BOL: Related items

Bioinformatics: The Bridge Between Curiosity and Discovery

BioStar — Mon, 24 Nov 2025 05:16:49 -0600

In the sprawling universe of modern science, bioinformatics stands as one of the most transformative and empowering fields of our time. It is where biology meets computation, where data becomes meaning, and where curiosity becomes discovery. If you’ve stepped into this world—or are considering it—here’s your reminder: you’re part of a revolution.

Why Bioinformatics Matters More Than Ever

Every day, our world generates massive amounts of biological data—from genome sequences to microbiome profiles to real-time pathogen surveillance. Hidden within these datasets are the answers to some of the greatest challenges humanity faces: emerging diseases, antimicrobial resistance, environmental stress, genetic disorders, sustainable agriculture, and more.

Bioinformatics isn’t just a skill.
It’s the language of the future of biology.

By mastering it, you give yourself the power to:

Decode genomes and understand life at its most fundamental level

Identify patterns no microscope could ever reveal

Predict disease outbreaks before they occur

Accelerate drug discovery with computational precision

Contribute to open-source tools that empower scientists worldwide

You don’t just follow science—you drive it.

Every Expert Was Once a Beginner

Many newcomers feel intimidated. Command-line interfaces. R scripts. Python packages. Next-generation sequencing data. Complex machine learning models.

But here’s the truth: every bioinformatician started exactly where you are now—curious, unsure, but excited.

No one writes perfect code on day one.

No one understands genomics pipelines immediately.

What makes you a bioinformatician is not perfection, but perseverance.

When your script throws a cryptic error…
When your data refuses to format…
When your pipeline runs for 6 hours only to crash…

Remember: this is part of the journey.
Every error teaches you. Every retry strengthens you. Every breakthrough energizes you.

Bioinformatics Is Not Just a Career—It’s a Mindset

It’s the mindset of:

Problem-solving.

Continuous learning.

Turning chaos into clarity.

Seeing what others can’t.

Bioinformaticians are detectives of biological complexity. You sit at the intersection of innovation, using tools that can shape public health, medicine, agriculture, and ecology. Few fields give you such direct impact on the world.

Your Contribution Matters

As you work on your script, pipeline, genome, or model, remember:

Somewhere, your analysis might contribute to:

A new therapy

A faster diagnostic test

A better understanding of a pathogen

A more resilient crop

An open-source dataset that helps thousands

A discovery that rewrites textbooks

Your code may be small, but its ripple effect is powerful.

The Future Is Bioinformatics—And You Are Part of It

The world is shifting. Wet labs are integrating AI. Hospitals rely on genomic insights. Farmers use gene-level predictions. Governments monitor disease in real time. Students launch pipelines that become global tools.

This is a golden era—and you are not late.
You are exactly where you need to be.

Keep Pushing. Keep Learning. Keep Discovering.

Bioinformatics is a journey filled with challenges, but also with unmatched rewards.

So the next time you feel stuck, frustrated, or overwhelmed, remember:
You’re building the science of tomorrow.

Be proud. Stay curious. Keep going.
Your work matters more than you think.

Bioinformatics Algorithm Demonstrations and Tutorials

Jitendra Narayan — Thu, 29 Aug 2013 09:23:51 -0500

Abstract

This project presents demonstrations of selected computer science algorithms important in bioinformatics, implemented in the spreadsheet program Microsoft Excel. Spreadsheets provide an interesting platform for demonstration of algorithms, since various steps of the calculations can be exposed in a manner that is easily comprehensible to users with little programming experience. The algorithms demonstrated include two approaches to approximate string matching (dynamic programming and Shift-AND numeric approximate matching), Hierarchical Clustering (used in phylogenetic studies and microarray analysis of gene expression), a Naive Bayes Classifier for simulated microarray gene expression data, and a simple Neural Network. These demonstrations are designed to serve as instructional aids in bioinformatics courses.

Tutorial @ http://www.cybertory.org/downloads/bae/BioinformaticsAlgorithmsInExcel.zip

One of the best resource for online bioinformatics learning is https://stepic.org/Bioinformatics-Algorithms-2 Enjoy the online learning.

Reference : cybertory

" Please add your favourite bioinformatics algorithms and tutorial links below in the comment section, for the benefit of bioinformatics and computational biology community ".

Address of the bookmark: http://www.cybertory.org/downloads/bae/BioinformaticsAlgorithmsExcelDoc.pdf

ViralQC: Checking If a Viral Genome Tells the Whole Story

LEGE — Tue, 08 Sep 2026 10:35:04 -0500

Imagine finding a mysterious piece of a puzzle and being told it belongs to a virus. Before studying it, you would want to know two things: Is the piece really viral, and how much of the puzzle is missing?

That is the problem ViralQC (https://github.com/ChengPENG-wolf/ViralQC) aims to solve.

Viral sequences recovered from metagenomic data can be incomplete or contaminated with microbial DNA. ViralQC uses information from both DNA sequences and predicted proteins to detect contamination and estimate how complete a viral contig is.

The authors compared ViralQC with CheckV and found that ViralQC performed particularly well for longer viral contigs, improving contamination detection and completeness estimation in several test cases.

Why does this matter? Because discovering a viral sequence is only the first step. If the sequence is contaminated or incomplete, downstream analyses—such as identifying viral functions or studying evolution—can be misleading.

ViralQC provides a useful quality check before researchers trust the viral genome they have discovered.

In a world where metagenomics is uncovering enormous numbers of unknown viruses, tools like ViralQC help us separate the real viral story from an incomplete or mixed-up one.

More at https://academic.oup.com/bioinformatics/article/42/Supplement_2/btag463/8767288

4273π: Bioinformatics education on low cost ARM hardware

Jitendra Narayan — Mon, 02 Sep 2013 07:02:43 -0500

Are you teaching bioinformatics at universities and found it complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. Hmm don't worry there is one new OS for the rescue. 4273π, an operating system image for Raspberry Pi based on Raspbian Linux. It provides an attractive, general-purpose computing environment, within which the course 4273π Bioinformatics for Biologists is embedded.

Though far slower than current desktop and laptop computers, the Raspberry Pi is notably faster than the Cray 1 supercomputer, a marvel of computer speed in its day. The Raspberry Pi approach includes all the benefits of the laptop approach, above, but at lower cost. In addition, the Raspberry Pi is a new and exciting computer system, which in itself can add interest to the course.

As the Raspbian operating system, Raspberry Pi firmware and hardware and 4273π Bioinformatics for Biologists teaching material develop, further releases of 4273π will be made available. It is anticipated that there will be a minimum of two releases per year during the next four years.

4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

Descriptive paper @ http://www.biomedcentral.com/1471-2105/14/243

Image source: BMC Bioinformatics

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/

Huber Lab

Mon, 09 Sep 2013 21:57:03 -0500

The Huber group develops computational and statistical methods to design and analyse novel experimental approaches in genetics and cell biology.

Future projects and goals

Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.
DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.
Cancer genomics, genomes as biomarkers, cancer phylogeny.
Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).

More @ http://www.embl.de/research/units/genome_biology/huber/index.html

Asst. PROF IN BIOINFORMATICS at JAIPUR NATIONAL UNIVERSITY

Thu, 12 Sep 2013 07:18:02 -0500

JAIPUR NATIONAL UNIVERSITY, SCHOOL OF LIFE SCIENCES (SIILAS CAMPUS) URGENTLY REQUIRES

Asst. PROF IN BIOINFORMATICS.

QUALIFICATION: AS PER UGC

DESIRABLE: 1 YEAR EXPERIENCE IN ACADEMICS

CONTACT immediately

Prof D.S.Bhatia
Director
9351288070

Last date within 7 days of the publication.

Find more @ http://jnujaipur.ac.in/downloads/AdvtDec2012.jpg

jobTree based python wrapper to run the genome simulation tool suite Evolver

Jit — Fri, 08 Dec 2017 16:26:32 -0600

evolverSimControl (eSC) can be used to simulate multi-chromosome genome evolution on an arbitrary phylogeny (Newick format). In addition to simply running evolver, eSC also automatically creates statistical summaries of the simulation as it runs including text and image files. Also included are convenience scripts to: check on a running simulation and see detailed status and logging information; extract fasta sequence files from the leaf nodes of a completed simulation; extract pairwise multiple alignment files (.maf) from leaf and branch nodes from a completed simulation and with the help of mafJoin, join them together into a single maf covering the entire simulation.

Address of the bookmark: https://github.com/dentearl/evolverSimControl

Research Associate@ School of Computational and Integrative Sciences under Jawaharlal Nehru University

Fri, 22 Nov 2013 19:06:44 -0600

School of Computational and Integrative Sciences under Jawaharlal Nehru University, New Delhi invited applications for filling up 4 posts of Research Associates (RA) and Junior Research Fellow (JRF) (2 posts each) purely on temporary basis, liable to be terminated at any time without prior notice or ceased/withdrawn by the funding agency. The vacancies are for a Department of Biotechnology, Government of India funded project entitled "Computational Core for Plant Metabolomics" (Project ID: 632) being administered by Prof Indira Ghosh. Interested candidates should send their applications till 13 December 2013.
Important Dates
Last Date for receipt of applications: 13 December 2013
Vacancy Details
Total Vacancies: 4 posts
Type of recruitment: Temporary
Sl. No.: 01
Name of the Post: Research Associate
No of Posts: 1 post
Remuneration: Rs. 23000 + 30%
Qualifications: PhD in Bioinformatics / computational biology / Biophysics / Physical Chemistry / Computer Science. Computational experience, proven by paper published, is a necessary qualification.
Sl. No.: 02
Name of the Post: Research Associate
No of Posts: 1 post
Remuneration: Rs. 23000 + 30%
Qualifications: PhD in Computational Biology / Bioinformatics & related subjects. Computational experience, proven by paper published, is a necessary qualification.
Sl. No.: 03
Name of the Post: Junior Research Fellow
No of Posts: 1 post
Remuneration: Rs. 12000 + 30%
Qualifications: M. Sc. / B. Tech. preferably in Computational Biology /Bioinformatics and related fields with experience in Website designing & maintenance of Database.
Sl. No.: 04
Name of the Post: Junior Research Fellow
No of Posts: 1 post
Remuneration: Rs. 12000 + 30%
Qualifications: M. Sc. / MCA / B. Tech. preferably in Computational Biology / Computer science with experience in Programming in Java / Python, C++ etc & designing of Database.
Selection Procedure: Selection will be done on the basis of candidates’ performance in the Interview.
Candidates short-listed / selected for Interview will be informed through email only.
How to Apply: Interested eligible candidates should send their applications, in the prescribed format, along with their current CV by post to “Prof Indira Ghosh, Project Investigator, Hall#6, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067” so as to reach the concerned authority by 13 December 2013.
Name of the post applied for’ must be superscripted on the envelope containing the application.
NOTE: For the post of Research Associates, only those candidates who have submitted thesis are eligible to apply. However, salary will be provided as per DBT / DST guidelines (i.e. candidates who have qualified NET /BET / BINC will have higher pay scale).
Candidates interested to register for PhD may not apply for JRF.
More @ http://www.jnu.ac.in/Career/currentjobs.htm

Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.

Jit — Tue, 26 Dec 2017 22:23:39 -0600

Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/