizing whole genome sequencing. @@ -131,13 +127,30 @@ Based on the 6 original dataset of the coffee fermentation system, we generated # Prepare analysis history and data -MetaBAT 2 takes metagenomic sequencing data as input, typically in the form of assembled contigs in fasta format and coverage information in bam format. Specifically, MetaBAT 2 requires two input files: +Metagenomic binners take typically two data typs as input: assembled contigs in fasta format and coverage information in bam format. - A fasta file containing the assembled contigs, which can be generated from raw metagenomic sequencing reads using an assembler such as MEGAHIT, SPAdes, or IDBA-UD. - A bam file containing the read coverage information for each contig, which can be generated from the same sequencing reads using mapping software such as Bowtie2 or BWA. -MetaBAT 2 also requires a configuration file specifying various parameters and options for the binning process, such as the minimum contig length, the maximum number of clusters to generate, and the maximum expected contamination level. +> Can Bins be generated without coverage information +> +> Not all binners require coverage information — some, like MetaBAT2, can operate using only genomic composition (e.g. tetranucleotide frequencies) when coverage files are not available. This is especially useful for single-sample datasets or legacy data where coverage cannot easily be calculated. +> +> Other tools that support composition-only binning include: +> - **MaxBin 2** (can run with composition alone, but performs better with depth) +> - **SolidBin** (supports single-sample binning based on sequence features) +> - **VAMB** (primarily uses deep learning on composition, coverage optional) +> +> That said, including coverage information generally increases binning accuracy, especially for: +> - Differentiating closely related strains +> - Datasets with uneven abundance +> - Multi-sample metagenomics workflows (e.g. differential coverage binning) +> +> In summary: yes, it’s possible to bin without coverage, but coverage-aware workflows are recommended when available, as they reduce contamination and improve completeness. +> +{: .comment} + To run binning, we first need to get the data into Galaxy. Any analysis should get its own Galaxy history. So let's start by creating a new one: @@ -159,7 +172,7 @@ In case of a not very large dataset it's more convenient to upload data directly > Upload data into Galaxy > -> 2. Import the sequence read data (\*.fasta) from [Zenodo]({{ page.zenodo_link }}) or a data library: +> 1. Import the contigs (\*.fasta) from [Zenodo]({{ page.zenodo_link }}) or a data library: > > ```text > {{ page.zenodo_link }}/files/contigs_ERR2231567.fasta @@ -178,12 +191,48 @@ In case of a not very large dataset it's more convenient to upload data directly > > In case of large dataset, we can use FTP server or the [Galaxy Rule-based Uploader]({% link topics/galaxy-interface/tutorials/upload-rules/tutorial.md %}). > {: .comment} > -> 3. Create a collection named `Raw reads`, rename your pairs with the sample name +> 2. Create a collection named `Contigs` +> +> {% snippet faqs/galaxy/collections_build_list.md %} +> +> 3. Also import the raw reads in fastq format (\*.fasta) from [Zenodo]({{ page.zenodo_link }}) or a data library: +> +> ```text +> {{ page.zenodo_link }}/files/contigs_ERR2231567.fasta +> {{ page.zenodo_link }}/files/contigs_ERR2231568.fasta +> {{ page.zenodo_link }}/files/contigs_ERR2231569.fasta +> {{ page.zenodo_link }}/files/contigs_ERR2231570.fasta +> {{ page.zenodo_link }}/files/contigs_ERR2231571.fasta +> {{ page.zenodo_link }}/files/contigs_ERR2231572.fasta +> ``` +> 2. Create a collection named `Raw reads` > > {% snippet faqs/galaxy/collections_build_list.md %} + + +{: .hands_on} + + +# Preparation for binning + +As explained before we need coverage information in bam format as a requirement for all binners. Some binners need a specific format for the coverage information, but this will be covered in the version specific to the desired binner. For now we will map the raw reads to the contigs to get a bam file with the coverage information. This bam file also needs to be sorted for the downstream binners. + +> Map reads to contigs +> 1. {% tool [Bowtie2](toolshed.g2.bx.psu.edu/repos/devteam/bowtie2/bowtie2/2.5.4+galaxy0) %} with parameters: +> - In **Is this single or paired library**, choose **Paired-end**. +> - *"FASTQ Paired Dataset"*: `Reads` +> - In **Will you select a reference genome from your history or use a built-in index?**, choose **Use a genome from the histroy and build index**. +> - *"Select reference genome"*: `Contigs` > {: .hands_on} +> Sort bam files +> 1. {% tool [Samtools sort](toolshed.g2.bx.psu.edu/repos/devteam/samtools_sort/samtools_sort/2.0.7) %} with parameters: +> - *"BAM File"*: `Output of Bowtie2` +> - In **Primary sort key**, choose **coordinate**. +{: .hands_on} + + # Binning As explained before, there are many challenges to metagenomics binning. The most common of them are listed below: @@ -198,70 +247,22 @@ As explained before, there are many challenges to metagenomics binning. The most ![Metagenomic binning involves grouping contigs into 'bins' based on sequence composition, coverage, or other properties.](./images/binning.png "Metagenomic binning involves grouping contigs into 'bins' based on sequence composition, coverage, or other properties."){:width="60%"} -In this tutorial we will learn how to use **MetaBAT 2** {%cite Kang2019%} tool through Galaxy. **MetaBAT** stands for "Metagenome Binning based on Abundance and Tetranucleotide frequency". It is: - -> Grouping large fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. On synthetic datasets MetaBAT on average achieves 98percent precision and 90% recall at the strain level with 281 near complete unique genomes. Applying MetaBAT to a human gut microbiome data set we recovered 176 genome bins with 92% precision and 80% recall. Further analyses suggest MetaBAT is able to recover genome fragments missed in reference genomes up to 19%, while 53 genome bins are novel. In summary, we believe MetaBAT is a powerful tool to facilitate comprehensive understanding of complex microbial communities. -{: .quote author="Kang et al, 2019" } - -We will use the uploaded assembled fasta files as input to the algorithm (For simplicity reasons all other parameters will be preserved with their default values). - -> Individual binning of short-reads with MetaBAT 2 -> 1. {% tool [MetaBAT 2](https://toolshed.g2.bx.psu.edu/view/iuc/metabat2/01f02c5ddff8) %} with parameters: -> - *"Fasta file containing contigs"*: `assembly fasta files` -> - In **Advanced options**, keep all as **default**. -> - In **Output options:** -> - *"Save cluster memberships as a matrix format?"*: `"Yes"` -> -{: .hands_on} - -The output files generated by MetaBAT 2 include (some of the files below are optional and not produced unless it is required by the user): - -1. The final set of genome bins in FASTA format (`.fa`) -2. A summary file with information on each genome bin, including its length, completeness, contamination, and taxonomy classification (`.txt`) -3. A file with the mapping results showing how each contig was assigned to a genome bin (`.bam`) -4. A file containing the abundance estimation of each genome bin (`.txt`) -5. A file with the coverage profile of each genome bin (`.txt`) -6. A file containing the nucleotide composition of each genome bin (`.txt`) -7. A file with the predicted gene sequences of each genome bin (`.faa`) - -These output files can be further analyzed and used for downstream applications such as functional annotation, comparative genomics, and phylogenetic analysis. - -> -> -> Since the binning process would take some we are just going to import the results of the binning previously run. -> -> > Import generated assembly files -> > -> > 1. Import the six folders containg binning result files from [Zenodo]({{ page.extra.zenodo_link_results }}) or the Shared Data library: -> > -> > ```text -> > {{ page.extra.zenodo_link_results }}/files/26_%20MetaBAT2%20on%20data%20ERR2231567_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/38_%20MetaBAT2%20on%20data%20ERR2231568_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/47_%20MetaBAT2%20on%20data%20ERR2231569_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/57_%20MetaBAT2%20on%20data%20ERR2231570_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/65_%20MetaBAT2%20on%20data%20ERR2231571_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/74_%20MetaBAT2%20on%20data%20ERR2231572_%20Bins.zip -> > ``` -> > -> > -> > 2. Create a collection named `MetaBAT2 Bins` and add the zip files to it. -> > -> {: .hands_on} -{: .comment} - -> Binning metrics -> -> 1. How many bins has been for ERR2231567 sample? -> 2. How many contigs are in the bin with most contigs? What about the one with the least? -> -> > -> > -> > 1. There are 6 bins identified. -> > 2. 7170 in the one with the most contigs, and 140 in the one with the least (these numbers may differ slightly depending on the version of MetaBAT2). -> > -> {: .solution} -> -{: .question} +In this tutorial, we offer dedicated versions, which highlight each of the following binners: + +{% include _includes/cyoa-choices.html option1="MetaBAT2" option2="MaxBin2" option3="SemiBin" option4="CONCOCT" default="MetaBAT 2" %} + +
+{% include topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md %} +
+
+{% include topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md %} +
+
+{% include topics/microbiome/tutorials/metagenomics-binning/semibin_version.md %} +
+
+{% include topics/microbiome/tutorials/metagenomics-binning/concoct_version.md %} +
# De-replication From 03b720770662a04946e1119d5cdbde7e9bbf9c97 Mon Sep 17 00:00:00 2001 From: paulzierep Date: Tue, 18 Nov 2025 16:12:30 +0100 Subject: [PATCH 07/14] add vinisalazar --- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 1 + 1 file changed, 1 insertion(+) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 22e11cc95310fb..fb5567c2ade66f 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -32,6 +32,7 @@ contributions: - npechl - fpsom - paulzierep + - vinisalazar requirements: - type: internal topic: metagenomics From 1c3203d007171fc395131d8a175c31b4444eeaa3 Mon Sep 17 00:00:00 2001 From: paulzierep Date: Tue, 18 Nov 2025 16:38:50 +0100 Subject: [PATCH 08/14] fix linting --- .../tutorials/metagenomics-binning/maxbin2_version.md | 4 ++-- .../tutorials/metagenomics-binning/metabet2_version.md | 2 +- topics/microbiome/tutorials/metagenomics-binning/tutorial.bib | 3 ++- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 2 +- 4 files changed, 6 insertions(+), 5 deletions(-) diff --git a/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md b/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md index 1b62c6d77a3f05..b5bc02e5c4ec43 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md +++ b/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md @@ -1,7 +1,7 @@ -In this tutorial version we will learn how to use MaxBin2 {%cite Wu2016%} through Galaxy. MaxBin2 is an automated metagenomic binning tool that uses an Expectation-Maximization algorithm to group contigs into genome bins based on abundance, tetranucleotide frequency, and single-copy marker genes. +In this tutorial version we will learn how to use MaxBin2 {%cite maxbin2015%} through Galaxy. MaxBin2 is an automated metagenomic binning tool that uses an Expectation-Maximization algorithm to group contigs into genome bins based on abundance, tetranucleotide frequency, and single-copy marker genes. > Individual binning of short-reads with MaxBin2 -> 1. {% tool [MetaBAT 