BOL: Related items

JRF/Traineeship/Studentship Bioinformatics

Thu, 10 Dec 2015 13:49:56 -0600

JRF/Traineeship/Studentship Bioinformatics

Eligibility : ME/M.Tech(Bio-Informatics/Bio-Chemistry Engg, CSE), MSc(Bio-Informatics, CS)

Location : Delhi

Last Date : 18 Dec 2015

Hiring Process : Walk - In
IARI - Job Details

JRF/Traineeship/Studentship Bioinformatics job position in Indian Agricultural Research Institute (IARI) purely temporary

JRF

Qualification: i) Master’s degree in Bioinformatics or Computer Science + NET qualification, or ii) M. Tech degree in Bioinformatics or Computer Science/Engineering

Desirable: Efficiency to handle agricultural databases and bioinformatics tool development

Pay Scale :Rs 25000/-

Age limit : 35 years

Traineeship/2 Post

Qualification: M.Sc./M. Tech (Bioinformatics) with 60 % marks from a recognized University

Pay Scale :Rs. 8000/-consolidated

Age limit : 35 years

Studentship/4 Post

Qualification: Final year M.Sc./ M.Tech (Bioinformatics) Students from a recognized University

Pay Scale :Rs. 8000/-consolidated

Age limit : 35 years
How to apply

Walk-in-Interview will be held on 18th December 2015 at 10:00 AM at AKMU, LBS Building,IARI, Pusa Campus, New Delhi-110012. Bring self attested copies and originals of all certificates ( class 10th )onwards along with biodata in the attached format, proof of date of birth, one passport size photo, NOC from present employer, if any.

More at http://www.iari.res.in/index.php?option=com_jumi&fileid=24&Itemid=664

Research Fellow Bioinformatics at Central University of Rajasthan

Tue, 02 Feb 2016 00:04:50 -0600

Research Fellow Bioinformatics

Eligibility : MSc(Bio-Chemistry, Bio-Informatics, Bio-Tech)
Location : Ajmer
Last Date : 13 Feb 2016
Hiring Process : Face to Face Interview
Central University of Rajasthan

Research Fellow Job vacancies in Central University of Rajasthan

Project Title : “Development of natural product derived febrifugine ananlogues as a novel therapeutics against visceral leishmaniasis”

No. of Post : 01

Qualification : Master of Biochemistry, Biotechnology, Bioinformatics and related biological sciences with minimum 55%, Age limit as per government rule.

Desirable Experience : Candidates with experience in cell culture, Chemoinformatics, and Parasitology will be preferred.

Fellowship : Rs. 25,000/- p.m. consolidated for NET qualified (14,000/- p.m. consolidated for Non-NET)
How to apply

The candidates may apply on a plain paper, along with their curriculum vitae (including name, date of birth, academic qualification starting from 10th class, summary of research experience, email id, phone number and passport size photograph) and email to vkprajapati@curaj.ac.in or post the hard copy to the Dr. Vijay Kumar Prajapati PI, DST-SERB Project (YSS/2015/000716), Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Dist- Ajmer, Rajasthan, 305 817 on or before 13th February 2016.

More at http://www.curaj.ac.in/Default.aspx?PageId=241

Bioinformatics course and lectures

Rahul Agarwal — Tue, 03 Sep 2013 16:41:02 -0500

http://openwetware.org/wiki/User:Jarle_Pahr/Bioinformatics

Address of the bookmark: http://gtpb.igc.gulbenkian.pt/bicourses/index.html

Key Bioinformatics Scientists

Rahul Agarwal — Wed, 09 Oct 2013 13:37:07 -0500

Address of the bookmark: http://www.iscb.org/iscb-leadership-a-staff-/officers-and-board-directors

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/

R package for visualising GO enrichment

Jit — Mon, 22 Jul 2013 12:25:09 -0500

An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations.

Links @ http://raivokolde.github.io/GOsummaries/

Lab @ http://biit.cs.ut.ee/about/main

Upcoming R Webinar

Jitendra Narayan — Wed, 11 Sep 2013 10:30:16 -0500

This webinar will describe an R based approach to considerably speed GWAS computation time on a notebook book computer.

More http://www.extension.org/pages/68354/upcoming-webinar:-fast-semi-parallel-linear-and-logistic-regression-for-genome-wide-association-studi#.UjCL9azyPqV

Surrogate Variable Analysis (SVA)

Jit — Thu, 30 Oct 2014 08:01:58 -0500

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways:

(1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS),

(2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and

(3) removing batch effects with known control probes (Leek 2014 biorXiv).

Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

More at http://www.bioconductor.org/packages/release/bioc/html/sva.html

Painless package development for R

Abhi — Tue, 03 May 2016 05:31:06 -0500

Devtools makes package development a breeze: it works with R’s existing conventions for code structure, adding efficient tools to support the cycle of package development. With devtools, developing a package becomes so easy that it will be your default layout whenever you’re writing a significant amount of code.

Before you get started be sure to check out:

Address of the bookmark: https://www.rstudio.com/products/rpackages/devtools/

CSBB-v1.0

Neel — Wed, 29 Jun 2016 07:33:05 -0500

CSBB is a command line based bioinformatics suite to analyze biological data acquired through varied avenues of biological experiments. CSBB is implemented in Perl, while it also leverages the use of R and python in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX, MAC OS and Windows platforms.

Currently CSBB provides 13 modules focused on analytical tasks like performing upper-quantile normalization on expression data or convert genome wide gene expression to z-scores when comparing expression data from different platforms.

More at https://github.com/skygenomics/CSBB-v1.0

Address of the bookmark: https://github.com/skygenomics/CSBB-v1.0