BOL: Related items

AGORA: Algorithm for Gene Order Reconstruction in Ancestors

Jit — Mon, 28 Feb 2022 23:26:21 -0600

AGORA stands for “Algorithm for Gene Order Reconstruction in Ancestors” and was developed by Matthieu Muffato in the DYOGEN Laboratory at the École normale supérieure in Paris in 2008.

    // | |     //   ) )  //   ) ) //   ) )  // | |
   //__| |    //        //   / / //___/ /  //__| |
  / ___  |   //  ____  //   / / / ___ (   / ___  |
 //    | |  //    / / //   / / //   | |  //    | |
//     | | ((____/ / ((___/ / //    | | //     | |

AGORA is used to generate ancestral genomes for the Genomicus online server for gene order comparison, and has been in constant use in the group since.

Address of the bookmark: https://github.com/DyogenIBENS/Agora

The Genes That Travel Together: A Genomic Detective Story

BioStar — Fri, 11 Sep 2026 13:00:36 -0500

Imagine looking through thousands of microbial genomes and discovering two genes that repeatedly appear together. The obvious conclusion is that they must somehow be connected—that perhaps they work together, participate in the same pathway, or depend on each other. But evolution has a way of leaving misleading clues. What if these two genes are found together simply because they were inherited from the same ancient ancestor? In that case, their apparent association may have little to do with their biological function; it may simply be a reflection of shared evolutionary history. This is the intriguing problem addressed by CORGIAS (https://github.com/ynishimuraLv/corgias), a computational framework developed to distinguish genuine gene associations from correlations created by common ancestry.

Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough—we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.

More at https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?

Bioinformatics tools to detect horizontal gene transfer (HGT) in genomes

Jit — Fri, 02 Mar 2018 04:56:23 -0600

Horizontal gene transfer (HGT), the “non-sexual movement of genetic material between two organisms” , is relatively common in prokaryotes and single-celled eukaryotes, but a number of factors combine to make it far rarer in multicellular eukaryotes. In order for a eukaryotic species to gain a gene by HGT, foreign DNA must enter the host nucleus, integrate into the genome, and in more complex organisms it must enter the sequestered germline in order to be transmitted to offspring. Once there, it must not experience strong negative selection, despite potential for genetic incompatibility with the host genome and mismatch between the niche of the donor and the host. Over the longer term, foreign DNA may become “domesticated” in the recipient genome and provide novel function.

Following are the popular tool to detect HGT in genomes:

T-REX / 3.22

HGT detection / download & compile

20525630

RANGER-DTL / 2.0

HGT detection / download binary

22689773

PhyloNet / 3.6.1

HGT detection / download binary

18662388

Jane / 4.01

HGT detection / download binary (!license!)

20181081

TREE-PUZZLE / 5.3.rc16

HGT detection / download & compile

11934758

CONSEL / 0.20

HGT detection / download

11751242

DarkHorse / 1.5 rev170

HGT detection / download & install

17274820

HGTector / 0.2.1

HGT detection / git clone

25159222

EGID / 1.0

HGT detection / download

22355228

GeneMarkS / 4.30

HGT detection / download binary (!license!)

9461475

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

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

GEO2R: compare two or more groups of Samples

Rahul Nayak — Sun, 20 Dec 2020 11:49:41 -0600

GEO2R to compare two or more groups of Samples in order to identify genes that are differentially expressed across experimental conditions.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/geo/geo2r/

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities

Jit — Mon, 07 Aug 2017 18:49:27 -0500

RAMI, which clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly.

Availability: RAMI is licensed under GNU GPL and can be run or downloaded from http://www.acgt.se/online.html.

Address of the bookmark: https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051

ForBio and UiB course: Introduction to phylogenetic methods

Rahul Nayak — Mon, 16 Dec 2019 09:39:12 -0600

The is an introductory course that is aimed at students who will need to preform phylogenetic analyses in their work, but who have little or no experience with phylogenetic analyses. The ForBio component of the course is focused on the practical aspects of phylogenetic analyses and students that have not attended the theoretical part of the course are expected to have read the relevant literature. The course will cover all basic aspects of phylogenetic analyses with emphasis on use of DNA data. Students will learn how to prepare their data, explore its properties and how to analyze it using distance, parsimony, likelihood and Bayesian methods. In the last two days students will also get an introduction to molecular dating with focus on the use of BEAST and to comparative methods used to study the evolution of discrete and continuous traits.

https://www.forbio.uio.no/events/courses/2020/Bergen_Phylogenetics_2020.html

ForBio travel grants
We have changed our travel grants rules. ForBio offers both incoming and outgoing travel grants to fund collaborations between ForBio members based in Norway and taxonomic experts up to NOK 14,000. Grants are given for taxonomic training with relevance to the main research project of the applicant, and aim to allow the student to do taxonomic research with specific experts, work with particular collections, or learn new preparation techniques directly from experts. Participation in external courses, conferences, fieldwork or other kinds of activities which are not classified as expert-in-training visits are no longer eligible for support. There are two application deadlines: March 1st 2020.

Swabs to Genomes: A Comprehensive Workflow

Rahul Nayak — Sun, 10 Aug 2014 03:01:21 -0500

The sequencing, assembly, and basic analysis of microbial genomes, once a painstaking and expensive undertaking, has become almost trivial for research labs with access to standard molecular biology and computational tools. However, there are a wide variety of options available for DNA library preparation and sequencing, and inexperience with bioinformatics can pose a significant barrier to entry for many who may be interested in microbial genomics. The objective of the present study was to design, test, troubleshoot, and publish a simple, comprehensive workflow from the collection of an environmental sample (a swab) to a published microbial genome; empowering even a lab or classroom with limited resources and bioinformatics experience to perform it.

Address of the bookmark: https://peerj.com/preprints/453.pdf