Efficient Local Alignment Discovery amongst Noisy Long Reads
Long read sequencers portend the possibility of producing reference quality genomes not only because the reads are long, but also because sequencing errors and read sampling are almost perfectly random. However, the error rates are as high as 15%, necessitating an efficient algorithm for finding local alignments between reads at a 30% difference rate, a level that current algorithm designs cannot handle or handle inefficiently. In this paper we present a very efficient yet highly sensitive, threaded filter, based on a novel sort and merge paradigm, that proposes seed points between pairs of reads that are likely to have a significant local alignment passing through them. We also present a linear expected-time heuristic based on the classic O(nd) difference algorithm  that finds a local alignment passing through a seed point that is exceedingly sensitive, failing but once every billion base pairs. These two results have been combined into a software program we call DALIGN that realizes the fastest program to date for finding overlaps and local alignments in very noisy long read DNA sequencing data sets and is thus a prelude to de novo long read assembly.
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- 2.Eid, R., Fehr, A., … (51 authors) … Korlach, J, Turner, S.W.: Real-Time DNA Sequencing from Single Polymerase Molecules. Science 323(5910), 133–138Google Scholar
- 5.Chin, C.S., Alexander, D.H., Marks, P., Klammer, A.A., Drake, J., Heiner, C., Clum, A., Copeland, A., Huddleston, J., Eichler, E.E., Turner, S.W., Korlach, J.: Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nature Methods 10, 563–569 (2013)CrossRefGoogle Scholar
- 9.Burrows, M., Wheeler, D.J.: A block sorting lossless data compression algorithm. Technical Report 124, Digital Equipment Corporation (1994)Google Scholar
- 12.Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms (3rd, 3rd edn., pp. 197–204. MIT Press (2009)Google Scholar