Pipeline for mRNA-seq analysis.
To be run after fastq files have been trimmed with cgat-flow readqc, runs standard mRNA-seq analysis and QC.
- mapping
- map reads with STAR and index BAM files
- if mapping is conducted with CGAT pipeline, these BAMs can be used as input, rather than running STAR
- readcounts
- count reads over genes with featureCounts
- summarystats
- collect mapping statistics with picard tools
- readquant
- calculate TPMs with Salmon
- coverage
- generate bigWig coverage tracks for visualisation
- fastq.gz formatted files. Can be paired or single end.
- should be named sample_r1.fastq.[1-2].gz (PE) or sample_r1.fastq.gz (SE)
- naming convention: sample names should be informative e.g. "group_condition_treatment_replicate.fastq.1.gz" as they're used to generate a sample information table to annotate plots and create comparisons for DESeq2
- fastq files should be placed in a folder named "data.dir/"
- alternatively BAMs can be placed in "bam.dir/" and only tasks after mapping will be run
- bam.dir: mapped reads, bigWigs
- read_counts.dir: raw read counts
- salmon.dir: TPMs
- csvdb: sqlite3 db containing all QC metrics, raw read counts, TPMs, etc.
- Jupyter notebook reports: QC and DESeq2 analysis
- cgat-core, cgat-flow, cgat-apps (https://github.com/cgat-developers)
- ruffus 2.8.3
- jupyter-notebook 6.0.2
- rpy2 3.2.4
- pandas 0.25.3
- numpy 1.17.3
- pybedtools 0.8.0
- seaborn 0.9.0
- matplotlib 3.1.1
- sqlite3
- yaml
- glob
- os
- re
- Salmon 0.11.3
- STAR 2.5.1b
- picard tools 2.10.9
- samtools 1.9
- subread 1.6.3
- deeptools 3.7.3
- ggplot2
- gplots
- gridExtra
- scales
- ggrepel
- wesanderson
- reshape2
- dplyr
- plyr
- grid
- RColorBrewer
- ComplexHeatmap
- circlize
- dendextend
- Rtsne
- DESeq2
- vsn
- topGO
- stringr