Bcbio RNA-seq ‘under the hood’

Bcbio is a configuration-based pipeline manager for common NGS workflows. It uses a YAML-config file to set all of the inputs and specifications for pipeline. I’ve used bcbio for dozens of RNA-seq projects, but I’ve never known exactly what it is doing during the pipeline itself. This is because in order to see the exact commands being run you have to either dig into the code, or dig through the log files.

Digging through code is difficult because the code base is large and there are many different pieces of code that call each other. Digging through the logs is difficult when there are dozens of samples (each command is repeated dozens of times, leading to log files with thousands of lines). Well, I finally gave in and sorted through the RNA-seq pipeline command logs to identify the unique steps that bcbio (version 1.0.8) is performing in order to produce its results. I was able to identify 21 unique steps that are performed on each sample.

The difficulty of figuring out exactly what a configuration-based pipeline like bcbio is going to do is one argument in favor of using software like snakemake or nextflow to create or adapt existing pipelines, where the actual steps in the pipeline are made very explicit in “process” blocks. I’m going to be writing more about NextFlow in upcoming posts.

Of these 21 steps, 17 steps all deal with creating a BAM file and then manipulating that BAM file or calculating something about the BAM file. The remainder mainly deal with pseudo-alignment using salmon. It’s somewhat ironic that most of the pipeline and computational time is taken up with creating and manipulating BAM files since I only ever use the salmon pseudo-alignments in my downstream analysis.

Here are the 21 steps of the bcbio RNA-seq workflow (I’ve deleted the long, user-specific file paths to show just the commands):

Step 1. Align with Hisat2

Step 2/3. Pipe to bamsormadup and redirect to sorted BAM

Step 4. Index BAM

Step 5. Samtools sort by read names

Step 6. Sambamba view to select only primary alignments

Step 7. FeatureCounts to count primary alignments in BAM

Step 8. Gffread to write a fasta file with spliced exons

Step 9. Build the salmon index

Step 10. Pseudo-alignment and quantification

Step 11. Convert salmon output to sleuth format

Step 12. Downsample BAM file with samtools view

Step 13. FASTQC on downsampled BAM

Step 14. Run Qualimap RNAseq on BAM

Step 15. A SED command (not sure exactly what it does)

Step 16. Mark duplicates on the BAM file

Step 17. Index de-duplicated BAM file

Step 18. Use Sambamba view to create duplicate metrics

Step 19. Use Sambamba to create mapping metrics

Step 20. Samtools stats on sorted BAM

Step 21. Samtools idxstats on sorted BAM

Calculate % mitochondrial for mouse scRNA-seq

Seurat is a popular R/Bioconductor package for working with single-cell RNA-seq data. As part of the very first steps of filtering and quality-controlling scRNA-seq data in Seurat, you calculate the % mitochondrial gene expression in each cell, and filter out cells above a threshold. The tutorial provides the following code for doing this in human cells:

Creating a catalog of mitochondrial genes by searching with ‘grep’ for any gene names that start with “MT-” works just fine for the human reference transcriptome. Unfortunately, it doesn’t work for mouse (at least for mm10, which is the reference assembly I’m working with). There are two workarounds for this, in my opinion.

The easiest is to change the regular expression in the “grep” command from “^MT-” to “^mt-” since a search through the mm10 reference (version 3.0.0) in the cellranger reference files reveals that for whatever reason, the MT genes are labeled with lowercase ‘mt’ instead.

A second, and perhaps more thorough, approach is to take advantage of the Broad Institute’s “Mouse Mitocarta 2.0” encyclopedia of mitochondrial genes (note that you could do this same procedure for human MT genes too).

By creating a list of the top 100-200 genes with the strongest evidence for MT expression, it seems likely that you more accurately capture true mitochondrial gene expression. Below is some code to use the “MitoCarta 2.0” (downloaded as a CSV file) for this procedure. You will need to import “tidyverse” to work with tibbles: