BOL: Related items

A comprehensive atlas of human gene activity released !!!

Abhimanyu Singh — Tue, 02 Sep 2014 14:20:24 -0500

A large international consortium of researchers has produced the first comprehensive, detailed map of the way genes work across the major cells and tissues of the human body. The findings describe the complex networks that govern gene activity, and the new information could play a crucial role in identifying the genes involved with disease.

We are able to pinpoint the regions of the genome that can be active in a disease and in normal activity, whether it’s in a brain cell, the skin, in blood stem cells or in hair follicles. This is a major advance that will greatly increase our ability to understand the causes of disease across the body.

The research is outlined in a series of papers published March 27, 2014, two in the journal Nature and 16 in other scholarly journals. The work is the result of years of concerted effort among 250 experts from more than 20 countries as part of FANTOM 5 (Functional Annotation of the Mammalian Genome). The FANTOM project, led by the Japanese institution RIKEN, is aimed at building a complete library of human genes.

Researchers studied human and mouse cells using a new technology called Cap Analysis of Gene Expression (CAGE), developed at RIKEN, to discover how 95% of all human genes are switched on and off. These “switches” — called “promoters” and “enhancers” — are the regions of DNA that manage gene activity. The researchers mapped the activity of 180,000 promoters and 44,000 enhancers across a wide range of human cell types and tissues and, in most cases, found they were linked with specific cell types.

Referene : www.kurzweilai.net/first-comprehensive-atlas-of-human-gene-activity-released

Manolis Kellis Lab

Sun, 31 Jan 2016 20:51:06 -0600

A major focus of our lab is understanding the effects of genetic variation on molecular phenotypes and human disease. We develop methods for integrating diverse functional genomic datasets of transcription, chromatin modifications, regulator binding, and their changes across multiple conditions to interpret genetic associations, identify causal variants, and predict the effects of genetic perturbations.

More at http://compbio.mit.edu

Gene Finding and Predictions

Poonam Mahapatra — Fri, 26 Aug 2016 07:26:27 -0500

In this exercise, a previously annotated gene will be used to measure the accuracy of different gene finding approaches. GRAIL, GENSCAN, geneid, FGENESH, GenomeScan, GrailEXP and GENEWISE will be used to annotate the sequence. Both search by signal, content and homology (protein and cDNA sequences) methods will be employed in order to improve the ab initio results. Weak conservation of Start codons will lead to wrong prediction of initial exons in most cases.

http://genome.crg.es/courses/Bioinformatics2003_genefinding/

Address of the bookmark: http://genome.crg.es/courses/Bioinformatics2003_genefinding/

OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation

Jit — Tue, 14 Nov 2017 09:30:35 -0600

Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.

http://www.comp.nus.edu.sg/~wongls/projects/orthoGNC/

Address of the bookmark: http://www.sciencedirect.com/science/article/pii/S1672022917301663

BioGPS: Spotlight on the Gene Expression Atlas

Jit — Thu, 18 Oct 2018 12:15:12 -0500

BioGPS opened 2016 with a publication in Nucleic Acids Research, right after the New Year holiday. Throughout the year, new designs for the site were being created, reviewed, adjusted, reviewed, adjusted, and more review/adjustments in anticipation of a site redesign for 2017. A Plugin registration Blitz was held in March and April; followed by a Plugin Review Blitz in May. The BioGPS spotlight series was also restarted, with spotlights on BGEE, Intermine, and other Intermine-related plugins.

There were ~910,000 requests made to BioGPS in 2016. Requests to BioGPS peaked in March and at the lowest in December.

Address of the bookmark: http://biogps.org/

Predict Gene Ontology with sequences !

LEGE — Wed, 08 Jul 2020 04:59:28 -0500

PANNZER (Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. The tool is designed to predict the functional description (DE) and GO classes.

PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use AAI-profiler to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.

http://ekhidna2.biocenter.helsinki.fi/sanspanz/

IterPro is for the beginners

h ttps://www.ebi.ac.uk/interpro/

You can find other comparative info at https://academic.oup.com/view-large/118391389

Address of the bookmark: http://ekhidna2.biocenter.helsinki.fi/sanspanz/

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation

Shruti Paniwala — Thu, 26 May 2022 00:59:49 -0500

CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.

Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to maarten.reijnders@unil.ch or create an issue.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075

Address of the bookmark: https://gitlab.com/mreijnders/crowdgo

Tigers genome sequenced

Rahul Agarwal — Tue, 17 Sep 2013 16:48:24 -0500

Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.

The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.

The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.

Reference:

http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690

http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms

Paper:

http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes

Surabhi Chaudhary — Sat, 12 May 2018 07:26:59 -0500

SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

Address of the bookmark: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

EGAD: Ultra-fast functional analysis of gene networks

Rahul Nayak — Fri, 14 Dec 2018 04:10:35 -0600

With the EGAD (Extending ‘Guilt-by-Association’ by Degree) package, we present a series of highly efficient tools to calculate functional properties in networks based on the guilt-by-association principle. These allow rapid controlled comparisons and analyses. Two of the core features are: a function prediction algorithm which is fully vectorized (neighbor_voting), allowing network characterization across even thousands of functional groups to be accomplished in minutes in cross-validation and an analytic determination of the optimal prior to guess candidates genes across multiple functional sets (calculate_multifunc, auc_multifunc).

Address of the bookmark: https://github.com/sarbal/EGAD