Bulk RNA-seq

Bulk RNA-seq pipeline in bcbio:

  • aligns reads with STAR (2pass), or hisat2 vs genome and transcriptome references (human, mouse, custom references);
  • quantifies expression counts with salmon, kallisto;
  • runs quality control;
  • calculates TPM with tximport;
  • detects rusions with arriba, pizzly;
  • creates a SummarizedExperiment object for downstream analysis in R;
  • calls variants with gatk or with vardict;
  • supports spike-in calibration

Workflow

This example processes 6 RNA-seq samples from the FDA’s Sequencing Quality Control project.

1. Install STAR index (if there is no STAR in the current bcbio installation)

bcbio_nextgen.py upgrade -u skip --genomes hg38 --aligners star --cores 10

2. Setup bcbio project

Download input data, create project structure and config files. This will download six samples from the SEQC project, three from the HBRR panel and three from the UHRR panel (100G download). :

wget https://raw.githubusercontent.com/bcbio/bcbio-nextgen/master/config/examples/rnaseq-seqc-getdata.sh
bash rnaseq-seqc-getdata.sh

Step 2 in detail:

2.1 Create input directory and download fastq files

mkdir -p seqc/input

2.2 Download a template yaml file describing RNA-seq analysis

wget --no-check-certificate https://raw.githubusercontent.com/bcbio/bcbio-nextgen/master/config/examples/rnaseq-seqc.yaml

rnaseq-seqc.yaml:

# Template for human RNA-seq using Illumina prepared samples
---
details:
  - analysis: RNA-seq
    genome_build: hg38
    algorithm:
      quality_format: standard
      aligner: star
      strandedness: unstranded
upload:
  dir: ../final
resources:
  star:
    cores: 10
    memory: 10G

2.3 Download fastq files into input dir

cd seqc/input
for SAMPLE in SRR950078 SRR950079 SRR950080 SRR950081 SRR950082 SRR950083
do 
   wget -c -O ${SAMPLE}_1.fastq.gz ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR950/${SAMPLE}/${SAMPLE}_1.fastq.gz
   wget -c -O ${SAMPLE}_2.fastq.gz ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR950/${SAMPLE}/${SAMPLE}_2.fastq.gz
done
cd ../../

2.4 Prepare a sample sheet

wget -c --no-check-certificate https://raw.githubusercontent.com/bcbio/bcbio-nextgen/master/config/examples/seqc.csv

seqc.csv:

samplename,description,panel
SRR950078,UHRR_rep1,UHRR
SRR950079,HBRR_rep1,HBRR
SRR950080,UHRR_rep2,UHRR
SRR950081,HBRR_rep2,HBRR
SRR950082,UHRR_rep3,UHRR
SRR950083,HBRR_rep3,HBRR

2.5 Generate yaml config file for analysis

project.yaml = template.yaml x sample_sheet.csv

bcbio_nextgen.py -w template rnaseq-seqc.yaml seqc.csv seqc/input/*.gz

In the result you should see a folder structure:

seqc
|---config
|---input
|---final
|---work

seqc/config/seqc.yaml is the main config file to run the bcbio project.

3. Run the analysis:

Single node:

cd seqc/work
bcbio_nextgen.py ../config/seqc.yaml -n 8

ipython: sbatch bcbio.sh:

#!/bin/bash

# https://slurm.schedmd.com/sbatch.html

#SBATCH --partition=priority        # Partition (queue)
#SBATCH --time=5-00:00:00           # Runtime in D-HH:MM format
#SBATCH --job-name=bulkrnatest      # Job name
#SBATCH -c 1
#SBATCH --mem-per-cpu=10G           # Memory needed per CPU
#SBATCH --output=project_%j.out     # File to which STDOUT will be written, including job ID
#SBATCH --error=project_%j.err      # File to which STDERR will be written, including job ID
#SBATCH --mail-type=NONE            # Type of email notification (BEGIN, END, FAIL, ALL)

bcbio_nextgen.py ../config/seqc.yaml -n 72 -t ipython -s slurm -q medium -r t=0-72:00 -r conmem=20 --timeout 3000 --tag "rna"