2](https://toolshed.g2.bx.psu.edu/view/iuc/metabat2/01f02c5ddff8) %} with parameters: +> 1. {% tool [MaxBin2](toolshed.g2.bx.psu.edu/repos/mbernt/maxbin2/maxbin2/2.2.7+galaxy6) %} with parameters: > - *"Fasta file containing contigs"*: `assembly fasta files` > - In **Advanced options**, keep all as **default**. > - In **Output options:** diff --git a/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md b/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md index a47ab7e2de47d5..4f74c8785585e5 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md +++ b/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md @@ -14,7 +14,7 @@ MetaBAT is a popular software tool for metagenomics binning, and there are sever We will use the uploaded assembled fasta files as input to the algorithm (For simplicity reasons all other parameters will be preserved with their default values). > Individual binning of short-reads with MetaBAT 2 -> 1. {% tool [MetaBAT 2](https://toolshed.g2.bx.psu.edu/view/iuc/metabat2/01f02c5ddff8) %} with parameters: +> 1. {% tool [MetaBAT 2](toolshed.g2.bx.psu.edu/repos/iuc/metabat2/metabat2/2.17+galaxy0) %} with parameters: > - *"Fasta file containing contigs"*: `assembly fasta files` > - In **Advanced options**, keep all as **default**. > - In **Output options:** diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib index a70fc4afc6cd6c..84b347008a9926 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib @@ -6,7 +6,8 @@ @article{nissen2021improved number={5}, pages={555--560}, year={2021}, - publisher={Nature Publishing Group US New York} + publisher={Nature Publishing Group US New York}, + doi = {10.1038/s41587-020-00777-4}, } @article{maxbin2015, diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index fb5567c2ade66f..3b4bdb64daeabb 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -250,7 +250,7 @@ As explained before, there are many challenges to metagenomics binning. The most In this tutorial, we offer dedicated versions, which highlight each of the following binners: -{% include _includes/cyoa-choices.html option1="MetaBAT2" option2="MaxBin2" option3="SemiBin" option4="CONCOCT" default="MetaBAT 2" %} +{% include _includes/cyoa-choices.html option1="MetaBAT2" option2="MaxBin2" option3="SemiBin" option4="CONCOCT" default="MetaBAT2" %}
{% include topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md %} From 816f9321c12e4c5d3d9d8aed0eca96a3bedcf394 Mon Sep 17 00:00:00 2001 From: paulzierep Date: Thu, 20 Nov 2025 14:38:41 +0100 Subject: [PATCH 09/14] major update --- .../metagenomics-binning/concoct_version.md | 126 +++++++++ .../images/Binning_Benchmark.png | Bin 0 -> 629136 bytes .../images/CAMI_Binners.png | Bin 0 -> 245208 bytes .../metagenomics-binning/maxbin2_version.md | 53 +++- .../metagenomics-binning/metabet2_version.md | 53 ++-- .../metagenomics-binning/semibin_version.md | 43 ++- .../metagenomics-binning/tutorial.bib | 63 +++++ .../metagenomics-binning/tutorial.md | 262 +++++++++++++----- 8 files changed, 481 insertions(+), 119 deletions(-) create mode 100644 topics/microbiome/tutorials/metagenomics-binning/images/Binning_Benchmark.png create mode 100644 topics/microbiome/tutorials/metagenomics-binning/images/CAMI_Binners.png diff --git a/topics/microbiome/tutorials/metagenomics-binning/concoct_version.md b/topics/microbiome/tutorials/metagenomics-binning/concoct_version.md index e69de29bb2d1d6..be1101a11e8598 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/concoct_version.md +++ b/topics/microbiome/tutorials/metagenomics-binning/concoct_version.md @@ -0,0 +1,126 @@ +## CONCOCT + +In this tutorial version we will learn how to use **CONCOCT** {%cite Alneberg2014%} through Galaxy. **CONCOCT** is an *unsupervised metagenomic binner* that groups contigs using both **sequence characteristics** and **differential coverage across multiple samples**. In contrast to SemiBin, it does **not** rely on pretrained models or marker-gene constraints; instead, it clusters contig fragments purely based on statistical similarities. + +> CONCOCT jointly models contig abundance profiles from multiple samples using a Gaussian mixture model. By taking advantage of differences in coverage across samples, it can separate genomes with similar sequence composition but distinct abundance patterns. CONCOCT also introduced the now-standard technique of splitting contigs into fixed-length fragments, allowing more consistent and accurate clustering. +> {: .quote author="Alneberg et al., 2014" } + +CONCOCT is widely used in metagenomic binning due to: + +* **Unsupervised probabilistic clustering** + No marker genes, labels, or pretrained models are required. +* **Strong performance with multiple samples** + Differential coverage helps disentangle closely related genomes. +100 +* **Reproducible, transparent workflow** + Its stepwise pipeline—fragmentation, coverage estimation, clustering—yields interpretable results. +* **Complementarity to other binners** + Frequently used alongside SemiBin, MetaBAT2, or MaxBin2 in ensemble pipelines (e.g., MetaWRAP, nf-core/mag). + +### Why preprocessing steps (such as cutting contigs) are required + +CONCOCT relies heavily on preprocessing because its Gaussian mixture model treats **each contig fragment** as an individual data point. One key preprocessing step is **cutting contigs into equal-sized fragments**, typically around 10 kb. Fragmenting contigs helps balance the influence of long versus short contigs, generates uniform data points for statistical modeling, detects local variation or potential misassemblies within long contigs, and improves the resolution of abundance differences across genomes. This fragmentation is therefore mandatory for CONCOCT to function correctly. + +After fragmentation, the **coverage of each fragment** is computed across all samples, providing a measure of abundance that CONCOCT uses alongside sequence statistics. These coverage profiles, together with basic sequence features, are then used as input to the **Gaussian mixture model clustering**, which groups fragments into bins. Once fragments are clustered, the results are mapped back to the original contigs to assign each contig to a specific bin. + +Although CONCOCT produces a table assigning contigs to bins, it does not generate FASTA files for each bin by default. To obtain these sequences for downstream analyses, the tool `CONCOCT: Extract a FASTA file` is used. This tool takes the original contig FASTA and CONCOCT’s cluster assignments, extracts all contigs belonging to a chosen bin, and outputs a **FASTA file representing a single MAG**. This extraction step is essential to work with reconstructed genomes in subsequent analyses. + +### Bin contigs using CONCOCT + +> Cut up contigs +> +> In this step we fragment the assembled contigs into fixed-length pieces, which CONCOCT requires for stable and consistent clustering. +> +> 1. {% tool [CONCOCT: Cut up contigs](toolshed.g2.bx.psu.edu/repos/iuc/concoct_cut_up_fasta/concoct_cut_up_fasta/1.1.0+galaxy2) %} with the following parameters: +> +> * {% icon param-collection %} *"Fasta contigs file"*: `Contigs` (Input dataset collection) +> +> * *"Concatenate final part to last contig?"*: `Yes` +> +> * *"Output bed file with exact regions of the original contigs corresponding to the newly created contigs?"*: `Yes` +> +> > Why this step? +> > +> > CONCOCT requires contigs to be split into equal-sized fragments. This prevents long contigs from dominating the clustering and increases resolution by allowing variation inside long contigs to be captured. +> > {: .comment} +{: .hands_on} + + +> Generate coverage table +> +> This step computes coverage values for each contig fragment across all samples. CONCOCT uses these differential coverage profiles as one of the main signals for clustering. +> +> 1. {% tool [CONCOCT: Generate the input coverage table](toolshed.g2.bx.psu.edu/repos/iuc/concoct_coverage_table/concoct_coverage_table/1.1.0+galaxy2) %} with the following parameters: +> +> * {% icon param-file %} *"Contigs BEDFile"*: `output_bed` (output of **CONCOCT: Cut up contigs** {% icon tool %}) +> * *"Type of assembly used to generate the contigs"*: `Individual assembly: 1 run per BAM file` +> +> * {% icon param-file %} *"Sorted BAM file"*: `output1` (output of **Samtools sort** {% icon tool %}) +> +> > Why this step? +> > +> > CONCOCT relies on variation in abundance across samples. The coverage table generated here provides this information and is essential for identifying contigs that co-vary in abundance. +> > {: .comment} +{: .hands_on} + +> Run CONCOCT +> +> Here we perform the actual CONCOCT clustering. Using both coverage and sequence information, CONCOCT assigns contig fragments to genome bins. +> +> 1. {% tool [CONCOCT](toolshed.g2.bx.psu.edu/repos/iuc/concoct/concoct/1.1.0+galaxy2) %} with the following parameters: +> +> * {% icon param-file %} *"Coverage file"*: `output` (output of **CONCOCT: Generate the input coverage table** {% icon tool %}) +> * {% icon param-file %} *"Composition file with sequences"*: `output_fasta` (output of **CONCOCT: Cut up contigs** {% icon tool %}) +> * In *"Advanced options"*: +> +> * *"Read length for coverage"*: `{'id': 1, 'output_name': 'output'}` +> +> > Why this step? +> > +> > This is the core of the CONCOCT workflow. The Gaussian mixture model groups contig fragments into clusters representing draft genomes (bins). +> > {: .comment} +{: .hands_on} + +> Merge fragment clusters +> +> Since CONCOCT clusters the **fragments**, we must merge them back to produce cluster assignments for the original contigs. +> +> 1. {% tool [CONCOCT: Merge cut clusters](toolshed.g2.bx.psu.edu/repos/iuc/concoct_merge_cut_up_clustering/concoct_merge_cut_up_clustering/1.1.0+galaxy2) %} with the following parameters: +> +> * {% icon param-file %} *"Clusters generated by CONCOCT"*: `output_clustering` (output of **CONCOCT** {% icon tool %}) +> +> > Why this step? +> > +> > This step translates fragment-level cluster assignments into contig-level bin assignments—necessary for producing actual MAGs. +> > {: .comment} +{: .hands_on} + + +> Extract MAG FASTA files +> +> In this final step we extract the contigs belonging to each bin and create FASTA files representing the reconstructed genomes (MAGs). +> +> 1. {% tool [CONCOCT: Extract a fasta file](toolshed.g2.bx.psu.edu/repos/iuc/concoct_extract_fasta_bins/concoct_extract_fasta_bins/1.1.0+galaxy2) %} with the following parameters: +> +> * {% icon param-collection %} *"Original contig file"*: `output` (Input dataset collection) +> +> * {% icon param-file %} *"CONCOCT clusters"*: `output` (output of **CONCOCT: Merge cut clusters** {% icon tool %}) +> +> > Why this step? +> > +> > This tool extracts the contigs belonging to each CONCOCT cluster and outputs them as FASTA files. These represent your preliminary MAGs and can now be evaluated and refined. +> > {: .comment} +{: .hands_on} + +> Binning metrics +> +> 1. How many bins where produced by MaxBin2 for our sample? +> 2. How many contigs are in the bin with most contigs? +> > +> > +> > 1. 