Skill UI

High-performance Rust toolkit with Python bindings for genomic interval analysis, covering overlap detection, coverage track generation, ML tokenization, reference sequence handling, fragment processing, and scoring workflows for sequencing datasets.

deepTools is a comprehensive command-line toolkit designed for processing and analyzing high-throughput sequencing data. It handles core genomics tasks including BAM to bigWig conversion, quality control assessment (PCA, correlation), normalization, and generating publication-quality visualizations (heatmaps, profiles) for ChIP-seq, RNA-seq, and ATAC-seq data. Essential for reproducible genomic research workflows.

This guide outlines the standardized requirements for declaring data and code availability in major scientific publications. It details mandatory best practices, including depositing raw data (e.g., sequencing, structures) and analysis code into approved, citable public repositories (such as GEO, PDB, Mendeley Data, and Zenodo). It also provides the required standardized three-bullet statement format for ensuring reproducibility.

A comprehensive guide for structuring a multi-study manuscript, particularly for high-tier journals. This skill focuses on building a coherent set of related studies—rather than a single experiment—to robustly test a major theory. It guides users through sequencing studies, determining necessary power, selecting appropriate designs (longitudinal, dyadic, experimental), and implementing preregistration to ensure convergence and internal validity.

This comprehensive guide outlines the standardized procedures and required repositories for ensuring data and code availability in scientific publications, particularly in molecular biology. It details where to deposit various data types (e.g., sequencing, structures, proteomics) in approved archives (GEO, PDB, PRIDE, Zenodo), provides the mandatory standardized statement format, and emphasizes the need to obtain accessions/DOIs before submission to ensure reproducibility.