Template repository for QuantSeq Fwd analysis, for RNA-seq data.
This contains a pipeline to process from reads to counts-per-mRNA, and a directory structure for some quality control using R/Rmarkdown.
EDIT THIS README FILE to describe the dataset that you are analysing
- quantseqfwd.nf - nextflow script for read processing and counting
- Rmd - directory for R markdown scripts and further data analysis after read processing
- input - directory for input
- input/annotation - genome or transcriptome annotation used for read alignment and counting
- input/experiment - other input files including sample sheet
- results - directory for output data and results
- results/counts - count data and analyses of those
- results/xxx - create specific subdirectories for other kinds of results
This is a read processing pipeline that does read QC, trimming, alignment, and counting per transcript. It is written in nextflow (DSL 1.0), see
We tested package installation using conda and bioconda, see bioconda installation instructions.
Once you have conda installed, create a conda environment called QS2022
by running the command:
conda create -n QS2022 nextflow=21.10 cutadapt=3.7 hisat2=2.2.1 samtools=1.15 fastqc=0.11.9 multiqc=1.12-0 bedtools=2.30.0 subread=2.0.1
These package versions run the pipeline as of June 2022. Others might break it or require troubleshooting/upgrading.
Note, we hope in the future to move to singularity containers for even cleaner package management.
Before starting, you have to choose and pick an annotation relevant to the species in question. Make sure it includes 3' ends of mRNAs because QuantSeq is 3'-targeted. This annotation requires:
- a fasta file of your genome (or transcriptome)
- a hisat2 index built from your fasta file
- a gff describing positions and strands of the mRNAs within the fasta file
If you don't already have an index, you can build it using commands like:
conda activate QS2022 # activate conda environment
mkdir input/annotation/index/ # make directory
hisat2-build input/annotation/ANNID_RENAME.fa \
input/annotation/index/ANNID_RENAME_hisat2
Remember to edit the fasta file name and index name as needed.
Then edit the parameters in quantseqfwd.nf
appropriate to your analysis:
params.input_fq_dir = 'EXPERIMENT_RENAME_fastq'
points to an input directory containing all of your fastq filesparams.output_dir = 'results/counts'
points to the output directory where you would like all the outputs stored.params.index_dir = 'input/annotation/index'
points to the directory with input annotation in itparams.index_prefix = 'ANNID_RENAME_hisat2'
name of hisat2 indexparams.mRNAgff = 'input/annotation/ANNID_RENAME_mRNAonly.gff'
name of gff file describing mRNA locationsparams.featuretype = 'mRNA'
in the mRNA gff file Type column, the feature corresponding to mRNA that you want to countparams.featurename = 'Name'
in the mRNA gff file attributes column, the field that contains the name of the feature to use in the counts fileparams.num_processes = 4
number of processes to use for parallelising adapter trimming and alignment. Increasing this can speed up running the pipeline on larger computersparams.adapters = 'AAAAAAAAAAAA'
is the sequencing adapter to remove, keep this as poly(A) for QuantSeq Fwd- `params.hisat2_other = '-k 2 --pen-cansplice 4 --pen-noncansplice 12 --min-intronlen 40 --max-intronlen 200' other hisat2 parameters, these are specialised for aligning intron-poor yeast genomes, see hisat2 manual for details
Remember that it is best to test & run carefully with subsampled data first, to find problems quickly! Point
params.input_fq_dir = 'EXPERIMENT_RENAME_fastq_subsample'
points to an input directory containing all of your fastq filesparams.output_dir = 'results/counts_subsample'
points to the output directory where you would like all the outputs stored.
Then run the command:
conda activate QS2022
nextflow run quantseqfwd.nf -with-dag flowchart.png -with-report counts_subsample/nextflow_report.html
The options -with-dag
and -with-report
are just to tell you a little more about the output, they are not strictly needed in order to run.
The flowchart shows what steps happen in the pipeline.
Troubleshoot (installation, filepaths, etc) until everything works.
At this point it's helpful to git commit
the subsampled run data in results/counts_subsample
, along with notes of anything you changed.
Edit
params.input_fq_dir
to point to the full input datasetparams.output_dir = 'results/counts'
or elsewhere as desired
Then run:
conda activate QS2022
nextflow run quantseqfwd.nf -with-report counts/nextflow_report.html
Save the results, i.e. the contents of results/counts
.
TBA
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