11/3/2019: V 0.61, Improve graphical visualization (thanks to reviewers). Interactive networks and much more.

5/20/2019: V.0.60, Annotation database updated to Ensembl 96. New bacterial and fungal genomes based on STRING-db! Just paste your gene list to get enriched GO terms and othe pathways for over 315 plant and animal species, based on annotation from Ensembl (Release 96), Ensembl plants (R. 43) and Ensembl Metazoa (R. 43). An additional 2031 genomes (including bacteria and fungi) are annotated based on STRING-db (v.10). In addition, it also produces KEGG pathway diagrams with your genes highlighted, hierarchical clustering trees and networks summarizing overlapping terms/pathways, protein-protein interaction networks, gene characterristics plots, and enriched promoter motifs. 

Address of the bookmark: http://bioinformatics.sdstate.edu/go/

Project :“Genetic Transformation and Development of Elite Transgenic Maize (Zea mays L.) for Biotic and Abiotic Stresses Tolerance”.

Qualification: Ph.D. degree in:Biotechnology/Bioinformatics/Biochemistry/Plant Molecular Biology/Plant Physiology/Botany or any related area with evidence of prior experience in maize transformation.

Additional experience in plant transformation of any cereal crop would be preferable.

The appointment would initially be for one year.

How to apply
Interested applicants should send their detailed CV including brief synopsis regarding the previous research experience (along withcontact email address by email) to: Dr. Tanushri Kaul (tanushri@icgeb.res.in). Group Leader, Nutritional Improvement of Crops Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi-110 067.

Closing date for applications: 22 August 2016.

More at http://www.icgeb.org/vacancies.html

1. the ontology itself, which is a set of terms with their precise definitions and defined relationships between them, and
2. the associations between gene products and GO terms, which are used to capture the existing knowledge about what each gene is known to do.

But the term gene ontology, or GO, is commonly used to refer to both, which is sometimes a source of potential confusion. In order to avoid this, here we will use the term “GO ontology” to describe the set of terms and their hierarchical structure and “GO annotations” to describe the set of associations between genes and GO terms.

There are 3 types of terms, or domains if you wish, in the gene ontology:

• Biological Processes (BP)
• Molecular Functions (MF)
• Cellular Components (CC)

Address of the bookmark: https://advaitabio.com/faq-items/understanding-gene-ontology/

More at http://www.nabi.res.in/Vacancies/NABI/ResearchFellowships/JRFSRFRA/2016/NABI8(18)2012-PME-3/Advt.pdf

https://diytranscriptomics.com

Address of the bookmark: https://diytranscriptomics.com

Sri Venkateswara College (University of Delhi)

New Delhi- 110021

1. Junior Research Fellow (1 Post)

Applications are invited for the post of Junior Research Fellow (JRF) under DST funded project which is purely temporary and is strictly for project duration only.

Title of project

No. of post

Remuneration (Rs.)

“Computational assisted Design and Synthesis of Novel Antimalarial Agents Embodying Structural Diversity Suitable for Protease Inhibitors”

(One)

Fellowship and HRA as per DST guidelines

Qualification

Post Graduate Degree in Basic Science (M.Sc./M.Tech in Bioinformatics/Biophysics) from a recognized University in India or abroad with at least 55% marks with NET qualification or Graduate Degree in Professional Course with NET Qualification or Post Graduate Degree in Professional Course.

Desirable

Fair knowledge of Computer Aided Drug Designing (CADD), Protein Structure modeling, molecular docking, and simulations are preferable.

2. Traineeship (1 Post)

Applications are invited for the position of traineeship in DBT-BTISnet funded Bioinformatics Infrastructure Facility (BIF) to carry out project work in the area of Bioinformatics.

Qualification

Applicant should be possess PG degree/PG diploma in Bioinformatics for traineeship. The traineeship is awarded for a period of six months from the date of joining and is not extendable. The selected candidates are entitled to receive a stipend of Rs. 8000/- per month (consolidate) for a period of 6 months.

=====================================================================

3. Studentship (1 Post)

Applications are invited for the position of Studentship in DBT-BTISnet funded Bioinformatics Infrastructure Facility (BIF) to carry out project work in the area of Bioinformatics.

Qualification

Candidates pursuing the Final Year of Post Graduate Degree in Basic Science (M.Sc.) or Post Graduate/ Graduate Degree in Professional Course (M.Tech/B.Tech) in Bioinformatics from a recognized University in India or abroad. The selected candidates are entitled to receive a stipend of Rs. 8000/- per month (consolidate) for a period of 6 months.

How to Apply?

Applicants are required to send applications on plain paper, stating the name, address, date of birth, educational qualification, experience and Institute, along with attested photocopies of mark sheets and certificates etc. by September 20, 2016 to:

The Coordinator

Bioinformatics Center, Sri Venkateswara College

Benito Juarez Road, Dhaula Kuan, New Delhi- 110021

Applications may also be sent by email to contact@bic-svc.ac.in. Strictly mention "Application for JRF, Traineeship or Studentship" in the subject line as the case may be.

Short listed candidates will be called for an interview. Canvassing in any form will be a disqualification. No TA/DA will be paid either for attending the interview or joining the post.

For more details visit our lab webpage: http://www.bic-svc.ac.in

Address of the bookmark: https://github.com/liviurotiul/PeGAS

Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

The Rearrangement Overview page describes the one or more breakpoints which make up a structural variant. A breakpoint is defined as a region or point where the sample sequence has altered from the reference sequence. Minimum interpretation is made of this data. One variant event can consist of one or multiple breakpoints. The Syntax (shown above the table) gives a detailed description of the variant and its location  (e.g. chr11:g.36585230_76606619del, a deletion of roughly 40Mb on chromosome 11). Syntax is based on HGVS mutation nomenclature recommendations [http://www.hgvs.org/rec.html]. 

http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview

Address of the bookmark: http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview

EDirect also provides an argument-driven function that simplifies the extraction of data from document summaries or other results that are returned in structured XML format. This can eliminate the need for writing custom software to answer ad hoc questions. Queries can move seamlessly between EDirect commands and UNIX utilities or scripts to perform actions that cannot be accomplished entirely within Entrez.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/books/NBK179288/