BOL: Related items

BioRG

Tue, 04 Aug 2015 20:52:52 -0500

This research group works on problems from the fields of Bioinformatics, Biotechnology, Data Mining, and Information Retrieval. The group's research projects includes Comparative Genomics of Bacterial genomes, Metagenomics, Genomic databases, Pattern Discovery in sequences and structures, micro-array data analysis, prediction of regulatory elements, primer design, probe design, phylogenetic analysis, medical image processing, image analysis, data integration, data mining, information retrieval, knowledge discovery in electronic medical records, and more.

More at http://biorg.cis.fiu.edu/

Foldit: Solve Puzzles for Science

Robert M Willioms — Thu, 21 Dec 2017 15:17:47 -0600

Foldit is an online puzzle video game about protein folding. It is part of an experimental research project developed by the University of Washington, Center for Game Science, in collaboration with the UW Department of Biochemistry. The objective of Foldit is to fold the structures of selected proteins as perfectly as possible

https://fold.it/portal/

Address of the bookmark: https://fold.it/

Protein-Protein Interaction Sites Predictions !

Poonam Mahapatra — Wed, 25 Apr 2018 04:53:20 -0500

The study of Protein–Protein Interactions (PPIs) has a crucial role in biology, medicine and the pharmaceutical industry. PPIs can be investigated from two aspects: The interaction partners of a specific protein and the amino acid residues participating in a given PPI. Information about a protein’s interaction partners allows scientists to construct protein interaction networks, such as signaling pathways, which in turn facilitate the understanding of many biological and clinical observations.

Following are the list of tools commonly used to PPIs predictions:

Protein-Protein Interaction Sites

PPISP

A consensus neural network method for predicting protein-protein interaction sites

HOMCOS

A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures

HotPOINT

Prediction of protein interfaces using an empirical model

ISIS

Prediction of interaction hotspots from sequence

KFC server

Automated decision-tree approach to predicting protein-protein interaction hot spots

meta-PPISP

A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate

ODA

Identification of optimal surface patches with the lowest docking desolvation energy values

PINUP

Protein binding site prediction with an empirical scoring function

Other Sites (DNA, RNA, Metals)

CHED

Web server for predicting soft metal binding sites in proteins

DBD-Hunter

A knowledge-based method for the prediction of DNA-protein interactions

DISPLAR

Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method

iDBPs

Predicts DNA binding proteins for proteins with known 3D structure.

PFplus

A tool for extracting and displaying positive electrostatic patches on protein surfaces which can be indicative of nucleic acid binding interfaces.

FANTASIA

BioStar — Wed, 20 Aug 2025 02:48:15 -0500

FANTASIA is an advanced pipeline for the automatic functional annotation of protein sequences using state-of-the-art protein language models. It integrates deep learning embeddings and in-memory similarity searches, retrieving reference vectors from a PostgreSQL database with pgvector, to associate Gene Ontology (GO) terms with proteins.

https://www.nature.com/articles/s42003-025-08651-2

Address of the bookmark: https://github.com/CBBIO/FANTASIA

RNA Sequencing Helps Identify Functional Variants from GWAS

Andaleeb — Fri, 22 Nov 2013 21:33:33 -0600

For Alzheimer’s and other complex disorders, mining the genome for disease-associated variants is no longer the obstacle. The challenge nowadays is figuring out how the identified loci relate to disease. As reported last month in Nature and its associated journals, advances in high-throughput RNA sequencing are providing new tools for understanding how disease loci influence gene expression—a starting point for understanding their connection to pathogenesis.

Address of the bookmark: http://schizophreniaforum.org/new/detail.asp?id=1953

MIT Computational Biology Group

Thu, 18 Dec 2014 14:47:01 -0600

My research group consists primarily of computer science graduate students and postdocs with expertise in algorithms, statistical inferences and machine learning, and sharing a passion for understanding fundamental biological problems.

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

MIRO : miRNA omics

Jit — Tue, 04 Oct 2016 14:50:48 -0500

The MIRO (the miRNA omics) pipeline is a flexible and powerful tool for the analysis of miRNA (or more generall short RNA) expression using short-read deep sequencing data. In its present implementation MIRO is especially adapted for the analysis of reads generated with the Illumina sequencing platform. MIRO allows to preprocess the Solexa-reads, map them flexibly to several reference genomes using one of four different mappers, create differential gene (miRNA) expression profiles and cluster reads using one of several algorithm. MIRO output is furthermore compatible with software such as genome browsers and miRDeep.

Address of the bookmark: http://seq.crg.es/download/software/Miro/

SEASTAR: Systematic Evaluation of Alternative STArt site in RNA

BioStar — Thu, 13 Aug 2020 09:54:27 -0500

SEASTAR (Systematic Evaluation of Alternative STArt site in RNA) is a software package for Transcription Start Site (TSS) identification and quantification using only RNA-seq data. It assembles novel TSSs based only on RNA-Seq data and merges them with known TSSs from a public database. This package enables high-quality TSS identification that is comparable to the highly sophisticated CAGE technology. This package is particularly useful for finding novel TSSs that contribute to transcriptome complexity along with identifying differential promoter utilization.

version 1.0.0 - updates several descriptions and tests. To achieve v0.9.4, one can visit https://github.com/zhyqin/SEASTAR-0.9.4 for download.