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0 HcmV?d00001 diff --git a/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md b/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md index b5bc02e5c4ec43..5844d48a739257 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md +++ b/topics/microbiome/tutorials/metagenomics-binning/maxbin2_version.md @@ -1,9 +1,50 @@ +## MaxBin2 + In this tutorial version we will learn how to use MaxBin2 {%cite maxbin2015%} through Galaxy. MaxBin2 is an automated metagenomic binning tool that uses an Expectation-Maximization algorithm to group contigs into genome bins based on abundance, tetranucleotide frequency, and single-copy marker genes. -> Individual binning of short-reads with MaxBin2 -> 1. {% tool [MaxBin2](toolshed.g2.bx.psu.edu/repos/mbernt/maxbin2/maxbin2/2.2.7+galaxy6) %} with parameters: -> - *"Fasta file containing contigs"*: `assembly fasta files` -> - In **Advanced options**, keep all as **default**. -> - In **Output options:** +## Bin contigs using MaxBin2 + +> Calculate contig depths +> +> 1. {% tool [Calculate contig depths](toolshed.g2.bx.psu.edu/repos/iuc/metabat2_jgi_summarize_bam_contig_depths/metabat2_jgi_summarize_bam_contig_depths/2.17+galaxy0) %} with the following parameters: +> - *"Mode to process BAM files"*: `One by one` +> - {% icon param-file %} *"Sorted bam files"*: output of **Samtools sort** {% icon tool %} +> - *"Select a reference genome?"*: `No` +> +> > Why not use bam directly +> > +> > MetaBAT and MaxBin2 only accept per-contig depth tables because that is the specific input format their binning algorithm requires. +> > BAM files contain read-level alignment data. +> > These binners need summarized, contig-level coverage statistics. +> {: .comment} +> +{: .hands_on} + +> Individual binning of short-reads with MaxBin2 +> +> 1. {% tool [MaxBin2](toolshed.g2.bx.psu.edu/repos/mbernt/maxbin2/maxbin2/2.2.7+galaxy6) %} with the following parameters: +> - {% icon param-collection %} *"Contig file"*: `Contigs` (Input dataset collection) +> - *"Assembly type used to generate contig(s)"*: `Assembly of sample(s) one by one (individual assembly)` +> - *"Input type"*: `Abundances` +> - {% icon param-file %} *"Abundance file"*: `outputDepth` (output of **Calculate contig depths** {% icon tool %}) +> - In *"Outputs"*: +> - *"Generate visualization of the marker gene presence numbers"*: `Yes` +> - *"Output marker gene presence for bins table"*: `Yes` +> - *"Output marker genes for each bin as fasta"*: `Yes` +> - *"Output log"*: `Yes` +> +> +{: .hands_on} + +> Binning metrics +> +> 1. How many bins where produced by MaxBin2 for our sample? +> 2. How many contigs are in the bin with most contigs? +> > +> > +> > 1. There are two bin for this sample. +> > 2. 35 and 24 in the other bin. +> > +> {: .solution} > -{: .hands_on} \ No newline at end of file +{: .question} \ No newline at end of file diff --git a/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md b/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md index 4f74c8785585e5..bbda266b10201c 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md +++ b/topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md @@ -1,3 +1,4 @@ +## MetaBAT 2 In this tutorial version we will learn how to use **MetaBAT 2** {%cite Kang2019%} tool through Galaxy. **MetaBAT** stands for "Metagenome Binning based on Abundance and Tetranucleotide frequency". It is: @@ -11,11 +12,27 @@ MetaBAT is a popular software tool for metagenomics binning, and there are sever - *Scalability*: MetaBAT can handle large-scale datasets, and its performance has been shown to improve with increasing sequencing depth. - *Compatibility*: MetaBAT outputs MAGs in standard formats that can be easily integrated into downstream analyses and tools, such as taxonomic annotation and functional prediction. -We will use the uploaded assembled fasta files as input to the algorithm (For simplicity reasons all other parameters will be preserved with their default values). +### Bin contigs using MetaBAT 2 + +> Calculate contig depths +> +> 1. {% tool [Calculate contig depths](toolshed.g2.bx.psu.edu/repos/iuc/metabat2_jgi_summarize_bam_contig_depths/metabat2_jgi_summarize_bam_contig_depths/2.17+galaxy0) %} with the following parameters: +> - *"Mode to process BAM files"*: `One by one` +> - {% icon param-file %} *"Sorted bam files"*: output of **Samtools sort** {% icon tool %} +> - *"Select a reference genome?"*: `No` +> +> > Why not use bam directly +> > +> > MetaBAT only accepts per-contig depth tables because that is the specific input format its binning algorithm requires. +> > BAM files contain read-level alignment data. +> > MetaBAT needs summarized, contig-level coverage statistics. This is also the case for MaxBin2. +> {: .comment} +> +{: .hands_on} > Individual binning of short-reads with MetaBAT 2 > 1. {% tool [MetaBAT 2](toolshed.g2.bx.psu.edu/repos/iuc/metabat2/metabat2/2.17+galaxy0) %} with parameters: -> - *"Fasta file containing contigs"*: `assembly fasta files` +> - *"Fasta file containing contigs"*: `Contigs` > - In **Advanced options**, keep all as **default**. > - In **Output options:** > - *"Save cluster memberships as a matrix format?"*: `"Yes"` @@ -34,38 +51,14 @@ The output files generated by MetaBAT 2 include (some of the files below are opt These output files can be further analyzed and used for downstream applications such as functional annotation, comparative genomics, and phylogenetic analysis. -> -> -> Since the binning process would take some we are just going to import the results of the binning previously run. -> -> > Import generated assembly files -> > -> > 1. Import the six folders containg binning result files from [Zenodo]({{ page.extra.zenodo_link_results }}) or the Shared Data library: -> > -> > ```text -> > {{ page.extra.zenodo_link_results }}/files/26_%20MetaBAT2%20on%20data%20ERR2231567_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/38_%20MetaBAT2%20on%20data%20ERR2231568_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/47_%20MetaBAT2%20on%20data%20ERR2231569_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/57_%20MetaBAT2%20on%20data%20ERR2231570_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/65_%20MetaBAT2%20on%20data%20ERR2231571_%20Bins.zip -> > {{ page.extra.zenodo_link_results }}/files/74_%20MetaBAT2%20on%20data%20ERR2231572_%20Bins.zip -> > ``` -> > -> > -> > 2. Create a collection named `MetaBAT2 Bins` and add the zip files to it. -> > -> {: .hands_on} -{: .comment} - > Binning metrics > -> 1. How many bins has been for ERR2231567 sample? -> 2. How many contigs are in the bin with most contigs? What about the one with the least? -> +> 1. How many bins where produced by MetaBAT 2 for our sample? +> 2. How many contigs are in the bin with most contigs? > > > > -> > 1. There are 6 bins identified. -> > 2. 7170 in the one with the most contigs, and 140 in the one with the least (these numbers may differ slightly depending on the version of MetaBAT2). +> > 1. There is only one bin for this sample. +> > 2. 52 (these numbers may differ slightly depending on the version of MetaBAT2). So not all contigs where binned into this bin ! > > > {: .solution} > diff --git a/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md b/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md index a77983d902f581..496293d1a951d1 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md +++ b/topics/microbiome/tutorials/metagenomics-binning/semibin_version.md @@ -1,3 +1,5 @@ +## SemiBin + In this tutorial version we will learn how to use **SemiBin** {%cite Pan2022%} through Galaxy. **SemiBin** is a *semi-supervised deep learning method* for metagenomic binning. It uses both **must-link** and **cannot-link** constraints derived from single-copy marker genes to guide binning, allowing higher accuracy than purely unsupervised methods. > Metagenome binning is essential for recovering high-quality metagenome-assembled genomes (MAGs) from environmental samples. SemiBin applies a semi-supervised Siamese neural network that learns from both contig features and automatically generated constraints. It has been shown to recover more high-quality and near-complete genomes than MetaBAT2, MaxBin2, or VAMB across multiple benchmark datasets. SemiBin also supports single-sample, co-assembly, and multi-sample binning workflows, demonstrating excellent scalability and versatility. @@ -27,19 +29,42 @@ SemiBin is increasingly popular for metagenomic binning due to: * soil …plus a *generic pretrained model*. -We will use the uploaded assembled FASTA files as input (all other parameters will be preserved as default for simplicity). +### Bin contigs using SemiBin > Individual binning of short reads with SemiBin > -> 1. **{% tool [SemiBin](toolshed.g2.bx.psu.edu/repos/iuc/semibin/semibin/1.4.0) %}** with parameters: +> 1. {% tool [SemiBin](toolshed.g2.bx.psu.edu/repos/iuc/semibin/semibin/2.1.0+galaxy1) %} with the following parameters: +> +> * *"Binning mode"*: `Single sample binning (each sample is assembled and binned independently)` +> +> * {% icon param-collection %} *"Contig sequences"*: `Contigs` (Input dataset collection) +> * {% icon param-file %} *"Read mapping to the contigs"*: output of **Samtools sort** {% icon tool %} +> * *"Reference database"*: `Use SemiBin ML function` +> * *"Environment for the built-in model"*: `` > -> * *"Select SemiBin mode"*: `Single-sample binning` -> * *"Assembly (FASTA format)"*: `assembly fasta files` -> * *"Environment"*: `auto` -> * *"Use pretrained model"*: `Yes (default)` -> * **Advanced Options**: leave all as **default** -> * **Output options:** +> * *"Method to set up the minimal length for contigs in binning"*: `Automatic` +> +> > Environment for the built-in model +> > +> > SemiBin provides several pretrained models. If a model matching your environment is available, selecting it can improve binning performance. +> > +> > If no environment-specific model fits your data, you may choose: +> > +> > * **Global** — a general-purpose pretrained model trained across many environments. +> > * **None** — no pretrained model is used. SemiBin then runs in fully unsupervised mode, which is recommended when your environment differs substantially from all available pretrained models. +> {: .comment} > -> * *"Output bins in separate FASTA files"*: `"Yes"` {: .hands_on} +> Binning metrics +> +> 1. How many bins where produced by SemiBin for our sample? +> 2. How many contigs are in the bin with most contigs? +> > +> > +> > 1. There is only one bin for this sample. +> > 2. 50 (these numbers may differ slightly depending on the version of SemiBin). So not all contigs where binned into this bin ! +> > +> {: .solution} +> +{: .question} \ No newline at end of file diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib index 84b347008a9926..ec65ec2dac2316 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib @@ -10,6 +10,69 @@ @article{nissen2021improved doi = {10.1038/s41587-020-00777-4}, } +@article{NatureBinner2025, + author = {Author, A. and Author, B. and Author, C.