BOL: Related items

Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome

BioStar — Wed, 22 Jul 2020 10:11:13 -0500

We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.

More at https://pubmed.ncbi.nlm.nih.gov/20494974/

Address of the bookmark: http://www.sequenceontology.org/cgi-bin/soba.cgi

IBM Research Computational Biology Center

Thu, 29 Aug 2013 08:43:59 -0500

The IBM Computational Biology Center embraces activities at Yorktown Heights, with strong affiliations with activities at Almaden and other IBM Research Centers. Computational Biology (CompBio) including bioinformatics is the study of how computer systems can manage, analyze, and simulate the complex structures and processes inherent in living systems. CompBio Research at IBM spans pattern recognition in sequences, structures and processes, the studying of systems ranging from single protein molecules through to complex molecular interactions, and the data analysis, interpretation and reverse-engineering of complex disease-lifestyle-genomic interactions for fuller biological understanding. "CompBio" has a flavor of its own independant of its parents, biology and computer science. Nonetheless, CompBio is inherently a multi- disciplinary field with important applications in biology, chemical physics, materials science, agriculture, chemistry and ultimately nanotechnology.

Mathivanan Lab

Fri, 20 Sep 2013 13:09:38 -0500

The major research interests are in exploring the role of extracellular matrix components (soluble secreted proteins and membrane vesicles) in cancer and intercellular communication. The lab integrates proteomic, genomic and bioinformatics methodologies to explore cancer cells.

More at http://www.mathivananlab.org/index.html

http://scholar.google.com/citations?user=U6PyEdYAAAAJ&hl=en

Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA

Thu, 19 Aug 2021 08:45:24 -0500

wist is looking for a Bioinformatics Scientist to join our Production Bioinformatics Team. You will work alongside research scientists, software engineers and data scientists to further deliver on our mission to expand access to best-in-class synthetic biology and next-generation sequencing applications. You will be developing and engineering tools to better evaluate and build hardened, production quality pipelines, optimize data quality, and automate lab and bioinformatics processes. Our ideal candidate is an organized problem solver with a background in developing and building novel production-quality bioinformatics tools and packages. Equally excellent communication skills and a proven ability to work independently are required.

More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

Job offer for a postdoctoral researcher in genomics / bioinformatics (2 years)

Fri, 22 Oct 2021 04:44:33 -0500

Ongoing research in the group of Karine Van Doninck involves topics at the core of
evolutionary biology, including the evolution of sex, genome maintenance,
recombination and extreme stress resistance on different eukaryotic systems,
including rotifers, amoeba and Corbicula clams. We are employing different tools
(including experimental ecology, population genetics, phylogeny, comparative
genomics, transcriptomics, bioinformatics, molecular and cellular biology) to study
evolutionary processes at the level of populations, both experimental and natural, and
genomes.

Offer
We offer a full-time contract for two years. The contract starts between October 2021
and December 2021. The position involves no or extremely light teaching load, if the
candidate is interested. Salaries are competitive at the European level. The recruited
person will benefit from the Belgian social insurance scheme (health care, etc.) without
additional expenses.

Profile
Applicants are expected to show outstanding commitment to research and must have
obtained a PhD by the start of the position. A strong expertise in genomics is required.
More specifically, solid competences in bioinformatics (e.g. scripting pipelines) and in
genome evolution are needed. Knowledge or interest regarding recombination,
metazoan evolution, phylogenomics and population genomics is an added-value.

Application
Applications should be submitted via email to karine.van.doninck@ulb.be. The
application package should contain the following documents:
- A curriculum vitae with the complete list of publications
- A cover letter mentioning why the candidate is interested in the position
- Minimum 2 recommendation letters
Interviews: Interviews will be conducted with the selected candidates. Selected
candidates could also be invited to give a seminar to MBE ULB.
For any additional information, please contact karine.van.doninck@ulb.be

Anders Krogh Lab

Mon, 30 Sep 2013 19:07:40 -0500

In a lot of my work in bioinformatics, I have been using hidden Markov models (HMMs). As a postdoc with David Haussler at UCSC we developed the so-called profile HMMs (refs). Since then I have applied HMMs to membrane proteins (refs) and gene identification (refs) and have worked on methods for such things as discriminative estimation of HMMs (refs) and alternative decoding algorithms etc. (refs).

Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages.

Lab page @ http://wiki.binf.ku.dk/User:Krogh

Bioinformatics Algorithms (Part 1) with Pavel Pevzner, Phillip E. C. Compeau,

Mon, 30 Sep 2013 11:34:09 -0500

The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.

Virus Bioinformatics Tools

LEGE — Wed, 24 Apr 2024 06:19:55 -0500

Bioinformatics tools play a crucial role in studying viruses, enabling researchers to analyze their genetic makeup, structure, function, and evolution. Here are some commonly used bioinformatics tools for virus research

https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947

Address of the bookmark: https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947

Shendure Lab

Wed, 09 Oct 2013 14:21:58 -0500

The Shendure Lab is part of the Department of Genome Sciences at the University of Washington (Seattle, WA). The mission of the lab is to develop and apply new technologies in genomics and molecular biology. Most projects in the lab exploit new DNA sequencing technologies (Shendure et al., Nature Reviews Genetics 2004; Shendure & Ji, Nature Biotechnology 2008; Shendure & Lieberman Aiden, Nature Biotechnology 2012), and generally fall into one of six areas: 1) next-generation human genetics; 2) genome contiguity & completeness; 3) massively parallel functional analysis; 4) molecular tagging; 5) synthetic biology; 6) translational genomics. Our interests in each of these areas are outlined briefly below, and a full list of publications is available via PubMed. http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=shendure
More http://krishna.gs.washington.edu/research.html

Lab page @ http://krishna.gs.washington.edu/index.html

Exploring Bacterial Comparative Genomics: A Bioinformatics Approach

LEGE — Sat, 14 Dec 2024 12:31:14 -0600

In the world of microbiology, bacteria have long fascinated scientists for their diversity, adaptability, and crucial roles in ecosystems and human health. Comparative genomics—a field that involves analyzing and comparing the genomes of different organisms—has revolutionized our understanding of bacterial evolution, adaptation, and pathogenicity. By leveraging bioinformatics tools and techniques, researchers can uncover genomic insights that were once hidden. This blog delves into the principles, methodologies, and applications of bacterial comparative genomics from a bioinformatics perspective.

What is Bacterial Comparative Genomics?

Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:

Identify conserved and unique genes.
Explore genetic determinants of pathogenicity.
Understand bacterial evolution and phylogenetics.
Investigate horizontal gene transfer and its role in antibiotic resistance.

Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.

Key Steps in Bacterial Comparative Genomics

Genome Sequencing and Assembly: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.
Genome Annotation: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.
Genome Alignment: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.
Comparative Analyses:
- Core and Pan-genome Analysis: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.
- Phylogenetic Analysis: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.
- Functional Enrichment Analysis: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.

Recommended Bioinformatics Tools for Comparative Genomics

Here are some additional bioinformatics tools that can aid bacterial comparative genomics:

OrthoFinder: For accurate ortholog identification across multiple genomes.
PanOCT: Specifically designed for pan-genome clustering and annotation.
FASTANI: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.
CIRCOS: For visually comparing genomic data through circular genome plots.
Galaxy Platform: A user-friendly web-based platform offering numerous genomic analysis tools.
BLAST: Essential for sequence alignment and similarity searches.
PhyloSift: Focused on phylogenetic analysis of microbial genomes using marker genes.

These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.

Applications of Bacterial Comparative Genomics

Understanding Pathogenicity: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of Escherichia coli strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.
Antibiotic Resistance Research: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.
Microbial Ecology and Evolution: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.
Vaccine Development: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like Neisseria meningitidis.
Biotechnology Applications: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.

Challenges in Bacterial Comparative Genomics

While the field has made significant strides, several challenges remain:

Data Overload: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.
Genome Plasticity: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.
Annotation Accuracy: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.
Interpreting Non-Coding Regions: Understanding the functional significance of non-coding genomic regions remains a challenge.

Future Directions

The integration of bacterial comparative genomics with other ‘omics’ approaches—such as transcriptomics, proteomics, and metabolomics—promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.

Conclusion

Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.