The IDBA-UD toolkit is available at our website http://www.cs.hku.hk/~alse/idba_ud

Motivation: Next-generation sequencing techniques allow us to generate reads from a microbial environment in order to analyze the microbial community. However, assembling of a set of mixed reads from different species to form contigs is a bottleneck of metagenomic research. Although there are many assemblers for assembling reads from a single genome, there are no assemblers for assembling reads in metagenomic data without reference genome sequences. Moreover, the performances of these assemblers on metagenomic data are far from satisfactory, because of the existence of common regions in the genomes of subspecies and species, which make the assembly problem much more complicated.Results: We introduce the Meta-IDBA algorithm for assembling reads in metagenomic data, which contain multiple genomes from different species. There are two core steps in Meta-IDBA. It first tries to partition the de Bruijn graph into isolated components of different species based on an important observation. Then, for each component, it captures the slight variants of the genomes of subspecies from the same species by multiple alignments and represents the genome of one species, using a consensus sequence. Comparison of the performances of Meta-IDBA and existing assemblers, such as Velvet and Abyss for different metagenomic datasets shows that Meta-IDBA can reconstruct longer contigs with similar accuracy.Availability: Meta-IDBA toolkit is available at our website http://www.cs.hku.hk/~alse/metaidba.Contact: chin@cs.hku.hk

Abstract. The de Bruijn graph assembly approach breaks reads into k-mers before assembling them into contigs. The string graph approach forms contigs by connecting two reads with k or more overlapping nucleotides. Both approaches must deal with the following problems: false-positive vertices, due to erroneous reads; gap problem, due to non-uniform coverage; branching problem, due to erroneous reads and repeat regions. A proper choice of k is crucial but for single k there is always a trade-off: a small k favors the situation of erroneous reads and non-uniform coverage, and a large k favors short repeat regions.We propose an iterative de Bruijn graph approach iterating from small to large k exploring the advantages of the in between values. Our IDBA outperforms the existing algorithms by constructing longer contigs with similar accuracy and using less memory, both with real and simulated data. The running time of the algorithm is comparable to existing algorithms. Availability: IDBA is available at