LOCAS - A Low Coverage Assembler for Resequencing Figure 7. Performance comparison of homology-guided assembly on real world data utilizing left-over reads. Illumina reads of the first chromosome of A. thaliana strain Ler-1 were aligned against the reference sequence (Col-0) and partitioned into blocks with SHORE of 25 kb. Local assemblies of reads are performed with SUPERLOCAS and VELVET in order to incorporate left-over reads. While SUPERLOCAS provides algorithms specifically adjusted to this task, VELVET had to assemble each block with the complete set of left-over reads. A barplot is shown for the avgN50 (average N50) size of both assemblers. doi:10.1371/journal.pone.0023455.g007 are constructed with the use of left-over reads often belong to insertion regions not represented in the reference genome. N50 sizes were higher for SUPERLOCAS with values consistently about 1500 bp (Figure 7). N50 sizes of VELVET ranged between 901 bp and 1,435 bp, showing much higher sensitivity to parameter choice. Furthermore, SUPERLOCAS performed best regarding CPU runtime. For SUPERLOCAS, one run was on average completed in 2 h 8 min, compared to 7 h 32 min for VELVET. VELVET performed best considering RAM usage. SUPERLOCAS used 3.99 GB of RAM while VELVET used only 1.73 GB. (See Table S3 and S4 for more details.). Discussion The assembly tool LOCAS is specifically designed for homology- guided assembly approaches in resequencing studies of eukaryotic genomes with short reads and low sequencing depth. In three performance studies adjusted to these conditions, LOCAS achieved usually better or at least similar results compared to existing short read assemblers. SUPERLOCAS, an extension of LOCAS, proved to be much faster than existing methods for the task of integrating left-over reads in consecutive executions of local reassemblies. The perfor- mance of SUPERLOCAS at low sequencing depth as measured by avgN50 and avgERR compares favorably to all other tools. LOCAS uses the overlap-layout-consensus approach because we found it to be better suited for low sequencing depth assembly in comparison to the de Bruijn paradigm used in VELVET, EULER-SR and ABYSS. We optimized overlap alignments between reads to span even very low covered regions. However, the number of overlaps increases by calculating exact alignments instead of submatches and often leads to a graph of higher complexity compared to a Bruijn graph. Moreover the graph cannot be reduced by using a coverage-based cutoff as typically applied in de Bruijn graph approaches, since the insufficient number of reads in low coverage assembly prevents the estimation of a reasonable value for a coverage-based cutoff. Further, the probability of using false overlaps increases for alignment calculations that allow for several mismatches compared to exact sub-matches, and the calculation of the overlap graph is slower than the construction of the de Bruijn graph. Nevertheless, longer contigs and a higher overall coverage of the sequenced genome are the payoff for dealing with a more complex graph and an increase in false alignments. In scenarios of high sequence coverage, LOCAS often produces shorter contigs compared to de Bruijn graph based de novo assemblers, which is a consequence of the highly sensitive overlap detection of LOCAS and the resulting overlap-graph of higher complexity.We believe that the method implemented in SUPERLOCAS is a good compromise between resource heavy de novo assembly and simple homology-guided assembly approaches that do not incorporate left-over reads. The integration of left-over reads allows for identification of polymor- phic or inserted regions in a de novo assembly fashion. Additionally, our assembly approach makes use of the exact positions of aligned reads against a reference in order to reduce assembly complexity and to reduce the number of false overlaps, as was formerly used to improve overlap calculation of Sanger reads [31]. Further, we PLoS ONE | www.plosone.org 8 August 2011 | Volume 6 | Issue 8 | e23455