Positions available

Applications were invited from for the following posts in an industry sponsored project. The project entitled "OsHK3b technology and Know How", valid for a period upto February, 2018.

Post 3: Senior Research Fellow (Computational Biologist / Metabolic engineering)

Salary: As per DBT rule.

Duration: All the above posts are purely temporary and liable to be terminated at any time without prior notice or ceased/withdrawn by the funding agency.

Age limit: The upper age limit for SRF shall be 32 years, which is relaxed upto 5 years in the case of candidates belonging to Schedule Castes/Schedule Tribes, Women, Physically Handicapped and OBC applicants.

Essential Qualifications: Masters/B Tech/Mtech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Computational Biology related to Database /portal building & maintenance, high throughput data handling and analysis etc. For M.Sc/B.Tech, Published paper in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must. Selection preference will be given to candidates with a good knowledge of Python and/or R. Knowledge of JAVA will also get a special consideration.

Desired Skills: Will be expected to manage ongoing research activities in the project, interact with Experimental group, manage the project data analysis, prepare file reports and associated project work etc. Familiarity with plant systems biology and genomics /metabolite resources related to plant metabolomics is desirable.

1. The post applied for must be clearly written on the Envelope containing the application
2. Applications received after last date shall not be entertained, School will not be responsible for any postal delay.
3. No application will be accepted via hand delivery or via e-mail. Please send printed & signed applications with detailed CV on or before 31st January, 2017 by post to the following address:

Prof. Ashwani Pareek
(Project Investigator)
Stress Physiology and Molecular Biology Laboratory (Room No-413),
School of Life Sciences,
Jawaharlal Nehru University,
New Delhi, India – 110067
Email: ashwanipareek@gmail.com

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

There are several pre-computed trees available for display, including the main Tree Of Life, described in Ciccarelli, et al., 2006. In addition to the precomputed trees, users can upload and display personal trees and data, using the 'Data upload' page or through a personal user account.

Address of the bookmark: http://itol.embl.de/

Training Resources:

• Bioinformatics Workshop (Webinars)
• Bioinformatics Training Slides

Specialized Applications:

• De Novo Assembly
• Transcriptome analysis
• Base Modification Analysis
• Barcoding
• Data Analysis Tools
• Minor Variants and Phasing Analysis

Address of the bookmark: https://github.com/PacificBiosciences/Bioinformatics-Training/wiki

Address of the bookmark: http://www.ub.edu/dnasp/

Basic usage:

Rscript Ideoplot.R --heatmap hm.bed --annotate annotations.bed --out ideogram.pdf
-or-
Rscript Ideoplot.R --annotate annotations.bed

Options
  --ideobed, i      A bed file of reference contig lengths/chromosome names
  --heatmap, -h     Fill chromosomes with normalized heatmap
                   (described below)
  --annotate, -a    Add character annotations.
  --out, -o         PDF output name.
  --stripes, -s     Specify a file containing the layout of the
                    annotations (description below)
  --bars, -b        Add track annotations
  --reference, -f   Either hg19, or hg38
  --topdown, r      Flag, when set, flips the orientation (P arms
                    drawn on top).

Address of the bookmark: https://github.com/mchaisso/Ideoplot

Address of the bookmark: http://abacas.sourceforge.net/Manual.html#9._Colour_code

Address of the bookmark: http://pasapipeline.github.io/

Some of the prominent features of the package are:

• visualizing polyploidy simultaneously on the same plot.
• annotating groups of elements as distinct colors.
• creating chromosome heatmaps.
• adjusting chromosome range or visualizing chromosome regions such as genes
• adding labels to the plot
• adding hyperlinks to each element

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html

You have a PhD in Computational Science, Bioinformatics, or equivalent, and expertise in analysis and modeling of human RNA-seq data, statistics, data mining and machine learning. Excellent communication skills in English (written and oral) is a must. Flexibility and willingness to work across multiple projects and technologies in a rapidly evolving scientific context is required.
Salary will depend on qualification and experience. Starting date: immediate.

Interested candidates should send to farina.cinthia@hsr.it:

1. CV (please show evidences of relevant titles, projects, courses, references, etc.)
2. One page with a list of research topics (i.e. ongoing projects)
3. earliest availability

4. 2-3 contact names