The duly completed application on prescribed format along with copies of supporting documents must reach to: Office of the Dean (Research & Consultancy), Motilal Nehru National Institute of Technology, Allahabad-211004 on or before 03/07/2015.

The position is purely temporary and will be governed by the funding agency rules & service conditions of Office of the Dean (Research & Consultancy), MNNIT Allahabad.

For detail advertisement see: www.mnnit.ac.in/images/newstories/Advertisement_for_the_post_of_Research_Assistant_in_UPCST_Project_of_Biotechnology_Department.pdf

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/imgag/ClinCNV

Eligibility : M Phil / Phd, MSc

Location : Delhi

Last Date : 27 Jun 2015

Hiring Process : Walk - In
Indian Agricultural Statistics Research Institute (IASRI) - Job DetailsDate of posting:03 Jun 15

Research Associate Statisticsjob position in Indian Agricultural Statistics Research Institute (IASRI)
on purely contractual temporary basis

Project : “ICAR-Network Project of Transgenic in Crops”

Qualification : Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application/ Life Science/ Biotechnology/ Agricultural Science or equivalent OR Post-Graduation in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application/ Life Science/ Biotechnology/ Agricultural Science or equivalent with 1st Division or 60% marks or equivalent with at least two years of research experience.

No.of Post: 01

Emoluments for RA: Consolidated Rs. 24000/- per month + 30% HRA for Ph.D holders and consolidated Rs. 23000/- per month + 30% HRA for Master Degree.

Age Limit : 40 years
How to apply

Walk-in-interview will be held on 27th June 2015, 10.30 A.M at IASRI, Pusa, New Delhi

More at http://iasri.res.in/employment/employment.htm

https://github.com/CSB5/lofreq

http://csb5.github.io/lofreq/installation/

https://github.com/CSB5/lofreq/tree/master/dist

Address of the bookmark: http://csb5.github.io/lofreq/

Starting 15th July 2015, the position relates to a project specifically for in silico drug docking, screening, design, optimisation and linkage with active chemists.

Experience in many docking softwares and operating systems is essential.

Additional experience in bioinformatics and computational biology tools will be useful.

Submit curriculum vitae to: sb.icgeb@gmail.com

Closing date: 5 July 2015

• Filters SNVs from any variant caller to remove false positives
• Calculates metrics based on BAM files and provides filtering not possible with other tools
• Fully user-configurable filtering (including which filters to use and their thresholds)
• Option to use filters identical to ICGC recommendations

FiNGS provides researchers with a tool to reproducibly filter somatic variants that is simple to both deploy and use, with filters and thresholds that are fully configurable by the user. It ingests and emits standard variant call format (VCF) files and will slot into existing sequencing pipelines. It allows users to develop and implement their own filtering strategies and simple sharing of these with others.

FiNGS reliably improves upon the precision of default variant caller outputs and performs better than other tools designed for the same task.

Address of the bookmark: https://github.com/cpwardell/FiNGS

Job Description

The Global Trait Discovery Informatics (GTDI) group located at the DuPont Knowledge Centre (DKC), Hyderabad, India is currently seeking applications for a highly motivated computational biologist. The GTDI group contributes to research programs in plant biotechnology at the DKC as well as across research centers located in DuPont Pioneer, Johnston, Iowa and at the DuPont Experimental Station in Wilmington, Delaware.

We are looking for candidates who have experience in analysis of high-throughput -omics datasets. The researcher will be primarily responsible for analyzing diverse -omics datatypes, such as transcriptomics, proteomics and metabolomics and actively contribute towards building streamlined solutions.

The candidate will be part of a diverse team of experimental biologists, computational biologists and software developers. A critical aspect of this position involves working with global teams across multiple locations and will require effective project coordination and communication skills. This is an exciting opportunity for candidates with strong data driven skills, who want to work at the interface of computational and experimental biology and contribute towards scientific discovery.

Responsibilities

·Integrate and analyze multiple datatypes in the context of experimental observations with a goal towards formulating testable hypothesis.

·Understanding the research questions from experimental biologists and formulate relevant in silico analyses.

·Establish and implement systematic analysis workflows starting from processing of raw data to biological interpretation.

·Critically analyze a wide variety of experimental data with a view to solving the underlying research questions.

·Identify and generate datasets for scientific testing and evaluation of algorithms.

Qualifications

PhD in computational biology, bioinformatics, population genetics, complex systems, computer sciences or any relevant physical or mathematical sciences, with experience in analyzing diverse -omics datasets.

Job Qualifications

Qualifications

PhD in computational biology, bioinformatics, population genetics, complex systems, computer sciences or any relevant physical or mathematical sciences, with experience in analyzing diverse -omics datasets.

More at http://careers.dupont.com/jobsearch/job-details/research-scientist/006077W-01/

Variant calling and/or genotyping

• DiscoSNP++ and discoSnpRAD: Reference-free small variant discovery (SNPs and indels)
• MindTheGap: Detection and assembly of large insertion variants
• TakeABreak: reference-free inversion discovery tool
• SVJedi: Structural Variant genotyper with long read data
• SVJedi-graph: Structural Variant genotyper with long read data using a variation graph

Sequence assembly

• MinYS: reference-guided genome assembly in metagenomics data
• MTG-link: local assembly tool for linked-read data
• Minia: De novo short read assembler
• de-novo pipeline: de-novo assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes
• Mapsembler2: Targeted assembly (not maintained)

Managing k-mers & indexation

• findere: simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
  • fimpera extends findere adding the abundance information.
• kmtricks: modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.
• kmindex is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.
• back to sequences: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.
• Backpack Quotient Filter: k-mer indexing data structure with abundance
• short read connector: Detect similar reads from potentially large read set
• DSK: Count K-mer in sequences

Pangenome graph manipulation

• Pancat: Pangenome Comparison and Analysis Toolkit
• GFAGraphs: a Python library to handle pangenome graph files in GFA format.

Comparative metagenomics with k-mers

• Simka and SimkaMin: Comparative metagenomics for large-scale datasets
• Comparead & Commet: comparison of metagenomic datasets

Species and bacterial strains identification

• ORI: software using long nanopore reads to identify bacteria present in a sample at the strain level
• StrainFLAIR: STRAIN-level proFiLing using vArIation gRaph

General-purpose sequencing data manipulation

• GASSST: long read mapper
• Leon: short read compressor (now included in GATB-core)
• Bloocoo: short read corrector
• BCALM: Construct compacted de Bruijn graphs (unitigs)

 Protein Structure

• A_Purva: Contact Map Overlap solver
• MD-Jeep: Distance Geometry solver
• CSA: Comparative Structural Alignment

Workflow

• SLICEE: parallel execution of bioinformatics workflows

Comparative Genomics

• CASSIS: detection of rearrangement breakpoints
• PLAST: intensive bank-to-bank sequence comparison
• DRJBreakpointFinder: detection and precise localization of excision sites in proviral segments