Fix #824

Author: drpatelh

CHANGELOG.md

@@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Enhancements & fixes
 
+- [[#824](https://github.com/nf-core/rnaseq/issues/824)] - Add explicit docs for usage of featureCounts in the pipeline
 - [[#828](https://github.com/nf-core/rnaseq/issues/828)] - Filter BAM output of UMI-tools dedup before passing to Salmon quant
 - Updated pipeline template to [nf-core/tools 2.4.1](https://github.com/nf-core/tools/releases/tag/2.4.1)
 

docs/usage.md

@@ -57,6 +57,14 @@ By default, the pipeline uses [STAR](https://github.com/alexdobin/STAR) (i.e. `-
 
 You also have the option to pseudo-align and quantify your data with [Salmon](https://salmon.readthedocs.io/en/latest/salmon.html) by providing the `--pseudo_aligner salmon` parameter. Salmon will then be run in addition to the standard alignment workflow defined by `--aligner`, mainly because it allows you to obtain QC metrics with respect to the genomic alignments. However, you can provide the `--skip_alignment` parameter if you would like to run Salmon in isolation. By default, the pipeline will use the genome fasta and gtf file to generate the transcripts fasta file, and then to build the Salmon index. You can override these parameters using the `--transcript_fasta` and `--salmon_index` parameters, respectively. The library preparation protocol (library type) used by Salmon quantification is inferred by the pipeline based on the information provided in the samplesheet, however, you can override it using the `--salmon_quant_libtype` parameter. You can find the available options in the [Salmon documentation](https://salmon.readthedocs.io/en/latest/library_type.html).
 
+## Quantification options
+
+The current options align with STAR and quantify using either Salmon (`--aligner star_salmon`) / RSEM (`--aligner star_rsem`). You also have the option to pseudo-align and quantify your data with Salmon by providing the `--pseudo_aligner salmon` parameter.
+
+Since v3.0 of the pipeline, featureCounts is no longer used to perform gene/transcript quantification, however it is still used to generate QC metrics based on [biotype](http://www.ensembl.org/info/genome/genebuild/biotypes.html) information available within GFF/GTF genome annotation files. This decision was made primarily because of the limitations of featureCounts to appropriately quantify gene expression data. Please see [Zhao et al., 2015](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141910#pone-0141910-t001) and [Soneson et al., 2015](https://f1000research.com/articles/4-1521/v1).
+
+For similar reasons, quantification will not be performed if using `--aligner hisat2` due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments - this may change in future releases (see [#822](https://github.com/nf-core/rnaseq/issues/822)). HISAT2 has been made available for those who have a preference for the alignment, QC and other types of downstream analysis compatible with it's output.
+
 ## Reference genome files
 
 The minimum reference genome requirements 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. those unavailable on [AWS iGenomes](https://nf-co.re/usage/reference_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.