At the other extreme from BGI’s 4000 member team, some bioinformaticians are suffering alone in various research labs. Mick Watson sends a helpful hand -
1. Make friends with local bioinformatics groups
2. Talk to your computing group
3. Obtain clear expectations
4. Rewrite your job description
6. Attend bioinformatics meetings
7. Try first, ask later
How about ‘threaten to quit and join Facebook’ every once in a while? :)
Popular sequence alignment tools such as BWA convert a reference genome to an indexing data structure based on the Burrows-Wheeler Transform (BWT), from which matches to individual query sequences can be rapidly determined. However the utility of also indexing the query sequences themselves remains relatively unexplored. Here we show that an all-against-all comparison of two sequence collections can be computed from the BWT of each collection with the BWTs held entirely in external memory, i.e. on disk and not in RAM. As an application of this technique, we show that BWTs of transcriptomic and genomic reads can be compared to obtain reference-free predictions of splice junctions that have high overlap with results from more standard reference-based methods.
Code to construct and compare the BWT of large genomic data sets is available at this http URL as part of the BEETL library.
Sequence squeeze: an open contest for sequence compression h/t: @rayanchikhi
Next-generation sequencing machines produce large quantities of data which are becoming increasingly difficult to move between collaborating organisations or even store within a single organisation. Compressing the data to assist with this is vital, but existing techniques do not perform as well as might be expected. The need for a new compression technique was identified by the Pistoia Alliance who commissioned an open innovation contest to find one. The dynamic and interactive nature of the contest led to some novel algorithms and a high level of competition between participants.
Sequence alignment is one of the oldest and the most famous problems in bioinformatics. Even after 45 years, for one reason or another, this problem is still actual; current solutions are trade-offs between execution time, memory consumption and accuracy. We purpose SW#, a new CUDA GPU enabled and memory efficient implementation of dynamic programming algorithms for local alignment. In this implementation indels are treated using the affine gap model. Although there are other GPU implementations of the Smith-Waterman algorithm, SW# is the only publicly available implementation that can produce sequence alignments on genome-wide scale. For long sequences, our implementation is at least a few hundred times faster than a CPU version of the same algorithm.