BOL: Related items

Binding Site Prediction in Protein !

Poonam Mahapatra — Wed, 25 Apr 2018 04:35:57 -0500

The interaction between proteins and other molecules is fundamental to all biological functions. In this section we include tools that can assist in prediction of interaction sites on protein surface and tools for predicting the structure of the intermolecular complex formed between two or more molecules (docking).

Pockets Identification

CASTp

Automatic Identification of pockets and cavities in proteins structure, and quantitation of their volumes using Delaunay triangulation. Available also as PyMOL plugin

Pocket-Finder

Automatic identification of pockets and cavities in proteins structure, and quantitation of their volumes.

PocketPicker

Grid-based technique for the analysis of protein pockets. PocketPicker available as a plugin for PyMOL

Binding Site Prediction

ConSurf

Identification of functional regions in proteins by surface-mapping of phylogenetic information

CRESCENDO

Identification protein interaction sites. It uses sequence conservation patterns in homologous proteins to distinguish between residues that are conserved due to structural restraints from those due to functional restraints.

Ligand Binding Sites

3DLigandSite

The server utilizes protein-structure prediction to provide structural models of the binding site. Ligands bound to structures are superimposed onto the model and use to predict the binding site.

FINDSITE

A threading-based method for ligand-binding site prediction and functional annotation based on binding-site similarity across superimposed groups of threading templates.

LIGSITE^csc

Prediction of binding site by pocket identification using the Connolly surface and degree of conservation

metaPocketA meta server for ligand-binding site prediction. metaPocket use LIGSITE^csc, PASS, Q-SiteFinder and SURFNET

Large Language Models in Bioinformatics: Transforming Data Analysis and Interpretation

LEGE — Thu, 02 Jan 2025 11:26:29 -0600

The integration of artificial intelligence (AI) into bioinformatics has ushered in a new era of computational biology. Among the most transformative advancements are large language models (LLMs), such as GPT and BERT, which leverage deep learning to process and interpret vast amounts of text data. These models are reshaping bioinformatics by enhancing data analysis, hypothesis generation, and literature mining.

Understanding Large Language Models

LLMs are AI systems trained on extensive datasets of natural language. Their ability to model context, identify patterns, and generate coherent language has proven invaluable across domains, including bioinformatics. By fine-tuning these models on biological datasets, researchers can unlock insights into molecular biology, systems biology, and beyond.

Key Applications of LLMs in Bioinformatics

1. Annotating Biological Data

Annotating genomic and proteomic data is fundamental yet labor-intensive. LLMs streamline this process by extracting functional annotations from literature and databases, predicting gene and protein functions, and providing automated insights.

2. Mining Scientific Literature

The exponential growth of publications presents a challenge for researchers to stay updated. LLMs can process large volumes of text to extract key findings, summarize papers, and identify trends, thereby facilitating efficient literature reviews.

3. Predicting Gene and Protein Functions

By leveraging sequence data and annotations, LLMs can predict the functions of uncharacterized genes and proteins. This capability is particularly useful for studying non-model organisms and orphan genes.

4. Drug Discovery and Repurposing

LLMs enable pattern recognition across chemical, genomic, and clinical datasets, identifying novel drug candidates and repurposing existing drugs for new therapeutic targets. They can simulate interactions between drugs and biological molecules, accelerating the discovery pipeline.

5. Generating Hypotheses for Research

LLMs analyze complex datasets to propose testable hypotheses. For example, they can predict protein-protein interactions, identify regulatory motifs, or model evolutionary processes in genomes.

Advantages of LLMs in Bioinformatics

Scalability: LLMs process massive datasets rapidly, reducing the time required for data analysis.
Versatility: These models adapt to diverse bioinformatics tasks, from genomic annotation to network analysis.
Contextual Insights: By synthesizing information across disparate datasets, LLMs provide integrative insights into biological systems.

Challenges in Applying LLMs

Despite their promise, LLMs face limitations:

Data Quality and Bias: Inaccurate or biased datasets can affect model predictions, necessitating rigorous data curation.
Interpretability: Understanding the decision-making process of LLMs remains a critical challenge, especially in high-stakes fields like genomics and medicine.
Resource Intensity: Training and deploying LLMs require substantial computational power, which can limit accessibility.
Ethical Concerns: Handling sensitive genomic data raises privacy and security issues, emphasizing the need for ethical guidelines.

