Address of the bookmark: http://samstat.sourceforge.net/

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

Input formats, automated data upload from the UCSC Genome Browser, gene annotation, annotation of repetitive elements, and progress report were previously described in the zPicture instructions and we refer the users to these materials for more details. This introduction is mainly focused on some novel features unique to the Mulan.

Address of the bookmark: https://mulan.dcode.org/mulanInstructions.php

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/

https://academic.oup.com/ve/article/7/1/veab042/6248116

Address of the bookmark: https://academic.oup.com/ve/article/7/1/veab042/6248116

Staff Scientists

Specialization: Applicant should have a Ph.D. with excellent academic credentials along with the track record of scientific productivity evidenced by publications/patents/products in the frontier areas of Plant Biology such as, Computational Biology, Genome Analysis and Molecular Mapping, Molecular Mechanism of Abiotic Stress Responses, Nutritional Genomics, Plant Development and Architecture, Plant Immunity, Molecular Breeding, Transgenics for crop improvement and other emerging areas based on plant genomics.

Remuneration: The length of experience and scientific accomplishments/quality of scientific productivity record will be major factors in deciding the level of appointment as Staff Scientist as well as starting salary in the Pay Bands of Rs 15,600-39,100 (with grade pay of 5400), and Rs 37,400-67,000 (with grade pay of 8,700 and 8,900) plus usual allowances admissible to the Central Government employees. However, NIPGR reserves the right to select candidates in the lower grade against the foregoing posts depending upon the qualifications and experience of the candidate. Reservation of posts shall be as per Govt. of India norms. Five posts (SC-2, ST-1, OBC-2) in the Pay Band of Rs 15,600-39,100 with Grade Pay of Rs 5400, are reserved.

More at http://www.nipgr.res.in/careers/vacancies_latest.php#

Apply online at http://www.nipgr.res.in/nipgr_recu/nipgr_recu.php

Form http://www.nipgr.res.in/files/careers/Application_Performa_2015.doc

It was first developed to transfer annotations between different genome assembly versions. However, it can also transfer annotations between strains and even different species, like Plasmodium chabaudi onto P. berghei, between different Leishmania species or Salmonella enterica onto other Salmonella serotypes. RATT is able to transfer any entries present on a reference sequence, such as the systematic id or an annotator's notes; such information would be lost in a de novo annotation.

More at http://ratt.sourceforge.net/

Address of the bookmark: http://ratt.sourceforge.net/

• Automatically improve draft assemblies
• Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

• Single base differences
• Small indels
• Larger indel or block substitution events
• Gap filling
• Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki