August 17, 2016

RGCB invites applications for the following positions from Indian citizens with prescribed qualifications. Full details including job description, additional desirable qualifications, etc. are described below.

Code No. 1

Senior Manager (Bioinformatics Operations)

(To download application format, click here )

Scale of Pay

PB-3 Rs.15600-39100 + Grade Pay Rs.6600/-

Number of Positions

1 (General)

Minimum Qualifications

PhD in Bioinformatics, Biotechnology, Life Sciences or Computer Science applied to biological questions.
A minimum of 5 years documented experience in national or state government R&D centers or state and central universities.
Track record of research funding and peer reviewed publications.
Proficiency using statistical analysis software or libraries such as R or Matlab.
Experience with a general scripting language such as Python, Ruby, or Pearl
Experience working with Next Generation Sequencing data
Proficiency with data visualization tools (Spotfire, Tableau, R, Python, etc.)
Experience with an object-oriented language such as Java, C++ or C# and familiarity with standard software development best practices: source code control, unit testing, in-code documentation and automated build environments.
Excellent listening, time management, organizational and interpersonal skills
Excellent communication skills, including the ability to illustrate problems and generate solutions
Management skills – demonstrated through the successful management of a team or large projects.
Broad and deep knowledge of computational methods for high-throughput sequence analysis and interpretation.
Extensive experience in delivering bioinformatics as a service and conducting training programs.
Experience of working with a production, customer-focused environment and business development projects.
Experience with management of funding and financial sustainability.
Demonstrated ability to work in a team environment and ability to lead and motivate an effective team, and also work as a good team player.
Good problem solver, able to logically identify solutions to technical problems.
Able to see the bigger picture and contribute towards strategic direction of Platforms and Pipelines teams.
Responsibilities

This position will involve cross-functional teamwork to build and develop bioinformatics tools and provide analysis for ongoing clinical trials.
Collaborate with biomarker scientists, clinical investigators and pipeline teams to build analytical tools.
Implement and evaluate new algorithms for R&D.
Support Research and Development teams by analyzing NGS data to identify predictive response markers
Lead training programs in Computational Biology and Bioinformatics.

More at http://rgcb.res.in/positions.php

Duration : Initially for a period of one year. Extendable based on the performance.

Qualification: (i) MSc in Bioinformatics, Biotechnology, Physics, Biophysics, Biochemistry,Computer Science with NET (UGC/CSIR/GATE/BINC/INSPIRE etc) qualification. (OR) (ii) M. Tech in Bioinformatics, Biotechnology

Additional qualification: Programming skills

Candidates who fulfill the requirements of IIT have the possibility to register for PhD.

Fellowship: Rs.25,000 and HRA.

Applicants are encouraged to send the CV to the coordinator by postal mail and e-mail. The deadline to receive the applications is 31st August 2016. The project coordinator has the discretion to restrict the number of candidates to be called for interview to a reasonable limit on the basis of qualifications and experience higher than the minimum prescribed in the announcement.

Project Co-ordinator:

Dr. M. Michael Gromiha
Department of Biotechnology
Indian Institute of Technology Madras
Chennai 600036
Email: gromiha@iitm.ac.in

ProteoClade helps you analyze two general categories of experiments:

1. Targeted Database Searches: Experiments in which a limited number of species are defined ahead of time, such as those involving Patient-Derived Xenografts (PDXs) or host-pathogen interactions. Reference protein sequence databases are used for targeted searches (ex: using Mascot, MaxQuant).

2. De Novo Searches: Experiments in which the organisms are unspecified ahead of time or involve samples of high taxonomic complexity. Mass spectra are analyzed in the absence of a reference database (ex: using PEAKS, PepNovo).

ProteoClade scales from two organisms to every organism in UniProt. Please refer to the complete documentation at proteoclade.readthedocs.io for installation, a user's guide, and examples.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007741

Address of the bookmark: https://github.com/HeldLab/ProteoClade

More at http://www.web.manit.ac.in/Year%202016/JRF/walk%20in.pdf

Duration of the Project: Six Months from the date of appointment can be extended further for six months
Essential Qualification and Experience: 1st Class Masters Degree in Bioinformatics or Life Sciences equivalent degree from a recognized University with 4 years R&D experience in Bioinformatics or relevant subjects from recognized institutes.
OR
Ph.D. degree in Bioinformatics or Life Sciences from recognized University.
M.Sc. degree obtained after a one year course will not be considered.
Experience: Research/teaching experience in Bioinformatics or relevant subjects form recognized Institute(s).

More at http://www.actrec.gov.in/data%20files/Vacancies/2016/AV-scin-stud-trainee-6-Sept-16.docx

Address of the bookmark: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

Note: Experience in the following field required: Molecular cancer cytogenetic and genetic toxicology Molecular drug Designing and targeted therapy Cancer genomics, proteomics, bioinformatics and next generation sequencing Therapeutic stem cell research and gene therapy Molecular cancer immunology and immunotherapy Molecular epidemiology Tumor endocrinology Translation research Ultra structural/tissue engg/development biology research Virus and cancer Molecular pathology No. of Posts: 11 (Eleven), (SC-1, OBC-3, UR-7)

Location: Kolkata (Calcutta) Salary: Rs.15600-39100 + Grade, Pay Rs.5400/-

For details kindly refer to the Employment News dated 24-30 September, 2016 and in the Institute’s Website: http://www.cnci.org.in

Last date for receipt of applications is 30 days from the date of notification in the Employment News. Director Chittaranjan National Cancer Institute 378, S.P.

Institute’s Website: http://www.cnci.org.in

ashr - https://github.com/stephens999/ashr, https://cran.r-project.org/web/packages/ashr/index.html

consensusDE - https://bioconductor.org/packages/release/bioc/html/consensusDE.html

DESeq2 - https://bioconductor.org/packages/release/bioc/html/DESeq2.html

edgeR - https://bioconductor.org/packages/release/bioc/html/edgeR.html

limma - https://kasperdanielhansen.github.io/genbioconductor/html/limma.html  https://bioconductor.org/packages/release/bioc/html/limma.html

MetaCycle - https://cran.r-project.org/web/packages/MetaCycle/index.html, https://github.com/gangwug/MetaCycle

RUVSeq - https://bioconductor.org/packages/release/bioc/html/RUVSeq.html

SARTools - https://github.com/PF2-pasteur-fr/SARTools

tximport - https://github.com/mikelove/tximport

The GO provides core biological knowledge representation for modern biologists, whether computationally or experimentally based. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Although extensively used in data analysis workflows, and widely incorporated into numerous data analysis platforms and applications, the general user of GO resources often misses fundamental distinctions about GO structures, GO annotations, and what can and can not be extrapolated from GO resources. Here are ten quick tips for using the Gene Ontology.

Read "Ten Quick Tips for Using the Gene Ontology" at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003343

Following are the most commonly used old and new GO term enrichment determination tools. These tools are recommended to people working in a wet-lab.

CLASSIFI (Department of Pathology, UT Southwestern Medical Center)

CLASSIFI (Cluster Assignment for Biological Inference) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene OntologyTM (GO) gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred.

http://pathcuric1.swmed.edu/pathdb/classifi.html

EasyGO (China Agricultural University)

EasyGO is designed to automate enrichment job for experimental biologists to identify enriched Gene Ontology (GO) terms in a list of microarray probe sets or gene identifiers (with expression information for PAGE analysis). Also EasyGO is also a GO annotation database, especially focus on agronomical species, supporting 30 species. It is user friendly, with advanced result browsing format and in-time update.

http://bioinformatics.cau.edu.cn/neweasygo/

http://bioinformatics.cau.edu.cn/easygo/

g:GOSt (Institute of Computer Science, University of Tartu)

g:GOSt retrieves most significant Gene Ontology (GO) terms, KEGG and REACTOME pathways, and TRANSFAC motifs to a user-specified group of genes, proteins or microarray probes. g:GOSt also allows analysis of ranked or ordered lists of genes, visual browsing of GO graph structure, interactive visualisation of retrieved results, and many other features. Multiple testing corrections are applied to extract only statistically important results.

