pyrpipe: a python package for RNA-Seq workflows

Abstract

Implementing RNA-Seq analysis pipelines is challenging as data gets bigger and more complex. With the availability of terabytes of RNA-Seq data and continuous development of analysis tools, there is a pressing requirement for frameworks that allow for fast and efficient development, modification, sharing and reuse of workflows. Scripting is often used, but it has many challenges and drawbacks. We have developed a python package, python RNA-Seq Pipeliner (pyrpipe) that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in python, in an object-oriented manner. pyrpipe provides high level APIs to popular RNA-Seq tools. Pipelines can be customized by integrating new python code, third-party programs, or python libraries. Researchers can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in python and is compatible with python versions 3.6 and higher. All source code is available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source or from PyPi (https://pypi.org/project/ pyrpipe).

orfipy: a fast and flexible tool for extracting ORFs

“…C and D. Runtimes on HPC (128 GB RAM; 28 cores) ( C ) and PC (8 GB RAM; 4 cores) ( D ) environments. Each analysis was run three times, via pyrpipe (12), and the mean runtime is reported. runtimes are faster than or comparable to for the large microbial and human transcriptome data.…”

The Great Deceiver: miR-2392’s Hidden Role in Driving SARS-CoV-2 Infection

“…The monocyte COVID-19 RNA-Seq data, published under the accession GSE159678 (Rother et al, 2020), was downloaded from SRA and gene expression was quantified using Salmon's selective alignment approach (Srivastava et al, 2020). The RNA-Seq processing pipeline was implemented using pyrpipe (Singh et al, 2021) (https://github.com/urmi-21/pyrpipe/tree/master/case_studies/Covid_RNA-Seq). Exploratory data analysis and differential expression analysis were performed using MetaOmGraph (Singh et al, 2020).…”

Foster thy young: Enhanced prediction of orphan genes in assembled genomes

“…To enable BIND and MIND in a best practices format, we have implemented all Direct Inference core RNA-Seq processing steps in python using pyrpipe 34 and the pipeline in Snakemake 39 (see methods for details), we provide MAKER and BRAKER in singularity containers, and we have developed full, documented MIND and BIND pipelines in a versatile reproducible open source framework.…”

“…If, as we advocate here, information on predicted non-canonical genes was easily accessible, experiments could be designed to prioritize these inferred genes for experimental study and to elucidate the potential roles of these transcripts 34,39,63 and to validate new pipelines by benchmarking against well-sequenced, well-annotated genomes. Furthermore, gene expression studies would include these predicted genes, and experimental biologists would gain a sense of how the genes might be acting.…”

“…To enable Findable, Accessible, Interoperable and Reusable (FAIR) 33 pipelines for MIND and BIND, we implemented the Direct Inference pipeline in an automated, reproducible manner using the python-based RNA-Seq processing workflow, pyrpipe 34 such that it can be easily customized; we included singularity containers for MAKER and BRAKER.…”