BOL: Related items

G-NEST: The Gene NEighborhood Scoring Tool

Neel — Fri, 25 Sep 2020 20:09:18 -0500

The Gene NEighborhood Scoring Tool (G-NEST) combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all window sizes. Primary author of final code = William F. Martin. Example data files are in the separate repository.

Address of the bookmark: https://github.com/dglemay/G-NEST

Tigers genome sequenced

Rahul Agarwal — Tue, 17 Sep 2013 16:48:24 -0500

Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.

The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.

The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.

Reference:

http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690

http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms

Paper:

http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes

Surabhi Chaudhary — Sat, 12 May 2018 07:26:59 -0500

SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

Address of the bookmark: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks

Rahul Nayak — Tue, 15 Jan 2019 09:39:30 -0600

GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks based on gene expression data and optional hub gene information.

It efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.

GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck

Address of the bookmark: http://lce.biohpc.swmed.edu/geneck/

OMArk: software for proteome (protein-coding gene repertoire) quality assessment

LEGE — Wed, 21 Feb 2024 15:01:20 -0600

OMArk is a software for proteome (protein-coding gene repertoire) quality assessment. It provides measures of proteome completeness, characterizes the consistency of all protein coding genes with regard to their homologs, and identifies the presence of contamination from other species. OMArk relies on the OMA orthology database, from which it exploits orthology relationships, and on the OMAmer software for fast placement of all proteins into gene families.

Address of the bookmark: https://github.com/DessimozLab/OMArk

KEGG Mapper – Reconstruct Pathway

Abhimanyu Singh — Wed, 12 Dec 2018 09:14:29 -0600

Reconstruct Pathway is a KEGG PATHWAY mapping tool that assists genome and metagenome annotations. The input data is a single gene list (for a single organism) or multiple gene lists (for multiple organisms) annotated with KEGG Orthology (KO) identifiers or K numbers. Each line of the gene list contains the user-defined gene identifier followed by, if any, the assigned K number. The mapping is performed through the K numbers against the KEGG reference pathways.

Address of the bookmark: https://www.kegg.jp/kegg/tool/map_pathway.html

PHD SCHOLARSHIP DENMARK

Fri, 13 Jun 2014 13:44:07 -0500

ne PhD position is available at the Bioinformatics Center, Department of Biology, University of Copenhagen, Denmark. The PhD position concerns protein structure prediction, and will be in the Structural Bioinformatics group of Associate professor Thomas Hamelryck. The group is an integrated part of the Bioinformatics Center, which is headed by Professor Anders Krogh, employs around sixty scientists (including PhD students) and focuses on non-coding RNA, eukaryotic gene regulation and protein structure prediction. The center provides a modern, pleasant, international working environment with excellent modern facilities, in the heart of Copenhagen.
The project will be supervised by Associate Professor Thomas Hamelryck.

The protein folding problem is of enormous practical, theoretical and medical importance - and in addition forms a fascinating intellectual challenge. The aim of this project is to develop and implement a probabilistic method to infer the structure of proteins, building on various probabilistic models of protein structure developed by the Hamelryck group. The method will also take the dynamic nature of proteins into account, and involves a close collaboration with the statistics department at the university within the interdisciplinary project "Dynamical Systems: Mathematical Modeling and Statistical Methods for the Social, Health, and Natural Sciences" (http://dsin.ku.dk/).

Qualifications
Knowledge of programming (C++) and statistics or machine learning. Knowledge of biology, physics or biophysics is a plus, but not a requirement.

The deadline for applications is June 15, 2014

More at : https://job.jobnet.dk/CV/FindJob/details.aspx/3695051%20

Gblocks: eliminates poorly aligned positions and divergent regions of a DNA or protein alignment

Poonam Mahapatra — Sat, 02 Jun 2018 07:36:05 -0500

Gblocks eliminates poorly aligned positions and divergent regions of a DNA or protein alignment so that it becomes more suitable for phylogenetic analysis. This server implements the most important features of the Gblocks program to make its use as simple as possible without loosing the functionality that it is necessary in most of the cases. Other options can be changed in the stand-alone program. You can see here an example output file showing the blocks selected from a protein alignment. Further information can be found in the online documentation.

Address of the bookmark: http://molevol.cmima.csic.es/castresana/Gblocks_server.html

PhD student / Bio-informatician in computational protein modeling

Sun, 23 Feb 2020 03:46:46 -0600

PhD student / Bio-informatician in computational protein modeling
Job Profile
You will perform research on drug/protein interaction analysis in the context of lung cancer, using computational protein modeling. You will implement existing models predicting drug efficacy, related to EGFR-driven cancer. You will translate these models to novel oncogenes, including ROS1. You will validate these models against experimental data from a parallel project, with the final goal of deployment of your methods into clinical decision making. Your work will be embedded in an international network consisting of both academic partners and ROS1-NSCLC patient organizations.

Requirements

You are (or soon will be) a master in bio-informatics. You have strong ICT skills and you are eager to fully submerge into the world of protein modeling. You have good experience with Linux and one or more programming languages as well as knowledge of tertiary structure analysis. Candidates with a Master degree in one of the life sciences (Biomedical sciences, Biochemistry, Bio-engineering, Biostatistics, …), with relevant interest and extended experience in this field are also welcome. A general background cancer biology and genetics is needed. You are willing and eligible to apply for a personal PhD fellowship with the Flemish FWO (FWO.be). Therefore, it is required that you hold a master degree from a European university, and have not obtained your master diploma more than three years ago (see FWO website for detailed conditions). Proficiency in English, and good communication skills, both oral and written, are required. You are highly motivated, and you like to work in an interactive research team. You are willing to work on a 4-year PhD project starting beginning of 2020.

What we offer

We offer a one year position, as a PhD student, which can be extended up to 4 year upon positive evaluation, even if a personal fellowship application is not successful. Wages are according to the standard Flemish bursary levels for PhD students.

Interested?
For additional information please contact dr. Geert Vandeweyer. To apply, send a copy of your CV including details of your relevant skills and a motivation letter by e-mail to dr. Geert Vandeweyer (geert.vandeweyer@uantwerpen.be) before March 15, 2020.

Source:https://academicpositions.be/ad/university-of-antwerp/2020/phd-student-bio-informatician-in-computational-protein-modeling/141252?utm_source=jooble&utm_medium=cpc&utm_campaign=jooble

Basics of BLAST Programs !

BioStar — Fri, 26 Jul 2024 06:04:26 -0500

The Basic Local Alignment Search Tool (BLAST) is a powerful bioinformatics program used to compare an input sequence (such as DNA, RNA, or protein sequences) against a database of sequences to find regions of similarity. Developed by the National Center for Biotechnology Information (NCBI), BLAST is widely used for identifying species, finding functional and evolutionary relationships between sequences, and predicting the function of novel sequences.

Key Features of BLAST:
1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.

2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.

3. Versatility: There are several versions of BLAST for different types of sequence comparisons:
- blastn: Compares a nucleotide query sequence against a nucleotide sequence database.
- blastp: Compares a protein query sequence against a protein sequence database.
- blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.
- tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.
- tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.

5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.

Applications of BLAST:
- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.
- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.
- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.
- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.

Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.