PT  - JOURNAL ARTICLE
AU  - Matthew D. Shirley
AU  - Viveksagar K. Radhakrishna
AU  - Javad Golji
AU  - Joshua M. Korn
TI  - PISCES: a package for rapid quantitation and quality control of large scale mRNA-seq datasets
AID  - 10.1101/2020.12.01.390575
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.12.01.390575
4099  - http://biorxiv.org/content/early/2020/12/02/2020.12.01.390575.short
4100  - http://biorxiv.org/content/early/2020/12/02/2020.12.01.390575.full
AB  - PISCES eases processing of large mRNA-seq experiments by encouraging capture of metadata using simple textual file formats, processing samples on either a single machine or in parallel on a high performance computing cluster (HPC), validating sample identity using genetic fingerprinting, and summarizing all outputs in analysis-ready data matrices. PISCES consists of two modules: 1) compute cluster-aware analysis of individual mRNA-seq libraries including species detection, SNP genotyping, library geometry detection, and quantitation using salmon, and 2) gene-level transcript aggregation, transcriptional and read-based QC, TMM normalization and differential expression analysis of multiple libraries to produce data ready for visualization and further analysis.PISCES is implemented as a python3 package and is bundled with all necessary dependencies to enable reproducible analysis and easy deployment. JSON configuration files are used to build and identify transcriptome indices, and CSV files are used to supply sample metadata and to define comparison groups for differential expression analysis using DEseq2. PISCES builds on many existing open-source tools, and releases of PISCES are available on GitHub or the python package index (PyPI).Competing Interest StatementThe authors have declared no competing interest.