New Bioinformatics Business Model - Make Software Free, Charge for Manual
@Assemblathon forwarded a new paper BLESS: Bloom-filter-based Error Correction Solution for High-throughput Sequencing Reads
Motivation: Rapid advances in next-generation sequencing (NGS) technology have led to exponential increase in the amount of genomic information. However, NGS sequencing reads contain far more errors than data from traditional sequencing methods, and downstream genomic analysis results can be improved by correcting the errors. Unfortunately, all the previous error correction methods required a large amount of memory, making it unsuitable to process reads from large genomes with commodity computers.
Results: We present a novel algorithm that produces accurate correction results with much less memory compared to previous solutions. The algorithm, named BLESS, uses a single minimum sized Bloom filter and is also able to tolerate a higher false positive rate, thus allowing us to correct errors with a 40X memory usage reduction on average compared to previous methods. Meanwhile, BLESS can extend reads like DNA assemblers to correct errors at the end of reads. Evaluations using real and simulated reads showed that BLESS could generate more accurate results than existing solutions. After errors were corrected using BLESS, 69% of initially unaligned reads could be aligned correctly. Additionally, de novo assembly results became 50% longer with 66% fewer assembly errors.
Availability: Freely available at http://sourceforge.net/p/bless-ec
That seemed interesting and we were curious about how different their approach is from Rayan Chikhi or Titus Brown’s work. Sadly, when we wanted to see the actual paper, we were greeted with this wonderful page.
