BOL: Related items

A Subway Map for the Genome

LEGE — Fri, 04 Sep 2026 04:14:14 -0500

Maya was looking at a genome graph on her computer. There were nodes, branches, and paths going in every direction.

“It’s like a subway map,” she said, “but for DNA.”

That idea is exactly what Sequence Tube Map brings to life.

Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.

This makes complex genome graphs much easier to explore.

Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.

What once looked like a tangled network now looked like a journey.

Sequence Tube Map turns the complexity of graph genomes into a visual story—making it easier for scientists to see how DNA can take different routes.

Read more at https://github.com/vgteam/sequencetubemap

and

https://academic.oup.com/bioinformatics/article/35/24/5318/5542397

Demo at https://vgteam.github.io/sequenceTubeMap/

Finding the Hidden Switches: The Story of KiNext and Protein Kinases

LEGE — Thu, 24 Sep 2026 02:51:28 -0500

Every newly sequenced genome contains thousands of proteins, but identifying what each protein does is a much harder task. Among these proteins are protein kinases, important molecular regulators that control processes such as cell growth, development, metabolism, stress responses, and signaling. Finding these kinases and determining which families they belong to can reveal important clues about how an organism functions and has evolved.

This is where KiNext comes into the picture. Introduced in a 2024 study published in BMC Bioinformatics, KiNext is a computational workflow designed to identify and classify protein kinases from predicted protein sequences. Instead of relying on a single search method, it brings together several approaches, including Hidden Markov Models, sequence alignment, phylogenetic analysis, and structural comparison.

The search begins with a simple question: does a protein contain the characteristics of a kinase? Protein kinases can change considerably during evolution, but important regions of their sequences often retain recognizable patterns. KiNext uses Hidden Markov Models, or HMMs, to detect these patterns. An HMM does not require a protein to be an exact match to a known kinase. Instead, it looks for a statistical sequence signature associated with kinase proteins, making it possible to detect more distant candidates.

Once potential kinases are identified, KiNext takes the analysis further. It distinguishes conventional eukaryotic protein kinases from atypical protein kinases and then attempts to classify them into different kinase groups and families. This distinction is important because simply identifying a protein as a kinase does not tell the complete story. Different kinase families can have very different evolutionary histories and biological functions.

The next stage brings evolution into the picture. KiNext can align kinase sequences and construct phylogenetic trees, allowing researchers to examine how newly identified proteins are related to previously characterized kinases. When sequence evidence alone is difficult to interpret, structural information can provide another clue. The workflow can incorporate AlphaFold-predicted structures and Foldseek-based structural comparisons to investigate whether an unusual protein resembles known kinase structures.

The researchers tested KiNext using two very different organisms: the Pacific oyster, Crassostrea gigas, and the green alga Ostreococcus tauri. In C. gigas, KiNext recovered previously reported kinases while identifying additional candidates. Structural analysis provided further evidence for many of the newly detected proteins. In O. tauri, the workflow similarly recovered most previously reported kinases and identified additional candidates while refining some of their classifications.

What makes KiNext particularly interesting is not just its ability to find kinases, but how the entire analysis is organized. The workflow uses Nextflow, allowing the different computational steps to be connected into a reproducible pipeline. Containers can also help manage software dependencies, making it easier to run the workflow across different computing environments.

This reproducibility becomes increasingly important as the number of available genomes continues to grow. A researcher studying one organism may be able to perform an analysis manually, but repeating the same process across hundreds or thousands of genomes quickly becomes impractical. A standardized workflow provides a way to perform the analysis consistently while keeping track of how the results were generated.

At its core, KiNext demonstrates a broader change taking place in modern genomics. Sequencing a genome provides an enormous amount of information, but the real scientific challenge begins afterward: understanding what all those sequences mean. Protein kinases are only one part of this larger puzzle, yet they are particularly important because they act as molecular switches throughout the cell.

By combining sequence profiles, evolutionary analysis, and structural evidence within a reproducible computational framework, KiNext provides researchers with a systematic way to uncover these molecular switches. Its real value lies not only in finding more kinases, but in making the process scalable, repeatable, and easier to apply to new genomes.

As genome sequencing continues to expand across the tree of life, tools such as KiNext can help turn enormous collections of protein sequences into meaningful biological stories—one kinase at a time.

AGORA: Algorithm for Gene Order Reconstruction in Ancestors

Jit — Mon, 28 Feb 2022 23:26:21 -0600

AGORA stands for “Algorithm for Gene Order Reconstruction in Ancestors” and was developed by Matthieu Muffato in the DYOGEN Laboratory at the École normale supérieure in Paris in 2008.

    // | |     //   ) )  //   ) ) //   ) )  // | |
   //__| |    //        //   / / //___/ /  //__| |
  / ___  |   //  ____  //   / / / ___ (   / ___  |
 //    | |  //    / / //   / / //   | |  //    | |
//     | | ((____/ / ((___/ / //    | | //     | |

AGORA is used to generate ancestral genomes for the Genomicus online server for gene order comparison, and has been in constant use in the group since.

Address of the bookmark: https://github.com/DyogenIBENS/Agora

Ancestral Genomes: a resource for reconstructed ancestral genes and genomes across the tree of life

Abhimanyu Singh — Fri, 02 Nov 2018 08:16:27 -0500

Ancestral Genomes (http://ancestralgenomes.org) is a resource for comprehensive reconstructions of these ‘fossil genomes’. Comprehensive sets of protein-coding genes have been reconstructed for 78 genomes of now-extinct species that were the common ancestors of extant species from across the tree of life.

Address of the bookmark: http://ancestralgenomes.org/

The Home Microbiome Project

Tue, 02 Sep 2014 15:21:49 -0500

The Home Microbiome Project is an initiative aimed at uncovering the dynamic co-associations between people's bacteria and the bacteria found in their homes.The hope is that the data and project will show that routine monitoring of the microbial diversity of your body and of the environment in which you live is possible. Computer animation courtesy the Biology & Built Environment (BioBE) Center, University of Oregon and Cameron Slayden at Cosmocyte. http://vimeo.com/90059732 BioBE on Vimeo: http://vimeo.com/user22991553

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.

Zombies like bacteria!!!

Rahul Agarwal — Tue, 03 Sep 2013 08:44:15 -0500

Do you believe in Zombies stories … Hmm confused? Don’t worry there is a news for you. Scientists from the Integrated Ocean Drilling Program have announced the findings of the long-lived bacteria, reproducing only once every 10,000 years, which have been found in rocks 2.5km (1.5 miles) below the ocean floor that are as much as 100 million years old.

" the microbes exist in very low concentrations, of around 1,000 microbes in every tea spoon full of rock, compared with billions or trillions of bacteria that would typically be found in the same amount of soil at Earth's surface."

Reference:

http://www.bbc.co.uk/news/science-environment-23855436

CLARK: Fast, accurate and versatile sequence classification system

Jit — Sat, 15 Feb 2020 01:49:01 -0600

CLARK, a method based on a supervised sequence classification using discriminative k-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.

http://clark.cs.ucr.edu/Spaced/

Address of the bookmark: http://clark.cs.ucr.edu/Spaced/

Public Databases for Bioinformatics !

Jit — Tue, 23 Mar 2021 05:32:15 -0500

https://www.nature.com/articles/s41467-020-17155-y

Server Infrastructure:

File Server:

dhara: Synology 3614 Storage Appliance
4 Core Xeon
108TB disk storage
10Gb ethernet to SCG3
Access atx: dhara:5000
Has btsync server (try it - its much better than dropbox)

Compute Servers:

nandi: Kundaje and Phi Server
24 intel cores
256GB RAM
500GB of SSD storage 
36TB RAID6 local storage
4 Intel Phi's (space for 4 more GPU's)


durga: Montgomery and sensitive data
24 intel cores
256GB RAM
500GB of SSD RAID0 storage 
60TB RAID6 local storage

mitra: Bassik and Web/DB Server
24 core
256GB RAM 
500GB of SSD RAID0 storage 
36TB RAID6 local storage

vayu: Kundaje GPU server
4 core
64GB RAM 
200GB of SSD storage 
8TB RAID10 local storage
4 Nvidia GTX 970 4GB GPUs

amold: Bickel and SGE server
32 AMD core
128GB RAM 
200GB of SSD storage 
12TB RAID5 local storage

wotan: Bickel and SGE server
64 AMD core
256GB RAM 
200GB of SSD storage 
12TB RAID5 local storage

Filesystem:

/users/$USER
default home directory
full backups nightly 
nfs mount to dhara
should store code, papers, and other highly processed data here

/mnt/data/
globally accessible data
should store common data here
e.g. genomes and indexes, annotations, ENCODE data  
if you dont want this to count towards your quote you must chown

/mnt/lab_data/$LAB/
lab accessible data
should store lab project data here 
e.g. ATAC-seq prediction data, enhancer prediction, motif calls

/srv/scratch/$USER
fast local storage
not backed up, but on raid and data will never be deleted
most analysis should be performed here

/srv/persistent/$USER
fast local storage
synced nightly, but not backed up
       ie if the hard drives fail or you delete something and notice 
       within 24 hours we can recover. Otherwise not. (vs home which is 
       properly backed up )  
intermediate analysis products that would be hard to recover should be stored here 
       e.g. stochastic analysis results that need to be kept so that paper 
       results can be reproduced

/srv/www/$LABNAME/
web accessible from mitra.stanford.edu
*NOT BACKED UP*

Some parallel programming patterns:

# gzip a bunch of files
parallel gzip -- *.FILESTOGZIP

# fork example in python:
(for more detailed examples look at 
 https://github.com/nboley/grit/ grit/lib/multiprocessing_utils.py)

import os
import time
import random

import multiprocessing

class ProcessSafeOPStream( object ):
    def __init__( self, writeable_obj ):
        self.writeable_obj = writeable_obj
        self.lock = multiprocessing.Lock()
        self.name = self.writeable_obj.name
        return
    
    def write( self, data ):
        self.lock.acquire()
        self.writeable_obj.write( data )
        self.writeable_obj.flush()
        self.lock.release()
        return
    
    def close( self ):
        self.writeable_obj.close()

def worker(queue, ofp):
    # Try without this
    random.seed()
    while True:
        i = queue.get()
        if i == 'FINISHED': return
        # simulate an expensive function
        x = random.random()
        time.sleep(x/10)
        print i, x
        ofp.write("%i\t%s\n" % (i, x))

NSIMS = 10000
NPROC = 25

# populate queue
todo = multiprocessing.Queue()
for i in xrange(NSIMS): todo.put(i)
for i in xrange(NPROC): todo.put('FINISHED')

ofp = ProcessSafeOPStream( open("output.txt", "w") )

pids = []
for i in xrange(NPROC):
    pid = os.fork()
    if pid == 0:
       worker(todo, ofp)
       os._exit(0)
    else:
       pids.append(pid)  

for pid in pids:
    os.waitpid(pid, 0)

ofp.close()

print "FINISHED"

For use case 1 we obtained the following ENCODE and ROADMAP datasets https://www.encodeproject.org/files/ENCFF446WOD/@@download/ENCFF446WOD.bed.gz, https://www.encodeproject.org/files/ENCFF546PJU/@@download/ENCFF546PJU.bam, https://www.encodeproject.org/files/ENCFF059BEU/@@download/ENCFF059BEU.bam. Blacklisted regions were obtained from http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/hg38-human/hg38.blacklist.bed.gz. The human genome version hg38 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz.

For use case 2 we used the set of narrowPeak files summarized in https://github.com/wkopp/janggu_usecases/tree/master/extra/urls.txt (archived version v1.0.1). The human genome version hg19 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz

For use case 3 we used the ENCODE datasets https://www.encodeproject.org/files/ENCFF591XCX/@@download/ENCFF591XCX.bam, https://www.encodeproject.org/files/ENCFF736LHE/@@download/ENCFF736LHE.bigWig, https://www.encodeproject.org/files/ENCFF177HHM/@@download/ENCFF177HHM.bam as we as the GENCODE annotation v29 from ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.annotation.gtf.gz.

Address of the bookmark: http://mitra.stanford.edu/