BOL: Related items

Bioinformatics -- Understanding of living systems through information science

Wed, 14 Aug 2013 11:50:17 -0500

Recently, the progress of the Human Genome Project, aiming to decode all human DNA sequences, has highlighted a research field called bioinformatics. In this new field, computers and techniques from information science are not just used as tools to advance life science research; they're expected to have a major impact on how we think about the life sciences. Q. The main feature of bioinformatics is, it utilizes computers to analyze life. One is example is the genome. In all organisms, DNA contains genetic information, and this is called the genome. But the amount of information involved is huge, so recently, it's been read using next-generation sequencers, and analyzed by computers. In bioinformatics research, what we do is utilize those genome information to investigate the principles of life. As an organism evolves, its genome sequence changes through sudden mutations. Additionally, at the genome level, mutations called rearrangements, such as inversions, transpositions, and duplications, occur. The genome comparison system developed by the Sakakibara Lab calculates homologous sequences called anchors, which are conserved between species. If the genome is considered as a long text, then anchors can be thought of as words. Q. We're coming to understand the genomes of various organisms - not just humans, but monkeys, chimpanzees, bacteria, and so on. The first method used to analyze a genome is comparing it with the genomes of other organisms, to see where it's the same and where it's different. In that way, the content of the genome is decoded bit by bit, using computers. By contrast, in our method, we've developed software called Murasaki, which we also use to analyze large genomes, by comparing them with those of other organisms. The Sakakibara Lab uses a next-generation sequencer at Keio University, along with a cluster machine with hundreds of CPUs. In this way, the Lab is analyzing genome mutations that cause cancer, and the genome of the natto production strain Bacillus subtilis. Until now, genome analysis could only be done in national-scale projects. But now, next-generation sequencer development has made genome analysis possible in an ordinary lab. In a world-first achievement, the Sakakibara Lab has decoded the natto bacillus genome, through analysis using Keio's next-generation sequencer. Q. In the future, biology and the life sciences may become almost entirely information science and computer science. And in healthcare, that may enable us, for example, to predict whether individuals are susceptible to cancer, or to certain lifestyle-related diseases, by understanding their personal genome data. So, I think it's amply possible that we can make use of such information effectively, to help people live longer and be free from disease, by thinking about their lifestyle habits. Bioinformatics is only two decades old. In this field, many areas are still unknown. Professor Sakakibara, having been involved since the beginning, will continue tackling new, challenging research projects.

Interactive Bioinformatics Resources !

Jit — Thu, 12 Aug 2021 00:09:00 -0500

Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

Bioinformatics Infrastructure Facility

Sun, 15 Sep 2013 09:22:25 -0500

The Bioinformatics Infrastructure Facility has started working in the year 2007 at Presidency College, Kolkata. It is one of the premier institutes of India and boasts of a rich heritage and great alumni. The Infrastructure Facility has a dedicated team headed by Sayak Ganguli and ably supported by Priayanka Dhar. The coordinator of the facility is Abhijit Datta of the Post Graduate Department of Botany. The lab mainly focusses on the analysis of the RNA Induced Silencing Complex. Recent highlights include the presentation of a paper at the RNAi World Congress.

More @ http://bioinfo-presiuniv.edu.in/index.php

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

PLOS Computational Biology: Translational Bioinformatics educational resources

Jitendra Narayan — Fri, 16 Aug 2013 12:24:56 -0500

PLOS present collection of Education articles: “Translational Bioinformatics”. This collection is presented as an online “book” which could serve as a reference tool for a graduate level introductory course, marking a step in an exciting new direction for the Education section of the journal.

Blog : http://blogs.plos.org/biologue/2012/12/28/translational-bioinformatics-plos-computational-biology-presents-an-educational-resource-for-an-emerging-field/

Educational Material : http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11

Address of the bookmark: http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11

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.

Translational Bioinformatics: Transforming 300 Billion Points of Data

Tue, 20 Aug 2013 19:03:47 -0500

Translational Bioinformatics: Transforming 300 Billion Points of Data into Diagnostics, Therapeutics, and New Insights into Disease Air date: Wednesday, June 20, 2012, 3:00:00 PM Time displayed is Eastern Time, Washington DC Local Description: There is an urgent need to translate genome-era discoveries into clinical utility, but the difficulties in making bench-to-bedside translations haven't been well described. The nascent field of translational bioinformatics may help. Dr. Butte's lab at Stanford University builds and applies tools that convert more than 300 billion points of molecular, clinical, and epidemiological data (measured by researchers and clinicians over the past decade) into diagnostics, therapeutics, and new insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his lab's work on using publicly available molecular measurements to find new uses for drugs, discovering new treatable mechanisms of disease in type 2 diabetes, and evaluating patients presenting with whole genomes sequenced. The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. For more information, visit: The NIH Director's Wednesday Afternoon Lecture Series Author: Atul Butte, M.D., Ph.D., Stanford University Runtime: 01:07:42 Permanent link: http://videocast.nih.gov/launch.asp?17321

MGRA: Breakpoint graphs and ancestral genome reconstructions

Jit — Tue, 25 Jul 2017 08:48:25 -0500

MGRA (Multiple Genome Rearrangements and Ancestors) is a tool for reconstruction of ancestor genomes and evolutionary history of extant genomes.

It takes as an input a set of genomes represented as sequences of genes (or synteny blocks) and produces such sequences for ancestral genomes at the internal nodes of the phylogenetic tree.

The phylogenetic tree may be also specified completely or partially, in the latter case MGRA can reconstruct conserved ancestral regions (CARs) of the ancestral genome of interest.

Since version 2 MGRA supports gene insertion and deletions in addition to genome rearrangements and allows the input genomes to have different gene content.

It also can reconstruct most plausible phylogenetic tree based on the rearrangement characters.

Address of the bookmark: http://mgra.cblab.org/

Bioinformatician Dreams

Jitendra Narayan — Wed, 21 Aug 2013 10:50:45 -0500

Bioinformatician life is interconnected, they always dream for a powerful server, little more space on server as they are generating lots of data per run, dream to publish results in good impact journals, meetings reminders :) and research analysis off course!!!

Ribbon: Visualizing complex genome alignments and structural variation:

Jit — Wed, 29 Nov 2017 07:40:22 -0600

Ribbon can be used for long reads, short reads, paired-end reads, and assembly/genome alignments. Instructions for each data format are available by clicking on "instructions" in each tab on the right.

Local installation:

You can install Ribbon locally from Github by following the instructions here: https://github.com/MariaNattestad/Ribbon

Address of the bookmark: http://genomeribbon.com/