BOL: Related items

Bioinformatics job in Genotypic Tech, India

Mon, 07 Apr 2014 08:20:54 -0500

Genotypic Technology, the first Genomics Company of India is poised to become the next generation life sciences company. We are hiring professionals for our high end Genomics Labs (Molecular Biology/ Microarray/NGS) and Bioinformatics groups.

Apply to Genotypic Technology if you are a PhD in Life Sciences/ Molecular Biology/ Biotechnology/ Human Genetics/ Bioinformatics with minimum 4-5 years post doctoral experience as well as publications in peer reviewed journals.

Source: http://www.genotypic.co.in/Careers/2/Current-Openings.aspx

17 Marie Curie PhD position available immediately

Tue, 23 Jun 2015 06:52:06 -0500

Kindly look into following webpage:
http://medhealth.leeds.ac.uk/info/1450/scholarships/1795/marie_curie_phd_training_network

The closing date for application will be 26 June 2015.

kraken: A universal genomic coordinate translator for comparative genomics

Jit — Thu, 07 Dec 2017 04:45:43 -0600

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

Scribl : HTML5 canvas genomics graphic library

Jit — Thu, 25 Oct 2018 09:38:53 -0500

Scribl is a javascript, Canvas-based graphics library that easily generates biological visuals of genomic regions, alignments, and assembly data. Scribl can also be used in conventional offline pipelines, since everything needed to generate charts can be contained in a single html file.

Address of the bookmark: http://chmille4.github.io/Scribl/

phyloXML: XML for evolutionary biology and comparative genomics

Jit — Sun, 08 Dec 2019 09:41:18 -0600

phyloXML (example) is an XML language designed to describe phylogenetic trees (or networks) and associated data. PhyloXML provides elements for commonly used features, such as taxonomic information, gene names and identifiers, branch lengths, support values, and gene duplication and speciation events. Using these standardized elements allows interoperability between various applications and databases. Furthermore, both due to extensible nature of XML itself and the provision of elements by phyloXML, extensibility as well as domain specific applications are ensured. The structure of phyloXML is described by XML Schema Definition (XSD) language.

http://www.phyloxml.org/archaeopteryx-js/adh.html

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

NGSymposium in Computational Biology

Mon, 09 Mar 2020 06:00:30 -0500

We have a great pleasure to invite you to the NGSymposium in Computational Biology to celebrate the 5th anniversary of the NGSchool Summer Schools. This international conference will make way for exchanging knowledge and experiences between experienced and early-stage researchers as well as bioinformaticians. The meeting will be held on 31.07 - 1.08.2020 in Warsaw. It will be a satellite event to the #NGSchool2020: Statistical Learning in Genomics. It will cover a wide range of topics from basic and applied biomedical sciences: bioinformatics, genomics, transcriptomics, computational biology, Machine Learning.

Registration of active participants will be open from February, 27 12 PM CET to April 17, 23:59 CET. In registration forms you will be asked for providing us with some basic information about yourself. You will also be able to submit your abstract. You can save your registration form after filling it partially and come back later to supply more data e.g. upload an abstract. Your registration will be completed only with the payment of the registration fee reaching our accounts - please make sure to transfer the money in advance!

Registration of passive participants will be open after closing of registration of active participants.

Details an registration: https://ngschool.eu/conference/

gggenomes: A grammar of graphics for comparative genomics

Surabhi Chaudhary — Mon, 01 Feb 2021 14:47:32 -0600

gggenomes is a versatile graphics package for comparative genomics. It extends the popular R visualization packageggplot2 by adding dedicated plot functions for genes, syntenic regions, etc. and verbs to manipulate the plot to, for example, quickly zoom in into gene neighborhoods.

Address of the bookmark: https://github.com/thackl/gggenomes

GEnView: A phylogeny based comparative genomics software to analyze the genetic environment of genes

Abhi — Tue, 28 Dec 2021 01:49:03 -0600

A phylogeny based comparative genomics software to analyze the genetic environment of genes. The user can select one or several taxa and provide one or several reference protein(s). Genomes and plasmids (based on user choice) will be downloaded from the NCBI Assembly/NR database and searched for the respective gene. Alternatively, custom genomes can be provided. User selected stretches (20kbp by default) of the genes genetic environment are extracted, annotated and aligned between all genomes. The sequences are then visualized, enabling comparison of synteny and gene content.

