BOL: Related items

NAViGaTOR: Network Analysis, Visualization and Graphing Toronto

Rahul Nayak — Tue, 03 Jul 2018 05:05:55 -0500

NAViGaTOR – Network Analysis, Visualization, & Graphing TORonto is a software system for scaleable visualizing and analyzing networks. The current version, NAViGaTOR 3, increases modularity, improves scaleability, extends input/output options, brings new network views and analysis algorithms. http://142.150.188.236/navigatorwp/

Address of the bookmark: http://142.150.188.236/navigatorwp/

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

Barrnap: Bacterial ribosomal RNA predictor

Abhimanyu Singh — Fri, 12 May 2017 09:24:41 -0500

Barrnap predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

It takes FASTA DNA sequence as input, and write GFF3 as output. It uses the new NHMMER tool that comes with HMMER 3.1 for HMM searching in RNA:DNA style. NHMMER binaries for 64-bit Linux and Mac OS X are included and will be auto-detected. Multithreading is supported and one can expect roughly linear speed-ups with more CPUs.

Address of the bookmark: https://github.com/tseemann/barrnap

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/

ERGO 2.0 Bioinformatics suites

Jit — Tue, 16 May 2017 08:14:10 -0500

ERGO 2.0 provides a systems biology informatics toolkit centered on comparative genomics to capture, query, and visualize sequenced genomes. Using Igenbio's proprietary algorithms, and the most comprehensive genomic database integrated with the largest collection of microbial metabolic and non-metabolic pathways, ERGO™ assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways, and gene products.

Address of the bookmark: https://www.igenbio.com/ergo/

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/

Senior Bioinformatics Software Developer, Hyderabad, Telangana

Mon, 03 Jul 2017 10:10:31 -0500

DuPont Pioneer is the world leader in plant biotechnology area including discovery, development and delivery of elite crop genetics. DuPont Pioneer is aggressively building Big Data and Predictive Analytics capabilities in order to deliver improved services to our customers. We are currently seeking Senior Bioinformatics Software Developer at the DuPont Knowledge Center in Hyderabad, India for our global Data Science and Informatics group. At DuPont Pioneer, you’ll become part of a work environment that nurtures your interests, ignites your passion, creates opportunities to serve and helps you attain success–both personally and professionally. The hiring level will be commensurate with the level of experience. This is a critical position with the potential to make immediate, significant impact on our business.
The successful candidate will have an extensive background in computer science and bioinformatics through courses or academic degrees, and proven experience in bioinformatics software development. We are looking for those creative, smart, model driven, agile individuals who enjoy giving their all to tackle diverse software needs.
Duties / Responsibilities

Job Qualifications
Education and Experience
• Master Degree in Bioinformatics, Computational biology, Scientific Computing or related field
• 3-5 years of Post-Master’s experience in Bioinformatics software development
• Proven experience developing high throughput bioinformatics applications
Required Competencies
• Strong proven experience in Python programming language in Linux environment
• Proven High Performance computing experience (LSF/SGE/OGE)
• Exposure in code versioning and repository management (GIT/SVN)
• Proven experience in Bioinformatics algorithm development
• Deep understanding in Bioinformatics tools, data types
Desired Competencies
• Familiarity working in a scientific computing environment (NumPy, SciPy, Pandas etc.)
• Familiarity working with Cloud technologies (AWS, Azure)
• Ability to demonstrate solid analytical skills and exceptional attention to detail.
• Experience in relational databases and data structures
• Proven experience working with teams using agile software development methodologies and processes
• Familiarity with Service Oriented Architecture (SOA)
• Familiarity with build tools (Jenkins, make, ANT, Maven)
• Exposure to project management tools (JIRA, Confluence, RED MINE, etc.)

More at http://careers.dupont.com/jobsearch/job-details/senior-bioinformatics-software-developer/012939W-01/

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

The 10th North East Bioinformatics Network (NEBINet) Annual Coordinators' Meet

Jit — Sat, 18 Nov 2017 15:02:44 -0600

The 10th North East Bioinformatics Network (NEBINet) Annual Coordinators' Meet organised by the Bioinformatics Centre, St Edmund's College, Shillong and sponsored by the Department of Biotechnology, Government of India, was held at St Edmund's College Auditorium here on Thursday. Meghalaya Governor Ganga Prasad graced the inaugural programme as chief guest.
In his inaugural address, the Governor said the panorama of scientific scenario has greatly changed over the years, the thrust areas have undergone a metamorphosis but the conceptual underpinning of the basic sciences still continues.
"Of late, the activity of basic research has been intricately intertwined with technology. And we are determined to carry forward this change, for it is through technology that science can actually reach the masses in our country and afar, and the changing times have also inculcated a culture of cross-departmental and interdisciplinary research. Science and technology has always played a pivotal role in taking a nation towards greater heights by ways of innovations and inventions," he added.
Prasad also hoped that discussions, suggestions and sharing of innovative ideas during the two-day 10th NEBINet Annual Coordinators' Meet will open up new avenues to make substantial advancement in Biological Sciences which will provide a platform for proper and effective delivery mechanism for the common man.
During the inaugural function, Advisor of Department of Biotechnology Dr T Madhan Mohan gave an overview of the NEBINet and Bioinformatics programme.
President of Epygen Biotech FZ LLC, Dubai, UAE, Dr Debayan Ghosh, delivered the keynote address.
St Edmund's College governing body secretary Brother Simon Coelho and St Edmund's College Principal Dr Sylvanus Lamare also spoke during the function.

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