<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/8509?offset=970 https://bioinformaticsonline.com/videolist/watch/3963/spotlight-on-genomics-understanding-our-genes-a-step-to-personalized-medicine Mon, 26 Aug 2013 17:07:44 -0500 https://bioinformaticsonline.com/videolist/watch/3963/spotlight-on-genomics-understanding-our-genes-a-step-to-personalized-medicine <![CDATA[Spotlight on Genomics: Understanding Our Genes - A Step to Personalized Medicine]]> (Visit: http://www.uctv.tv/) Learn about the essential role of genomics in the development of stem cell based therapies. Craig Venter, president and founder of the J. Craig Venter Institute and Catriona Jamieson, director for stem cell research at the UCSD Moores Cancer Center, speak about the future of personalized medicine in which genomics, the study of genes and their function, is applied to pinpoint specific treatments for patients. Sandra Dillon, a clinical trial participant, gives a patient's perspective. [7/2013] [Health and Medicine] [Show ID: 24530]]]> 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/news/view/12883/breaking-chromosomes-to-study-cancer Fri, 18 Jul 2014 05:42:09 -0500 https://bioinformaticsonline.com/news/view/12883/breaking-chromosomes-to-study-cancer <![CDATA[Breaking chromosomes to study cancer !!!]]> Chromosomes are present in every cell of our body and they contain the information the body needs to develop and function properly. This information is carried in genes that are arranged along the chromosomes. There are usually 46 chromosomes in every cell. These chromosomes come in pairs, one from our mother and one from our father. The chromosomes can be sorted into 23 pairs by looking at them down a microscope.

Most people who have a balanced translocation have the right amount of chromosome material but it has been rearranged in some way. This may happen if two chromosomes swap pieces (a reciprocal translocation). In other cases two whole chromosomes may become stuck together (a Robertsonian translocation). This page describes what happens when someone has a reciprocal translocation.

Reciprocal chromosomal translocations occur following double-strand breaks (DSBs) in DNA when a section of one chromosome is exchanged with that of another, non-homologous chromosome. These exchanges may produce a dysfunctional fusion gene that disrupts cell growth and survival pathways, such as the translocations seen in leukemia and childhood sarcomas.

Chromosomal translocations have been well studied in cancer cell lines which are associated with two types of cancer, acute myeloid leukemia and Ewing's sarcoma, but determining how they contribute to cancer development is complicated by additional mutations and altered gene expression profiles in these cultured cells. Now, Juan Carlos Ramirez, head of the Viral Vector Facility at the Fundacion Centro Nacional de Investigaciones Cardiovasculares (CNIC) and his colleagues Raul Torres at CNIC and Sandra Rodriguez-Peralez at the Spanish National Cancer Center (CNIO) in Madrid, Spain have used a new genome editing tool, CRISPR-Cas9, to induce chromosomal translocations for the first time in a human cell line and in primary cells. The study's authors conclude by stating that the use of this technology will allow for the clarification of how and why chromosomal translocation occurs, which without doubt will allow new anti-cancer therapeutic strategies to be tackled.

Using RNA-Guided Endonuclease (RGEN) technology or CRISPR/Cas9 genome engineering technology, CNIO and CNIC researchers have shown that it is possible to obtain such chromosomal translocations. The CRISPR-Cas9 system is extremely simple to introduce a cut at the desired locus, easier to design, and cheaper than many other systems. Using the CRISPR-Cas9 system, Ramirez and his colleagues reproduced the translocations observed in Ewing’s Sarcoma (ES) and Acute Myeloid Leukemia (AML) patient cell lines in HEK293 cells and also generated the ES translocation in human mesenchymal stem cells and the AML translocation in umbilical cord blood cells.

By focusing on chromosomal translocation without the confounding characteristics of established cell lines, these new cells lines should help answer the fundamental question of what causes a cell to become cancerous. Ramirez and his team now look forward to modeling other chromosome translocations in a variety of cell types.

Reference:

http://en.wikipedia.org/wiki/Chromosomal_translocation

http://www.nature.com/ncomms/2014/140603/ncomms4964/abs/ncomms4964.html

]]> 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/file/view/37581/comparativegenomics-exercise2 Wed, 22 Aug 2018 22:10:56 -0500 https://bioinformaticsonline.com/file/view/37581/comparativegenomics-exercise2 <![CDATA[ComparativeGenomics Exercise2]]> COMPARATIVE MICROBIAL GENOMICS ANALYSIS WORKSHOP  @ cbs.dtu.dk

Free Bioinformatics workbench https://www.mn.uio.no/ifi/english/research/networks/clsi/earlier_seminars/2012/tammivesth_osloseminarfinal.pdf

]]> Neel 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