BOL: Related items

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/

G-NEST: The Gene NEighborhood Scoring Tool

Neel — Fri, 25 Sep 2020 20:09:18 -0500

The Gene NEighborhood Scoring Tool (G-NEST) combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all window sizes. Primary author of final code = William F. Martin. Example data files are in the separate repository.

Address of the bookmark: https://github.com/dglemay/G-NEST

The Gene Detectives: How a New Computational Tool Is Helping Us Read the History Written in Our Genes

BioStar — Thu, 10 Sep 2026 21:23:42 -0500

Imagine opening an ancient family album with no names, dates, or captions. Some photographs are missing, others have been copied, and a few show people who look almost identical. Your challenge is to discover who is related to whom and reconstruct the family story. In evolutionary biology, scientists face a similar mystery when they study genes. Every genome contains clues about its past, but genes can be duplicated, lost, and changed over millions of years, making their history difficult to trace.

A recent study published in PLOS Computational Biology introduces REvolutionH-tl 2.0 (https://pypi.org/project/revolutionhtl/), a computational tool designed to help solve this mystery. The software analyzes protein sequences and identifies evolutionary relationships between genes. It can help researchers detect important events such as gene duplication, gene loss, and speciation, allowing them to reconstruct how genes evolved across different organisms.

What makes the tool especially interesting is its focus on both speed and interpretation. The researchers compared REvolutionH-tl 2.0 with several existing computational tools and reported that it achieved competitive accuracy while requiring less computational time in their tests. The tool also includes visualization features, helping researchers turn complicated genetic data into evolutionary stories that are easier to explore.

Genes are more than simple sequences of DNA; they are records of life’s long history. Each surviving gene carries traces of the changes that shaped it. By bringing together computational analysis and visualization, REvolutionH-tl 2.0 acts like a detective, helping scientists uncover these hidden stories. Ultimately, tools like this can improve our understanding of how genomes evolved and how genes acquired the roles they perform today.

More at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013017

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/

genoPlotR - plot gene and genome maps project!

Abhimanyu Singh — Wed, 12 Dec 2018 08:33:41 -0600

genoPlotR is a R package to produce reproducible, publication-grade graphics of gene and genome maps. It allows the user to read from usual format such as protein table files and blast results, as well as home-made tabular files.

Features

Linear representation of several segments of DNA
Comparisons represented by areas between the segments (like Artemis, for example)
Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
Plot several subsegments of the same segment on the same line, separated by a //
Automatic or manual placement of the segments on the plot
Add annotations to all the lines
Create smart, automatic annotations for genomes, based on gene names
Add a user-generated tree
Add a global scale or a scale to each line
Use user-defined graphical functions to represent genes

Address of the bookmark: http://genoplotr.r-forge.r-project.org/

gggenes: a ggplot2 extension for drawing gene arrow maps.

BioStar — Tue, 02 Jun 2020 11:43:34 -0500

Install the stable version of gggenes from CRAN:

install.packages("gggenes")

If you want the development version, install it from GitHub:

devtools::install_github("wilkox/gggenes")

More at https://github.com/wilkox/gggenes

Address of the bookmark: http://wilkox.org/gggenes

GenomeQC: a quality assessment tool for genome assemblies and gene structure annotations

Neel — Thu, 19 May 2022 04:29:05 -0500

The GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license. All source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6568-2

Address of the bookmark: https://github.com/HuffordLab/GenomeQC

Tool: Gene Set Clustering based on Functional annotation (GeneSCF)

EagleEye — Fri, 24 Jun 2016 17:30:22 -0500

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Gene Set Clustering based on Functional annotation

GeneSCF serves as command line tool for clustering the list of genes given by the users based on functional annotation (Gene Ontology, KEGG, REACTOME and NCG 4.0). It requires gene list in the form of Entrez Gene ID (UIDs) or Official gene symbols as a input. GeneSCF supports more organisms from V1.1. Examples to download database as simple text file using GeneSCF "prepare_database" module, 1) https://www.biostars.org/p/197414/#197416 , 2) https://www.biostars.org/p/191532/#191540

The advantage of using GeneSCF over other enrichment tools is that, it performs enrichment analysis in real-time (v1.1 and above) by accessing source databases. With command-line versions of tools, as you know you can run multiple gene list simultaneously.

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Home page:

http://genescf.kandurilab.org/

Requirement:

GeneSCF only works on Linux system, it has been successfully tested on Ubuntu, Mint and Cent OS. Other distributions of Linux might work as well.

Documentation:

http://genescf.kandurilab.org/documentation.php

Report issues on Biostars or GitHub Project page

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Enzyme Portal

Rahul Agarwal — Tue, 03 Sep 2013 18:06:06 -0500

Enzyme Portal- To look for information about the biology of a protein with enzymatic activity.

The enzyme portal integrates many resources, most of them hosted by EBI and also external ones such as BioPortal. Its main goal is to provide information about enzymes in a suitable format, with a usable interface designed for intended users. Instead of reinventing the wheel, it makes use of available and reliable resources to that end.

Related Literature:

http://nar.oxfordjournals.org/content/41/D1/D773.full

http://www.biomedcentral.com/1471-2105/14/103

Address of the bookmark: http://www.ebi.ac.uk/enzymeportal/

%MinMax: A versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding.

Surabhi Chaudhary — Thu, 24 May 2018 02:53:31 -0500

%MM calculates whether a given gene sequence encodes amino acids using the most common codons possible, the least common codons possible, or (most typically) some combination of these extremes. See our PLoS ONE paper for more details on how the %MinMax algorithm works. %MinMax results are averaged over an 18-codon sliding window; hence the result for "codon window = 1" is the average codon usage for codons 1-18, codon window 2 = codons 2-19, etc.

Address of the bookmark: http://www.codons.org/