BOL: Related items

HiBC: Human Intestinal Bacteria Collection

BioStar — Wed, 07 May 2025 05:49:19 -0500

The human gut is home to trillions of microorganisms, forming one of the most complex and dynamic microbial ecosystems known to science. The Human Intestinal Bacteria Collection (HiBC) is a pioneering initiative aimed at cataloging, preserving, and studying the diverse bacterial species that inhabit the human gastrointestinal tract. This curated collection serves as a critical resource for researchers working on microbiome-related health, disease, and therapeutics.

What is HiBC?

The Human Intestinal Bacteria Collection (HiBC) is a comprehensive, high-quality reference repository of bacterial isolates derived from human fecal samples. It focuses on anaerobic and facultative anaerobic bacteria that play pivotal roles in digestion, immune modulation, vitamin synthesis, and pathogen resistance. The collection includes both culturable strains and genomic data from unculturable taxa, bridging the gap between culture-dependent and -independent microbiome studies.

Why is HiBC Important?

Understanding Microbiome-Host Interactions
HiBC enables deeper insight into the functions of specific bacterial taxa in the gut. With well-characterized isolates, researchers can conduct mechanistic studies to explore how certain bacteria influence metabolism, inflammation, or mental health.
Precision Probiotics and Therapeutics
By providing access to native human gut microbes, HiBC supports the development of next-generation probiotics, live biotherapeutic products (LBPs), and fecal microbiota transplantation (FMT) alternatives.
Standardization and Reproducibility
With standardized cultivation and genomic protocols, HiBC ensures consistency across microbiome research studies, improving reproducibility and comparability of findings.
Antimicrobial Resistance (AMR) Surveillance
HiBC includes metadata on antibiotic resistance genes (ARGs), helping track the spread of AMR in commensal gut bacteria and understanding its implications for human health.

Key Features of HiBC

Culturable Bacteria Repository: A living collection of anaerobic and facultative strains isolated from healthy and diseased individuals worldwide.
Metadata-rich Entries: Each isolate is annotated with host details (age, health status, diet), geographical origin, phenotypic traits, and antibiotic susceptibility profiles.
Whole Genome Sequencing (WGS): High-quality genome assemblies for most strains to support functional and comparative genomics.
Interactive Database Access: User-friendly search and filtering options for strain selection based on taxonomy, function, or clinical relevance.
Cross-linking with Other Databases: Integration with NCBI, GOLD, and Human Microbiome Project (HMP) data for broader context and validation.

Applications of HiBC

Microbiome-based diagnostics and biomarker discovery
Host-microbe interaction studies in gnotobiotic mouse models
Gut microbiome modulation through diet, drugs, or engineered bacteria
Longitudinal studies of gut flora across age, geography, and lifestyle
Environmental and evolutionary microbiology of human-associated bacteria

Accessing HiBC

Researchers and interested parties can explore the HiBC database through its official website: https://www.hibc.rwth-aachen.de/. The platform offers comprehensive information on bacterial isolates, including taxonomy, cultivation conditions, and genomic data, facilitating advanced research in human gut microbiome studies.

Final Thoughts

The HiBC is a cornerstone resource in the rapidly evolving field of microbiome research. As science moves toward personalized medicine and microbial therapeutics, having a reliable and diverse collection of human gut bacteria is not just useful — it's essential. Whether you're a microbiologist, clinician, computational biologist, or biotechnologist, HiBC offers tools to accelerate discovery and innovation in gut microbiome science.

HumCFS: a database of fragile sites in human chromosomes

Abhimanyu Singh — Sun, 21 Apr 2019 20:17:29 -0500

Fragile sites are specific chromosomal region that exhibit an increased frequency of chromosdomal breakge when cells are exposed to replicative stress. Since from the discovery of chromosomal fragile sites/regions (CFS), several line of evidence suggests their involvement in human pathologies and they have been recognized as a preferential site for integration of exogenous oncogenic DNA viruses and hotspots for chromosomal re-arrangement. There is large gap in our knowledge of human CFS region as knowledge about CFS are unequally distributed in literature, which impose a problem in studying these region. In order to address these issues, we develop this platform HumCFS, which provides comprehensive information about experimentally identified CFS at a single source.