Future Prospects

The continued development of LLMs tailored for bioinformatics promises exciting advancements. Specialized models trained on omics data, open-access platforms, and interdisciplinary collaborations will expand the utility of LLMs. Moreover, integrating LLMs with other AI technologies, such as graph neural networks and reinforcement learning, can unlock deeper biological insights.

Conclusion

Large language models are revolutionizing bioinformatics by addressing longstanding challenges in data annotation, literature mining, and function prediction. Their ability to analyze complex biological datasets efficiently positions them as indispensable tools for modern research. As bioinformatics embraces AI, the synergy between LLMs and biological sciences holds the potential to unravel the complexities of life with unprecedented precision and scale.

poRe: an R package for the visualization and analysis of nanopore sequencing data

Jit — Thu, 23 Nov 2017 09:55:57 -0600

Motivation: The Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693

Research Associate Bioinformatics in IISc Recruitment 2020

Tue, 23 Jun 2020 21:53:34 -0500

Research Associate Bioinformatics in IISc Recruitment 2020

Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.

Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.

Desirable Qualifications (Research Associate/s): i) Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment).

ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv) Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.

The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.

Emoluments: As per DST, GOI norms and commensurate with experience.

More at https://www.iisc.ac.in/positions-open/

Bioinformatics Scientist/Research Software Engineer at University of Dundee Dundee, United Kingdom

Wed, 26 Aug 2020 10:31:25 -0500

We are recruiting for an exceptional individual to join us as a computational scientist, bioinformatician, or (research) software engineer with an interest in interactive data analysis platforms for biology and medicine within our Jalview (www.jalview.org) research software engineering team.

More at https://www.jobs.dundee.ac.uk/fe/tpl_uod01.asp?s=4A515F4E5A565B1A&jobid=104342,2382988671&key=147934117&c=99413415238921&pagestamp=sesxbbuyifokdsfygf

Last date: 30th August 2020

Informal enquiries about this position may be made to Prof. Geoff Barton (gjbarton@dundee.ac.uk) or Dr Jim Procter (jprocter@dundee.ac.uk). To find out more about Jalview research software engineering team please visit www.jalview.org and www.compbio.dundee.ac.uk

Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees

Rahul Nayak — Wed, 27 Nov 2019 05:32:33 -0600

The Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees (although clustering trees or any other tree-like data structure are also supported).

Other tools

https://github.com/shenwei356/taxonkit

ETE, version: 3.1.1
BioPython, version: 1.73
taxadb, version: 0.10.1
TaxonKit, version: 0.5.0

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

Breedbase is a comprehensive breeding management and analysis software

BioStar — Wed, 06 Jan 2021 19:45:21 -0600

Breedbase is a comprehensive breeding management and analysis software. It can be used to design field layouts, collect phenotypic information using tablets, support the collection of genotyping samples in a field, store large amounts of high density genotypic information, and provide Genomic Selection related analyses and predictions. Breedbase supports the BrAPI standard.

Address of the bookmark: https://breedbase.org/

Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory

Wed, 18 Aug 2021 00:19:48 -0500

Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

NGS course Medical Genomics, scheduled for 17-20 September 2013 in UZ Leuven (Belgium).

Poonam Mahapatra — Mon, 12 Aug 2013 12:08:24 -0500

This course is open to all students and postdocs and registration for all academic participants is free of charge. To help us in organizing the course, please register online via http://gc.uzleuven.be where the preliminary program is also available.

This course is organized with support from the IAP “Belgian Medical Genomics Initiative”, SymBioSys and the Genomics Core.

For inquiries, please email Ms Narcisse Opdekamp ( narcisse.opdekamp@uzleuven.be ).

More at >> http://gc.uzleuven.be/

Amity University Bioinformatics Summer Program - Kolkata

eliabrodsky — Tue, 11 Jun 2019 21:27:10 -0500

Registrations are now open for the 2019 Summer Bioinformatics Training program at Amity University, Kolkata. The program will focus on introductory topics for life science students. We will review important history, topics and challenges bioinformatics can help address in the context of basic research, discovery and industry.

Read more: https://edu.t-bio.info/amity-university-summer-bioinformatics-program-registrations-are-open/