BOL

Useful resources to therapy, disease, drug and drug-target network data

github.com - The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without...

academic.oup.com - LoReTTA (Long Read Template-Targeted Assembler), a tool designed for performing de novo assembly of long reads generated from viral genomes on the PacBio platform. LoReTTA exploits a reference genome to guide the assembly process, an approach that...

orthovenn3.bioinfotoolkits.net - OrthoVenn3 is a powerful tool for comparative genomics analysis, used as a web server for full genome comparisons, annotation, and evolutionary analysis of orthologous clusters across multiple species. It has already been used by thousands of users...

biokit.readthedocs.io - BioKit is a set of tools dedicated to bioinformatics, data visualisation (biokit.viz), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis...

bakrep.computational.bio - 2,438,386 bacterial genomes at your fingertips consistently processed & characterized, enriched with metadata, accessible via a flexible search engine. BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data. It...

http://ga4gh.org/#/ - GA4GH Data Working Group Led by David Haussler (UCSC) and Richard Durbin (Sanger Institute), the Data Working Group (DWG) of the Global Alliance brings together the leading Genome Institutes and Centers with IT industry leaders to create global...

The genome of 130 mammals was sequenced by a large international consortium and the data was analyzed together with 110 existing genomes to allow scientists to identify the important positions in the DNA.

github.com - ARG-ANNOT. PMID: 24145532 CARD. PMID: 23650175 MEGARes PMID: 27899569 NCBI BioProject: PRJNA313047 plasmidfinder PMID: 24777092 resfinder. PMID: 22782487 VFDB. PMID: 26578559 SRST2's version...

In today’s era of big biology, we’re generating more data than ever before—genomes, transcriptomes, proteomes, metabolomes, microbiomes… you name it. But raw biological data doesn’t speak for itself. Making sense of it requires more than traditional...