Lists (2)
Stars
Toolbox for biostatistics and clinical data analysis in R. Developed by the biostatistics team at Laboratorios Sophia S.A. de C.V.
An analysis and visualization platform for 'omics data
The COnstraint-Based Reconstruction and Analysis Toolbox. Documentation:
An SRA tool that takes as input local fastq file from a clinical infection sample, identifies and removes any significant human read, and outputs the edited (cleaned) fastq file that can safely be …
Ultra-rapid detection of viral variants directly from sequencing data
machine-readable file format for genomic library sequence and structure
Supercomputing. Seamlessly. Open, Interactive HPC Via the Web
List of software packages for multi-omics analysis
A list of positions (mostly biology-related) open to apply
This is the repository for hiplex proteome sequencing project.
ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.
An open and interoperable data framework for spatial omics data
Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc.)
Testing building mulled containers for multi-requirement tools.
Methods for detecting doublets in single-cell sequencing data
🔬 Path to a free self-taught education in Bioinformatics!
An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
Pipeline for testing drug response prediction models in a statistically and biologically sound way.
Vitessce is a visual integration tool for exploration of spatial single-cell experiments.
Hone computational biology skills by re-enacting Figure 1 of modern biology papers
Code for the MARSY model with sample input and response. I included figures to show the architecture and format of input samples.
scRNAseq analysis notes from Ming Tang
A visible neural network model for drug response prediction
Deep Reinforcement Learning for de-novo Drug Design