Parameters

  • transcript_assembler If set, will assemble novel genes and transcripts and merge the results into the known annotation. Can have multiple values set in a list. Supports [’cufflinks’, ‘stringtie’].
  • transcriptome_align If set to True, will also align reads to just the transcriptome, for use with EBSeq and others.
  • expression_caller A list of optional expression callers to turn on. Supports [’cufflinks’, ‘express’, ‘stringtie’, ‘dexseq’, ‘kallisto’]. Salmon and count based expression estimation are run by default. Sailfish is deprecated.
  • fusion_caller A list of optional fusion callers to turn on. Supports [oncofuse, pizzly, ericscript, arriba].
  • variantcaller Variant calling algorithm to call variants on RNA-seq data. Supports [gatk-haplotype] or [vardict].
  • spikein_fasta A FASTA file of spike in sequences to quantitate. There are quantitated separately, so should be things you are not expecting to match anywhere to the genome or trancriptome of the species you are working with.
  • quantify_genome_alignments If set to True, run Salmon quantification using the genome alignments from STAR, when available. If STAR alignments are not available, use Salmon’s SA mode with decoys.
  • transcriptome_gtf A GTF file to use to specify transcripts which will override the bcbio-installed versions. This is used if you have an alternate transcriptome you want to quantitate. You can also use this option to add your own transcripts to a set to quantitate, just append them to your GTF file and sort it.

QC and Basic DE analysis

By default, bcbio runs simple R scripts. To make them work, you need >1 sample, and category in the metadata for each sample:

algorithm:
  aligner: star
  strandedness: unstranded
metadata:
  category: normal

If there is no category in the yaml, the script assigns the same fake_category to all samples.

A more comprehensive QC and DE analysis is possible with bcbiornaseq

  • bcbiornaseq A dictionary of key-value pairs to be passed as options to bcbioRNAseq. Currently supports organism as a key and takes the latin name of the genome used (mus musculus, homo sapiens, etc) and interesting_groups which will be used to color quality control plots:
algorithm:
  tools_on: [bcbiornaseq]
  bcbiornaseq:
    organism: homo sapiens
    interesting_groups: [treatment, genotype, etc, etc]

Output

RNA-seq

Project directory:

├── counts
    ├── tximport-counts.csv -- gene-level counts for DE analysis generated from salmon counts by tximport
    ├── bcbio-se.rds -- SummarizedExperiment object with all counts
bcbio-se.html -- a simple Rmd QC report
├── annotated_combined.counts -- gene counts with symbols from featureCounts (don't use this)
├── bcbio-nextgen-commands.log -- commands run by bcbio
├── bcbio-nextgen.log -- logging information from bcbio run
├── combined.counts -- gene counts with gene IDs from featureCounts (don't use this)
├── metadata.csv -- provided metadata about each sample
├── multiqc
    ├── multiqc_report.html -- multiQC report
├── programs.txt -- program versions of tools run
├── project-summary.yaml -- YAML description of project, with derived
  metadata
└── tx2gene.csv -- transcript to gene mappings for use with tximport

Sample directories:

S1
├── S1-ready.bam -- coordinate-sorted whole genome alignments
├── S1-ready.counts -- featureCounts counts (don't use this)
├── S1-transcriptome.bam -- alignments to the transcriptome
├── salmon
│   ├── abundance.h5 -- h5 object, usable with sleuth
│   └── quant.sf -- salmon quantifications, usable with tximport
└── STAR
    ├── S1-SJ.bed -- STAR junction file in BED format
    └── S1-SJ.tab -- STAR junction file in tabular format

bcbioRNASeq directory:

