LOCAS - A Low Coverage Assembler for Resequencing Figure 6. Performance comparison of homology-guided assembly on real world data without utilizing left-over reads. Paired-end reads were produced by Illumina GAIIx with a length of 80 bp to a depth of ,76 for the first chromosome of A. thaliana strain Ler-1. Reads were aligned against the complete reference sequence (Col-0) and partitioned into blocks with SHORE of 25 kb. LOCAS and VELVET are applied in paired- end mode for all blocks which contain reads that are aligned to the same region of the reference sequence. The x-axis shows the avgN50 (average N50) and the y-axis the avgDISS (average dissimilarity). The runs of LOCAS produced with different parameter setting are drawn in red and those of VELVET in blue. doi:10.1371/journal.pone.0023455.g006 Next, all left-over reads were pooled. Assemblies of the blocks incorporating the left-over reads were performed applying the assembly tools SUPERLOCAS and VELVET. As VELVET does not provide a special mode for left-over incorporation, we used VELVET as follows: each local assembly was given the complete set of left-over reads as an additional input. We omitted EULER- SR, SOAPdenovo and ABYSS due to their insufficient perfor- mance shown in the first study on data with a low sequencing depth. In Figure 4, the resulting avgN50 size and avgERR are shown. SUPERLOCAS performed robustly with different parameters and overall outperformed VELVET in our study. We observed a maximum avgN50 size of 3,132 bp and 2,446 bp for SUPER- LOCAS and VELVET, respectively. The error rates range from 0.17% to 0.21% for SUPERLOCAS, and from 0.09% to 0.53% for VELVET. The average maximal contig size of SUPER- LOCAS was larger (with up to 17,821 bp) in comparison with VELVET (up to 12,996 bp). (See Table S2 for more details.) The CPU runtimes ranged from 3 h 12 min for SUPERLOCAS to 7 h 51 min for VELVET per run. SUPERLOCAS used 433 MB of RAM while VELVET required 224 MB. In addition, we examined the contigs of both tools regarding the appearance of insertion regions. These regions are present in the target genome but not in the reference genome. Most insertion regions with a length of at least 100 bp could only be assembled with the help of left-over reads. Figure 5 shows the number of insertion regions that were assembled without errors by SUPERLOCAS and by VELVET, respectively, separated by insertion length. Both tools performed this task equally well, assembling a similar number of the insertions. Real World Data To test performance on real world data, we used sequence reads from the first chromosome of A. thaliana strain Landsberg erecta (Ler-1) produced within the 1001 Genomes Project [30]. Ler-1 was sequenced on the Illumina GAIIx with 80 bp paired-end reads to a depth of ,76. Reads were aligned against the complete reference sequence and all alignments on chromosome 1 have been partitioned into subregions of at most 40 kb using GenomeMapper [29] and SHORE [6]. We first applied LOCAS and VELVET to the assembly of the reads in each block without left-over reads. Instead of the avgERR, we estimated the average relative dissimilarity to the reference sequence over all blocks, denoted as avgDISS. For avgDISS values higher than 1%, VELVET performed best considering avgN50 size, while for avgDISS values lower than 1%, LOCAS showed the best avgN50 sizes (Fig. 6). We observed a maximum avgN50 size of 1,606 bp and of 1,526 bp for VELVET and LOCAS, respectively. We then applied SUPERLOCAS and VELVET. For VEL- VET, each block was assembled with the complete set of left-over reads as in the second study. Contigs were used for analysis if they featured a similarity with the reference sequence of at least 75%. We allowed this high percentage of dissimilarity since contigs that PLoS ONE | www.plosone.org 7 August 2011 | Volume 6 | Issue 8 | e23455