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Huge numbers of short reads are being generated for mapping back to thegenome to discover the frequency of transcripts, miRNAs, DNAase hypersensitivesites, FAIRE regions, nucleosome occupancy, etc. Since these reads aretypically short (e.g., 36 base pairs) and since many eukaryotic genomes,including humans, have highly repetitive sequences then many of these reads mapto two or more locations in the genome. Current mapping of these reads, gradingthem according to 0, 1 or 2 mismatches wastes a great deal of information.These short sequences are typically mapped with no account of the accuracy ofthe sequence, even in company software when per base error rates are beingreported by another part of the machine. Further, multiply mapping locationsare frequently discarded altogether or allocated with no regard to where otherreads are accumulating. Here we show how to combine probabilistic mapping ofreads with an EM algorithm to iteratively improve the empirical likelihood ofthe allocation of short reads. Mapping using LAST takes into account the perbase accuracy of the read, plus insertions and deletions, plus anticipatedoccasional errors or SNPs with respect to the parent genome. The probabilisticEM algorithm iteratively allocates reads based on the proportion of readsmapping within windows on the previous cycle, along with any prior informationon where the read best maps. The methods are illustrated with FAIRE ENCODE datalooking at the very important head-to-head gene combination of TP53 and WRAP53.

Expectation-Maximization (EM) Algorithms for Mapping Short Reads Illustrated with FAIRE data and the TP53-WRAP53 Gene Region