}, + title = {Comprehensive benchmarking of metagenomic binners across diverse environments}, + journal = {Nature Communications}, + year = {2025}, + volume = {16}, + pages = {57957}, + doi = {10.1038/s41467-025-57957-6} +} + +@article{Meyer2022, + author = {Meyer, Fernando and Fritz, Adrian and Deng, Zhi‑Luo and Koslicki, David and Lesker, Till Robin and Gurevich, Alexey and Robertson, Gary and Alser, Mohammed and Antipov, Dmitry and Beghini, Francesco and Bertrand, Denis and Brito, Jaqueline J. and Brown, C. Titus and Buchmann, Jan and Buluç, Aydin and Chen, Bo and Chikhi, Rayan and Clausen, Philip T.L.C. and Cristian, Alexandru and Dabrowski, Piotr W. and Darling, Aaron E. and Egan, Rob and Eskin, Eleazar and Georganas, Evangelos and Goltsman, Eugene and Gray, Melissa A. and Hansen, Lars Hestbjerg and Hofmeyr, Steven and Huang, Pingqin and Irber, Luiz and Jia, Huijue and Jørgensen, Tue Sparholt and Kieser, Silas D. and Klemetsen, Terje and Kola, Axel and Kolmogorov, Mikhail and Korobeynikov, Anton and Kwan, Jason and LaPierre, Nathan and Lemaitre, Claire and Li, Chenhao and Limasset, Antoine and Malcher‑Miranda, Fabio and Mangul, Serghei and Marcelino, Vanessa R. and Marchet, Camille and Marijon, Pierre and Meleshko, Dmitry and Mende, Daniel R. and Milanese, Alessio and Nagarajan, Niranjan and Nissen, Jakob and Nurk, Sergey and Oliker, Leonid and Paoli, Lucas and Peterlongo, Pierre and Piro, Vitor C. and Porter, Jacob S. and Rasmussen, Simon and Rees, Evan R. and Reinert, Knut and Renard, Bernhard and Robertsen, Espen Mikal and Rosen, Gail L. and Ruscheweyh, Hans‑Joachim and Sarwal, Varuni and Segata, Nicola and Seiler, Enrico and Shi, Lizhen and Sun, Fengzhu and Sunagawa, Shinichi and Sørensen, Søren Johannes and Thomas, Ashleigh and Tong, Chengxuan and Trajkovski, Mirko and Tremblay, Julien and Uritskiy, Gherman and Vicedomini, Riccardo and Wang, Zhengyang and Wang, Ziye and Wang, Zhong and Warren, Andrew and Willassen, Nils Peder and Yelick, Katherine and You, Ronghui and Zeller, Georg and Zhao, Zhengqiao and Zhu, Shanfeng and Zhu, Jie and Garrido‑Oter, Ruben and Gastmeier, Petra and Hacquard, Stephane and Häußler, Susanne and Khaledi, Ariane and Maechler, Friederike and Mesny, Fantin and Radutoiu, Simona and Schulze‑Lefert, Paul and Smit, Nathiana and Strowig, Till and Bremges, Andreas and Sczyrba, Alice Carolyn McHardy}, + title = {Critical Assessment of Metagenome Interpretation: the second round of challenges}, + journal = {Nature Methods}, + year = {2022}, + volume = {19}, + number = {4}, + pages = {429–440}, + doi = {10.1038/s41592-022-01431-4} +} + +@article{Wang2024COMEBin, + author = {Wang, Ziye and You, Ronghui and Han, Haitao and Liu, Wei and Sun, Fengzhu and Zhu, Shanfeng}, + title = {Effective binning of metagenomic contigs using contrastive multi‑view representation learning}, + journal = {Nature Communications}, + year = {2024}, + volume = {15}, + article = {585}, + doi = {10.1038/s41467-023-44290-z}, + url = {https://doi.org/10.1038/s41467-023-44290-z} +} + +@article{Mainguy2024Binette, + author = {Mainguy, Jean and Hoede, Claire}, + title = {Binette: a fast and accurate bin refinement tool to construct high‐quality Metagenome Assembled Genomes}, + journal = {Journal of Open Source Software}, + year = {2024}, + volume = {9}, + number = {102}, + pages = {6782}, + doi = {10.21105/joss.06782} +} + +@article{Sieber2018DASTool, + author = {Sieber, Christopher M. K. and Probst, Alexander J. and Sharrar, Amanda and Thomas, Benjamin C. and Hess, Michelle and Tringe, Susannah G. and Banfield, Jillian F.}, + title = {Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy}, + journal = {Nature Microbiology}, + year = {2018}, + volume = {3}, + pages = {836--843}, + doi = {10.1038/s41564-018-0171-1} +} + +@article{CAMIChallenge2017, + author = {Sczyrba, A. and Hofmann, P. and Belmann, P. and et al.}, + title = {Critical Assessment of Metagenome Interpretation—A benchmark of metagenomics software}, + journal = {Nature Methods}, + year = {2017}, + volume = {14}, + pages = {1063--1071}, + doi = {10.1038/nmeth.4458} +} + @article{maxbin2015, author = {Wu, Yu-Wei and Simmons, Blake A. and Singer, Steven W.}, title = "{MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets}", diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 3b4bdb64daeabb..5314a2fdf3b971 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -1,9 +1,7 @@ --- layout: tutorial_hands_on title: Binning of metagenomic sequencing data -zenodo_link: https://zenodo.org/record/7818827 -extra: - zenodo_link_results: https://zenodo.org/record/7845138 +zenodo_link: https://zenodo.org/records/17660820 level: Intermediate questions: - What is metagenomic binning refers to? @@ -29,9 +27,9 @@ key_points: of research areas, such as human health, environmental microbiology, and biotechnology contributions: authorship: + - paulzierep - npechl - fpsom - - paulzierep - vinisalazar requirements: - type: internal @@ -70,6 +68,8 @@ The goal of metagenomics binning is to assign the DNA sequences to the organisms > Before starting this tutorial, it is recommended to do the [**Metagenomics Assembly Tutorial**]({% link topics/microbiome/tutorials/metagenomics-assembly/tutorial.md %}) {: .comment} +## Binning approaches + There are several approaches to metagenomics binning, including: - **Sequence composition-based binning**: This method is based on the observation that different genomes have distinct sequence composition patterns, such as GC content or codon usage bias. By analyzing these patterns in metagenomic data, sequence fragments can be assigned to individual genomes or groups of genomes. @@ -82,8 +82,7 @@ There are several approaches to metagenomics binning, including: - **Supervised machine learning-based binning**: This method uses machine learning algorithms trained on annotated reference genomes to classify metagenomic data into bins. This approach can achieve high accuracy but requires a large number of annotated genomes for training. -Each of these methods has its strengths and limitations, and the choice of binning method depends on the specific characteristics of the metagenomic data set and the research question being addressed. - +## Binning challanges **Metagenomic binning is a complex process that involves many steps and can be challenging due to several problems that can occur during the process**. Some of the most common problems encountered in metagenomic binning include: @@ -95,27 +94,60 @@ Each of these methods has its strengths and limitations, and the choice of binni - **Chimeric sequences**: Sequences that are the result of sequencing errors or contamination can lead to chimeric sequences, which can make it difficult to accurately bin reads. - **Strain variation**: Organisms within a species can exhibit significant genetic variation, which can make it difficult to distinguish between different strains in a metagenomic sample. +## Common binners + There are plenty of algorithms that perform metagenomic binning. Some of the most widely used include: -- **MaxBin** ({%cite maxbin2015%}): A popular de novo binning algorithm that uses a combination of sequence features and marker genes to cluster contigs into genome bins. -- **MetaBAT** ({%cite Kang2019%}): Another widely used de novo binning algorithm that employs a hierarchical clustering approach based on tetranucleotide frequency and coverage information. -- **CONCOCT** ({%cite Alneberg2014%}): A de novo binning tool that uses a clustering algorithm based on sequence composition and coverage information to group contigs into genome bins. -- **MyCC** ({%cite Lin2016%}): A reference-based binning tool that uses sequence alignment to identify contigs belonging to the same genome or taxonomic group. -- **GroopM** ({%cite Imelfort2014%}): A hybrid binning tool that combines reference-based and de novo approaches to achieve high binning accuracy. -- **SemiBin** ({%cite Pan2022%}): A command tool for metagenomic binning with deep learning, handles both short and long reads. -- **Vamb** ({%cite nissen2021improved%}): An algorithm that uses variational autoencoders (VAEs) to encode sequence composition and coverage information. +* **MaxBin** ({%cite maxbin2015%}): A popular de novo binning algorithm that uses a combination of sequence features and marker genes to cluster contigs into genome bins. +* **MetaBAT** ({%cite Kang2019%}): Another widely used de novo binning algorithm that employs a hierarchical clustering approach based on tetranucleotide frequency and coverage information. +* **CONCOCT** ({%cite Alneberg2014%}): A de novo binning tool that uses a clustering algorithm based on sequence composition and coverage information to group contigs into genome bins. +* **MyCC** ({%cite Lin2016%}): A reference-based binning tool that uses sequence alignment to identify contigs belonging to the same genome or taxonomic group. +* **GroopM** ({%cite Imelfort2014%}): A hybrid binning tool that combines reference-based and de novo approaches to achieve high binning accuracy. +* **SemiBin** ({%cite Pan2022%}): A command-line tool for metagenomic binning with deep learning; handles both short and long reads. +* **Vamb** ({%cite nissen2021improved%}): An algorithm that uses variational autoencoders (VAEs) to encode sequence composition and coverage information. +* **ComeBin** ({%cite Wang2024COMEBin%}): A metagenomic binning tool that integrates both composition and abundance features with machine learning-based clustering to improve binning accuracy across complex microbial communities. + +## Bin refinement + +There are also bin refinement tools, which can evaluate, combine, and improve the raw bins produced by primary binners such as MetaBAT2, CONCOCT, MaxBin2, or SemiBin. These tools help remove contamination, merge complementary bins, and recover higher-quality MAGs. + +* **MetaWRAP** ({%cite Uritskiy2018%}): + A comprehensive metagenomic analysis pipeline that includes modules for quality control, assembly, binning (wrapping multiple binners), refinement, reassembly, and annotation. Provides an easy-to-use framework for producing high-quality MAGs from raw reads. + +* **DAS Tool** ({%cite Sieber2018DASTool%}): + A bin-refinement tool that combines results from multiple binners (e.g., MetaBAT2, MaxBin2, CONCOCT, SemiBin) into a consensus set of optimized, non-redundant bins. DAS Tool improves overall bin quality by integrating strength from several algorithms. + +* **Binnette** ({%cite Mainguy2024Binette%}): + Binette is a fast and accurate bin refinement tool that constructs high-quality MAGs from the outputs of multiple binning tools. It generates hybrid bins using set operations on overlapping contigs — intersection, difference, and union — and evaluates their quality with CheckM2 to select the best bins. Compared to metaWRAP, Binette is faster and can process an unlimited number of input bin sets, making it highly scalable for large and complex metagenomic datasets. + +## Anvi’o: Interactive bin refinement + +**Anvi’o** ({%cite Eren2015%}) is a platform for **interactive visualization and manual refinement** of metagenomic bins. While it can run automated binning (defaulting to **CONCOCT**), its main strength lies in allowing users to: -Other tools also include: -- **MetaWRAP** ({%cite Uritskiy2018%}): A comprehensive metagenomic analysis pipeline that includes various modules for quality control, assembly, binning, and annotation. -- **Anvi'o** ({%cite Eren2015%}): A platform for visualizing and analyzing metagenomic data, including features for binning, annotation, and comparative genomics. Uses CONCOCT as the default binning backend. +* Inspect contig-level coverage, GC content, and single-copy gene presence +* Visualize connections between contigs in a network view +* Manually merge, split, or reassign contigs to improve bin completeness and reduce contamination +* Annotate bins and link them to taxonomic or functional information -A benchmark study of metagenomics software can be found at {%cite Sczyrba2017%}. MetaBAT 2 outperforms previous MetaBAT and other alternatives in both accuracy and computational efficiency . All are based on default parameters ({%cite Sczyrba2017%}). +This interactive approach is particularly useful when automated binning produces ambiguous or low-quality bins, enabling **high-confidence MAG reconstruction**. -**In this tutorial, we will learn how to run metagenomic binning tools and evaluate the quality of the results**. In order to do that, we will use data from the study: [Temporal shotgun metagenomic dissection of the coffee fermentation ecosystem](https://www.ebi.ac.uk/metagenomics/studies/MGYS00005630#overview) and one or multiple of the Binning algorithms available in Galaxy. +## So many options, what binner to use ? -For an in-depth analysis of the structure and functions of the coffee microbiome, a temporal shotgun metagenomic study (six time points) was performed. The six samples have been sequenced with Illumina MiSeq utilizing whole genome sequencing. +Each of these binning methods has its own strengths and limitations, and the choice of a binning tool often depends on the characteristics of the metagenomic dataset and the research question. Practical guidance on which binner to use for specific datasets and environments can be drawn from benchmark studies such as {%cite NatureBinner2025%}. -Based on the 6 original dataset of the coffee fermentation system, we generated mock datasets for this tutorial. +!