The two sets of sequencing reads (454 and Illumina) from each sample were assembled using a hybrid de novo assembly strategy. Illumina reads were trimmed for base sequence quality using FASTQC (Andrews S: FastQC: A quality control tool for high throughput sequence data. [ http://www.bioinformatics.babraham.ac.uk/projects/fastqc/]). Per base sequence quality control (QC) cutoffs of 20% were used to trim illumina reads and then assembled into de novo contigs using VelvetOptimiser61, which is a wrapper that uses the Velvet denovo assembler and automatically estimates the optimal k-mer size that will produce the best possible assembly. The range of K-mer sizes specified were between 35 to 70. The best Illumina contigs obtained were then fragmented into shorter segments of ~400 bp, similar to the read length from 454. These 400 bp Illumina-fragmented contigs were then pooled with the reads generated from the 454 machine. A second de novo assembly using Newbler was then performed. The resulting de novo contigs were used for the downstream analysis mentioned below.