bcbioRNASeq/
├── data
│ ├── bcb.rda -- bcbioRNASeq object with gene-level data
├── data-transcript
│ ├── bcb.rda -- bcbioRNASeq object with transcript-level data
├── quality_control.html -- quality control report
├── quality_control.Rmd -- RMarkdown that generated quality control report
├── results
│ └── 2019-11-21
│ ├── gene -- gene level information
│ │ └── counts
│ │ ├── counts.csv.gz -- count matrix from tximport, suitable for count-based analyses
│ │ └── metadata.csv.gz -- metadata and quality control data for samples
│ ├── quality_control
│ │ └── tpm.csv.gz -- TPM from tximport, use for visualization
│ └── transcript -- transcript level information
│ └── counts
│ ├── counts.csv.gz -- transcript level count matrix, suitable for count-based analyses needed transcript-level data
│ └── metadata.csv.gz -- metadata and quality control for samples

Workflow for analysis:

For gene-level analyses, we recommend loading the gene-level counts.csv.gz and the metadata.csv.gz and using DESeq2 to do the analysis. For a more in-depth walkthrough of how to use DESeq2, refer to our DGE_workshop.

For transcript-level analyses, we recommend using sleuth with the bootstrap samples. You can load the abundance.h5 files from Salmon, or if you set kallisto as an expression caller, use the abundance.h5 files from that.

Another great alternative is to use the Salmon quantification to look at differential transcript usage (DTU) instead of differential transcript expression (DTE). The idea behind DTU is you are looking for transcripts of genes that have been flipped from one isoform to another. The Swimming downstream tutorial has a nice walkthrough of how to do that.

Steps

Step are outlined here

Description

RNA-seq pipeline includes steps for quality control, adapter trimming, alignment, variant calling, transcriptome reconstruction and post-alignment quantitation at the level of the gene and isoform.

We recommend using the STAR aligner for all genomes. Use Tophat2 only if you do not have enough RAM available to run STAR (about 30 GB).

Our current recommendation is to run adapter trimming only if using the Tophat2 aligner. Adapter trimming is very slow, and aligners that soft clip the ends of reads such as STAR and hisat2, or algorithms using pseudoalignments like Salmon handle contaminant sequences at the ends properly. This makes trimming unnecessary. Tophat2 does not perform soft clipping so if using Tophat2, trimming must still be done.

Salmon, which is an extremely fast alignment-free method of quantitation, is run for all experiments. Salmon can accurately quantitate the expression of genes, even ones which are hard to quantitate with other methods (see this paper for example for Sailfish, which performs similarly to Salmon). Salmon can also quantitate at the transcript level which can help gene-level analyses (see this paper for example). We recommend using the Salmon quantitation rather than the counts from featureCounts to perform downstream quantification.

Still, we had at least two projects with initally low % mapped (STAR) at 50-70% which greatly benefited from trimming both in terms of % mapped and N mapped reads. We trimmed with bcbio.yaml:

algorithm:
  trim_reads: read_through 
  adapters: illumina

or with fastp

#!/bin/bash
fastp -I {input.R1]} -I {input.R2} -o {output.R1_trim_fastq} -O {output.R2_trim_fastq} -h {output.fastp_html} -g -x -5 -3

# Additional flag definitions:
# -g trims polyG, common artifact in Illumina 2-dye sequencing chemistry (MiniSeq, NextSeq, Novaseq)
# -x trims polyX, any other homopolymeric stretch
# -5 sliding window 5’ quality trimming
# -3 sliding window 3’ quality trimming

Although we do not recommend using the featureCounts based counts, the alignments are still useful because they give you many more quality metrics than the quasi-alignments from Salmon.

After a bcbio RNA-seq run there will be in the upload directory a directory for each sample which contains a BAM file of the aligned and unaligned reads, a salmon directory with the output of Salmon, including TPM values, and a qc directory with plots from FastQC and qualimap.

In addition to directories for each sample, in the upload directory there is a project directory which contains a YAML file describing some summary statistics for each sample and some provenance data about the bcbio run. In that directory is also a combined.counts file with the featureCounts derived counts per cell.