<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/11249?offset=1410 https://bioinformaticsonline.com/bookmarks/view/43859/mumco-is-a-simple-bash-script-that-uses-whole-genome-alignment-information-provided-by-mummer-v4-to-detect-variants Wed, 27 Apr 2022 04:34:12 -0500 https://bioinformaticsonline.com/bookmarks/view/43859/mumco-is-a-simple-bash-script-that-uses-whole-genome-alignment-information-provided-by-mummer-v4-to-detect-variants <![CDATA[MUM&Co is a simple bash script that uses Whole Genome Alignment information provided by MUMmer (v4) to detect variants.]]> MUM&Co is able to detect:
Deletions, insertions, tandem duplications and tandem contractions (>=50bp & <=150kb)
Inversions (>=1kb) and translocations (>=10kb)

Address of the bookmark: https://github.com/SAMtoBAM/MUMandCo

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/44537/the-atcc-genome-portal Wed, 15 May 2024 14:24:16 -0500 https://bioinformaticsonline.com/bookmarks/view/44537/the-atcc-genome-portal <![CDATA[The ATCC Genome Portal]]> The ATCC Genome Portal (AGP, https://genomes.atcc.org/) is a database of authenticated genomes for bacteria, fungi, protists, and viruses held in ATCC’s biorepository. It now includes 3,938 assemblies (253% increase) produced under ISO 9000 by ATCC. Here, we present new features and content added to the AGP for the research community.

Address of the bookmark: https://genomes.atcc.org/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/44489/proksee Wed, 27 Mar 2024 11:11:54 -0500 https://bioinformaticsonline.com/bookmarks/view/44489/proksee <![CDATA[Proksee]]> Proksee is an expert system for genome assembly, annotation and visualization. To begin using Proksee, provide a complete genome sequence, sequencing reads or a CGView/Proksee map JSON file.

Please Cite the Following
Grant JR, Enns E, Marinier E, Mandal A, Herman EK, Chen C, Graham M, Van Domselaar G, and Stothard P
Nucleic Acids Research, 2023, gkad326, https://doi.org/10.1093/nar/gkad326

Address of the bookmark: https://proksee.ca/

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment Mon, 05 Aug 2024 23:01:29 -0500 https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment <![CDATA[UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment]]> UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment

  • Using state of the art tools, easily extended for other viruses

  • Tool and database updates for critical components via Conda

  • Built using modern design patterns with Conda and Snakemake

  • Extensible and easy to customize

  • Submission Ready Genomes

  • Customizable reporting with comprehensive visualization

https://ikim-essen.github.io/uncovar/

Github https://github.com/IKIM-Essen/uncovar

 

 

Address of the bookmark: https://ikim-essen.github.io/uncovar/

]]> BioStar https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design Fri, 21 Feb 2025 10:39:47 -0600 https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design <![CDATA[NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design]]> 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.

]]> BioStar https://bioinformaticsonline.com/researchlabs/view/22393/narcis-fernandez-fuentes-lab Mon, 25 May 2015 07:30:00 -0500 <![CDATA[Narcis Fernandez-Fuentes Lab]]> Welcome to our web-site compiling all the research-related activities of the group. Our research interests relate to a number of areas within Bioinformatics. We have a long-standing interest in protein structure prediction and structure-to-function relationships. We work in the study of biomolecular interactions, modeling of protein complexes, the study and characterization of protein-protein interactions, peptide design, modeling of genetic variation, structure-based protein design and different aspects of Plant Bioinformatics. Take a look at the our databases and servers and the list of publications for more information.

More at http://www.bioinsilico.org/

]]> https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping Tue, 08 May 2018 04:15:25 -0500 https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping <![CDATA[MITObim - mitochondrial baiting and iterative mapping]]> This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found here. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for Illumina data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the Iontorrent, 454 and PacBio sequencing platforms.

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

]]> Rahul Nayak https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes Mon, 25 Jun 2018 17:22:24 -0500 https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes <![CDATA[chromoMap-An R package for Interactive visualization and mapping of human chromosomes]]> chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

https://cran.r-project.org/web/packages/chromoMap/index.html

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books Fri, 30 May 2014 04:48:04 -0500 https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books <![CDATA[Next generation sequencing(NGS) books]]> Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream.

This bookmark is created to provide NGS online books links.

Address of the bookmark: http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version

]]> Abhimanyu Singh https://bioinformaticsonline.com/poll/view/23590/will-minion-nanopore-sequencing-increase-the-number-of-next-generation-sequencing-projects Tue, 04 Aug 2015 05:14:07 -0500 https://bioinformaticsonline.com/poll/view/23590/will-minion-nanopore-sequencing-increase-the-number-of-next-generation-sequencing-projects <![CDATA[Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?]]> Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?

]]> Strand