Informed and Automated k-Mer Size Selection for Genome Assembly

Genome assembly tools based on the de Bruijn graph framework rely on a parameter k, which represents a trade-off between several competing effects that are difficult to quantify. There is currently a lack of tools that would automatically estimate the best k to use and/or quickly generate histograms of k-mer abundances that would allow the user to make an informed decision. We develop a fast and accurate sampling method that constructs approximate abundance histograms with a several orders of magnitude performance improvement over traditional methods. We then present a fast heuristic that uses the generated abundance histograms for putative k values to estimate the best possible value of k. We test the effectiveness of our tool using diverse sequencing datasets and find that its choice of k leads to some of the best assemblies. Our tool KmerGenie is freely available at: http://kmergenie.bx.psu.edu/Comment: HiTSeq 201

Dualities in Tree Representations

A characterization of the tree $T^*$ such that $\mathrm{BP}(T^*)=\overleftrightarrow{\mathrm{DFUDS}(T)}$, the reversal of $\mathrm{DFUDS}(T)$ is given. An immediate consequence is a rigorous characterization of the tree $\hat{T}$ such that $\mathrm{BP}(\hat{T})=\mathrm{DFUDS}(T)$. In summary, $\mathrm{BP}$ and $\mathrm{DFUDS}$ are unified within an encompassing framework, which might have the potential to imply future simplifications with regard to queries in $\mathrm{BP}$ and/or $\mathrm{DFUDS}$. Immediate benefits displayed here are to identify so far unnoted commonalities in most recent work on the Range Minimum Query problem, and to provide improvements for the Minimum Length Interval Query problem.Comment: CPM 2018, extended versio

Disk Compression of k-mer Sets

K-mer based methods have become prevalent in many areas of bioinformatics. In applications such as database search, they often work with large multi-terabyte-sized datasets. Storing such large datasets is a detriment to tool developers, tool users, and reproducibility efforts. General purpose compressors like gzip, or those designed for read data, are sub-optimal because they do not take into account the specific redundancy pattern in k-mer sets. In our earlier work (Rahman and Medvedev, RECOMB 2020), we presented an algorithm UST-Compress that uses a spectrum-preserving string set representation to compress a set of k-mers to disk. In this paper, we present two improved methods for disk compression of k-mer sets, called ESS-Compress and ESS-Tip-Compress. They use a more relaxed notion of string set representation to further remove redundancy from the representation of UST-Compress. We explore their behavior both theoretically and on real data. We show that they improve the compression sizes achieved by UST-Compress by up to 27 percent, across a breadth of datasets. We also derive lower bounds on how well this type of compression strategy can hope to do

Mapsembler, targeted assembly of larges genomes on a desktop computer

Background: The analysis of next-generation sequencing data from large genomes is a timely research topic. Sequencers are producing billions of short sequence fragments from newly sequenced organisms. Computational methods for reconstructing sequences (whole-genome assemblers) are typically employed to process such data. However, one of the main drawback of these methods is the high memory requirement. Results: We present Mapsembler, an iterative targeted assembler which processes large datasets of reads on commodity hardware. Mapsembler checks for the presence of given regions of interest in the reads and reconstructs their neighborhood, either as a plain sequence (consensus) or as a graph (full sequence structure). We introduce new algorithms to retrieve homologues of a sequence from reads and construct an extension graph. Conclusions: Mapsembler is the rst software that enables de novo discovery around a region of interest of gene homologues, SNPs, exon skipping as well as other structural events, directly from raw sequencing reads. Compared to traditional assembly software, memory requirement and execution time of Mapsembler are considerably lower, as data indexing is localized. Mapsembler can be used at http://mapsembler.genouest.or

Localized genome assembly from reads to scaffolds: practical traversal of the paired string graph

International audienceNext-generation de novo short reads assemblers typically use the following strategy: (1) assemble unpaired reads using heuristics leading to contigs; (2) order contigs from paired reads information to produce scaffolds. We propose to unify these two steps by introducing localized assembly: direct construction of scaffolds from reads. To this end, the paired string graph structure is introduced, along with a formal framework for building scaffolds as paths of reads. This framework leads to the design of a novel greedy algorithm for memory-efficient, parallel assembly of paired reads. A prototype implementation of the algorithm has been developed and applied to the assembly of simulated and experimental short reads. Our experiments show that our methods yields longer scaffolds than recent assemblers, and is capable of assembling diploid genomes significantly better than other greedy methods

MindTheGap: integrated detection and assembly of short and long insertions

Voir : http://mindthegap.genouest.orgInternational audienceMotivation: Insertions play an important role in genome evolution. However, such variants are difficult to detect from short read sequencing data, especially when they exceed the paired-end insert size. Many approaches have been proposed to call short insertion variants based on paired-end mapping. However, there remains a lack of practical methods to detect and assemble long variants. Results: We propose here an original method, called MINDTHEGAP, for the integrated detection and assembly of insertion variants from re-sequencing data. Importantly, it is designed to call insertions of any size, whether they are novel or duplicated, homozygous or heterozygous in the donor genome. MINDTHEGAP uses an efficient k-mer based method to detect insertion sites in a reference genome, and subsequently assemble them from the donor reads. MINDTHEGAP showed high recall and precision on simulated datasets of various genome complexities. When applied to real C. elegans and human NA12878 datasets, MINDTHEGAP detected and correctly assembled insertions longer than 1 kb, using at most 14 GB of memory.Availability: http://mindthegap.genouest.or