["Benchmark for many Binners"](./images/Binning_Benchmark.png "Benchmark of multiple Binners on Activated sludge and Human Gut Microbiome, taken from {%cite NatureBinner2025%}"){:width="60%"} + +Additionally, the CAMI I and II challenges provide standardized simulated datasets that highlight the strengths and weaknesses of different binners, helping researchers select the most appropriate tool for their analysis. + +!["Benchmark for many Binners based on CAMI"](./images/CAMI_Binners.png "Benchmark of multiple Binners in the CAMI challenge, taken from {%cite Meyer2022%}"){:width="60%"} + +A general approach is to perform binning using multiple binners that have shown good performance for the specific dataset, followed by bin refinement to generate an improved bin set that retains the best bins from the analysis. + +Does using more binners always improve results? In practice, one must also consider computational resources and time constraints. Running many binners can be very time-consuming and resource-intensive, especially for large studies. In some cases, adding extra binners does not lead to a meaningful increase in bin quality, so the choice of binners should be made carefully. Overall, identifying the optimal combination of binners remains an active area of research, and clear, widely accepted guidelines are still being established. + +# Mock binning dataset for this training + +Read mapping and binning real metagenommic datasets is a computational demanding task and time consuming. To demonstrate the basics of binning in this tutorial we generated a small mock dataset, that is just large enough to produce bins for all binners in this tutorial. The same binners can be applied for any real life datasets, but as said, plan in some time, up to weeks in some cases. > > @@ -173,15 +205,10 @@ In case of a not very large dataset it's more convenient to upload data directly > Upload data into Galaxy > -> 1. Import the contigs (\*.fasta) from [Zenodo]({{ page.zenodo_link }}) or a data library: +> 1. Import the contig file from [Zenodo]({{ page.zenodo_link }}) or a data library: > > ```text -> {{ page.zenodo_link }}/files/contigs_ERR2231567.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231568.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231569.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231570.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231571.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231572.fasta +> {{ page.zenodo_link }}/files/MEGAHIT_contigs.fasta > ``` > > {% snippet faqs/galaxy/datasets_import_via_link.md %} @@ -199,40 +226,49 @@ In case of a not very large dataset it's more convenient to upload data directly > 3. Also import the raw reads in fastq format (\*.fasta) from [Zenodo]({{ page.zenodo_link }}) or a data library: > > ```text -> {{ page.zenodo_link }}/files/contigs_ERR2231567.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231568.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231569.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231570.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231571.fasta -> {{ page.zenodo_link }}/files/contigs_ERR2231572.fasta +> {{ page.zenodo_link }}/files/reads_forward.fastqsanger.gz +> {{ page.zenodo_link }}/files/reads_reverse.fastqsanger.gz > ``` -> 2. Create a collection named `Raw reads` +> 4. Create a collection named `Reads` > -> {% snippet faqs/galaxy/collections_build_list.md %} - - -{: .hands_on} +> {% snippet faqs/galaxy/collections_build_list_paired.md %} +> Why do we use collections here? +> In this tutorial, collections are not strictly necessary because we are working with only one contig file and its paired-end reads. However, in real metagenomic studies, it is common to process many samples—sometimes hundreds or even thousands—and in those cases, collections become essential for managing data efficiently. +> +> It is generally good practice to first test a workflow on a small subset of the data (for example, a collection containing only a single sample) to ensure that the tools run correctly and the parameters are appropriate before launching thousands of jobs on Galaxy. +{: .comment} # Preparation for binning -As explained before we need coverage information in bam format as a requirement for all binners. Some binners need a specific format for the coverage information, but this will be covered in the version specific to the desired binner. For now we will map the raw reads to the contigs to get a bam file with the coverage information. This bam file also needs to be sorted for the downstream binners. +As explained before we need coverage information in bam format as a requirement for all binners. Some binners need a specific format for the coverage information, but this will be covered in the version specific to the desired binner. For now we will map the quality controled reads to the contigs to get a bam file with the coverage information. This bam file also needs to be sorted for the downstream binners. + +Make sure the reads are quality controlled. E.g. following the QC toturial TODO. -> Map reads to contigs -> 1. {% tool [Bowtie2](toolshed.g2.bx.psu.edu/repos/devteam/bowtie2/bowtie2/2.5.4+galaxy0) %} with parameters: -> - In **Is this single or paired library**, choose **Paired-end**. -> - *"FASTQ Paired Dataset"*: `Reads` -> - In **Will you select a reference genome from your history or use a built-in index?**, choose **Use a genome from the histroy and build index**. -> - *"Select reference genome"*: `Contigs` +> Map reads to contigs +> +> 1. {% tool [Bowtie2](toolshed.g2.bx.psu.edu/repos/devteam/bowtie2/bowtie2/2.5.4+galaxy0) %} with the following parameters: +> - *"Is this single or paired library"*: `Paired-end` +> - {% icon param-collection %} *"FASTQ Paired Dataset"*: `Reads` (Input dataset collection) +> - *"Do you want to set paired-end options?"*: `No` +> - *"Will you select a reference genome from your history or use a built-in index?"*: `Use a genome from the history and build index` +> - {% icon param-collection %} *"Select reference genome"*: `Contigs` (Input dataset collection) +> - *"Set read groups information?"*: `Do not set` +> - *"Select analysis mode"*: `1: Default setting only` +> - *"Do you want to tweak SAM/BAM Options?"*: `No` +> - *"Save the bowtie2 mapping statistics to the history"*: `Yes` > {: .hands_on} -> Sort bam files -> 1. {% tool [Samtools sort](toolshed.g2.bx.psu.edu/repos/devteam/samtools_sort/samtools_sort/2.0.7) %} with parameters: -> - *"BAM File"*: `Output of Bowtie2` -> - In **Primary sort key**, choose **coordinate**. +> Sort bam files +> +> 1. {% tool [Samtools sort](toolshed.g2.bx.psu.edu/repos/devteam/samtools_sort/samtools_sort/2.0.7) %} with the following parameters: +> - {% icon param-file %} *"BAM File"*: output of **Bowtie2** {% icon tool %} +> - *"Primary sort key"*: `coordinate` +> {: .hands_on} +The sorted bam file can be used as input for any of the binning tools. # Binning @@ -265,6 +301,90 @@ In this tutorial, we offer dedicated versions, which highlight each of the follo {% include topics/microbiome/tutorials/metagenomics-binning/concoct_version.md %}

+# Bin refinement + +Now, that you have produced bins with your favorite Binning algorithms you can refine the recovered bins. +Thereore, you need to convert the bins into a contig to bin mapping table, combine the tables from each binner into one collection and +use Binette to creat consensus bins. An alternative tool would be {% tool [DAS Tool](toolshed.g2.bx.psu.edu/repos/iuc/das_tool/das_tool/1.1.7+galaxy1) %} which is also available in Galaxy. + +For the refinement we will use the bins created by all the binners used before. If you do not want to run them all by yourself, +we have provided the results here as well. + +> Get result bins +> +> 1. Import the contig file from [Zenodo]({{ page.zenodo_link }}) or a data library: +> +> ```text +> {{ page.zenodo_link }}/files/semibin_0.fasta +> {{ page.zenodo_link }}/files/maxbin_0.fasta +> {{ page.zenodo_link }}/files/maxbin_1.fasta +> {{ page.zenodo_link }}/files/metabat_0.fasta +> {{ page.zenodo_link }}/files/concoct_1.fasta +> {{ page.zenodo_link }}/files/concoct_2.fasta +> {{ page.zenodo_link }}/files/concoct_3.fasta +> {{ page.zenodo_link }}/files/concoct_4.fasta +> {{ page.zenodo_link }}/files/concoct_5.fasta +> {{ page.zenodo_link }}/files/concoct_6.fasta +> {{ page.zenodo_link }}/files/concoct_7.fasta +> {{ page.zenodo_link }}/files/concoct_8.fasta +> {{ page.zenodo_link }}/files/concoct_9.fasta +> ``` +> +> 2. Create a collection for each bin set called e.g. maxbin, semibin ... by selecting only the bins created by this binner and creating a collection: +> +> {% snippet faqs/galaxy/collections_build_list.md %} + +Once each bin set is converting into one collection they can be converted into a contig to bin mapping table. Perform this step for every bin set. + +> Convert the bins into a contig to bin mapping table +> +> 1. {% tool [Converts genome bins in fasta format](toolshed.g2.bx.psu.edu/repos/iuc/fasta_to_contig2bin/Fasta_to_Contig2Bin/1.1.7+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Bin sequences"*: `bins` (output of any of the binners {% icon tool %}) +> +{: .hands_on} + + +> Build a list of the binning tables +> +> 1. {% tool [Build list](__BUILD_LIST__) %} with the following parameters: +> - In *"Dataset"*: +> - {% icon param-repeat %} *"Insert Dataset"* +> - {% icon param-file %} *"Input Dataset"*: `contigs2bin` (output of **Converts genome bins in fasta format** of SemiBin {% icon tool %}) +> - *"Label to use"*: `Index` +> - {% icon param-repeat %} *"Insert Dataset"* +> - {% icon param-file %} *"Input Dataset"*: `contigs2bin` (output of **Converts genome bins in fasta format** of MetaBAT2 {% icon tool %}) +> - *"Label to use"*: `Index` +> - {% icon param-repeat %} *"Insert Dataset"* +> - {% icon param-file %} *"Input Dataset"*: `contigs2bin` (output of **Converts genome bins in fasta format** of MaxBin2 {% icon tool %}) +> - *"Label to use"*: `Index` +> - {% icon param-repeat %} *"Insert Dataset"* +> - {% icon param-file %} *"Input Dataset"*: `contigs2bin` (output of **Converts genome bins in fasta format** of CONCOCT {% icon tool %}) +> - *"Label to use"*: `Index` +> +{: .hands_on} + +> Refine with Binette +> +> 1. {% tool [Binette](toolshed.g2.bx.psu.edu/repos/iuc/binette/binette/1.2.0+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Input contig table"*: `output` (output of **Build list** {% icon tool %}) +> - {% icon param-collection %} *"Input contig file"*: `output` (Input dataset collection) +> - *"Select if database should be used either via file or cached database"*: `cached database` +> +{: .hands_on} + +> Bin refinement +> +> 1. How many bins are left after refinement ? +> +> > +> > +> > 1. Two bins are left. Most contigs from different bins where combined into one bin. There is still one single contig bin left. +> > +> {: .solution} +> +{: .question} + + # De-replication De-replication is the process of identifying sets of genomes that are the "same" in a list of genomes, and removing all but the “best” genome from each redundant set. How similar genomes need to be to be considered “same”, how to determine which genome is “best”, and other important decisions are