BOL: Related items

Project Scientist at National Agri-Food Biotechnology Institute (NABI)

Thu, 25 Aug 2016 05:49:36 -0500

Advt. No. NABI/8(18)/2012-PME-3
Project Scientist recruitment in National Agri-Food Biotechnology Institute (NABI)
Project Title : “Transfer and Evaluation of Indian Banana with Pro-Vitamin A (PVA) Constructs”
Essential qualifications: Ph.D. thesis submitted/awarded in any branch of life/plant sciences. Desirable qualification: a) Excellent academic record with research experience in area relevant to plant metabolic engineering, molecular biology and bioinformatics supported with high quality publications. b) Knowledge and experience of Chromatography and Mass Spectrometry based technological analysis of samples. c) Knowledge and experience of in-silico analysis such as trascriptomics, proteomics and genomics. c) Relevant research publications in reputed international journals with high impact factors.
No. of Post : 01
Age: 35 years
Emoluments: Rs.40,000/- per month.
How to apply
Walk-In-Interview on 29/08/2016 at National Agri-Food Biotechnology Institute, C-127, Industrial Area, Phase VIII, S.A.S. Nagar, Mohali-160 071 Email: siddharth@nabi.res.in

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

DIY Transcriptomics

Abhi — Wed, 31 Jul 2024 01:19:26 -0500

A semester-long course covering best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies, with a primary focus on empowering students to be independent in the use of lightweight and open-source software using the R programming language and the Bioconductor suite of packages. This course follows a hybrid format in which online lectures are paired with in-person labs where students participate in hands-on, live coding exercises using real ‘omic datasets. The course is focused on datasets and topics central to infectious disease research, immunology, and One-Health, but the concepts and approaches covered are applicable to any genomic study.

https://diytranscriptomics.com

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

Bioinformatics openings at Sri Venkateswara College, University of Delhi

Tue, 20 Sep 2016 05:43:24 -0500

Bioinformatics center

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.

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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

PeGAS: A Comprehensive Bioinformatic Solution for Pathogenic Bacterial Genomic Analysis

LEGE — Mon, 01 Sep 2025 01:18:10 -0500

This is PeGAS, a powerful bioinformatic tool designed for the seamless quality control, assembly, and annotation of Illumina paired-end reads specific to pathogenic bacteria. This tool integrates state-of-the-art open-source software to provide a streamlined and efficient workflow, ensuring accurate insights into the genomic makeup of pathogenic microbial strains.

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

DECIPHER

Anjana — Fri, 30 Sep 2016 09:33:12 -0500

DECIPHER is a software toolset that can be used to maintain, analyze, and decipher large amounts of DNA sequence data. To install DECIPHER, see the Downloads page.

To begin using DECIPHER read the "Getting Started DECIPHERing" tutorial. Refer to the PDF documents below for instructions on how to use DECIPHER for various tasks.

Address of the bookmark: http://decipher.cee.wisc.edu/Documentation.html

Single Cell RNAseq data analysis tutorial !!

Robert M Willioms — Mon, 27 Nov 2017 16:24:29 -0600

A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since
Measures the average expression level for each gene across a large population of input cells
Useful for comparative transcriptomics, e.g. samples of the same tissue from different species
Useful for quantifying expression signatures from ensembles, e.g. in disease studies
Insufficient for studying heterogeneous systems, e.g. early development studies, complex tissues (brain)
Does not provide insights into the stochastic nature of gene expression

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

COSMIC

Jit — Sat, 01 Oct 2016 15:04:10 -0500

The accurate description and annotation of structural variants can be complex. This is due to the different resolution that variants are reported from traditional cytogenetic coordinates down to the actual base pair positions. Furthermore, multiple rearrangements in a single area of the genome can make cataloguing and interpreting their effects challenging.

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

Delta: a new Web-based 3D genome visualization and analysis platform

Jit — Wed, 20 Dec 2017 08:49:55 -0600

Delta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Deltafeatures a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs.

https://github.com/zhangzhwlab/delta

Address of the bookmark: https://github.com/zhangzhwlab/delta

Entrez Direct: E-utilities on the UNIX Command Line

Anjana — Wed, 19 Oct 2016 08:06:24 -0500

Entrez Direct (EDirect) is an advanced method for accessing the NCBI's suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a UNIX terminal window. Functions take search terms from command-line arguments. Individual operations are combined to build multi-step queries. Record retrieval and formatting normally complete the process.

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/

MGcV: the microbial genomic context viewer for comparative genome analysis

Jit — Mon, 29 Jan 2018 04:55:46 -0600

MGcV is an interactive web-based visalization tool tailored to facilitate small scale genome analysis. To start using MGcV:

Supply your genes/genomic segments/phylogenetic tree of interest in the input-box by
- selecting the type of identifier and pasting identifiers (one per line)
- or by using the gene ID search tool
- or with the BLAST search tool
Click "Visualize context".

Consult the documentation to learn more about MGcV.

Address of the bookmark: http://mgcv.cmbi.ru.nl/