MOTIVATION: de Bruijn graphs have been proposed as a data structure to facilitate the analysis of related whole genome sequences, in both a population and comparative genomic settings. However, current approaches do not scale well to many genomes of large size (such as mammalian genomes). RESULTS: In this article, we present TwoPaCo, a simple and scalable low memory algorithm for the direct construction of the compacted de Bruijn graph from a set of complete genomes. We demonstrate that it can construct the graph for 100 simulated human genomes in less than a day and eight real primates in < 2 h, on a typical shared-memory machine. We believe that this progress will enable novel biological analyses of hundreds of mammalian-sized genomes. AVAILABILITY AND IMPLEMENTATION: Our code and data is available for download from github.com/medvedevgroup/TwoPaCo. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Bacterial genomes are simpler than mammalian ones, and yet assembling the former from the data currently generated by high-throughput short-read sequencing machines still results in hundreds of contigs. To improve assembly quality, recent studies have utilized longer Pacific Biosciences (PacBio) reads or jumping libraries to connect contigs into larger scaffolds or help assemblers resolve ambiguities in repetitive regions of the genome. However, their popularity in contemporary genomic research is still limited by high cost and error rates. In this work, we explore the possibility of improving assemblies by using complete genomes from closely related species/strains. We present Ragout, a genome rearrangement approach, to address this problem. In contrast with most reference-guided algorithms, where only one reference genome is used, Ragout uses multiple references along with the evolutionary relationship among these references in order to determine the correct order of the contigs. Additionally, Ragout uses the assembly graph and multi-scale synteny blocks to reduce assembly gaps caused by small contigs from the input assembly. In simulations as well as real datasets, we believe that for common bacterial species, where many complete genome sequences from related strains have been available, the current high-throughput short-read sequencing paradigm is sufficient to obtain a single high-quality scaffold for each chromosome. AVAILABILITY: The Ragout software is freely available at: https://github.com/fenderglass/Ragout.

UNLABELLED: Next-generation sequencing (NGS) technologies have raised a challenging de novo genome assembly problem that is further amplified in recently emerged single-cell sequencing projects. While various NGS assemblers can use information from several libraries of read-pairs, most of them were originally developed for a single library and do not fully benefit from multiple libraries. Moreover, most assemblers assume uniform read coverage, condition that does not hold for single-cell projects where utilization of read-pairs is even more challenging. We have developed an exSPAnder algorithm that accurately resolves repeats in the case of both single and multiple libraries of read-pairs in both standard and single-cell assembly projects. AVAILABILITY AND IMPLEMENTATION: http://bioinf.spbau.ru/en/spades

MOTIVATION: The rapidly increasing set of sequenced genomes highlights the importance of identifying the synteny blocks in multiple and/or highly duplicated genomes. Most synteny block reconstruction algorithms use genes shared over all genomes to construct the synteny blocks for multiple genomes. However, the number of genes shared among all genomes quickly decreases with the increase in the number of genomes. RESULTS: We propose the Duplications and Rearrangements In Multiple Mammals (DRIMM)-Synteny algorithm to address this bottleneck and apply it to analyzing genomic architectures of yeast, plant and mammalian genomes. We further combine synteny block generation with rearrangement analysis to reconstruct the ancestral preduplicated yeast genome. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.