bollito

bollito implements a Snakemake-based computational pipeline for comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, covering quality control, read alignment, quantification, cell-level QC, dimensionality reduction, clustering, marker detection, differential expression, functional analysis, trajectory inference, and RNA velocity.

Key Features:

• Workflow management: Snakemake-based orchestration for reproducible, automated, and parallelizable execution.
• Input formats: Accepts raw FASTQ files or preprocessed expression matrices.
• Tool integration: Integrates over 30 state-of-the-art tools for end-to-end scRNA-seq processing and analysis.
• Quality control and quantification: Performs initial QC, read alignment, and quantification.
• Cell-specific QC: Implements cell-level quality control measures to assess data integrity per cell.
• Dimensionality reduction: Applies dimensionality reduction techniques to simplify high-dimensional single-cell data.
• Clustering: Includes clustering algorithms to identify distinct cell populations.
• Marker detection: Detects cell-type or state-specific marker genes.
• Differential expression: Performs differential expression analysis across conditions or clusters.
• Functional analysis: Enables analysis of biological processes and pathways within cell subsets.
• Trajectory inference: Reconstructs developmental trajectories and cellular differentiation paths.
• RNA velocity: Estimates RNA velocity to predict future cell states and transcriptional dynamics.
• Modularity: Modular design allows incorporation of additional tools and extension of functionality.

Scientific Applications:

• Preprocessing and QC: Processing raw FASTQ or expression matrices and performing sample- and cell-level quality control.
• Cell type identification: Identifying and characterizing distinct cell populations via clustering and marker detection.
• Differential expression analysis: Detecting genes with significant expression changes between conditions or clusters.
• Functional and pathway analysis: Investigating active biological processes and pathways in cell subsets.
• Trajectory and lineage reconstruction: Inferring developmental trajectories and differentiation paths.
• RNA velocity analysis: Predicting future transcriptional states of individual cells.
• Large-scale scRNA-seq analysis: Scalable processing of large single-cell datasets through automated, parallel execution.

Methodology:

Snakemake-based workflow that accepts FASTQ or expression matrices, integrates over 30 tools, and executes QC, read alignment, quantification, cell-level QC, dimensionality reduction, clustering, marker detection, differential expression, functional analysis, trajectory inference, and RNA velocity.