Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

The Job detail

Advancing the SNP-discovery and polymorphism assessment work across several germplasm panels representing global genetic diversity
Population genetic and genomic analyses, testing the hypothesis related to adaptation in multiple geographic regions
Develop SNP assays from large scale GBS and other re-sequencing data for several target traits utilizing available phenotyping data
Combined analyses of genotypic and phenotypic data for discovery of marker-trait associations, and conducting GWAS
Processing, analyzing, and archiving large-scale genomic data sets, assessing data quality, conducting analyses, interpreting findings, and communicating findings to others including preparation of reports, presentations, posters and journal articles
Providing support to MSc and PhD students on topic related to its major core of research
Any other work assigned by the supervisor

The Person:

PhD in bioinformatics, genetics, computational biology preferably with 1 to 2 years of experience;
familiar with standard bioinformatics tools and scripting languages and emerging and evolving software platforms relevant to bioinformatics and computational biology;
ability to create new analytical pipelines; experience with handling large data sets;
ability to program in at least two of the following: C++, PERL, Python, R, Java.
will use next-generation sequencing technologies to generate marker data for genetic mapping and transcriptome data for expression QTL mapping, and will be responsible for data generation as well as data analysis.

Period and Remuneration: The assignment is for a period of two years, and can be extended for another year depending on performance. ICRISAT pays a very attractive all inclusive lump sum assignment fee payable in Indian Rupees.

How to Apply: Please send your application by email to icrisatjobs@cgiar.org, stating the job title (Special project Scientist-Sorghum Genomics) clearly in the subject column, addressed to the Director, Human Resources and Operations, ICRISAT, Patancheru, Andhra Pradesh 502 324, India, latest by 10 June 2014. The application should include an up-to-date Curriculum Vitae, a short statement of competencies and experience for the position, and the names and addresses (including phone/e-mail) of three referees. Only short-listed candidates will be contacted.

More at: http://www.icrisat.org/careers/Special-Project-Scientist-Sorghum-Genomics.htm

Walk- in- Test cum Interview (based on test) for the selection of Research Associate

under the scheme “Distributed Information Sub Centre –DISC” & Research Assistant under scheme “Phytophthora, Fusarium and Ralstonia diseases of Horticultural and Field Crops” will be held at this Institute as per details indicated below.

WALK -IN- TEST CUM INTERVIEW

Name of the post : Research Associate

Date of Interview : 21-05-2014 at 10.00 AM

No. of posts : One

Qualifications : a)Essential

Ph.D Degree in Bioinformatics OR : Masters degree in Bioinformatics with a minimum of
60% marks or equivalent OGPA with at least two years research experience as evidenced from fellowship/ associateship/training/published papers etc.

b)Desirable: Experience in NGS data analysis.

Emoluments : Rs. 23,000/- per month + HRA (Masters Degree Holders)

Rs. 24,000/- per month + HRA (Ph.D Degree Holders)

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till 31-03-2017.

Title of Assigment : Research Assistant (on contract basis)

No. of vacancy : One

Qualification : Essential : Post Graduation in Bioinformatics and Minimum one year experience in NGS data analysis

Desirable : Experience in Perl/Python/R

Remuneration : Rs. 20,000/- per month (consolidated)

Scope of work :

1. Analysis of different file formats and their conversions.

2. Assessing the quality of data and filtering of raw reads.
3. Assembling the raw reads-de novo as well as reference mapping.
4. Compression of aligned reads using Jam tools
5. RNA-seq. Analysis
6. Differential expression testing involving Normalization, Statistical testing, heat map generation & hierarchical clustering
7. Annotating the assembled genome and geneet testing and their validation
8. Metabolic pathway analysis
9. Comparative genomics
10. Setting up of genome browsers.

Period of Assigment : Initially for six months.

Date & Venue of Interview : 21-05-2014 at IISR, Kozhikode at 10.00 AM

More at http://www.spices.res.in/pdf/disc-advtmnt.pdf

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/

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

There are two options to run ShRec3D (on linuX only so far): the first one uses the Matlab complier runtime environment (MCR), the second one doesn't need any other library to be installed but only works with the latest versions of Linux (equivalent to Fedora 19 and above).

Address of the bookmark: https://sites.google.com/site/julienmozziconacci/#TOC-Downloads