PHAT
Point and click, cross platform suite for analysing and visualizing next-generation sequencing datasets.
README
Latest Stable Release: PHAT@0.11.0 Documentation Quicklinks How to Get PHAT Working With PHAT Projects Inputting Files Running Quality Control (QC) Reports Running Alignments Reports and Analyses Circular Visualization BLASTing Alignments Application Architecture Overview Contributing Repository Structure Performance The Pathogen-Host Analysis Tool (PHAT) is an application for processing and analyzing next-generation sequencing (NGS) data as it relates to relationships between pathogen and host…
- Repository
- github.com/chgibb/phat
Source attribution
- GitHub — github.com/chgibb/phat
- Awesome Bioinformatics — github.com/chgibb/phat
Related resources
Sequence manipulation toolkit for FASTA/FASTQ files written in Nim.
Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme().
Provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks.
Unified Python framework for bulk, single-cell, and spatial RNA-seq multi-omics analysis with deep learning deconvolution (VAE) and graph neural networks, bridging Bindea, Bindea, scanpy and squidpy ecosystems (Nature Communications 2024)
First architecture deeply integrating a DNA foundation model with an LLM for multimodal biological reasoning, achieving 98% accuracy on KEGG disease pathway prediction and 15%+ average gains on variant effect prediction with interpretable step-by-step reasoning traces (bowang-lab, 390+ stars)
Unified framework for state-of-the-art pre-trained bio foundation models across genomics and transcriptomics, providing standardized interfaces and pipelines for DNA, RNA, and single-cell models including Evo 2, Geneformer, scGPT, and UCE with streamlined inference, benchmarking, and fine-tuning workflows (213+ stars, 2024-2025)