PISCES

PISCES processes mRNA-seq datasets to perform transcript quantitation, genetic fingerprinting, species detection, library-geometry assessment, quality control, TMM normalization, and differential expression analysis.

Key Features:

• Metadata Management: Captures comprehensive sample metadata using simple textual file formats to support reproducibility and traceability.
• Scalable Processing: Supports execution on single machines and high-performance computing (HPC) clusters with parallel execution.
• Species Detection and SNP Genotyping: Identifies species present in samples and performs single nucleotide polymorphism (SNP) genotyping for sample validation.
• Library Geometry Detection: Detects sequencing library geometry to assess library structure and data integrity.
• Quantitation Using Salmon: Performs transcript-level quantitation using Salmon for accurate transcript abundance estimation.
• Gene-Level Transcript Aggregation: Aggregates transcript-level estimates to gene-level counts for downstream analyses.
• Quality Control: Conducts transcriptional and read-based quality control assessments across libraries.
• TMM Normalization: Applies Trimmed Mean of M-values (TMM) normalization to adjust for compositional differences between samples.
• Differential Expression with DESeq2: Identifies differentially expressed genes using DESeq2 on count data.
• Configuration and Implementation: Implemented in Python3 with configuration via JSON files for transcriptome indices and CSV files for sample metadata and group definitions.

Scientific Applications:

• Large-scale mRNA-seq processing: Processing and analysis of high-throughput mRNA-seq datasets across many libraries.
• Transcript quantitation and gene expression measurement: Accurate estimation of transcript and gene expression levels using Salmon and gene-level aggregation.
• Sample identity validation: Validation of sample identity and contamination detection via genetic fingerprinting and SNP genotyping.
• Differential expression analysis: Detection of differentially expressed genes across sample groups using TMM normalization and DESeq2.
• Species detection in mixed samples: Identification of species composition within sequencing samples.

Methodology:

Two primary modules are implemented: a compute cluster-aware analysis that performs species detection, SNP genotyping, library-geometry detection, and transcript quantitation using Salmon; and a gene-level transcript aggregation module that aggregates transcripts to genes, performs transcriptional and read-based QC, applies TMM normalization, and runs differential expression analysis with DESeq2; configuration uses JSON for transcriptome indices and CSV for sample metadata and group definitions, and the package is implemented in Python3.