<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26325?offset=1160 https://bioinformaticsonline.com/news/view/5963/make-genomic-research-less-ethnically-biased Wed, 30 Oct 2013 16:08:17 -0500 https://bioinformaticsonline.com/news/view/5963/make-genomic-research-less-ethnically-biased <![CDATA[Make Genomic Research Less Ethnically-Biased]]> Mexican billionaire Carlos Slim Hélu, the world’s 2nd-richest man, is giving an additional $74 million to a genomics center in Boston in order to right a bias in the field–a kind of scientific racism, you might call it. The problem: most samples of DNA analyzed in biomedical research come from people of European descent.

Find more detail news at http://www.forbes.com/sites/erincarlyle/2013/10/30/carlos-slim-gives-another-74-million-to-make-genomic-research-less-ethnically-biased/?utm_campaign=forbesfbsf&utm_source=facebook&utm_medium=social

]]> Shikha Logwani https://bioinformaticsonline.com/bookmarks/view/42985/janggu-deep-learning-for-genomics Tue, 23 Mar 2021 05:14:43 -0500 https://bioinformaticsonline.com/bookmarks/view/42985/janggu-deep-learning-for-genomics <![CDATA[Janggu - Deep learning for Genomics]]> Janggu is a python package that facilitates deep learning in the context of genomics. The package is freely available under a GPL-3.0 license.

Detail tutorial at https://janggu.readthedocs.io/en/latest/

USE cases

https://github.com/wkopp/janggu_usecases

Address of the bookmark: https://github.com/BIMSBbioinfo/janggu

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34515/metasim-a-sequencing-simulator-for-genomics-and-metagenomics Mon, 04 Dec 2017 07:18:20 -0600 https://bioinformaticsonline.com/bookmarks/view/34515/metasim-a-sequencing-simulator-for-genomics-and-metagenomics <![CDATA[MetaSim A Sequencing Simulator for Genomics and Metagenomics.]]> Our software can be used to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to design a metagenome by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a simulation of a number of different sequencing technologies. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree. 

Address of the bookmark: http://ab.inf.uni-tuebingen.de/software/metasim/

]]> Jit https://bioinformaticsonline.com/opportunity/view/36647/bioinformatics-jobs-at-nibmg Wed, 16 May 2018 02:57:15 -0500 <![CDATA[Bioinformatics jobs at NIBMG]]> NIBMG are looking for bright and motivated people in our big projects on cutting edge biomedical genomics research

http://www.nibmg.ac.in/academic/SyMeC-ICGC/SyMeC%20&%20ICGC_May%202018.pdf

http://www.nibmg.ac.in/academic/plp/15_05_2018/AdvertisementMay2018.pdf

]]> https://bioinformaticsonline.com/researchlabs/view/42137/plant-computational-genomics-lab-%E2%80%93-jill-wegrzyn Thu, 20 Aug 2020 19:49:12 -0500 <![CDATA[PLANT COMPUTATIONAL GENOMICS LAB – JILL WEGRZYN]]> Our research focuses on the computational analysis of genomic and transcriptomic sequences from non-model plant species. We do this by developing approaches to examine gene finding, gene expression, transcriptome assembly, and conserved element identification, through machine learning and computational statistics. We use these novel methods to address questions related to genome biology and population genomics.

We also develop web-based applications that integrate data across domains to facilitate the forest geneticist or ecologist’s ability to analyze, share, and visualize their data. Such integration requires the implementation of semantic technologies and ontologies to connect genotype, phenotype, and environmental data.

http://plantcompgenomics.com/

]]> https://bioinformaticsonline.com/opportunity/view/42712/scientist-c-non-medical-it-expert-computer-professionalgenomicsbioinformatic-at-nimr Mon, 01 Feb 2021 13:54:06 -0600 <![CDATA[Scientist C - Non-Medical (IT Expert- Computer Professional/Genomics/Bioinformatic) at NIMR]]> Applications are invited upto 12th February 2021 in the prescribed format (available on the websites of ICMR-NIMR) through link http://onlineapply.nimr.org.in/ up to 05:00 PM on 12th February 2021 for the following post on contract basis at NIMR, Sector-8, Dwarka, New Delhi.

Scientist C - Non-Medical (IT Expert- Computer Professional/Genomics/Bioinformatic)No. of posts: 01 (UR)

Salary (Fixed): Rs.51,000/- + HRA

Essential Qualification: Candidate should possess 1st class master degree in relevant subjects from a recognized university with 4 years experience
OR
2nd class M.Sc + Ph.D degree in relevant subjects from a recognized university with 4 years experience.Desirable Qualification: Candidates should possess a PhD degree in any field of science.
Preference will be given to those who have published scientific papers in international journals and who have a track record of working in infectious diseases.

The candidate must know the following for further consideration: (a) data processing and analysis using statistical softwares, (d) programming, (e) presentation of complex data from excel files and related skills.
Understanding of GIS and malaria will be an advantage. Experience and interest in functional genomics and genomic sequencing will be important.

Age Limit: 40 YearsDuration: 30.09.2021

More at http://onlineapply.nimr.org.in/

]]> https://bioinformaticsonline.com/blog/view/43896/list-of-comparative-genomics-resources Tue, 28 Jun 2022 04:08:06 -0500 https://bioinformaticsonline.com/blog/view/43896/list-of-comparative-genomics-resources <![CDATA[List of comparative genomics resources !]]>

Compare structural and functional annotations of proteins between sequenced genomes.

