autosome-ru Goto Github PK

adastra-pipeline

ananastra

armnet

asb-backend

asb-frontend

asb-ml

ML analysis of ASB data

babachi

dmmpmm

Drosophila Melanogaster Major Position Matrix Motifs (DMMPMM). Drosophila transcription factor binding motifs built by careful usage of footprinting data

draw_motif_logo

Python3 scripts to draw motif logo in svg format

epifactors_webapp

Web-site for EpiFactors database of epigenetic factors, corresponding genes and products.

hocomoco_rsnp_benchmarks

hocomoco_v9

Old version of HOCOMOCO TFBS motif database, version 9: https://autosome.ru/HOCOMOCO

hocomoco_webapp

Web-site for HOCOMOCO collection of transcription factor binding sites motifs.

ht-selex-kmer-filtering

K-mer filtering algorithm for HT-SELEX data processing

human_legnet

ibis-challenge

Repository with source code and metadata for IBIS challenge

idmmpmm

Drosophila Melanogaster Major Position Matrix Motifs (DMMPMM). Drosophila transcription factor binding motifs built by integrating data from different available experimental sources

kmer-motif-enrichment

legnet

accurate prediction of promoter activity and variant effects from massive parallel reporter assays

macro-perfectos-ape

MACRO-APE and PERFECTOS-APE tools

mixalime

mixalime_reproduction_material

Reproduction material for numerical tests in a MixALime paper

molotool

Sequence Motif Location Tool (MoLoTool)

motif_benchmarking_data

Motif benchmarking supplementary data for paper submission

motif_benchmarking_paper

Supplementary scripts to analyze motif benchmarking results

motif_benchmarks

Benchmarks collection for GRECO TFBS Benchmarking Initiative

opera_webface

Webface for OperaHouse (included inside)

papolarity

Tools to deal with coverage profile on a transcript level. Allows to analyse ribosome positional preferences (such as polarity score) based on Ribo-Seq data.

pasio

Pasio is a tool for denoising profiles coming from DNA-sequencing data like ATAC-seq, DNA-seq or ChIP-seq.

pwm-evaluation-using-snp-selex

Supplementary data for "Positional weight matrices have sufficient prediction power for analysis of noncoding variants"