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/

Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search

Jit — Tue, 28 Jan 2020 03:34:23 -0600

Reference free SNP search for comparative population genomics: multiple samples run simultanously. **experimental phase, compiles and runs with OpenMPI-1.8.8 with Intel Compiler only

Cycles enumeration (aka Bubbles) as part of de novo de bruijn graphs assembly using colours can be unpractical for large error prone genomes which makes the assembly process produce an excessive number of false positive cycles. Our solution is to search the graph in multicores shared memory parallel mode using graph decomposition then use filtering method to generate good quality SNPs.

https://arxiv.org/abs/1809.06700

https://github.com/redayounsi/2KP2P

/2kp2omp/bin/main_2kp2_K63_C2 -i fastq_files.txt -o fungus_bub.fasta -r stat_fungus.txt -c cov_fungus_hash.txt -k 63 -h 20 -b 100 -g 600 -l 100 -f 16 -t 5.0 -x 1 -v 0 -p 1 -y 1 -u 1

Address of the bookmark: https://github.com/redayounsi/2KP2P

pggb: the pangenome graph builder

biogeek — Sun, 13 Sep 2020 20:54:20 -0500

This pangenome graph construction pipeline renders a collection of sequences into a pangenome graph (in the variation graph model). Its goal is to build a graph that is locally directed and acyclic while preserving large-scale variation. Maintaining local linearity is important for the interpretation, visualization, and reuse of pangenome variation graphs.

Address of the bookmark: https://github.com/pangenome/pggb

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

Spines

Jit — Mon, 28 Nov 2016 05:33:26 -0600

Spines is a collection of software tools, developed and used by the Vertebrate Genome Biology Group at the Broad Institute. It provides basic data structures for efficient data manipulation (mostly genomic sequences, alignments, variation etc.), as well as specialized tool sets for various analyses. It also features three sequence alignment packages: Satsuma, a highly parallelized program for high-sensitivity, genome-wide synteny; Papaya, an all-purpose alignment tool for less diverged sequences; and SLAP, a context-sensitive local aligner for diverged sequences with large gaps.

Access Spines here.

Address of the bookmark: https://www.broadinstitute.org/genome-sequencing-and-analysis/spines

orthodotter: Synteny plots (oxford grid)

Jit — Wed, 09 Aug 2017 07:16:16 -0500

orthodotter -h
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orthodotter - Plot orthologous genes on an oxford grid.
       -f      : input file, containing orthologous genes, default is stdin
                       species chr-name start end species chr-name start end
       -toPlot  : give the x and y sets and the color separated by double-dots,
                       for example set1:set2:red will plot set1 on x, set2 on y with
                       red points. Could give several -toPlot arguments.
                       To launch the clustering of dots, use extra-option 1=dist,min_nb_genes
                       where dist is the minimal distance (euclidian) between two points and min_nb_genes the minimal
                       number of genes in a cluster to be valid.
       -o      : output file, default is stdout
       -x       : resolution of x axis, default is 600
       -y       : resolution on y axis, default is 600
       -r       : radius of circle representing orthologous genes
       -format       : could be png, gif, jpg, pdf or ps. Default is png.
       -fg           : foreground color, default is black
       -bg           : background color, default is transparent
       -fSize   : fontSize, default is 1
       -filter       : check chromosome names
       -h            : help
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orthodotter -f Vigne_Banane.ortho -toPlot Vigne:Banane:black:1=10,5 -x 1200 -y 1200 -bg white -o Vigne_vs_Banane.png > Vigne_vs_Banane.clusters
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Address of the bookmark: https://github.com/institut-de-genomique/orthodotter

Quota synteny alignment

Jit — Mon, 31 Jul 2017 04:11:57 -0500

Typically in comparative genomics, we can identify anchors, chain them into syntenic blocks and interpret these blocks as derived from a common descent. However, when comparing two genomes undergone ancient genome duplications (plant genomes in particular), we have large number of blocks that are not orthologous, but are paralogous. This has forced us sometimes to use ad-hoc rules to screen these blocks. So the question is: given the expected depth (quota) along both x- and y-axis, select a subset of the anchors with maximized total score.

Address of the bookmark: https://github.com/tanghaibao/quota-alignment

flexidot: Highly customizable, ambiguity-aware dotplots for visual sequence analyses

Jit — Fri, 24 Apr 2020 08:39:28 -0500

FlexiDot is a cross-platform dotplot suite generating high quality self, pairwise and all-against-all visualizations. To improve dotplot suitability for comparison of consensus and error-prone sequences, FlexiDot harbors routines for strict and relaxed handling of mismatches and ambiguous residues. The custom shading modules facilitate dotplot interpretation and motif identification by adding information on sequence annotations and sequence similarities to the images. Combined with collage-like outputs, FlexiDot supports simultaneous visual screening of a large sequence sets, allowing dotplot use for routine screening.

Address of the bookmark: https://github.com/molbio-dresden/flexidot