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.
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.
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.
Yesterday we looked into the newly released ‘kmc tools’. Today we will work out another
simple problem so that you feel familiar with it. We really love this powerful program,
because, as the authors have shown, they could reproduce the results of many previously
published bioinformatics papers with only a few commands.
Happy New Year ! Here is a great way to bring some fun and challenges to your new year. We got
a note from Nikolay Vyahhi, who helped build Rosalind and Stepik, that their organization is hosting a bioinformatics competition. The details are posted below -
A number of recent papers are proposing to use multidimensional Bloom filters to identify genes from a
large collection of RNAseq libraries. This post provides general perspective on these papers. In a later
post, we will go in depth and explain the algorithm of the recent preprint by carrying out an
Job Title: Postdoctoral Scholar Position in Comparative Plant Genomics and Bioinformatics
The Computational Plant Genomics Lab invites applications for a Postdoctoral position in the Department of Ecology and Evolutionary Biology at the University of Connecticut. We focus on developing computational approaches that integrate next generation sequence data to address questions in non-model plants, particularly forest trees. The lab has the following ongoing projects: 1) Understanding the evolution of alternative translation initiation using RNA-seq data 2) Integrating new and existing approaches to gene prediction to improve the annotation of complex genomes 3) Analysis of gene family evolution and related comparative genomics questions 4) Detecting variation in populations from GBS and related sequence data.
Abstract: Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. The Zipper plot is an application that enables users to interrogate putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These features are obtained from publicly available datasets including CAGE-sequencing (CAGE-seq), ChIP-sequencing (ChIP-seq) for histone marks and DNase-sequencing (DNase-seq). The Zipper plot application requires three input fields (chromosome, genomic coordinate (hg19) of the TSS and strand) and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot.
This is a fascinating talk that our readers from both computational and life sciences sides
will enjoy. The author realized shortcomings of common programming languages in solving
his domain-specific task and developed Clasp starting from common Lisp.