http://biit.cs.ut.ee/gprofiler/

DAVID : Gene Functional Classification (Laboratory of Immunopathogenesis and Bioinformatics, NIAID)

The Functional Classification Tool provides a rapid means to organize large lists of genes into functionally related groups to help unravel the biological content captured by high throughput technologies.

http://david.abcc.ncifcrf.gov/gene2gene.jsp

http://david.abcc.ncifcrf.gov/

API https://github.com/chrisamiller/davidapi

GOEAST (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences)

GOEAST is web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods.

http://omicslab.genetics.ac.cn/GOEAST/

GOstat (Walter and Eliza Hall Institute of Medical Research)

Find statistically overrepresented GO terms within a group of genes

http://gostat.wehi.edu.au/

GOrilla (Technion - Laboratory of Computational Biology , Israel Institute of Technology)

GOrilla is a tool for identifying and visualizing enriched GO terms in ranked lists of genes.
It uses two approaches, first by searching for enriched GO terms that appear densely at the top of a ranked list of genes  or by searching for enriched GO terms in a target list of genes compared to a background list of genes.

GOrilla makes nice pictures !!!!

http://cbl-gorilla.cs.technion.ac.il/

Gene Ontology for Functional Analysis (GOFFA)

GOFFA is a tool developed for ArrayTrack™ that takes a list of genes and identifies terms in Gene Ontology (GO) disclaimer icon associated with those genes.

It provides several tools to view/access the GO term hierarchy, full listing of GO terms annotated with the genes associated with a given term with statically useful report.

http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm233315.htm

GOAT (The University of Manchester)

The aim of the GOAT project is to create an application that will guide users, especially biomedical researchers, in the annotation of gene products with terms from the Gene Ontology.

http://goat.man.ac.uk/

Script https://github.com/tanghaibao/goatools/

REVIGO ( Rudjer Boskovic Institute, Croatia)

REViGO is a web server that can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds.

http://revigo.irb.hr/

QuickGo (EMBL-EBI Institute)

It uses extensive computational filters to allow the generation of specific subsets of GO annotations, mapped to sequence identifiers of your choice. Then GO slims are used which is collective list of GO full set of terms available from the Gene Ontology project.

http://www.ebi.ac.uk/QuickGO/

GOLEM

An interactive graph-based gene-ontology navigation and analysis tool. GOLEM is a userful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy.

http://reducio.princeton.edu/GOLEM/

BGI Web Gene Ontology (WEGO) Annotation Plot (Beijing Genomics Institute)

WEGO () is a useful tool for plotting GO annotation results. It has been widely used in many important biological research projects, such as the rice genome project [Yu, J. et al. Science 296, 79-92 (2002); Yu, J. et al. PLoS Biol 3, e38 (2005)] and the silkworm genome project [Xia, Q. et al. Science 306, 1937-40 (2004)]. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO along with two other tools, namely External to GO Query and GO Archive Query, are freely available for all users. Any suggestions are welcome at wego@genomics.org.cn. Here is a sample output generated by WEGO

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

GeneGO MetaCore (MIT)

GeneGo is a leading provider of data mining & analysis solutions in systems biology. MetaCore, GeneGo's flapship product, is an integrated software suite for functional analysis of experimental data. MetaCore is based on a curated database of human protein-protein, protein-DNA interactions, transcription factors, signaling and metabolic pathways, disease and toxicity, and the effects of bioactive molecules.

https://portal.genego.com/

GOEx (Stony Brook University)

GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics.

http://pcarvalho.com/patternlab

GOssTo

GOssTo and GOssToWeb are tools to calculate the semantic similarity between genes or terms in the Gene Ontology.