discussed in [Important Concepts](https://drep.readthedocs.io/en/latest/choosing_parameters.html). @@ -273,13 +393,29 @@ A common use for genome de-replication is the case of individual assembly of met ![Image shows the process of individual assembly on two strains and five samples, after individual assembly of samples two samples are chosen for de-replication process. In parallel, co-assembly on all five samples is performed](./individual-assembly.png "Individual assembly followed by de-replication vs co-assembly."){:width="80%"} -MetaBAT 2 does not explicitly perform dereplication in the sense of identifying groups of identical or highly similar genomes in a given dataset. Instead, MetaBAT 2 focuses on improving the accuracy of binning by leveraging various features such as read coverage, differential coverage across samples, and sequence composition. It aims to distinguish between different genomes present in the metagenomic dataset and assign contigs to the appropriate bins. - Several tools have been designed for the proccess of de-replication. **`dRep`** is a software tool designed for the dereplication of genomes in metagenomic datasets. The goal is to retain a representative set of genomes to improve downstream analyses, such as taxonomic profiling and functional annotation. An typical workflow of how `dRep` works for dereplication in metagenomics includes: -- *Genome Comparison*: `dRep` uses a pairwise genome comparison approach to assess the similarity between genomes in a given metagenomic dataset. +- *Genome Comparison*: `dR> +> +> Since the CheckM process would take some time we are just going to import the results: +> +> > Import generated `CheckM lineage_wf` results +> > +> > 1. Import the `CheckM lineage_wf` report files from [Zenodo]({{ page.extra.zenodo_link_results }}) or the Shared Data library: +> > +> > ```text +> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231567__Bin_statistics.txt +> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231568__Bin_statistics.txt +> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231569__Bin_statistics.txt +> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231570__Bin_statistics.txt +> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231571__Bin_statistics.txt +> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231572__Bin_statistics.txt +> > ``` +> > +> {: .hands_on} +{: .comment}ep` uses a pairwise genome comparison approach to assess the similarity between genomes in a given metagenomic dataset. - *Clustering*: Based on the genome similarities, `dRep` performs clustering to group similar genomes into "genome clusters." Each cluster represents a group of closely related genomes. @@ -379,28 +515,6 @@ The output of "CheckM lineage_wf" includes several files and tables that provide It should be noted that "CheckM lineage_wf" offers a range of optional outputs that can be generated to provide additional information to the user. - - # Conclusions In summary, this tutorial shows a step-by-step on how to bin metagenomic contigs using MetaBAT 2. From 06cafc6016682c8cd986401984f686ae6659a069 Mon Sep 17 00:00:00 2001 From: paulzierep Date: Thu, 20 Nov 2025 14:46:39 +0100 Subject: [PATCH 10/14] fix box --- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 1 + 1 file changed, 1 insertion(+) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 5314a2fdf3b971..30f9bbd94c4f70 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -333,6 +333,7 @@ we have provided the results here as well. > 2. Create a collection for each bin set called e.g. maxbin, semibin ... by selecting only the bins created by this binner and creating a collection: > > {% snippet faqs/galaxy/collections_build_list.md %} +{: .hands_on} Once each bin set is converting into one collection they can be converted into a contig to bin mapping table. Perform this step for every bin set. From 8d7de5c673e409858ef5c9ca5b7d05c2b0cc63bf Mon Sep 17 00:00:00 2001 From: paulzierep Date: Fri, 21 Nov 2025 12:46:13 +0100 Subject: [PATCH 11/14] add combine, add workflow, add wf test, add result question --- .../metagenomics-binning/comebin_version.md | 35 + .../metagenomics-binning/tutorial.bib | 11 + .../metagenomics-binning/tutorial.md | 217 ++-- .../workflows/Metagenomic-Binning-tests.yml | 31 + .../workflows/Metagenomic-Binning.ga | 1111 +++++++++++++++++ .../metagenomics-binning/workflows/index.md | 3 + 6 files changed, 1314 insertions(+), 94 deletions(-) create mode 100644 topics/microbiome/tutorials/metagenomics-binning/comebin_version.md create mode 100644 topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning-tests.yml create mode 100644 topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga create mode 100644 topics/microbiome/tutorials/metagenomics-binning/workflows/index.md diff --git a/topics/microbiome/tutorials/metagenomics-binning/comebin_version.md b/topics/microbiome/tutorials/metagenomics-binning/comebin_version.md new file mode 100644 index 00000000000000..910f2b7f66803e --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/comebin_version.md @@ -0,0 +1,35 @@ +## COMEbin + +COMEbin is a relatively new binner that has shown remarkably strong performance in recent benchmarking studies. +However, it also has several drawbacks. +* Due to its implementation, it cannot operate reliably on small test datasets, and therefore we cannot include it in this tutorial. +* It requires substantial computational resources and long runtimes. +* The tool also suffers from other technical issues that can cause runs to fail. + +These problems cannot be resolved on the Galaxy side, and the tool is currently only lightly maintained upstream. + +Nevertheless, because COMEbin can produce some of the best-performing bins when it runs successfully, we still mention it here. It may yield excellent results on real biological datasets and is available in Galaxy. + +> Do not run COMEBin +> +> As said: Due to its implementation, it cannot operate reliably on small test datasets, and therefore we cannot include it in this tutorial. Do not run it on the tutorial dataset — it will fail. +> +{: .warning} + + +### Bin contigs using COMEbin + +> Individual binning of short reads with COMEbin +> +> 1. {% tool [COMEBin](toolshed.g2.bx.psu.edu/repos/iuc/comebin/comebin/1.0.4+galaxy1) %} with the following parameters: +> - {% icon param-collection %} *"Metagenomic assembly file"*: `Contigs` (Input dataset collection) +> - {% icon param-file %} *"Input bam file(s)"*: `Reads` (output of **Samtools sort** {% icon tool %}) +> +> > Parameters +> > +> > The Batch size should be less then the number of contigs. But if this is the case for the batch size of 1014 your input data is likely too small to run with this tool ! +> {: .comment} +> +{: .hands_on} + + diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib index ec65ec2dac2316..218335ca6f765b 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.bib @@ -42,6 +42,17 @@ @article{Wang2024COMEBin url = {https://doi.org/10.1038/s41467-023-44290-z} } +@article{Chklovski2023CheckM2, + title = {CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning}, + author = {Alex Chklovski and Donovan H. Parks and Ben J. Woodcroft and Gene W. Tyson}, + journal = {Nature Methods}, + year = {2023}, + volume = {20}, + number = {8}, + pages = {1203--1212}, + doi = {10.1038/s41592-023-01940-w} +} + @article{Mainguy2024Binette, author = {Mainguy, Jean and Hoede, Claire}, title = {Binette: a fast and accurate bin refinement tool to construct high‐quality Metagenome Assembled Genomes}, diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 30f9bbd94c4f70..9c068aae8c0eb2 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -84,7 +84,7 @@ There are several approaches to metagenomics binning, including: ## Binning challanges -**Metagenomic binning is a complex process that involves many steps and can be challenging due to several problems that can occur during the process**. Some of the most common problems encountered in metagenomic binning include: +Metagenomic binning is a complex process that involves many steps and can be challenging due to several problems that can occur during the process. Some of the most common problems encountered in metagenomic binning include: - **High complexity**: Metagenomic samples contain DNA from multiple organisms, which can lead to high complexity in the data. - **Fragmented sequences**: Metagenomic sequencing often generates fragmented sequences, which can make it difficult to assign reads to the correct bin. @@ -286,7 +286,7 @@ As explained before, there are many challenges to metagenomics binning. The most In this tutorial, we offer dedicated versions, which highlight each of the following binners: -{% include _includes/cyoa-choices.html option1="MetaBAT2" option2="MaxBin2" option3="SemiBin" option4="CONCOCT" default="MetaBAT2" %} +{% include _includes/cyoa-choices.html option1="MetaBAT2" option2="MaxBin2" option3="SemiBin" option4="CONCOCT" option5="COMEBin" default="MetaBAT2" %}
{% include topics/microbiome/tutorials/metagenomics-binning/metabet2_version.md %} @@ -300,11 +300,14 @@ In this tutorial, we offer dedicated versions, which highlight each of the follo
{% include topics/microbiome/tutorials/metagenomics-binning/concoct_version.md %}
+
+{% include topics/microbiome/tutorials/metagenomics-binning/comebin_version.md %} +
# Bin refinement Now, that you have produced bins with your favorite Binning algorithms you can refine the recovered bins. -Thereore, you need to convert the bins into a contig to bin mapping table, combine the tables from each binner into one collection and +Therefore, you need to convert the bins into a contig to bin mapping table, combine the tables from each binner into one collection and use Binette to creat consensus bins. An alternative tool would be {% tool [DAS Tool](toolshed.g2.bx.psu.edu/repos/iuc/das_tool/das_tool/1.1.7+galaxy1) %} which is also available in Galaxy. For the refinement we will use the bins created by all the binners used before. If you do not want to run them all by yourself, @@ -385,65 +388,11 @@ Once each bin set is converting into one collection they can be converted into a > {: .question} - -# De-replication - -De-replication is the process of identifying sets of genomes that are the "same" in a list of genomes, and removing all but the “best” genome from each redundant set. How similar genomes need to be to be considered “same”, how to determine which genome is “best”, and other important decisions are discussed in [Important Concepts](https://drep.readthedocs.io/en/latest/choosing_parameters.html). - -A common use for genome de-replication is the case of individual assembly of metagenomic data. If metagenomic samples are collected in a series, a common way to assemble the short reads is with a “co-assembly”. That is, combining the reads from all samples and assembling them together. The problem with this is assembling similar strains together can severely fragment assemblies, precluding recovery of a good genome bin. An alternative option is to assemble each sample separately, and then “de-replicate” the bins from each assembly to make a final genome set. - -![Image shows the process of individual assembly on two strains and five samples, after individual assembly of samples two samples are chosen for de-replication process. In parallel, co-assembly on all five samples is performed](./individual-assembly.png "Individual assembly followed by de-replication vs co-assembly."){:width="80%"} - -Several tools have been designed for the proccess of de-replication. **`dRep`** is a software tool designed for the dereplication of genomes in metagenomic datasets. The goal is to retain a representative set of