BOL: Related items

Gene Ontology Consortium

Jit — Fri, 11 Jan 2019 05:51:02 -0600

The GO knowledgebase is composed of two primary components:

the Gene Ontology (GO), which provides the logical structure of the biological functions (‘terms’) and their relationships to one another, manifested as a directed acyclic graph
the corpus of GO annotations, evidence-based statements relating a specific gene product (a protein, non-coding RNA, or macromolecular complex, which we often refer to as ‘genes’ for simplicity) to a specific ontology term

Together, the ontology and annotations aim to describe a comprehensive model of biological systems. Currently, the GO knowledgebase includes experimental findings from over 140 000 published papers, represented as over 600 000 experimentally-supported GO annotations. These provide the core dataset for additional inference of over 6 million functional annotations for a diverse set of organisms spanning the tree of life.

In addition to this core knowledgebase, GOC resources also include software to edit and perform logical reasoning over the ontologies, web access to the ontology and annotations, and analytical tools that use the GO knowledgebase to support biomedical research.

Address of the bookmark: http://www.geneontology.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

DarkHorse: a method for genome-wide prediction of horizontal gene transfer

Jit — Thu, 22 Jun 2017 07:58:35 -0500

A new approach to rapid, genome-wide identification and ranking of horizontal transfer candidate proteins is presented. The method is quantitative, reproducible, and computationally undemanding. It can be combined with genomic signature and/or phylogenetic tree-building procedures to improve accuracy and efficiency. The method is also useful for retrospective assessments of horizontal transfer prediction reliability, recognizing orthologous sequences that may have been previously overlooked or unavailable. These features are demonstrated in bacterial, archaeal, and eukaryotic examples.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/

GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks

Rahul Nayak — Tue, 15 Jan 2019 09:39:30 -0600

GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks based on gene expression data and optional hub gene information.

It efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.

GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck

Address of the bookmark: http://lce.biohpc.swmed.edu/geneck/

PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes

BioStar — Thu, 13 Aug 2020 10:06:19 -0500

PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

homologizer: Phylogenetic phasing of gene copies into polyploid subgenomes

Abhi — Sat, 03 Jun 2023 19:19:10 -0500

This tutorial describes the usage of homologizer to phase gene copies into polyploid subgenomes. The tutorial is an abbreviated version of a soon-to-be published paper in Methods in Molecular Biology. Please see that paper for many more details and practical considerations for running homologizer analyses. If you use homologizer, please cite the paper in which we first describe the method:

Freyman, W.A., Johnson, M.G., and C.J. Rothfels. 2022. Homologizer: phylogenetic phasing of gene copies into polyploid subgenomes. bioRxiv 2020.10.22.351486v4

homologizer is implemented in RevBayes. Please see http://revbayes.com to download and install RevBayes. For users without previous RevBayes experience, we recommend the tutorials at http://revbayes.com.

Address of the bookmark: https://github.com/wf8/homologizer

Explore taxdump files !

Jit — Sat, 08 Feb 2020 04:44:55 -0600

This is an extract of taxdump-readme.txt to be found at 
ftp://ftp.ncbi.nih.gov/pub/taxonomy/

The content of the archive
--------------------------

It may look like this:

delnodes.dmp
division.dmp
gencode.dmp
merged.dmp
names.dmp
nodes.dmp
readme.txt

The readme.txt file gives a brief description of *.dmp files. These files
contain taxonomic information and are briefly described below. Each of the
files store one record in the single line that are delimited by "\t|\n"
(tab, vertical bar, and newline) characters. Each record consists of one 
or more fields delimited by "\t|\t" (tab, vertical bar, and tab) characters.
The brief description of field position and meaning for each file follows.

nodes.dmp
---------

This file represents taxonomy nodes. The description for each node includes 
the following fields:

	tax_id					-- node id in GenBank taxonomy database
 	parent tax_id				-- parent node id in GenBank taxonomy database
 	rank					-- rank of this node (superkingdom, kingdom, ...) 
 	embl code				-- locus-name prefix; not unique
 	division id				-- see division.dmp file
 	inherited div flag  (1 or 0)		-- 1 if node inherits division from parent
 	genetic code id				-- see gencode.dmp file
 	inherited GC  flag  (1 or 0)		-- 1 if node inherits genetic code from parent
 	mitochondrial genetic code id		-- see gencode.dmp file
 	inherited MGC flag  (1 or 0)		-- 1 if node inherits mitochondrial gencode from parent
 	GenBank hidden flag (1 or 0)            -- 1 if name is suppressed in GenBank entry lineage
 	hidden subtree root flag (1 or 0)       -- 1 if this subtree has no sequence data yet
 	comments				-- free-text comments and citations

names.dmp
---------
Taxonomy names file has these fields:

	tax_id					-- the id of node associated with this name
	name_txt				-- name itself
	unique name				-- the unique variant of this name if name not unique
	name class				-- (synonym, common name, ...)

division.dmp
------------
Divisions file has these fields:
	division id				-- taxonomy database division id
	division cde				-- GenBank division code (three characters)
	division name				-- e.g. BCT, PLN, VRT, MAM, PRI...
	comments

gencode.dmp
-----------
Genetic codes file:

	genetic code id				-- GenBank genetic code id
	abbreviation				-- genetic code name abbreviation
	name					-- genetic code name
	cde					-- translation table for this genetic code
	starts					-- start codons for this genetic code

delnodes.dmp
------------
Deleted nodes (nodes that existed but were deleted) file field:

	tax_id					-- deleted node id

merged.dmp
----------
Merged nodes file fields:

	old_tax_id                              -- id of nodes which has been merged
	new_tax_id                              -- id of nodes which is result of merging

PubMed opens for comment

Rahul Nayak — Thu, 24 Oct 2013 12:40:17 -0500

The informal conversations that researchers have at scientific meetings look set to move online, if a new initiative by the US National Center for Biotechnology Information (NCBI) has its way. On 22 October, the NCBI of Bethesda, Maryland, launched the pilot phase of a programme called PubMed Commons. This will allow users to comment on published abstracts on the PubMed website, which indexes some 22 million papers.

For now, only a select group of researchers and their invited guests can use the system. But the NCBI's director David Lipman, who helped to develop the programme, says that soon any PubMed author will be allowed to comment under his or her real name and anyone will be able to read the comments.

More @ http://www.nature.com/news/pubmed-opens-for-comment-1.14023

News source Nature.

NCBI Remap

Jit — Thu, 11 Feb 2016 11:02:26 -0600

NCBI Remap. This tool is conceptually similar to liftOver in that in manages conversions between a pair of genome assemblies but it uses different methods to achieve these mappings. It is also available through a simple web interface or you can use the API for NCBI Remap.

More at http://www.ncbi.nlm.nih.gov/genome/tools/remap

API http://www.ncbi.nlm.nih.gov/genome/tools/remap/docs/api

Address of the bookmark: http://www.ncbi.nlm.nih.gov/genome/tools/remap