BOL: Related items

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.

R Graphs !!

Jit — Fri, 04 Nov 2016 10:48:00 -0500

The blog is a collection of script examples with example data and output plots. R produce excellent quality graphs for data analysis, science and business presentation, publications and other purposes. Self-help codes and examples are provided. Enjoy nice graphs !!

Address of the bookmark: http://rgraphgallery.blogspot.be/

Glia: a Graph/Smith-Waterman (partial order) aligner/realigner

Jit — Tue, 28 Jan 2020 04:02:58 -0600

glia's main use is as a local realigner. It will realign reads to a set of known (or putative) variants in a VCF, both consuming and producing an ordered stream of BAM alignments.

More at https://github.com/ekg/glia

glia -f ~/human_g1k_v37.fasta -t 20:62900077-62902077 -v variants.vcf.gz \
     -s AAATGTAAACATTTTATAGGGGATTCCCCTAAAAACAAAAAAACTTTCTGGGAAAGATTTTTCAAAAAATAAAA

Address of the bookmark: https://github.com/ekg/glia

JumboDB: tool for de Bruijn graph construction

BioStar — Tue, 17 Aug 2021 13:33:46 -0500

jumboDB tool for fast de Bruijn graph construction from long sequences (reads or genomes) with very low error rate. JumboDB is not a genome assembler by itself but rather a subroutine that translates a set of reads into compressed de Bruijn graph.

More at https://github.com/AntonBankevich/jumboDB

Address of the bookmark: https://github.com/AntonBankevich/jumboDB

GFAffix : Identifies walk-preserving shared affixes in variation graphs and collapses them into a non-redundant graph structure.

BioStar — Thu, 28 Aug 2025 03:11:25 -0500

GFAffix identifies walk-preserving shared affixes in variation graphs and collapses them into a non-redundant graph structure.

Address of the bookmark: https://github.com/codialab/GFAffix

The anatomy of successful computational biology software

Jitendra Narayan — Thu, 10 Oct 2013 11:53:08 -0500

Creators of software widely used in computational biology discuss the factors that contributed to their success

Nature Biotechnology spoke with Altschul and several other originators of computational biology software programs widely used today (Table 1). The conversations explored what makes certain software tools successful, the unique challenges of developing them for biological research and how the field of computational biology, as a whole, can move research agendas forward. What follows is an edited compilation of interviews.

Detail @ http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html

News Source @ Nature

Software developed in pevsner lab

Robert M Willioms — Mon, 06 Oct 2014 12:41:26 -0500

DRAGON: Database Referencing of Array Genes Online

SNOMAD: Standardization and Normalization of Microarray Data

SNPduo: SNP Analysis Between Two Individuals

SNPtrio: Analyzing and Visualizing and Inheritance Patterns in Trios

SNPscan: Data Analysis and Visualization of SNP Data

pediSNP: Analyze SNP Data From a Pedigree of Two Generations

kcoeff: Calculate Cotterman Coefficients of SNP Genotype Data

triPOD: Detects chromosomal abnormalities in parent-child trio-based microarray data

Address of the bookmark: http://pevsnerlab.kennedykrieger.org/php/?q=software

Cytoscape

Anjana — Mon, 23 May 2016 02:32:00 -0500

Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of Apps are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.

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

ERGO 2.0 Bioinformatics suites

Jit — Tue, 16 May 2017 08:14:10 -0500

ERGO 2.0 provides a systems biology informatics toolkit centered on comparative genomics to capture, query, and visualize sequenced genomes. Using Igenbio's proprietary algorithms, and the most comprehensive genomic database integrated with the largest collection of microbial metabolic and non-metabolic pathways, ERGO™ assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways, and gene products.

Address of the bookmark: https://www.igenbio.com/ergo/

MOSS: A System for Detecting Software Similarity

Jit — Sat, 09 Dec 2017 08:59:07 -0600

Moss (for a Measure Of Software Similarity) is an automatic system for determining the similarity of programs. To date, the main application of Moss has been in detecting plagiarism in programming classes. Since its development in 1994, Moss has been very effective in this role. The algorithm behind moss is a significant improvement over other cheating detection algorithms (at least, over those known to us).

Moss can currently analyze code written in the following languages:

C, C++, Java, C#, Python, Visual Basic, Javascript, FORTRAN, ML, Haskell, Lisp, Scheme, Pascal, Modula2, Ada, Perl, TCL, Matlab, VHDL, Verilog, Spice, MIPS assembly, a8086 assembly, a8086 assembly, MIPS assembly, HCL2.

Address of the bookmark: https://theory.stanford.edu/~aiken/moss/