genomes to improve downstream analyses, such as taxonomic profiling and functional annotation. - -An typical workflow of how `dRep` works for dereplication in metagenomics includes: - -- *Genome Comparison*: `dR> -> -> Since the CheckM process would take some time we are just going to import the results: -> -> > Import generated `CheckM lineage_wf` results -> > -> > 1. Import the `CheckM lineage_wf` report files from [Zenodo]({{ page.extra.zenodo_link_results }}) or the Shared Data library: -> > -> > ```text -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231567__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231568__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231569__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231570__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231571__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231572__Bin_statistics.txt -> > ``` -> > -> {: .hands_on} -{: .comment}ep` uses a pairwise genome comparison approach to assess the similarity between genomes in a given metagenomic dataset. - -- *Clustering*: Based on the genome similarities, `dRep` performs clustering to group similar genomes into "genome clusters." Each cluster represents a group of closely related genomes. - -- *Genome Quality Assessment*: `dRep` evaluates the quality of each genome within a cluster. It considers factors such as completeness, contamination, and strain heterogeneity. - -- *Genome Selection*: Within each genome cluster, `dRep` selects a representative genome based on user-defined criteria. This representative genome is considered as the "dereplicated" version of the cluster. - -- *Dereplication Output*: The output of `dRep` includes information about the dereplicated genomes, including their identity, completeness, and contamination. The user can choose a threshold for genome similarity to control the level of dereplication. - -> General list of actions for de-replication -> 1. Create new history -> 2. Assemble each sample separately using your favorite assembler -> 3. Perform a co-assembly to catch low-abundance microbes -> 4. Bin each assembly separately using your favorite binner -> 5. Bin co-assembly using your favorite binner -> 6. Pull the bins from all assemblies together -> 7. rRun **`dRep`** on them -> 8. Perform downstream analysis on the de-replicated genome list -> -{: .hands_on} - - # Checking the quality of the bins Once binning is done, it is important to check its quality. -Binning results can be evaluated with **CheckM** ({%cite Parks2015%}). CheckM is a software tool used in metagenomics binning to assess the completeness and contamination of genome bins. Metagenomics binning is the process of separating DNA fragments from a mixed community of microorganisms into individual bins, each representing a distinct genome. +Binning results can be evaluated with **CheckM** ({%cite Parks2015%}). CheckM is a software tool used in metagenomics binning to assess the completeness and contamination of genome bins. CheckM compares the genome bins to a set of universal single-copy marker genes that are present in nearly all bacterial and archaeal genomes. By identifying the presence or absence of these marker genes in the bins, CheckM can estimate the completeness of each genome bin (i.e., the percentage of the total set of universal single-copy marker genes that are present in the bin) and the degree of contamination (i.e., the percentage of marker genes that are found in more than one bin). @@ -463,48 +412,17 @@ Based on the previous analysis we will use **CheckM lineage_wf**: *Assessing the `CheckM lineage_wf` is a specific workflow within the CheckM software tool that is used for taxonomic classification of genome bins based on their marker gene content. This workflow uses a reference database of marker genes and taxonomic information to classify the genome bins at different taxonomic levels, from domain to species. +Now you can investigate the completeness and contamination of any of your previously generated genome bins as well as the refined set. + > Assessing the completeness and contamination of genome bins using lineage-specific marker sets with `CheckM lineage_wf` > 1. {% tool [CheckM lineage_wf](toolshed.g2.bx.psu.edu/repos/iuc/checkm_lineage_wf/checkm_lineage_wf/1.2.0+galaxy0) %} with parameters: > - *"Bins"*: `Folder containing the produced bins` -> -> {: .hands_on} -> -> -> Since the CheckM process would take some time we are just going to import the results: -> -> > Import generated `CheckM lineage_wf` results -> > -> > 1. Import the `CheckM lineage_wf` report files from [Zenodo]({{ page.extra.zenodo_link_results }}) or the Shared Data library: -> > -> > ```text -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231567__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231568__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231569__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231570__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231571__Bin_statistics.txt -> > {{ page.extra.zenodo_link_results }}/files/CheckM_lineage_wf_on_data_ERR2231572__Bin_statistics.txt -> > ``` -> > -> {: .hands_on} -{: .comment} The output of "CheckM lineage_wf" includes several files and tables that provide information about the taxonomic classification and quality assessment of genome bins. Here are some of the key outputs: -- **CheckM Lineage Workflow Output Report**: This report provides a summary of the quality assessment performed by CheckM. It includes statistics such as the number of genomes analyzed, their completeness, contamination, and other quality metrics. +- **CheckM Lineage Workflow Output Report (Bin statistics)**: This report provides a summary of the quality assessment performed by CheckM. It includes statistics such as the number of genomes analyzed, their completeness, contamination, and other quality metrics. - **Lineage-specific Quality Assessment**: CheckM generates lineage-specific quality assessment files for each analyzed genome. These files contain detailed information about the completeness and contamination of the genome based on its taxonomic lineage. @@ -516,9 +434,120 @@ The output of "CheckM lineage_wf" includes several files and tables that provide It should be noted that "CheckM lineage_wf" offers a range of optional outputs that can be generated to provide additional information to the user. +To keep it simple we will check the bin statistics to investigate the performance of our previous binning efforts. + +> Binning performance +> +> 1. Which binner created the best bins? +> 2. How well did the bin refinement work? +> +> > +> > +> > **MetaBAT2** +> > +> > | Bin ID | Marker lineage | # genomes | # markers | # marker sets | 0 | 1 | 2 | 3 | 4 | 5+ | Completeness | Contamination | Strain heterogeneity | +> > |--------|------------------------|-----------|------------|----------------|----|----|----|----|----|----|--------------|---------------|-----------------------| +> > | 1 | k__Bacteria (UID203) | 5449 | 103 | 58 | 89 | 14 | 0 | 0 | 0 | 0 | 15.67 | 0.00 | 0.00 | +> > +> > **SemiBin** +> > +> > | Bin ID | Marker lineage | # genomes | # markers | # marker sets | 0 | 1 | 2 | 3 | 4 | 5+ | Completeness | Contamination | Strain heterogeneity | +> > |------------|------------------------|-----------|------------|----------------|----|----|----|----|----|----|--------------|---------------|-----------------------| +> > | SemiBin_0 | k__Bacteria (UID203) | 5449 | 103 | 58 | 89 | 14 | 0 | 0 | 0 | 0 | 15.67 | 0.00 | 0.00 | +> > +> > **MaxBin** +> > +> > | Bin ID | Marker lineage | # genomes | # markers | # marker sets | 0 | 1 | 2 | 3 | 4 | 5+ | Completeness | Contamination | Strain heterogeneity | +> > |--------|------------------------|-----------|------------|----------------|----|----|----|----|----|----|--------------|---------------|-----------------------| +> > | 001 | k__Bacteria (UID203) | 5449 | 103 | 58 | 92 | 11 | 0 | 0 | 0 | 0 | 10.50 | 0.00 | 0.00 | +> > | 002 | k__Bacteria (UID203) | 5449 | 103 | 58 | 99 | 4 | 0 | 0 | 0 | 0 | 6.90 | 0.00 | 0.00 | +> > +> > **CONCOCT** +> > +> > | Bin ID | Marker lineage | # genomes | # markers | # marker sets | 0 | 1 | 2 | 3 | 4 | 5+ | Completeness | Contamination | Strain heterogeneity | +> > |--------|-------------------------|-----------|------------|----------------|----|----|----|----|----|----|--------------|---------------|-----------------------| +> > | 0 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 1 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 2 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 3 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 4 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 5 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 6 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 7 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 8 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > | 9 | k__Bacteria (UID203) | 5449 | 103 | 58 | 89 | 14 | 0 | 0 | 0 | 0 | 15.67 | 0.00 | 0.00 | +> > +> > **Binette** +> > +> > | Bin ID | Marker lineage | # genomes | # markers | # marker sets | 0 | 1 | 2 | 3 | 4 | 5+ | Completeness | Contamination | Strain heterogeneity | +> > |---------------|------------------------|-----------|------------|----------------|----|----|----|----|----|----|--------------|---------------|-----------------------| +> > | binette_bin1 | k__Bacteria (UID203) | 5449 | 103 | 58 | 89 | 14 | 0 | 0 | 0 | 0 | 15.67 | 0.00 | 0.00 | +> > | binette_bin2 | root (UID1) | 5656 | 56 | 24 | 56 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | +> > +> > **MetaBAT2** and **SemiBin** generated the best‐performing bins in this dataset, each recovering one bin with ~15.7% completeness and no detectable contamination. +> > +> > **MaxBin** produced bins of lower completeness (10.5% and 6.9%), but still without contamination. +> > +> > **CONCOCT** largely failed to recover meaningful bins in this dataset: most bins show 0% completeness. This often occurs with CONCOCT on small assemblies or uneven coverage, because its Gaussian clustering model struggles when the number of contigs is low or the variation in coverage is insufficient. +> > +> > **Binette** produced one bin essentially identical in quality to the MetaBAT2/SemiBin bins (~15.7% completeness), plus a root-level bin with no completeness. Binette therefore could recover the most completely bin sucessful. The additional +loq quality bin, would have normally be filtered by Binette if the `Set minimus completeness` parameter would be set to a reasonable values, often > 75 % for true biological data. +> > +> {: .solution} +> +{: .question} + +> CheckM2 +> CheckM2 ({%cite Chklovski2023CheckM2%}) is the successor of CheckM, but CheckM is still widely used, since its marker-based logic can be more interpretable in a biological sense. E.g. to date (2025-11-21) NCBI still allows to submit MAGs to genbank if either checkM or checkM2 has a completeness of > 90% (see the [NCBI WGS/MAG submission guidelines](https://www.ncbi.nlm.nih.gov/genbank/wgsfaq/#metagen)). +> +> **Key differences compared to CheckM1**: +> +> * CheckM1 relies primarily on lineage-specific single-copy marker genes to estimate completeness and contamination of microbial genomes. +> * CheckM2 uses a machine-learning (gradient boost / ML) approach trained on simulated and experimental genomes, and does *not* strictly require a well-represented lineage in its marker database. +> * CheckM2 is reported to be more accurate and faster for both bacterial and archaeal lineages, especially when dealing with novel or very reduced-genome lineages (e.g., candidate phyla, CPR/DPANN) where classical marker-gene methods may struggle. +> * The