While teaching R to biologists, a common complaint I hear is that “there are too many functions”. Therefore, I decided to take a minimalist approach and not teach students new functions unless those are absolutely necessary. Using existing functions for new tasks has two benefits - (i) it keeps the brain clutter-free from too many function names, (ii) it gives students more practice on the existing functions thus reinforcing their knowledge.
These days, many biologists are performing RNAseq and other NGS experiments. The immediate challenges after collecting the data are (i) where to store them, (ii) where to analyze them and (iii) how to give access to all lab members in an efficient and secure manner.
Dear readers, over the years many of you requested more organized content and complete tutorials on bioinformatics. Three years back, we started posting them in our membership section. All content in the membership section had been free with registration.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.