Quantifying Introgression via Branch Lengths

WGDI: A user-friendly toolkit for evolutionary analyses of whole-genome duplications and ancestral karyotypes

Informatics for RNA-seq: A web resource for analysis on the cloud. Educational tutorials and working pipelines for RNA-seq analysis including an introduction to: cloud computing, critical file form…

A feature SNP selection tool specifically designed to handle large-scale deep whole-genome sequencing data.

Phylogenetic orthology inference for comparative genomics

Code repository for the Global Ant Genomics Alliance

基于Vue3 + WebRTC + Nodejs + Electron搭建的远程桌面控制

Telomere-to-telomere assembly of accurate long reads (PacBio HiFi, Oxford Nanopore Duplex, HERRO corrected Oxford Nanopore Simplex) and Oxford Nanopore ultra-long reads.

High-precision TE Annotator

De novo clustering of long transcript reads into genes

所有小初高、大学PDF教材。

This is the standalone version of the EviAnn pipeline

Data files useful for BLUPF90 programs

Annotation and Ranking of Structural Variation

Evolutionary Sparse Learning - Paired Species Contrast

OrthoSLC: A pipeline to get Orthologous Genes using Reciprocal Best Blast Hit (RBBH) Single Linkage Clustering, indenpendent of relational database management system

HGTector2: Genome-wide prediction of horizontal gene transfer based on distribution of sequence homology patterns.

Evaluating genome assemblies

nsfc - 国家自然科学基金项目LaTeX模版(面青地)

moffee: Make Markdown Ready to Present

RNA-seq workflow using STAR and DESeq2

🥡 Git recipes in Chinese by Zhongyi Tong. 高质量的Git中文教程.

Git的奇技淫巧

Portable solution to generate genome alignment chains using lastz

regenie is a C++ program for whole genome regression modelling of large genome-wide association studies.

FastOMA is a scalable software package to infer orthology relationship.

Improved Inference of Ortholog Groups using Hidden Markov Models

CAAStools is a bioinformatics toolbox that allows the user to identify and validate CAAS on MSA of orthologous proteins.

Large-scale analysis of type usage in R.

Covers the basics of mixed models, mostly using @lme4