Friday, 24 December 2010

Exome sequencing reveals mutations in previously unconsidered candidate genes

Excerpted from Bio-IT World 

December 20, 2010 | Doctors at the Medical College in Wisconsin have published in the journal Genetics and Medicine the results of exome sequencing in a seriously ill boy with undiagnosed bowel disease. The study (using 454 sequencing) revealed mutations in a gene called XIAP, which interestingly was not previously considered among more than 2,000 candidate genes before the DNA sequencing was performed. full article

Monday, 20 December 2010

As R&D Budgets Shrink and Data Grows, Bioinformatics Service Providers Could Gain in Popularity

After this article in Nature Singapore's Salad days are over. There's another article in GenomeWeb that talks about shrinking R&D budgets and bioinformatics outsourcing. Are labs worldwide facing a cut in budgets? Or is it just year end panic? Hmmmm

Tuesday, 14 December 2010

1000 Genomes Project Tutorial Videos

The 1000 Genomes Project has released the data sets for the pilot projects and for more than 1000 samples for the full-scale project. A tutorial for how to use the data was held at the 2010 American Society of Human Genetics (ASHG) annual convention on November 3.
Videos for each of the tutorial sessions are now available. The tutorial describes 1000 Genomes Project data, how to access it and how to use it. Each of the speakers and their topics are listed below along with their tutorial videos and PowerPoint slides.

Gil McVean, Ph.D.
Professor of Statistical Genetics
University of Oxford

Description of the 1000 Genomes Data
Gabor Marth, D.Sc.
Associate Professor of Biology
Boston College

How to Access the Data
Steve Sherry, Ph.D.
National Center for Biotechnology Information
National Library of Medicine
National Institutes of Health. Bethesda, Md.

How to Use the Browser
Paul Flicek, Ph.D.
European Molecular Biology Laboratory
Vertebrate Genomics Team
European Bioinformatics Institute (EBI)

Stuctural Variants
Jan Korbel, Ph.D.
Group Leader, Genome Biology Research Unit
Joint Appointment with EMBL-EBI
European Molecular Biology Laboratory (Heidelberg, Germany)

How to Use the Data in Disease Studies
Jeffrey Barrett, Ph.D.
Team Leader, Statistical and Computational Genetics
Wellcome Trust Sanger Institute
Hinxton, United Kingdom

Friday, 3 December 2010

When Playing games is working (if you are biologist that is)

Check out this flash game Phylo
If you are thinking it's related to phylogenetics then Bingo.. Kudos for excellent idea and excellent graphics and interface but wished they had a better name and less verbose introduction for laymen.
waiting eagerly for the iphone/ipod version to be out..


What's Phylo all about?
Though it may appear to be just a game, Phylo is actually a framework for harnessing the computing power of mankind to solve a common problem; Multiple Sequence Alignments.

What is a Multiple Sequence Alignment? A sequence alignment is a way of arranging the sequences of D.N.A, R.N.A or protein to identify regions of similarity. These similarities may be consequences of functional, structural, or evolutionary relationships between the sequences.
From such an alignment, biologists may infer shared evolutionary origins, identify functionally important sites, and illustrate mutation events. More importantly, biologists can trace the source of certain genetic diseases.

The Problem Traditionally, multiple sequence alignment algorithms use computationally complex heuristics to align the sequences.
Unfortunately, the use of heuristics do not guarantee global optimization as it would be prohibitively computationally expensive to achieve an optimal alignment. This is due in part to the sheer size of the genome, which consists of roughly three billion base pairs, and the increasing computational complexity resulting from each additional sequence in an alignment.

Our Approach Humans have evolved to recognize patterns and solve visual problems efficiently.
By abstracting multiple sequence alignment to manipulating patterns consisting of coloured shapes, we have adapted the problem to benefit from human capabilities.
By taking data which has already been aligned by a heuristic algorithm, we allow the user to optimize where the algorithm may have failed.

The Data All alignments were generously made available through UCSC Genome Browser.
Infact, all alignments contain sections of human DNA which have been speculated to be linked to various genetic disorders, such as breast cancer.
Every alignment is received, analyzed, and stored in a database, where it will eventually be re-introduced back into the global alignment as an optimization.          

Datanami, Woe be me