http://www.paccanarolab.org/gosstoweb/

GO Workbench

The Gene Ontology Analysis Viewer allows direct browsing of the Gene Ontology, and also the visualization of GO Term analysis results.

http://wiki.c2b2.columbia.edu/workbench/index.php/Gene_Ontology_Viewer

Some other useful list of GO software and tools is available at http://www.geneontology.org/GO.tools.shtml#browser

Yet another useful webpage with list of GO tools at http://neurolex.org/wiki/Category:Resource:Gene_Ontology_Tools

You see, for the past couple of years, I’ve been a bit frustrated because OS X does not come with a whole lot of Perl modules pre-installed, and for all I googled, I couldn’t find an “idiot’s” guide for moderately-savvy-but-not-expert users like myself to install modules and dependencies on demand.

The only instructions I could find point to Fink, which basically installs modules in a path that isn’t included in the Perl @INC variable, meaning you have to manually specify the full path to the modules in every script — which is not a lot of fun if you’re developing on OS X and deploying on Red Hat, for instance.

Moreover, Fink doesn’t seem to make every module available, and it’s not very easy to determine which Fink package you need to install if you need a particular module.

So, with a script that called on several apparently unavailable modules, and a deadline looming, I finally decided to suck it up and figure out how to use CPAN to install them:

1) Make sure you have the Apple Developer Tools (XCode) installed.

These are on one of your install discs, or available as a huge but free download from the Apple Developer Connection [free registration required] or the Mac App Store. I thought I had them, but apparently when we upgraded that computer to Tiger, they went missing.

If you don’t have this stuff installed, your installation will fail with errors about unavailable commands.

1.5) Install Command Line Tools (Recent XCode versions only)

(Thank you to Tom Marchioro for informing me about this step.)

Older versions of XCode installed the command line tools (which are required to properly install CPAN modules) by default, but apparently newer ones do not. To check whether you have the command line tools already installed, run the following from the Terminal:

$ which make

This command checks the system for the “make” tool. If it spits out something like /usr/bin/make you’re golden and can skip ahead to Step 2. If you just get a new prompt and no output, you’ll need to install the tools:

1. Launch XCode and bring up the Preferences panel.
2. Click on the Downloads tab
3. Click to install the Command Line Tools

If you like, you can run which make again to confirm that everything’s installed correctly.

2) Configure CPAN.

$ sudo perl -MCPAN -e shell

perl> o conf init

This will prompt you for some settings. You can accept the defaults for almost everything (just hit “return”). The two things you must fill in are the path to make (which should be /usr/bin/make or the value returned when you run which make from the command line) and your choice of CPAN mirrors (which you actually choose don’t really matter, but it won’t let you finish until you select at least one). If you use a proxy or a very restrictive firewall, you may have to configure those settings as well.

If you skip Step 2, you may get errors about make being unavailable.

3) Upgrade CPAN

$ sudo perl -MCPAN -e 'install Bundle::CPAN'

Don’t forget the sudo, or it’ll fail with permissions errors, probably when doing something relatively unimportant like installing man files.

This will spend a long time downloading, testing, and compiling various files and dependencies. Bear with it. It will prompt you a few times about dependencies. You probably want to enter “yes”. I agreed to everything it asked me, and everything turned out fine. YMMV of course. If everything installs properly, it’ll give you an “OK” at the end.

4) Install your modules. For each module….

$ sudo perl -MCPAN -e 'install Bundle::Name'

or

$ sudo perl -MCPAN -e 'install Module::Name'

This will install the module and its dependencies. Nice, eh? Again, don’t forget the sudo.

The first time you run this after upgrading CPAN, it may prompt you to configure again (see Step 2). If you accept its offer to try to configure itself automatically, it may just run through everything without a problem.

There are a couple of potential pitfalls with specific modules (such as theLWP::UserAgent / HEAD issue), but most have workarounds, and I haven’t run into anything that wasn’t easily recoverable.

And that’s it!

Did you find this useful? Is there anything I missed?