Address of the bookmark: https://github.com/Xinglab/SEASTAR

piRNA and Bioinformatics: Decoding the Guardians of the Genome

LEGE — Sat, 07 Dec 2024 02:15:11 -0600

In the symphony of small RNAs, PIWI-interacting RNAs (piRNAs) stand out as the protectors of genomic integrity. These small, non-coding RNAs play critical roles in silencing transposable elements, regulating gene expression, and maintaining germline stability. The rise of bioinformatics has revolutionized our understanding of piRNAs, enabling researchers to decipher their biogenesis, functions, and evolutionary significance.

What Are piRNAs?

piRNAs are the largest class of small non-coding RNAs, typically 24–32 nucleotides in length. Unlike microRNAs (miRNAs) and small interfering RNAs (siRNAs), piRNAs do not rely on Dicer enzymes for maturation. Instead, they are processed from long single-stranded precursors and associate with PIWI proteins, a subclass of the Argonaute protein family.

The primary functions of piRNAs include:

Silencing Transposable Elements: By targeting transposons, piRNAs prevent genomic instability, particularly in germline cells.
Regulating Gene Expression: piRNAs modulate gene expression at transcriptional and post-transcriptional levels.
Epigenetic Modulation: They guide epigenetic modifications, such as DNA methylation, to specific genomic loci.

Challenges in piRNA Research

Studying piRNAs is fraught with challenges, including:

Short Length: Their small size complicates sequencing and alignment.
Lack of Sequence Conservation: Unlike miRNAs, piRNAs exhibit limited sequence conservation across species.
Complex Biogenesis: The intricate pathways of piRNA generation require sophisticated computational tools to unravel.

Bioinformatics: Illuminating the World of piRNAs

Bioinformatics has emerged as an indispensable tool for studying piRNAs, facilitating their discovery, annotation, and functional analysis. Here's how bioinformatics is transforming piRNA research:

1. Identification and Annotation

The discovery of piRNAs relies on next-generation sequencing (NGS) data. Bioinformatics tools such as piRNApredictor and Piano identify piRNA clusters and predict potential targets. Databases like piRBase and piRNAdb curate information about known piRNAs, their sequences, and associated proteins.

2. Mapping and Alignment

piRNAs often originate from repetitive regions, making their alignment challenging. Tools like Bowtie and STAR handle the unique mapping requirements of piRNAs, enabling accurate identification of piRNA clusters in genomes.

3. Functional Analysis

Bioinformatics approaches predict piRNA functions by analyzing their interactions with transposons, genes, and epigenetic marks. Algorithms such as TargetFinder and RIblast explore piRNA-mRNA interactions, shedding light on regulatory networks.

4. Evolutionary Studies

piRNAs are evolutionarily diverse, reflecting their roles in species-specific genomic defense. Comparative genomics tools help trace the evolution of piRNA clusters and their associated PIWI proteins across species.

5. Epigenomic Insights

piRNAs are key players in epigenetic regulation. Bioinformatics pipelines integrate piRNA data with chromatin immunoprecipitation sequencing (ChIP-seq) and DNA methylation data to uncover their role in shaping the epigenome.

Case Study: piRNAs in Germline Integrity

One of the hallmark functions of piRNAs is the suppression of transposable elements in the germline. For example, in Drosophila melanogaster, piRNAs target retrotransposons like gypsy and copia. Bioinformatics analyses revealed that these piRNAs guide PIWI proteins to transposon-derived RNA, ensuring genome stability during gametogenesis.

Clinical Relevance of piRNAs

Recent studies suggest that piRNAs may serve as biomarkers for diseases such as cancer, infertility, and neurodegenerative disorders. For instance:

Cancer: Dysregulated piRNA expression has been linked to tumorigenesis, making them potential targets for cancer therapies.
Infertility: Aberrant piRNA pathways are implicated in male infertility due to their role in spermatogenesis.
Neurodegeneration: piRNAs may regulate neuronal gene expression, highlighting their potential in neurological research.

Future Directions

The integration of bioinformatics with emerging technologies offers exciting opportunities for piRNA research:

Single-Cell Sequencing: Unveiling cell-specific piRNA expression and function.
Machine Learning: Predicting piRNA functions and targets with greater accuracy.
CRISPR-Based Tools: Editing piRNA clusters to explore their roles in vivo.

Conclusion

piRNAs are the unsung guardians of the genome, safeguarding genetic material from transposable elements and contributing to gene regulation and epigenetic programming. Bioinformatics has opened the floodgates of discovery, unraveling the complexities of piRNAs and their myriad roles in biology and disease.

As we continue to decode the piRNA landscape, these small RNAs promise to unveil big secrets about genome stability, evolution, and human health, cementing their place as a fascinating frontier in molecular biology.

swgis v2.0 : a seqword genomic island sniffer

Abhimanyu Singh — Thu, 01 Nov 2018 12:35:52 -0500

swgis v2.0 is the modified version of the seqword genomic island sniffer. this version is specifically optimized for predicting genomic islands in eukaryotic genomes. swgis v2.0 was tested on several eukaryotic species of different lineages. all identified genomic islands were deposited in the eugi database.

download swgis v2.0

Address of the bookmark: http://eugi.bi.up.ac.za/eugi_download_swgis.php