nf-core/rnavar

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Introduction

This document describes the output produced by the pipeline based on a public dataset.

The directories listed below are created in the results/ directory after the pipeline has finished. All paths are relative to the top-level results directory.

Dataset

RNAseq data has been taken from GM12878 GIAB sample SRA Accession SRR5665260. The dataset has 38.6 million PE reads of read length 2x151 bp from NextSeq 500 sequencing platform.

Samplesheet

A sample sheet has been prepared in the following way to set the FASTQ files to run the analysis.

sample,fastq_1,fastq_2,strandedness
GM12878,/data/GM12878/SRR5665260_1.fastq.gz,/data/GM12878/SRR5665260_2.fastq.gz,reverse

Execution

The pipeline has been executed with the following command:

nextflow run nf-core/rnavar -profile <institutional_config>,docker --input samplesheet.csv --genome GRCh38 --annotate_tools merge --outdir results

The <institutional_config> used in this experiment can be found here. However, you can create your own institutional config and place it on nf-core/configs and then use the config name directly in the command instead of <institutional_config> to use your own data and parameters.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

• Preprocessing
  • cat - Merge re-sequenced FastQ files
• Alignment
  • STAR - Fast spliced aware genome alignment
• Alignment post-processing
  • Picard MarkDuplicates - Duplicate read marking
  • GATK4 SplitNCigarReads - Splits reads that contain Ns in their cigar string (e.g. spanning splicing events in RNAseq data)
  • GATK4 Base Quality Score Recalibration (BQSR) - Estimate and correct systematic bias that affect the assignment of base quality scores by the sequencer
• Variant calling
  • GATK4 HaplotypeCaller - Call SNPs and indels via local re-assembly of haplotypes
• Variant filtering
  • GATK4 VariantFiltration - Hard-filtering variant calls based on certain criteria
• Variant annotation
  • snpEff
  • VEP
• QC and reporting
  • QC
    • FastQC - Raw read QC
    • GATK MarkDuplicates reports
    • samtools stats
    • snpEff reports
    • VEP reports
  • Reporting
    • MultiQC - Present QC for raw reads, alignment, base quality recalibration as well as variant annotation summary.
• Workflow reporting and genomes
  • Reference genome files - Saving reference genome indices/files
  • Pipeline information - Report metrics generated during the workflow execution

Preprocessing

cat

If multiple libraries/runs have been provided for the same sample in the input samplesheet (e.g. to increase sequencing depth) then these will be merged at the very beginning of the pipeline in order to have consistent sample naming throughout the pipeline. Please refer to the usage documentation to see how to specify these samples in the input samplesheet.

Alignment

STAR

STAR is a read aligner designed for splice aware mapping typical of RNA sequencing data. STAR stands for Spliced Transcripts Alignment to a Reference, and has been shown to have high accuracy and outperforms other aligners by more than a factor of 50 in mapping speed, but it is memory intensive.

Alignment post-processing

MarkDuplicates

GATK MarkDuplicates locates and tags duplicate reads in a BAM file. The tool’s main output is a new BAM file, in which duplicates have been identified in the SAM flags field for each read.

If desired, duplicates can be removed using the --remove_duplicates true option.

SplitNCigarReads

GATK SplitNCigarReads is executed for post-processing RNA reads aligned against the reference. The tool’s main output is a new BAM file with reads split at N CIGAR elements and CIGAR strings updated.

Currently, the pipeline does not produce the new BAM file in the output directory.

Base (Quality Score) Recalibration

Base quality recalibration step runs by default and it produces the recalibrated BAM file for variant calling, as described below. However, you can turn off this step by using --skip_baserecalibration true option, and in that case, no recalibrated BAM file is produced and the pipeline uses the un-calibrated BAM file will be used for variant calling.

GATK BaseRecalibrator

GATK BaseRecalibrator generates a recalibration table based on various co-variates.

Currently, the pipeline does not produce the recalibration table file in the output directory.

GATK ApplyBQSR

GATK ApplyBQSR recalibrates the base qualities of the input reads based on the recalibration table produced by the GATK BaseRecalibrator tool.

