BOL: Related items

CGView - Circular Genome Viewer

Rahul Nayak — Wed, 20 Jun 2018 10:15:57 -0500

CGView is a Java package for generating high quality, zoomable maps of circular genomes. Its primary purpose is to serve as a component of sequence annotation pipelines, as a means of generating visual output suitable for the web. Feature information and rendering options are supplied to the program using an XML file, a tab delimited file, or an NCBI ptt file. CGView converts the input into a graphical map (PNG, JPG, or Scalable Vector Graphics format), complete with labels, a title, legends, and footnotes. In addition to the default full view map, the program can generate a series of hyperlinked maps showing expanded views. The linked maps can be explored using any web browser, allowing rapid genome browsing, and facilitating data sharing. The feature labels in maps can be hyperlinked to external resources, allowing CGView maps to be integrated with existing web site content or databases. For examples of the various output types, see the CGView gallery. http://wishart.biology.ualberta.ca/cgview/gallery.html http://stothard.afns.ualberta.ca/downloads/CCT/index.html https://www.gview.ca/wiki/GView/WebHome https://server.gview.ca/ http://stothard.afns.ualberta.ca/cgview_server/ Paper https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbx081/4037458

Address of the bookmark: http://wishart.biology.ualberta.ca/cgview/

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Rahul Nayak — Sat, 07 Mar 2020 05:52:20 -0600

GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.

https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

Address of the bookmark: https://github.com/wanyuac/GeneMates

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.

CONTIGuator !

BioStar — Fri, 04 Oct 2019 01:27:58 -0500

CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).

Address of the bookmark: https://sourceforge.net/projects/contiguator/

PHYMMBL

Jit — Mon, 10 Oct 2016 08:56:34 -0500

Metagenomics sequencing projects collect samples of DNA from uncharacterized environments that may contain hundreds or even thousands of species. One of the main challenges in analyzing a metagenome is phylogenetic classification of raw sequence reads into groups representing the same or similar species. Such classification is a useful prerequisite for genome assembly and for analysis of the biological diversity present in a sample. The newest sequencing technologies have simultaneously made metagenomics easier, by making the sequencing process faster, and more difficult, by producing shorter read lengths than previous technologies. Methods for classifying sequences as short as 100 base pairs (bp) have until now been relatively inaccurate, requiring metagenomics projects to use older, long-read technologies. Phymm, a new classification approach for metagenomics data which uses interpolated Markov models (IMMs) to taxonomically classify DNA sequences, can accurately classify reads as short as 100 bp. Its accuracy for short reads represents a significant leap forward over previous composition-based classification methods. PhymmBL (rhymes with "thimble"), the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Address of the bookmark: http://www.cbcb.umd.edu/software/phymm/

A Beginner's Guide to Using Kraken for Taxonomic Classification

Neel — Fri, 13 Dec 2024 11:29:03 -0600

Kraken is a popular bioinformatics tool designed for fast and accurate taxonomic classification of metagenomic sequences. Its efficiency and precision make it a go-to resource for analyzing microbial communities, including bacteria, viruses, archaea, and fungi. Whether you're new to bioinformatics or experienced in the field, Kraken is an indispensable tool for taxonomic analysis.

In this blog, we’ll walk through the basics of Kraken, from installation to running an analysis, and highlight its key features and applications.

What is Kraken?

Kraken is a sequence classification tool that assigns taxonomic labels to DNA sequences using exact k-mer matching. It uses a reference database of genomes, dividing sequences into k-mers and identifying matches in a computationally efficient way.

Key Features of Kraken

Speed: Kraken processes data much faster than alignment-based methods.
Accuracy: It uses a precise k-mer matching algorithm for high-resolution taxonomic assignments.
Scalability: It can handle large metagenomic datasets.
Custom Databases: You can build and use custom databases tailored to your research needs.

Installing Kraken

System Requirements
- A Unix-based operating system (Linux/macOS).
- Sufficient computational resources for database building (RAM and disk space).
Installation Steps
- Clone the Kraken repository from GitHub:
  
  git clone https://github.com/DerrickWood/kraken.git cd kraken
- Compile the Kraken binaries:
  
  make
- Add Kraken to your PATH for easy access:
  
  export PATH=$PATH:/path/to/kraken

Preparing a Database

Kraken requires a database of reference genomes. You can use a pre-built database or create a custom one.

Downloading a Pre-built Database
Kraken offers pre-built databases, such as the MiniKraken database, which is lightweight and suitable for smaller datasets. Download it using:

kraken-build --download-library minikraken
Building a Custom Database
To include specific genomes, download FASTA files and build the database:

kraken-build --download-library bacteria --threads 4 --db my_database kraken-build --build --db my_database

This process may take considerable time and resources, depending on the size of the database.

Running Kraken

Once the database is ready, you can classify sequences.

Basic Usage
Use the following command to classify sequences:

kraken --db my_database --threads 4 --fastq-input input_sequences.fastq --output kraken_output.txt

Key options:
- --db: Specifies the database.
- --threads: Number of threads for parallel processing.
- --fastq-input: Indicates input file format (FASTQ/FASTA).
Interpreting Results
Kraken generates an output file with columns for sequence IDs, taxonomic classifications, and the confidence score.

Visualizing Kraken Results

Kraken results can be visualized using tools like Krona or converted to human-readable reports using kraken-report.

Generate a Report

kraken-report --db my_database kraken_output.txt > kraken_report.txt
Krona Visualization
Install Krona and convert Kraken output for visualization:

cut -f2,3 kraken_output.txt | ktImportTaxonomy -o krona_output.html

Open the HTML file in your browser to interactively explore the taxonomic classifications.

Advanced Usage

Confidence Thresholds
Adjust the confidence threshold for classification using the --confidence option. Higher values reduce false positives but may miss some true positives:

kraken --db my_database --confidence 0.1 --fastq-input input.fastq
Paired-End Reads
For paired-end sequencing data, use:

kraken --db my_database --paired reads_1.fastq reads_2.fastq
Customizing K-mers
Kraken allows you to set custom k-mer lengths during database building for specific applications.

Applications of Kraken

Microbial Ecology: Characterizing microbial communities in soil, water, and the human microbiome.
Pathogen Detection: Identifying pathogens in clinical samples.
Fungal Research: Analyzing fungal diversity in metagenomic datasets.
Environmental Monitoring: Tracking microbial populations in diverse habitats.

Conclusion

Kraken is a versatile and efficient tool for taxonomic classification in metagenomics. Its speed, accuracy, and flexibility make it a favorite among bioinformaticians. By following this guide, you can set up and use Kraken to unlock insights into microbial and fungal communities, paving the way for discoveries in ecology, medicine, and biotechnology.

CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs)

Jit — Mon, 18 May 2020 10:53:32 -0500

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formated appropriately (see Usage).

Address of the bookmark: https://github.com/dutilh/CAT

Understanding DUMP files from NCBI Taxonomy database !

Shruti Paniwala — Fri, 15 Jul 2022 04:29:05 -0500

*.dmp files are bcp-like dump from GenBank taxonomy database

General information.

Field terminator is "\t|\t"

Row terminator is "\t|\n"

nodes.dmp file consists of taxonomy nodes. The description for each node includes the following

fields:

tax_id -- node id in GenBank taxonomy database

parent tax_id -- parent node id in GenBank taxonomy database

rank -- rank of this node (superkingdom, kingdom, ...)

embl code -- locus-name prefix; not unique

division id -- see division.dmp file

inherited div flag (1 or 0) -- 1 if node inherits division from parent

genetic code id -- see gencode.dmp file

inherited GC flag (1 or 0) -- 1 if node inherits genetic code from parent

mitochondrial genetic code id -- see gencode.dmp file

inherited MGC flag (1 or 0) -- 1 if node inherits mitochondrial gencode from parent

GenBank hidden flag (1 or 0) -- 1 if name is suppressed in GenBank entry lineage

hidden subtree root flag (1 or 0) -- 1 if this subtree has no sequence data yet

comments -- free-text comments and citations

Taxonomy names file (names.dmp):

tax_id -- the id of node associated with this name

name_txt -- name itself

unique name -- the unique variant of this name if name not unique

name class -- (synonym, common name, ...)

Divisions file (division.dmp):

division id -- taxonomy database division id

division cde -- GenBank division code (three characters)

division name -- e.g. BCT, PLN, VRT, MAM, PRI...

comments

Genetic codes file (gencode.dmp):

genetic code id -- GenBank genetic code id

abbreviation -- genetic code name abbreviation

name -- genetic code name

cde -- translation table for this genetic code

starts -- start codons for this genetic code

Deleted nodes file (delnodes.dmp):

tax_id -- deleted node id

Merged nodes file (merged.dmp):

old_tax_id -- id of nodes which has been merged

new_tax_id -- id of nodes which is result of merging

Citations file (citations.dmp):

cit_id -- the unique id of citation

cit_key -- citation key

pubmed_id -- unique id in PubMed database (0 if not in PubMed)

medline_id -- unique id in MedLine database (0 if not in MedLine)

url -- URL associated with citation

text -- any text (usually article name and authors).

-- The following characters are escaped in this text by a backslash:

-- newline (appear as "\n"),

-- tab character ("\t"),

-- double quotes ('\"'),

-- backslash character ("\\").

taxid_list -- list of node ids separated by a single space

Dengue Lineages !

LEGE — Fri, 16 Aug 2024 04:40:14 -0500

Our dengue virus lineage system splits up the current genotypes into major and minor lineages to provide additional spatiotemporal resolution and a common language to discuss important genomic diversity. A full description of the lineage system can be found here.

https://dengue-lineages.org/

Address of the bookmark: https://dengue-lineages.org/

lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data

BioJoker — Tue, 27 Nov 2018 04:43:57 -0600

lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.

lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

Address of the bookmark: https://github.com/vpc-ccg/lordfast