SUMMARY: Bioinformatics applications increasingly rely on ad hoc disk storage of k-mer sets, e.g. for de Bruijn graphs or alignment indexes. Here, we introduce the K-mer File Format as a general lossless framework for storing and manipulating k-mer sets, realizing space savings of 3-5× compared to other formats, and bringing interoperability across tools. AVAILABILITY AND IMPLEMENTATION: Format specification, C++/Rust API, tools: https://github.com/Kmer-File-Format/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Considering a set of intervals on the real line, an interval graph records these intervals as nodes and their intersections as edges. Identifying (i.e. merging) pairs of nodes in an interval graph results in a multiple-interval graph. Given only the nodes and the edges of the multiple-interval graph without knowing the underlying intervals, we are interested in the following questions. Can one determine how many intervals correspond to each node? Can one compute a walk over the multiple-interval graph nodes that reflects the ordering of the original intervals? These questions are closely related to linked-read DNA sequencing, where barcodes are assigned to long molecules whose intersection graph forms an interval graph. Each barcode may correspond to multiple molecules, which complicates downstream analysis, and corresponds to the identification of nodes of the corresponding interval graph. Resolving the above graph-theoretic problems would facilitate analyses of linked-reads sequencing data, through enabling the conceptual separation of barcodes into molecules and providing, through the molecules order, a skeleton for accurately assembling the genome. Here, we propose a framework that takes as input an arbitrary intersection graph (such as an overlap graph of barcodes) and constructs a heuristic approximation of the ordering of the original intervals.

MOTIVATION: Genome assembly is increasingly performed on long, uncorrected reads. Assembly quality may be degraded due to unfiltered chimeric reads; also, the storage of all read overlaps can take up to terabytes of disk space. RESULTS: We introduce two tools: yacrd for chimera removal and read scrubbing, and fpa for filtering out spurious overlaps. We show that yacrd results in higher-quality assemblies and is one hundred times faster than the best available alternative. AVAILABILITY AND IMPLEMENTATION: https://github.com/natir/yacrd and https://github.com/natir/fpa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Long-read genome assembly tools are expected to reconstruct bacterial genomes nearly perfectly; however, they still produce fragmented assemblies in some cases. It would be beneficial to understand whether these cases are intrinsically impossible to resolve, or if assemblers are at fault, implying that genomes could be refined or even finished with little to no additional experimental cost. RESULTS: We propose a set of computational techniques to assist inspection of fragmented bacterial genome assemblies, through careful analysis of assembly graphs. By finding paths of overlapping raw reads between pairs of contigs, we recover potential short-range connections between contigs that were lost during the assembly process. We show that our procedure recovers 45% of missing contig adjacencies in fragmented Canu assemblies, on samples from the NCTC bacterial sequencing project. We also observe that a simple procedure based on enumerating weighted Hamiltonian cycles can suggest likely contig orderings. In our tests, the correct contig order is ranked first in half of the cases and within the top-three predictions in nearly all evaluated cases, providing a direction for finishing fragmented long-read assemblies. AVAILABILITY AND IMPLEMENTATION: https://gitlab.inria.fr/pmarijon/knot . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.