https://link.springer.com/epdf/10.1186/s12864-018-5330-5?author_access_token=ICASEpyMAQaxLlKw--fyCG_BpE1tBhCbnbw3BuzI2RMA57KLmXk5bZabRUiDQzRFHXd6hjm4kWSiLV3mU5XVMitqXUwFMSo4x5vbfty0EDQ9PW1sd1h923_TYXkvJ5niSwAyZ7BklJ0ujFAFhcKtjw%3D%3D

Address of the bookmark: https://webs.iiitd.edu.in/raghava/humcfs/

Should you get sequenced? Not all bad genes predict disease

Rahul Agarwal — Thu, 29 Aug 2013 15:10:53 -0500

“What we really don’t know yet is whether the predictive aspects of the genome are going to turn out to be beneficial or potentially harmful”

“As we roll out genomic medicine we are fighting against this society-wide misconception that having the bad gene means you’re going to get the disease. That’s only true in a very few cases.”

Source:Today Health

Address of the bookmark: http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154

Comparison of Short Read De Novo Alignment Algorithms

Rahul Agarwal — Wed, 21 Aug 2013 07:56:01 -0500

Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.

Title: Comparison of Short Read De Novo Alignment Algorithms

Author: Nikhil Gopal

Address of the bookmark: http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf

RNA Bioinformatics and High Throughput Analysis Jena

Sat, 09 Nov 2013 20:03:56 -0600

Research Topics:

High Throughput Sequencing Analysis
Comparative Genomics
Identification and Annotation of Non-coding RNAs
Bioinformatic Analysis and System Biology of Viruses
Coevolution of Proteins and RNAs
Algorithmic Bioinformatics
Phylogenetic Analysis

http://www.rna.uni-jena.de/index.php

Bio-Rad Acquires GnuBIO

Rahul Agarwal — Sat, 19 Apr 2014 10:36:36 -0500

http://www.businesswire.com/news/home/20140411005331/en/Bio-Rad-Acquires-GnuBIO-Developer-Droplet-Based-DNA-Sequencing#.U1KXnPm1b8o

Deadly Human Pathogen Cryptococcus Sequenced

Rahul Agarwal — Fri, 25 Apr 2014 11:02:21 -0500

"Now, researchers have sequenced the entire genome and all the RNA products of the most important pathogenic lineage of Cryptococcus neoformans, a strain called H99. The results, which appear in PLOS Genetics, also describe a number of genetic changes that can occur after laboratory handling of H99 that make it more susceptible to stress, hamper its ability to sexually reproduce and render it less virulent."

Source:

http://www.biosciencetechnology.com/news/2014/04/deadly-human-pathogen-cryptococcus-fully-sequenced

Paper:

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004292

Drawback of Exome Sequencing

Rahul Agarwal — Mon, 02 Jun 2014 05:46:43 -0500

Dr Eric Londin, Assistant Professor, Thomas Jefferson University, USA, stated that analysis of 44 exome datasets from four different testing kits showed that they missed a high proportion of clinically relevant regions in the 56 ACMG genes. "At least one gene in each exome method was missing more than 40 percent of disease-causing genetic variants, and we found that the worst-performing method missed more than 90 percent of such variants in four of the 56 genes," he says.

Source: http://www.eurekalert.org/pub_releases/2014-05/esoh-pco052914.php

Pathway Analysis

Rahul Agarwal — Fri, 03 Oct 2014 08:51:13 -0500

Pathway Analysis is usually performed with aim to enrich the genes with their functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to disclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, their transcripts, and their gene products. However, existing tools and dbs mainly based on knowledge driven approach in which pathways will be identified by finding the correlation between the information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) and gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.

Introductory Articles/ppt/sources:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375

http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf

http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html

http://davetang.org/muse/tag/pathway/

https://www.biostars.org/p/42219/

http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf

http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf

Impotant Database and Tools:

GeneMANIA, Cytoscape, IPA and Metacore (Commerical ), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go

Popular R based tools:

Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat

More:

http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+

LoRMA: a tool for correcting sequencing errors in long reads such those produced by Pacific Biosciences sequencing machines

Jit — Wed, 15 Jun 2016 17:18:36 -0500

LoRMA is a tool for correcting sequencing errors in long reads such those produced by Pacific Biosciences sequencing machines.

Publication:

L. Salmela, R. Walve, E. Rivals, and E. Ukkonen: Accurate selfcorrection of errors in long reads using de Bruijn graphs. Accepted to RECOMB-Seq 2016.

Download:

Address of the bookmark: https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/