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Please refer to the [nf-core website](https://nf-co.re/usage/reference_genomes) for general usage docs and guidelines regarding reference genomes.
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The minimum reference genome requirements for this pipeline are a FASTA and GTF file, all other files required to run the pipeline can be generated from these files. However, it is more storage and compute friendly if you are able to re-use reference genome files as efficiently as possible. It is recommended to use the `--save_reference` parameter if you are using the pipeline to build new indices (e.g. custom genomes that are unavailable on [AWS iGenomes](https://nf-co.re/usage/reference_genomes#custom-genomes)) so that you can save them somewhere locally. The index building step can be quite a time-consuming process and it permits their reuse for future runs of the pipeline to save disk space. You can then either provide the appropriate reference genome files on the command-line via the appropriate parameters (e.g. `--star_index '/path/to/STAR/index/'`) or via a custom config file. Another option is to run the pipeline once with `--save_reference --skip_alignment --skip_pseudo_alignment` to generate and save all of the required reference files and indices to the results directory. You can then move the reference files in `<RESULTS_DIR>/genome/` to a more permanent location and use these paths to override the relevant parameters in the pipeline e.g. `--star_index`.
- If `--genome` is provided then the FASTA and GTF files (and existing indices) will be automatically obtained from AWS-iGenomes unless these have already been downloaded locally in the path specified by `--igenomes_base`.
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The minimum reference genome requirements for this pipeline are a FASTA and GTF file, all other files required to run the pipeline can be generated from these files. For example, the latest reference files for human can be derived from Ensembl like:
These files can then be specified to the workflow with the `--fasta` and `--gtf` parameters.
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Notes:
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- Compressed reference files are supported by the pipeline i.e. standard files with the `.gz` extension and indices folders with the `tar.gz` extension.
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- If `--gff` is provided as input then this will be converted to a GTF file, or the latter will be used if both are provided.
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- If `--gene_bed` is not provided then it will be generated from the GTF file.
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- If `--additional_fasta` is provided then the features in this file (e.g. ERCC spike-ins) will be automatically concatenated onto both the reference FASTA file as well as the GTF annotation before building the appropriate indices.
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- When using `--aligner star_rsem`, both the STAR and RSEM indices should be present in the path specified by `--rsem_index` (see [#568](https://github.com/nf-core/rnaseq/issues/568)).
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#### Indices
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By default, indices are generated dynamically by the workflow for tools such as STAR and Salmon. Since indexing is an expensive process in time and resources you should ensure that it is only done once, by retaining the indices generated from each batch of reference files:
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When using `--aligner star_rsem`, both the STAR and RSEM indices should be present in the path specified by `--rsem_index` (see [#568](https://github.com/nf-core/rnaseq/issues/568)).
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- the `--save_reference` parameter will save your indices in your results directory
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- the `--skip_alignment --skip_pseudo_alignment` will disable other processes if you'd like to do an 'indexing only' workflow run.
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> **NB:** Compressed reference files are also supported by the pipeline i.e. standard files with the `.gz` extension and indices folders with the `tar.gz` extension.
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Once you have the indices from a workflow run you should save them somewhere central and reuse them in subsequent runs using custom config files or command line parameters such as `--star_index '/path/to/STAR/index/'`.
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As of v3.7 of the pipeline, if you are using a genome downloaded from AWS iGenomes and using `--aligner star_salmon` (default) the version of STAR to use for the alignment will be auto-detected (see [#808](https://github.com/nf-core/rnaseq/issues/808)).
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#### Gencode
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If you are using [GENCODE](https://www.gencodegenes.org/) reference genome files please specify the `--gencode` parameter because the format of these files is slightly different to ENSEMBL genome files:
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- The `--gtf_group_features_type` parameter will automatically be set to `gene_type` as opposed to `gene_biotype`, respectively.
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- If you are running Salmon, the `--gencode` flag will also be passed to the index building step to overcome parsing issues resulting from the transcript IDs in GENCODE fasta files being separated by vertical pipes (`|`) instead of spaces (see [this issue](https://github.com/COMBINE-lab/salmon/issues/15)).
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## Prokaryotic genome annotations
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####Prokaryotic genome annotations
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This pipeline uses featureCounts to generate QC metrics based on [biotype](http://www.ensembl.org/info/genome/genebuild/biotypes.html) information available within GFF/GTF genome annotation files. The format of these annotation files can vary significantly depending on the source of the annotation and the type of organism. The default settings in the pipeline are tailored towards Ensembl GTF annotations available for eukaryotic genomes. Prokaryotic genome annotations tend to be distributed in GFF format which are structured differently in terms of the feature naming conventions. There are a number of ways you can tune the behaviour of the pipeline to cater for differences/absence of biotype information:
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Please get in touch with us on the #rnaseq channel in the [nf-core Slack workspace](https://nf-co.re/join) if you are having problems or need any advice.
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### iGenomes (not recommended)
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If the `--genome` parameter is provided (e.g. `--genome GRCh37`) then the FASTA and GTF files (and existing indices) will be automatically obtained from AWS-iGenomes unless these have already been downloaded locally in the path specified by `--igenomes_base`.
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However this is no longer recommended because:
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- Gene annotations in iGenomes are extremely out of date. This can be particularly problematic for RNA-seq analysis, which relies on accurate gene annotation.
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- Some iGenomes references (e.g., GRCh38) point to annotation files that use gene symbols as the primary identifier. This can cause issues for downstream analysis, such as the nf-core [differential abundance](https://nf-co.re/differentialabundance) workflow where a conventional gene identifier distinct from symbol is expected.
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Notes:
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- As of v3.7 of the pipeline, if you are using a genome downloaded from AWS iGenomes and using `--aligner star_salmon` (default) the version of STAR to use for the alignment will be auto-detected (see [#808](https://github.com/nf-core/rnaseq/issues/808)).
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## Running the pipeline
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The typical command for running the pipeline is as follows:
> **NB:** Loading iGenomes configuration remains the default for reasons of consistency with other workflows, but should be disabled when not using iGenomes, applying the recommended usage above.
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This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.
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Note that the pipeline will create the following files in your working directory:
Copy file name to clipboardExpand all lines: nextflow_schema.json
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"type": "string",
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"description": "Name of iGenomes reference.",
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"fa_icon": "fas fa-book",
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"help_text": "If using a reference genome configured in the pipeline using iGenomes, use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. `--genome GRCh38`. \n\nSee the [nf-core website docs](https://nf-co.re/usage/reference_genomes) for more details."
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"help_text": "If using a reference genome configured in the pipeline using iGenomes (not recommended), use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. `--genome GRCh38`. \n\nSee the [nf-core website docs](https://nf-co.re/usage/reference_genomes) for more details."
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