Variant calling

GATK HaplotypeCaller is used to call SNVs and small indels in the sample. The Recalibrated BAM file is used as an input to this process and the output file is produced in VCF format.

Variant filtering

GATK VariantFiltration is used for hard-filtering variant calls based on certain criteria. Records are hard-filtered by changing the value in the FILTER field to something other than PASS. Filtered records will be preserved in the output.

Variant annotation

This directory contains results from the final annotation steps: two tools are used for annotation, snpEff and VEP.

snpEff

snpeff is a genetic variant annotation and effect prediction toolbox. It annotates and predicts the effects of variants on genes (such as amino acid changes) using multiple databases for annotations. The generated VCF header contains the software version and the used command line.

To annotate variants using snpeff, you can use --annotate_tools snpeff or --annotate_tools merge. The annotated variant files in VCF format can be found in results/variant_annotation folder.

For further reading and documentation see the snpEff manual

VEP

VEP (Variant Effect Predictor), based on Ensembl, is a tool to determine the effects of the variants. The generated VCF header contains the software version, also the version numbers for additional databases like Clinvar or dbSNP used in the VEP line. The format of the consequence annotations is also in the VCF header describing the INFO field.

Currently, it contains:

• Consequence: impact of the variation, if there is any
• Codons: the codon change, i.e. cGt/cAt
• Amino_acids: change in amino acids, i.e. R/H if there is any
• Gene: ENSEMBL gene name
• SYMBOL: gene symbol
• Feature: actual transcript name
• EXON: affected exon
• PolyPhen: prediction based on PolyPhen
• SIFT: prediction by SIFT
• Protein_position: Relative position of amino acid in protein
• BIOTYPE: Biotype of transcript or regulatory feature

To annotate variants using vep, you can use --annotate_tools vep. The annotated variant files in VCF format can be found in results/variant_annotation folder.

When --annotate_tools merge option is used, the annotation from both snpeff and vep are combined into a single VCF file which can be found with the following naming convention.

For further reading and documentation see the VEP manual

QC and Reporting

QC

FastQC

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

Plots will show:

• Sequence counts for each sample.
• Sequence Quality Histograms: The mean quality value across each base position in the read.
• Per Sequence Quality Scores: The number of reads with average quality scores. Shows if a subset of reads has poor quality.
• Per Base Sequence Content: The proportion of each base position for which each of the four normal DNA bases has been called.
• Per Sequence GC Content: The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
• Per Base N Content: The percentage of base calls at each position for which an N was called.
• Sequence Length Distribution.
• Sequence Duplication Levels: The relative level of duplication found for every sequence.
• Overrepresented sequences: The total amount of overrepresented sequences found in each library.
• Adapter Content: The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

GATK MarkDuplicates reports

More information in the GATK MarkDuplicates section

Duplicates can arise during sample preparation e.g. library construction using PCR. Duplicate reads can also result from a single amplification cluster, incorrectly detected as multiple clusters by the optical sensor of the sequencing instrument. These duplication artifacts are referred to as optical duplicates.

For further reading and documentation see the MarkDuplicates manual.

samtools stats

samtools stats collects statistics from BAM files and outputs in a text format.

Plots will show:

• Alignment metrics.

For further reading and documentation see the samtools manual

snpEff reports

snpeff is a genetic variant annotation and effect prediction toolbox. It annotates and predicts the effects of variants on genes (such as amino acid changes) using multiple databases for annotations.

Plots will shows :

• locations of detected variants in the genome and the number of variants for each location.
• the putative impact of detected variants and the number of variants for each impact.
• the effect of variants at protein level and the number of variants for each effect type.
• the quantity as function of the variant quality score.

VEP reports

VEP (Variant Effect Predictor), based on Ensembl, is a tools to determine the effects of all sorts of variants, including SNPs, indels, structural variants, CNVs.

For further reading and documentation see the VEP manual

Reporting

MultiQC

MultiQC is a visualization tool that generates a single HTML report summarizing all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability.

For more information about how to use MultiQC reports, see https://multiqc.info.

Pipeline information

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.