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RNA-seq pipeline

This simple RNA-seq pipeline processes most RNA-seq protocols. It uses the ultra-fast pseudomapping-based kallisto to generate transcript abundance estimations from unmapped reads.

Installation and configuration

Prequisites

Python packages. This pipeline uses pypiper and looper. You can do a user-specific install of these like this:

pip install --user https://github.com/epigen/pypiper/zipball/v0.6
pip install --user https://github.com/epigen/looper/zipball/v0.7.2

Required executables. You will need some common bioinformatics software installed. The list is specified in the pipeline configuration file (rnaseq.yaml) tools section.

Static files. This pipeline requires static files which are specified in the pipeline configuration file.

Usage

  • Clone the pipeline repository: git clone git@github.com:epigen/open_pipelines.git;
  • Adapt the pipeline configuration file to point to specific software if needed;
  • Create a sample annotation sheet containing the variables sample_name, protocol, and organism;
  • Create a project configuration file that points to the pipeline interface file and the sample annotation sheet;
  • Run pipelines using looper looper run project_config.yaml.

More detailed instructions or creating a project configuration file and sample annotation sheet canbe found in the Looper documentation.

In the particular case of the RNA-seq pipeline, one special column in the annotation sheet can be used to pair samples for peak calling. Add a column named "compare_sample" containing the name ("sample_name" column) of the sample to use as background.

Pipeline steps

Merging input files

If given more than one BAM file as input, the pipeline will merge them before begining processing. The merged, unmapped inpu BAM file will be output in $sample_name/unmapped. This file is temporary and will be removed if the pipeline finishes successfully.

Sequencing read quality control with FASTQC

FastQC is ran on the unaligned input BAM files for quality control.

An HTML report and accompaning zip file will be output in the root directory of each sample.

Read trimming

Reads are trimmed for adapters prior to alignment.

Adapter sequences to be trimmed can be specified in a FASTA file which is stated in the pipeline configuration file under resources: adapters.

Two trimming programs can be selected: trimmomatic and skewer in the pipeline configuration file under parameters: trimmer. While rigorous benchmarking of both trimmers could be performed, the reason to use skewer is its considerable speed compared with trimmomatic and the fact that it is available as a binary executable rather than a Java jar.

These produce FASTQ files for each read pair and one file with unpaired reads, which are stored under $sample_name/unmapped/. These files are temporary and will be removed if the pipeline finishes sucessfully.

Expression quantification trimming

This pipeline uses Kallisto for transcript quantification without the need of alignment.

Kallisto needs a transcriptome index which should be specified in the pipeline configuration file under resources: kallisto_index. This can be easily created with the kallisto index command.

A TSV file containing estimation of transcript abundances is created under $sample_name/kallisto/.

Collecting statistics from pipeline runs

You can easily collect statistics from all runs using looper: lopper summarize project_config.yaml

Quality control and Statistics

Due to the minimal pipeline size/steps, statistics produced are limited. Here are the reported statistics and their description:

  • fastqc_GC_perc: GC percentage of all sequenced reads from FASTQC report.
  • fastqc_read_length: read length as determined from FASTQC report.
  • fastqc_total_pass_filter_reads: number of pass filter reads from FASTQC report.
  • fastqc_poor_quality_perc: number of poor quality reads from FASTQC report
  • trim_short_perc: percentage of reads dropped because of too short length after trimming
  • trim_empty_perc: percentage of reads dropped because empty after trimming
  • trim_trim_loss_perc: percentage of reads lost during trimming
  • trim_surviving_perc: percentage of reads surviving after trimming
  • trim_trimmed_perc: percentage of reads that were trimmed
  • trim_untrimmed_perc: percentage of reads that were untrimmed
  • transcripts: number of transcripts quantified
  • zero-count_transcripts: number of transcripts with estimated zero counts.
  • non-zero-count_transcripts: number of transcripts with more than zero estimated counts.
  • log2tpm_mean: Mean expression (log2(1 + TPM)) of all transcripts
  • log2tpm_median: Median expression (log2(1 + TPM)) of all transcripts
  • log2tpm_iqr: Interquantile range (IQR) of the expression (log2(1 + TPM)) of all transcripts
  • non-zero_log2tpm_mean: Mean expression (log2 TPM) of transcripts with >0 counts
  • non-zero_log2tpm_median: Mean expression (log2 TPM) of transcripts with >0 counts
  • non-zero_log2tpm_iqr: Interquantile range (IQR) of the expression (log2(1 + TPM)) of transcripts with >0 counts
  • Time: pipeline run time
  • Success: time pipeline run finished

Contributing

Pull requests welcome. Active development should occur in the development branch.

Contributors