BOL: Related items

RAVEN: a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models

Jit — Wed, 24 Oct 2018 22:38:05 -0500

The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models (GEMs). It makes use of published models and/or KEGG, MetaCyc databases, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology.

Address of the bookmark: https://github.com/SysBioChalmers/RAVEN

Research Project at IIT, Madras

Wed, 17 Aug 2016 03:26:06 -0500

Two project positions are available to work on (i) molecular modeling and molecular dynamics simulations and (ii) development of bioinformatics databases and tools at Protein Bioinformatics Lab, Department of Biotechnology, IIT Madras.

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

ShinyGO v0.61: Gene Ontology Enrichment Analysis + more

Jit — Wed, 03 Jun 2020 08:00:30 -0500

2/3/2020: Now published by Bioinformatics.

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/

JRF Bioinformatics at MANIT, India

Thu, 18 Aug 2016 02:48:53 -0500

Advt. No.: Maths./577/2016 Date: 12/08/2016
JRF Bioinformatics Job Position in Maulana Azad National Institute of Technology (MANIT) purely temporary basis
Project Title : “Computational Approach to Study Complex Biological Network of Diseases using Molecular Data”
Essential Qualifications & experience: M.Tech in Bioinformatics/ Computational System biology/Computer Science or M.Sc. in Bio informatics/Biotechnology/Mathematics/Statistics from recognized University/ Institute. Preference will be given to GATE/NET qualified candidates.
No. of Post : 01
Fellowship: INR 12000

How to apply
The duly completed application on prescribed format along with copies of supporting documents must reach to: office of the Dr. Usha Chouhan, Principal Investigator, Department of Mathematics, Bioinformatics & Computer Applications, Maulana Azad National Institute of Technology, Bhopal-462003 on or before 31/08/2016. A soft copy of the application should also be sent to ycchouhan@gmail.com email address of Principal Investigator.

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

Understanding GO analysis

Neel — Wed, 08 Feb 2023 04:22:01 -0600

The confusion about gene ontology and gene ontology analysis can start right from the term itself. There are actually two different entities that are commonly referred to as gene ontology or “GO”:

the ontology itself, which is a set of terms with their precise definitions and defined relationships between them, and
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/

Scientist at Advanced Centre for Treatment, Research and Education in Cancer - Navi Mumbai, Maharashtra

Tue, 30 Aug 2016 04:16:15 -0500

Scientist
Advanced Centre for Treatment, Research and Education in Cancer - Navi Mumbai, Maharashtra
Scientist (One position)
Project: Bioinformatics centre DBT- Sub-DIC at ACTREC
Funding agency: DBT Grant No.232

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

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 jobs at Chittaranjan National Cancer Institute

Thu, 29 Sep 2016 09:36:33 -0500

Chittaranjan National Cancer Institute Advertisement No.497/2016 Invites Applications For Senior Scientific Officer, Gr. II

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

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

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