More at https://pubmed.ncbi.nlm.nih.gov/34951622/

Address of the bookmark: https://github.com/EbmeyerSt/GEnView

LoVis4u: Locus Visualisation tool for comparative genomics

LEGE — Tue, 17 Sep 2024 02:30:57 -0500

Description

LoVis4u is a bioinformatics tool for Loci Visualisation.

LoVis4u, a command-line tool and Python API designed for highly customizable and fast visualisation of multiple genomic loci. LoVis4u generates vector images in PDF format based on annotation data from GenBank or GFF files. It is capable of visualising entire genomes of bacteriophages as well as plasmids and user-defined regions of longer prokaryotic genomes. Additionally, LoVis4u offers optional data processing steps to identify and highlight accessory and core genes in input sequences.

https://art-egorov.github.io/lovis4u/

Address of the bookmark: https://github.com/art-egorov/lovis4u

Dr. Ken Buetow: IS & Personalized Medicine Lecture-Part 1

Sun, 29 Sep 2013 20:11:09 -0500

Full Lecture Abstract Creating an Evidence Engine to Support Personalized Medicine Personalized medicine is transforming biomedical research and healthcare service delivery. Disease definition, diagnosis, treatment, and prevention are being fundamentally altered by the capacity to routinely perform comprehensive molecular characterization. Nowhere is this change happening faster than in the field of cancer. Increasingly sophisticated technology provides the capacity to describe, in multiple molecular dimensions, the tumor and the individual in which it has developed. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of the disease process. The generation of this unprecedented amount of data presents us with the challenge contextualizing that data and converting into actionable information. Currently, the context is drawn from fragmented research literature generated by "siloed" reductionist basic science investigations, t incomplete outcomes of clinical research designed for regulatory approval, t out-of-date recommendations made by bodies of experts, and day-to-day clinical experience of the practitioner. The integration and interpretation of this complex multidimensional information into the evidence necessary to support clinical care exceeds the raw human cognitive capacity. Information systems have the capacity to provide the needed "tool" to tackle this challenge -- to generate the necessary evidence to support the delivery of personalized medicine. Arizona State University's (ASU) Complex Adaptive Systems team is building such an Evidence Engine in its Next Generation Cyber Capability (NGCC). The ASU NGCC -- composed of networks, hardware, software, and people transforms "Big Data" to information and creates the evidence necessary to enable personalized medicine. Bio Dr. Buetow currently serves as Director of Computational Sciences and Informatics within Arizona State University's (ASU) Complex Adaptive Systems Initiative (CASI). CASI applies systems approaches that leverage ASU's interdisciplinary research strengths to address complex global challenges. The Computational Sciences and Informatics program is developing and applying information technology to connect and enhance trans-disciplinary knowledge both within ASU and across the broader knowledge-generating ecosystem to address problems in biomedicine, the environment, and national security. Dr. Buetow previously served as the Director of the Center for Biomedical Informatics and Information Technology within the National Institutes of Health's National Cancer Institute (NCI). In that capacity he initiated and oversaw the NCI's efforts to connect the global cancer community through community-developed, standards-based, interoperable informatics capabilities that enable secure exchange and use of biomedical data. Buetow designed and built one of the largest biomedical computing efforts in the world. He was responsible for coordinating biomedical informatics and information technology. The NCI center he led focused on speeding scientific discovery and facilitated translational research by coordinating, developing and deploying biomedical informatics systems, infrastructure, tools and data in support of NCI research initiatives. The primary focus of the Buetow laboratory is the application of computational technologies to solve major biomedical challenges, particularly the role of genetics in complex human diseases such as cancer. It undertakes this mission through a systems approach in which genetic analytic approaches are applied to multiple high-throughput molecular characterizations integrated through informatics. The Buetow laboratory approaches diseases such as cancer as a complex adaptive system. The Buetow laboratory has a long history of developing and applying bioinformatics methods to find genetic components underlying complex traits. The laboratory was instrumental in the earliest studies developing and applying linkage disequilibrium methods as genetic mapping tools. The laboratory also developed methods and pipelines to generate and apply genome-wide genetic maps. In early work with genome-wide gene sequence data, the laboratory developed approaches to efficiently and accurately computationally identify variants. More recently, the analytic approaches have been extended to systematically identify insertion/deletion variation, translocations, and rearrangements. In application to transcriptome data, these methods facilitate identification of splicing and alternative transcripts. More information available at: https://sols.asu.edu/people/kenneth-buetow-0