genome-analysis 开源项目推荐（212 个）

MAGICS-LAB /DNABERT_2

[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome

exon-research /genomi

Local-first, open-source Claude Science alternative, before Claude Science is a thing. Turn your AI agent into personal DNA expert.

marbl /Winnowmap

Long read / genome alignment software

biocommons /hgvs

Python library to parse, format, validate, normalize, and map sequence variants according to HGVS Nomenclature (https://hgvs-nomenclature.org/).

mbhall88 /rasusa

Randomly subsample sequencing reads or alignments

TobyBaril /EarlGrey

Earl Grey: A fully automated TE curation and annotation pipeline

XMU-Kuangnan-Fang-Team /GENetLib

A Python library for Gene–environment interaction analysis via deep learning

asntech /intervene

Intervene: a tool for intersection and visualization of multiple genomic region and gene sets

mcveanlab /mccortex

De novo genome assembly and multisample variant calling

fmalmeida /bacannot

Generic but comprehensive pipeline for prokaryotic genome annotation and interrogation with interactive reports and shiny app.

moshi4 /COGclassifier

A tool for classifying prokaryote protein sequences into COG(Cluster of Orthologous Genes) functional category

ganlab /GALA

Long-reads Gap-free Chromosome-scale Assembler

kishwarshafin /helen

H.E.L.E.N. (Homopolymer Encoded Long-read Error-corrector for Nanopore)

CMU-SAFARI /RawHash

RawHash 能够实时准确高效地将原始纳米孔信号映射到不同大小的参考基因组（例如从病毒到人类基因组），无需碱基调用。由 Firtina 等人描述（发表于 https://academic.oup.com/bioinformatics/article/39/Supplement_1/i297/7210440 年）。

lynnlangit /TeamTeri

GCP、AWS或Azure上的生物信息学

NBChub /bgcflow

Snakemake workflow for the analysis of biosynthetic gene clusters across large collections of genomes (pangenomes)

happykhan /BRIG

BRIG 是一款跨平台（Windows/Mac/Linux）应用程序，使用 BLAST 显示多个基因组的圆形对比图像。

biocommons /seqrepo

non-redundant, compressed, journalled, file-based storage for biological sequences

CMU-SAFARI /BLEND

BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applications: 1) finding overlapping reads and 2) read mapping. Described by Firtina et al. (published in NARGAB https://doi.org/10.1093/nargab/lqad004)

pievos101 /PopGenome

An Efficient Swiss Army Knife for Population Genomic Analyses in R