About 8,000 years ago, the gene EGLN1 changed by a single DNA base pair. Today, a relatively short time later on the scale of human history, 88 percent of Tibetans have the genetic variation, and it was virtually absent from closely related lowland Asians. The findings indicate the genetic variation endows its carriers with an advantage.

In those without the adaptation, low oxygen caused their blood to become thick with oxygen-carrying red blood cells, an attempt to feed starved tissues, which could cause long-term complications such as heart failure. The researchers found that the newly identified genetic variation protected Tibetans by decreasing the over-response to low oxygen.

Reference: http://www.nature.com/nature/journal/v512/n7513/abs/nature13408.html

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

Responsibilities

Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.
Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.
Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.
Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.
Create scientifically rigorous visualizations, communications, and presentations of results.

Reference @ https://www.google.com/about/careers/search#!t=jo&jid=62095001

Address of the bookmark: http://www.vital-it.ch/

More at http://bioinfo.vanderbilt.edu/zhanglab/

http://www.stratosgenomics.com/stratos-genomics-technology

Bioinformatics supports to researchers
Customized training in Bioinformatics for researchers and faculty members
Support in Installing, implementing and maintaining software on computer.
Create awareness for taking preventive measure against data security
Organize workshops on thrust ares of Bioinformatics
Research Training to students of Biotechnology and Bioinformatics

More at http://bioinfo.bisr.res.in/index.php

Rajiv Gandhi Centre for Biotechnology (RGCB) invites applications for the following three faculty scientist positions:

Scientist E-II or F in Bioinformatics & Computational Biology

SCIENTIST E-II OR F IN COMPUTATIONAL BIOLOGY & BIOINFORMATICS

Highly motivated and innovative individual who will pursue basic research, solve biological problems with emphasis on computational and quantitative experimental methods and build active bridges to translational research. The scientist will also provide computational biology support to ongoing research programs in disease biology, provide assistance to analyze complex data sets generated by RGCB scientists and collaborators inclusive of including high dimensional “omics” data and next generation sequencing data, such as whole genome, exome, RNA-seq and ChIP-seq as well as provide leadership for high quality training for junior scientists and regular teaching programs of the institute. Areas of research of interest to RGCB include but are not limited to computational, systems, or quantitative biology with applications to cell biology, developmental biology, metabolism, genomics, proteomics, biophysics, biological information systems, network pharmacology, drug design and cancer research. The scientist’s responsibilities include efforts for the integration of DNA variant annotation with statistical genetic analysis methods including linkage, imputation and association methods, adopting novel and innovative methodologies to analyze, integrate and interpret high dimensional data sets, provision of annotation to robust genetics and genomics findings using several data sources and methods, data management of exploratory clinical and R&D studies in partnership with other lines of genetic data generated from internal and external studies, delivery and documentation of genomic information to support genetic studies, ensuring high-quality genetic and genomic data is incorporated into exploratory- clinical research programs, developing tools that make maximum use of multiple data sources to support annotation of DNA variation and contributes to systems biology initiatives within RGCB

More at http://rgcb.res.in/scientist-positions/

Application Form http://rgcb.res.in/wp-content/uploads/2015/11/APPLICATION-FORMAT-FOR-SCIENTISTS.docx

More at https://github.com/dentearl/n50PlottingTools

Address of the bookmark: https://github.com/dentearl/n50PlottingTools

Address of the bookmark: https://arvados.org/