Bioinformatics

The Hardest Easy Problem in Bioinformatics

Based on my experience of teaching bioinformatics to new programmers, the question - “extract the coding sequence of a multi-exon gene from the human (or other large eukaryotic) genome and translate it to find the protein sequence.” - can be classified as the hardest easy problem. Experienced bioinformaticians can answer the question without blinking, but those in this game for the first time find it extremely challenging.

R is the Most Powerful Language, but not for Bioinformatics

Tutorials - An Absolute Beginner's Guide to Bioinformatics

Python Sandbox and Other Helpful Resources for Biology/Bioinformatics

A student in our online class on bioinformatics mentioned that she would have to learn Python/R/linux within a month to be allowed to work at her research lab. This is the new reality in biology. Almost every researcher I know is collecting massive amounts NGS data, whereas the skills to make sense of data are in dire need.

(Remotely Taught Module) - Data Visualization in R

We are offering a new remotely taught module on data visualization in R. You will learn some of the most essential tools needed for exploratory data analysis. Especially, if you heard about the powerful ggplot library, but its logic appears complicated, this module is perfect for you.

A Bioinformatics Study Guide for the Biologists - (i)

Increasingly all biologists and biochemists are feeling the need to learn bioinformatics. The required skill-sets go way beyond being able to run BLAST searches at NCBI or find information on genes and genomes from the online databases. Believe it or not, doing those tasks used to be called “bioinformatics” in biology departments a few years back. That situation changed with next-generation sequencing. Now that sequencing is so cheap, every lab has tons of raw data sitting in their hard-drives and they need help in their analysis.

SibeliaZ - An Extremely Fast Aligner for Multiple Genomes

Readers may enjoy a new paper posted at biorxiv by Ilia Minkin and Paul Medvedev. It shows a method for aligning against multiple closely-related genomes that is order(s) of magnitude faster than the competing approaches. In bioinformatics, such dramatic improvement in speed is not seen often.

Go is Now the Best Programming Languages for Full-fledged Bioinformatics - Really?

Bioinformaticians writing in twitter appear considerably bemused by a new paper that appeared in biorxiv late Friday. Here is the abstract.

Unpacking S4 Objects in R

Modern statistics was invented by a doctor, whose income from curing people was just not enough. To make more money on the side from gambling, he came up with the earliest versions of the rules of probability.

Annual Bioinformatics Contest from the Rosalind Team

A Terrific Post-doc Opportunity to Learn Bioinformatics

Here is a great opportunity to learn cutting-edge algorithms in bioinformatics. Heng Li, who developed several popular NGS bioinformatics programs like Samtools, BWA and Minimap, is moving to Dana Farber Cancer Institute. He is hiring new post-docs to work with him.

Mantis and the Counting Quotient Filter

Bioinformatics Contest - 2018

It is that time of the year again. Our friends from Rosalind, Stepik and Bioinformatics Institute are hosting another bioinformatics contest with qualifying round starting on Feb. 3rd. Details below.

DIY Ancestry Analysis using the GPS Algorithm

For those interested in trying out the cutting-edge tools in ancestry research on real data, I am open-sourcing my own genotype information in this github project along with all analysis steps. You need to install two programs - plink and admixture. Then by following the steps given in the README file, you should be able to find the geographic origin of the given sample, (which is me).

Minimizer - An Introductory Tutorial

This is a condensed version of our longer tutorial on minimizer algorithms available here. Many bioinformatics algorithms use short substrings of a longer sequence, commonly known as k-mers, for indexing, search or assembly. Minimizers allow efficient binning of those k-mers so that some information about the sequence contiguity is preserved.

Compact Universal Set of Minimizers

There has been a number of interesting recent developments on minimizers likely to make bioinformatics algorithms even more efficient. In this post, we like to mention three papers by Y. Orenstein, G. Marçais and collaborators.

Another Tutorial - This Time on Pevzner's Videos

Grab them here on the left sidebar in bioinformatics courses section at the link ‘Pevzner Course’. I am still in the process of annotating the sets, including cross-linking similar sections.

A Tutorial with Ben Langmead's Bioinformatics Videos

Tuesday Review - SAVE your day for CRISPR, Nature Fake News and Other Stories

1. SAVE your day for CRISPR

Two biorxiv papers cover the important topic of making CRISPR analysis user-friendly. In this context, we also included references to several other available CRISPR analysis tools for the benefit of our readers.

Monday review - Myers' dBG Paper, Pacbio's Multiplexing and Bioinformaticians' Foray into Escapism

1. Correcting Long Noisy Reads Using de Bruijn Graphs

Great news - the algorithmic concepts for short read assembly developed over the last decade need not be unlearned. In the two papers presented below, Myers, Pevzner and their colleagues use de Bruijn graphs for assembly and error correction of long noisy reads.

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