View AREs in the human transcriptome and study the comparative genomics of AREs in model organisms.

Find information about orthologous genes in prokaryotes.

Search for publicly available QTL data on livestocks and animal species.

Find information about bovine genomics data.

A multi-organism web-based resource system designed to easily retrieve, correlate and interpret data across species.

A database resource of developmentally associated conserved non-coding elements.

Delineate conserved non-coding blocks from upstream regions of putative orthologous gene pairs from man, mouse, rat, fugu, Mus musculus, Danio rerio, and zebrafish.

Find coexpressed gene lists and networks in human and mouse.

Construct phylogenetic tree of microorganisms based on oligopeptide content of their complete proteomes.

A novel database server that classifies GenBank's dbEST (database of expressed gene sequences) libraries and removes contaminants.

Find information about the ancestral relationship between genes.

Provides an overview of the genomic organization of microRNAs and extent of conservation during evolution in different metazoan species.

Conduct comparative biometric analysis of chromosomes of different organisms.

Search for Indices of gene and transcripts in human and mouse.

Search and compare 12 new and old Drosophila genomes.

Access to whole genome alignments of human, mouse, rat and fish sequences.

Find eukaryotic paralog/paralogon information.

Analyze the evolutionary process of overlapping genes when comparing different species.

The first publicly available system reported to handle inter-species gene mention normalization.

A web-based software/database system aimed at an improved and accelerated annotation of prokaryotic genomes.

Visualize gene indices of human, mouse, Arabidopsis, Zebrafish, Drosophila and Sheep.

Find information about mutated mouse genes.

A data management and analysis system for metagenomes

Search for influenza sequence, vaccine, and drug resistance information.

LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal models for their research.

The missing link between multi-species full genome comparisons and functional analysis.

Conduct comparative analysis of completely sequenced microbial genomes.

A biologist-centric software for evolutionary analysis of DNA and protein sequences.

Conduct single nucleotide polymorphisms diversity measurements among homologous sequences from the Mammalia class.

Find information about 1000s of microbial genomes.

A database dedicated to the study of genome conservation.

Explore orthologous relations across 352 complete genomes.

Browse complete genomes in several clades.

A database of orthologous genomic markers for placental mammal phylogenetics.

Conduct genome wide functional and structural analysis.

Conduct genome-wide cis-regulatory module (CRM) predictions for both the human and the mouse genomes.

Compare phenotypes of a given gene or gene set in different model organisms.

Phylemon is a web server that integrates a selected suite of more than 20 different tools from the most popular stand-alone programs of phylogenetic and evolutionary analysis.

Use this database to see where in the evolution some phylogenetic lineages were started, and over which species they were contained.

Search for genomic information on nematode satellite species Pristionchus pacificus.

Find information about related protein sequences.

Database hosting genomics and post-genomics data from multiple protozoans.

A database of pseudogene families based on the protein families from the Pfam database.

Find genomic information about mammals.

Find information about predicted regulons in prokaryotic transcription regulation.

Perform systematic comparison of proteome data among species.

A comparative map viewer dedicated to the biology of the Solanaceae family.

Analyze data from functional analysis on fragmented microbial genetic material.

Visualize and explore comparative genomic hybridization data sets.

Search for genome-wide annotations of regulatory sites in yeast and prokaryotes genomes.

Search for information on adaptive evolution in gene families of higher plants and chordate.

Generate graphical maps of circular genomes that show sequence features, base composition plots, analysis results and sequence similarity plots.

Conduct a comprehensive analysis of genes and genomes.

An interactive online poster presentation on the Macaque genome, including high-quality images, video clips, and Web resources

Search for annotated genetic information of expressed sequence tags (ESTs) in different eukaryotic organisms.

Find mapping and expression information for a unigene cluster (ESTs and full-length mRNA sequences organized into clusters that each represent a unique known or putative gene)

A public online resource for identifying or designing 'universal' overgo-hybridization probes from conserved sequences that can be used to efficiently screen one or more genomic libraries from a designated group of species.

Comprehensive suite of programs and databases for comparative analysis of genomic sequences.

Find metabolic networks and phylogenomic information on a taxonomically diverse range of eukaryotes.

]]> Shruti Paniwala https://bioinformaticsonline.com/researchlabs/view/44650/manthey-research-group-%E2%80%93-evolutionary-genomics Thu, 22 Aug 2024 06:25:55 -0500 <![CDATA[Manthey Research Group – Evolutionary Genomics]]> We focus on fundamental questions in genomics, ecology, and evolution. Our methods include fieldwork and labwork, but most of our time is spent analyzing genomics data using computational biology approaches.

Ant / bacteria co-evolution, landscape genomics, and population genomics
Vertebrate and/or invertebrate genome evolution

If you might be interested in joining our research group, send an email with your intent and why this group would potentially be a good fit for your future goals along with a CV / Resume to jdmanthey (at) gmail (dot) com

More at https://mantheylab.org/

]]> 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/bookmarks/view/34549/kraken-a-universal-genomic-coordinate-translator-for-comparative-genomics Thu, 07 Dec 2017 04:45:43 -0600 https://bioinformaticsonline.com/bookmarks/view/34549/kraken-a-universal-genomic-coordinate-translator-for-comparative-genomics <![CDATA[kraken: A universal genomic coordinate translator for comparative genomics]]> If you planning on conducting a study involving dozens of large genomes, then you do not have to run all pairwise synteny alignments .. simply try kraken: A universal genomic coordinate translator for comparative genomics

Address of the bookmark: https://github.com/nedaz/kraken

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