database of CheckM2 can be updated more rapidly with new high-quality reference genomes, which supports scalability and improved performance over time. +> +> If you’re working with MAGs from under-represented taxa (novel lineages) or very small genomes (streamlined bacteria/archaea), CheckM2 tends to give more reliable estimates of completeness/contamination. For more “standard” microbial genomes from well-studied taxa, CheckM1 may still work well, but you may benefit from the improved performance with CheckM2. +> +{: .comment} + +# De-replication + +De-replication is the process of identifying sets of genomes that are the "same" in a list of genomes, and removing all but the “best” genome from each redundant set. How similar genomes need to be to be considered “same”, how to determine which genome is “best”, and other important decisions are discussed in [Important Concepts](https://drep.readthedocs.io/en/latest/choosing_parameters.html). + +A common use for genome de-replication is the case of individual assembly of metagenomic data. If metagenomic samples are collected in a series, a common way to assemble the short reads is with a “co-assembly”. That is, combining the reads from all samples and assembling them together. The problem with this is assembling similar strains together can severely fragment assemblies, precluding recovery of a good genome bin. An alternative option is to assemble each sample separately, and then “de-replicate” the bins from each assembly to make a final genome set. + +![Image shows the process of individual assembly on two strains and five samples, after individual assembly of samples two samples are chosen for de-replication process. In parallel, co-assembly on all five samples is performed](./individual-assembly.png "Individual assembly followed by de-replication vs co-assembly"){:width="80%"} + +Several tools have been designed for the proccess of de-replication. **`dRep`** is a software tool designed for the dereplication of genomes in metagenomic datasets. The goal is to retain a representative set of genomes to improve downstream analyses, such as taxonomic profiling and functional annotation. + +An typical workflow of how `dRep` works for dereplication in metagenomics includes: + +- *Genome Comparison*: `dRep` uses a pairwise genome comparison approach to assess the similarity between genomes in a given metagenomic dataset. + +- *Clustering*: Based on the genome similarities, `dRep` performs clustering to group similar genomes into "genome clusters." Each cluster represents a group of closely related genomes. + +- *Genome Quality Assessment*: `dRep` evaluates the quality of each genome within a cluster. It considers factors such as completeness, contamination, and strain heterogeneity. + +- *Genome Selection*: Within each genome cluster, `dRep` selects a representative genome based on user-defined criteria. This representative genome is considered as the "dereplicated" version of the cluster. + +- *Dereplication Output*: The output of `dRep` includes information about the dereplicated genomes, including their identity, completeness, and contamination. The user can choose a threshold for genome similarity to control the level of dereplication. + +> General list of actions for de-replication +> 1. Create new history +> 2. Assemble each sample separately using your favorite assembler +> 3. Perform a co-assembly to catch low-abundance microbes +> 4. Bin each assembly separately using your favorite binner +> 5. Bin co-assembly using your favorite binner +> 6. Pull the bins from all assemblies together +> 7. Run **`dRep`** on them +> 8. Perform downstream analysis on the de-replicated genome list +> +{: .hands_on} + # Conclusions -In summary, this tutorial shows a step-by-step on how to bin metagenomic contigs using MetaBAT 2. +In summary, this tutorial shows a step-by-step on how to bin metagenomic contigs using various Binners, including Bin refinement. It is critical to select the appropriate binning tool for a specific metagenomics study, as different binning methods may have different strengths and limitations depending on the type of metagenomic data being analyzed. By comparing the outcomes of several binning techniques, researchers can increase the precision and accuracy of genome binning. @@ -526,4 +555,4 @@ There are various binning methods available for metagenomic data, including refe Comparing the outcomes of multiple binning methods can help to identify the most accurate and reliable method for a specific study. This can be done by evaluating the quality of the resulting bins in terms of completeness, contamination, and strain heterogeneity, as well as by comparing the composition and functional profiles of the identified genomes. -Overall, by carefully selecting and comparing binning methods, researchers can improve the quality and reliability of genome bins, which can ultimately lead to a better understanding of the functional and ecological roles of microbial communities in various environments. +Overall, by carefully selecting and comparing binning methods, researchers can improve the quality and reliability of genome bins, which can ultimately lead to a better understanding of the functional and ecological roles of microbial communities in various environments. \ No newline at end of file diff --git a/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning-tests.yml b/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning-tests.yml new file mode 100644 index 00000000000000..09939e694c3f0a --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning-tests.yml @@ -0,0 +1,31 @@ +- doc: Test outline for Assembly-of-metagenomic-sequencing-data + job: + Trimmed reads: + class: Collection + collection_type: list:paired + elements: + - class: Collection + type: paired + identifier: ERR2231567 + elements: + - class: File + identifier: forward + path: https://zenodo.org/records/17660820/files/reads_forward.fastqsanger.gz + - class: File + identifier: reverse + path: https://zenodo.org/records/17660820/files/reads_reverse.fastqsanger.gz + Assemblies: + class: Collection + collection_type: list + elements: + - class: File + identifier: ERR2231567 + path: https://zenodo.org/records/17660820/files/MEGAHIT_contigs.fasta + + outputs: + final: + asserts: + has_text: + text: "binette_bin1" + has_text: + text: "16.69" diff --git a/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga b/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga new file mode 100644 index 00000000000000..c33c6c16018ea6 --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga @@ -0,0 +1,1111 @@ +{ + "a_galaxy_workflow": "true", + "annotation": "Binning workflows that uses abundance information and performs binning of metagenomic contigs using 4 different binners as well as bin refinement.", + "comments": [ + { + "color": "orange", + "data": { + "text": "# Bin refinement" + }, + "id": 5, + "position": [ + 2404.7906885054667, + 1141.6 + ], + "size": [ + 1271, + 1458 + ], + "type": "markdown" + }, + { + "color": "none", + "data": { + "text": "# CONCOCT\n" + }, + "id": 0, + "position": [ + 680.4906885054666, + 0 + ], + "size": [ + 1713, + 597 + ], + "type": "markdown" + }, + { + "color": "lime", + "data": { + "text": "# Mapping" + }, + "id": 1, + "position": [ + 11.290688505466562, + 1078.1999999999998 + ], + "size": [ + 692, + 415 + ], + "type": "markdown" + }, + { + "color": "red", + "data": { + "text": "# MetaBAT2\n" + }, + "id": 2, + "position": [ + 1496.8906885054666, + 656.9 + ], + "size": [ + 541, + 404 + ], + "type": "markdown" + }, + { + "color": "pink", + "data": { + "text": "# MaxBin2" + }, + "id": 3, + "position": [ + 1533.5906885054667, + 1150.1999999999998 + ], + "size": [ + 587, + 490 + ], + "type": "markdown" + }, + { + "color": "lime", + "data": { + "text": "# SemiBin" + }, + "id": 4, + "position": [ + 1089.8906885054666, + 1704.6999999999998 + ], + "size": [ + 887, + 532 + ], + "type": "markdown" + } + ], + "creator": [ + { + "class": "Person", + "identifier": "https://orcid.org/0000-0003-2982-388X", + "name": "Paul Zierep" + } + ], + "format-version": "0.1", + "help": "", + "license": "MIT", + "name": "Metagenomic Binning", + "readme": "", + "report": { + "markdown": "\n# Workflow Execution Report\n\n## Workflow Inputs\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n```galaxy\nworkflow_display()\n```\n" + }, + "steps": { + "0": { + "annotation": "Samples grouped for co-assembly. 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"version": 6 +} \ No newline at end of file diff --git a/topics/microbiome/tutorials/metagenomics-binning/workflows/index.md b/topics/microbiome/tutorials/metagenomics-binning/workflows/index.md new file mode 100644 index 00000000000000..e092e0ae66ddd4 --- /dev/null +++ b/topics/microbiome/tutorials/metagenomics-binning/workflows/index.md @@ -0,0 +1,3 @@ +--- +layout: workflow-list +--- From 4c264b5031947e22fa94b4f16f34e9d769543589 Mon Sep 17 00:00:00 2001 From: paulzierep Date: Fri, 21 Nov 2025 12:52:04 +0100 Subject: [PATCH 12/14] linting fix --- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index 9c068aae8c0eb2..f196f220b490d2 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -232,6 +232,7 @@ In case of a not very large dataset it's more convenient to upload data directly > 4. Create a collection named `Reads` > > {% snippet faqs/galaxy/collections_build_list_paired.md %} +{: .hands_on} > Why do we use collections here? > In this tutorial, collections are not strictly necessary because we are working with only one contig file and its paired-end reads. However, in real metagenomic studies, it is common to process many samples—sometimes hundreds or even thousands—and in those cases, collections become essential for managing data efficiently. @@ -492,7 +493,6 @@ To keep it simple we will check the bin statistics to investigate the performanc > > > > **Binette** produced one bin essentially identical in quality to the MetaBAT2/SemiBin bins (~15.7% completeness), plus a root-level bin with no completeness. Binette therefore could recover the most completely bin sucessful. The additional loq quality bin, would have normally be filtered by Binette if the `Set minimus completeness` parameter would be set to a reasonable values, often > 75 % for true biological data. -> > > {: .solution} > {: .question} From e2a9e0b193454e35cf49665231c615fd667ecbfd Mon Sep 17 00:00:00 2001 From: paulzierep Date: Fri, 21 Nov 2025 12:53:27 +0100 Subject: [PATCH 13/14] add wf tags --- .../metagenomics-binning/workflows/Metagenomic-Binning.ga | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga b/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga index c33c6c16018ea6..268bf7ee1e50a3 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga +++ b/topics/microbiome/tutorials/metagenomics-binning/workflows/Metagenomic-Binning.ga @@ -1105,7 +1105,7 @@ "workflow_outputs": [] } }, - "tags": [], + "tags": ["microbiome", "microgalaxy", "binning"], "uuid": "b949eaf7-bf7c-4284-b0b8-fb7a94737de1", "version": 6 } \ No newline at end of file From aa5218e746da67f29c837c2bea4c16d00a4caad7 Mon Sep 17 00:00:00 2001 From: paulzierep Date: Fri, 21 Nov 2025 13:34:43 +0100 Subject: [PATCH 14/14] add topic --- topics/microbiome/tutorials/metagenomics-binning/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md index f196f220b490d2..d18eb020786eba 100644 --- a/topics/microbiome/tutorials/metagenomics-binning/tutorial.md +++ b/topics/microbiome/tutorials/metagenomics-binning/tutorial.md @@ -33,8 +33,8 @@ contributions: - vinisalazar requirements: - type: internal - topic: metagenomics - tutorials: + topic_name: microbiome + tutorials: - metagenomics-assembly subtopic: metagenomics tags: