BOL: Related items

Bioinformatics 101 - Running BLAST

Tue, 03 Sep 2013 14:59:50 -0500

How to format the database for BLAST, run the command, view the output file, and use BioPerl and Perl to parse the output. By David Francis, Ohio State University. Delivered live at the Tomato Disease Workshop 2010. For more information, please visit http://www.extension.org/pages/32521/bioinformatics-101-video.

Bioinformatics Training Material !

BioStar — Sat, 18 Mar 2023 11:26:18 -0500

Glittr is a curated list of bioinformatics training material.
All material is:

In a GitHub or GitLab repository
Free to use
Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

CSIR-INSTITUTE OF GENOMICS & INTEGRATIVE BIOLOGY

Thu, 29 Aug 2013 05:22:03 -0500

CSIR-INSTITUTE OF GENOMICS & INTEGRATIVE BIOLOGY, Mall Road, Delhi 110007

POSITIONS OPEN FOR TEMPORARY RESEARCH PROJECT POSTS

(Date of interview 23rd September 2013 at 10:30 AM)

CSIR-Institute of Genomics & Integrative Biology (IGIB), desires to engage qualified incumbents on purely temporary basis as detailed below:

Project Code/Title (Project Code BSC0123)

Genome dynamics in Cellular Organization, Differentiation and Enantiostasis (GENCODE)

Project Fellow

First Class M.Sc./M.Tech in bioinformatics/Human Genetics/Genomics

Rs. 16,000/- + 30 % HRA per month

Sr. Project Fellow

First Class M.Sc./M.Tech in bioinformatics/Human Genetics/Genomics

With two years of experience in NGS data analysis.

Rs. 18,000/- + 30 % HRA per month

Age relaxation as per Govt. of India instructions.

Engagement is for the project and on behalf of the funding agency and the tenure shall be as mentioned above. The duration of the post is initially for One year or till the closing date of the project, whichever is earlier. Tenure may be extendable up to project duration. Contract may be terminated at any time by giving one-month notice by either side. The applicants will have no claim implicit or explicit for consideration against any CSIR/IGIB post.

How to Apply:

It is mandatory for eligible applicants to apply by both the processes as given below:

1. Sending the resume in MS Word format directly to hrd@igib.res.in (Mentioning the Project Code-Post Code in the Subject Line of the email example:GAP0059-1)

2. They also need to fill up proforma by clicking on the following link HR Online Form.

3. Candidate cannot apply for more than two posts.

Last date of receiving application is 02-09-2013.

No application would be entertained with result awaited status or after due date.

The email will be sent to the short listed candidates.

No TA/DA will be paid to the candidates to attend the interview. The engagement shall be as per guidelines of CSIR/Funding agency. Candidates will have an option to give reply in Hindi.

Note: The shortlisted candidates, who will receive the email for interview, have to report at 09:00 AM on the day of interview along with any Photo ID card and original certificates for entry purpose. Entry will be closed by 10:00 AM.

More @ http://www.igib.res.in/sites/default/files/23092013.htm

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.

Python and BioPython Tutorial

Manshi Raghubanshi — Fri, 23 Aug 2013 06:47:40 -0500

A quickstart tutorial that allows to become familiar with the Python language. The exercises expect knowledge of basic concepts of programming. A group of 2nd year computer science students with no previous Python knowledge required 60'-90' to complete the exercises. With about 3 hours time, the exercise is suitable for non-programmers as well.

Address of the bookmark: http://www.biotnet.org/training-materials/python-programmers

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/

Research Project Posts for CSIR Project, Delhi

Tue, 27 Aug 2013 04:31:41 -0500

Positions Open For Temporary Research Project Posts for CSIR Project, Delhi
CSIR is looking for bright young candidates to get involved in building algorithms and platforms for large biological data analyses in the areas of comparative genomics, computational workflows, disease association studies, simulating virtual organelles, etc. Anyone who fulfills the eligibility criteria mentioned below may appear for a walk-in interview on 3rd September 2013 at CSIR Headquarters, Anusandhan Bhawan, 2 Rafi Marg, Delhi – 110001.
you can go to link for details or download PDF

http://www.csir.res.in/External/Heads/aboutcsir/announcements/ProjectPost_130813.pdf

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

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

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