BOL: Related items

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Rahul Nayak — Sat, 07 Mar 2020 05:52:20 -0600

GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.

https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

Address of the bookmark: https://github.com/wanyuac/GeneMates

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

NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design

BioStar — Fri, 21 Feb 2025 10:39:47 -0600

NVIDIA and the Arc Institute have introduced Evo 2, a groundbreaking AI model designed to understand, predict, and generate DNA sequences. This marks a major advancement in computational biology, offering scientists an unprecedented tool to decode the genetic blueprint of life and even design entirely new biological systems.

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

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

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/

HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers

Jit — Mon, 21 Jan 2019 06:50:05 -0600

A computational pipeline for genome-wide detection of putative horizontal gene transfer (HGT) events based on sequence homology search hit distribution statistics

Authors: Qiyun Zhu (qiyunzhu@gmail.com), Katharina Dittmar (katharinad@gmail.com)

Affiliation: Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, USA

Zhu Q, Kosoy M, Dittmar K. HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers. BMC Genomics. 2014. 15:717.

Usage: Simply execute perl HGTector.pl, or, open GUI.html in a web browser to see a step-by-step wizard.

Download HGTector 0.2.2.

Address of the bookmark: https://github.com/DittmarLab/HGTector

MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data

BioStar — Thu, 26 Nov 2020 11:05:04 -0600

The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings:

1) A cell line expressing or lacking single TF,

2) Breast tumors divided along PAM50 designations

3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype

4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels.

In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

More at https://uwmadison.app.box.com/s/8j90e5h2rjrsz3bacaxnq8kor2o64vyg

Address of the bookmark: https://github.com/asroopra/MAGIC

JPred4: A Protein Secondary Structure Prediction Server

Poonam Mahapatra — Fri, 29 Dec 2017 16:14:28 -0600

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction.

Address of the bookmark: http://www.compbio.dundee.ac.uk/jpred4/

RNAcon: web-server for the prediction and classification of non-coding RNAs

Shruti Paniwala — Mon, 17 Jul 2017 04:55:11 -0500

RNAcon is a web-server for the prediction and classification of non-coding RNAs. It uses SVM-based model for the discrimination between coding and ncRNAs and RandomForest-based prediction model for the classification of ncRNAs into different classes. The structural information based graph properties were used for the development of prediction model.

The standalone version (Linux-based command-line) of RNAcon is freely available for the global scientific community.

Reference: Panwar, B.; Arora, A. and Raghava, G.P.S. (2014) Prediction and classification of ncRNAs using structural informationBMC Genomics 2014, 15:127

Address of the bookmark: http://crdd.osdd.net/raghava/rnacon/

Five points for bioinformatics software/tools

Jitendra Narayan — Mon, 05 Aug 2013 04:12:32 -0500

In the bioinformatics sector we mostly spend time on computational analysis of huge amounts of data and try to make sense of it, biologically. But, most of the newbie bioinformaticians are faced with dilemma when they receive biological sequence data for the first time. They mostly found confusing over open source, user friendly GUI, and commercial bioinformatics software. Don’t be surprise this is true and also not an easy task to decide, because analytical step is the most crucial part and believe to be the biggest bottleneck in publishing paper in high impact journals. Through this blog I would like to address the pros and cons of both kind of software/tools and try to assist (Hmmm not really, It looks convince) you to make decision on your software selections.

The most common newbie questions are:

Should I try to use these free open source programs? Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.