BOL: Related items

GA4GH Data Working Group

Jitendra Prajapati — Sun, 20 Mar 2016 23:13:07 -0500

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 standards and tools for the secure, privacy respecting and interoperable sharing of Genomic data.

More at http://ga4gh.org/#/

Address of the bookmark: http://ga4gh.org/#/

NCBI to assist in Virus Hunting Data Science Hackathon

BioStar — Thu, 15 Nov 2018 12:55:01 -0600

NCBI Hackathon are pleased to announce the second installment of the SoCal Bioinformatics Hackathon. From January 9-11, 2019, the NCBI will help run a bioinformatics hackathon in Southern California hosted by the Computational Sciences Research Center at San Diego State University!

NCBI Hackathon specifically looking for folks who have experience in computational virus hunting or adjacent fields to identify known, taxonomically-definable and novel viruses from a few hundred thousand metagenomic datasets that we’ll put on cloud infrastructure. This event is for researchers, including students and postdocs, who are already engaged in the use of bioinformatics data or in the development of pipelines for virological analyses from high-throughput experiments. If this describes you, please apply! The event is open to anyone selected for the hackathon and willing to travel to SDSU (see below).

https://ncbiinsights.ncbi.nlm.nih.gov/2018/11/09/ncbi-sdsu-virus-hunting-data-science-hackathon-january-2019/

DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution

Rahul Nayak — Tue, 03 Mar 2020 01:12:47 -0600

DeepHiC is a GAN-based model for enhancing Hi-C data resolution. We developed this server for helping researchers to enhance their own low-resolution data by a few steps of clicks. Ab initio training could be performed according to our published code. We provided trained models for various depth of low-coverage sequencing Hi-C data. The depth of input data is estimated by its distribution comparing with those of the downsampled Hi-C data we used in training

Address of the bookmark: http://sysomics.com/deephic

AMR Database !

LEGE — Tue, 04 Jun 2024 13:37:21 -0500

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 of ARG-ANNOT. PMID: 25422674.
VirulenceFinder PMID: 24574290.

Address of the bookmark: https://github.com/sanger-pathogens/ariba/wiki/Task%3A-getref

BRaVa Browser

Jitendra Narayan — Sun, 23 Aug 2026 10:32:19 -0500

BRaVa Browser is an interactive web tool for exploring rare genetic variant associations. It allows users to search and visualize rare variant association results across genes and traits, making it easier to investigate potential relationships between genetic variation and disease or other phenotypes.

More at https://nikbaya.github.io/brava_browser/#/

Address of the bookmark: https://nikbaya.github.io/brava_browser/#/

FMLRC: a long-read error correction tool using the multi-string Burrows Wheeler Transform

Neel — Fri, 10 Aug 2018 13:29:28 -0500

FMLRC, or FM-index Long Read Corrector, is a tool for performing hybrid correction of long read sequencing using the BWT and FM-index of short-read sequencing data. Given a BWT of the short-read sequencing data, FMLRC will build an FM-index and use that as an implicit de Bruijn graph. Each long read is then corrected independently by identifying low frequency k-mers in the long read and replacing them with the closest matching high frequency k-mers in the implicit de Bruijn graph. In contrast to other de Bruijn graph based implementations, FMLRC is not restricted to a particular k-mer size and instead uses a two pass method with both a short "k-mer" and a longer "K-mer". This allows FMLRC to correct through low complexity regions that are computational difficult for short k-mers.

Address of the bookmark: https://github.com/holtjma/fmlrc

World of Omics

Rahul Agarwal — Tue, 16 Jul 2013 17:11:48 -0500

How many variants of "omics" techniques presently in use ?

Should you get sequenced? Not all bad genes predict disease

Rahul Agarwal — Thu, 29 Aug 2013 15:10:53 -0500

“What we really don’t know yet is whether the predictive aspects of the genome are going to turn out to be beneficial or potentially harmful”

“As we roll out genomic medicine we are fighting against this society-wide misconception that having the bad gene means you’re going to get the disease. That’s only true in a very few cases.”

Source:Today Health

Address of the bookmark: http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154