Dualities in Tree Representations

A characterization of the tree T^* such that BP(T^*)=ova{DFUDS(T)}, the reversal of DFUDS(T) is given. An immediate consequence is a rigorous characterization of the tree T^ such that BP(T^)=DFUDS(T). In summary, BP and DFUDS are unified within an encompassing framework, which might have the potential to imply future simplifications with regard to queries in BP and/or 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

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

Dualities in tree representations

A characterization of the tree T∗ such that BP(T∗) = ↔ DFUDS(T), the reversal of DFUDS(T) is given. An immediate consequence is a rigorous characterization of the tree T such that BP( T^) = DFUDS(T^). In summary, BP and DFUDS are unified within an encompassing framework, which might have the potential to imply future simplifications with regard to queries in BP and/or 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

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

Fast and Scalable Minimal Perfect Hashing for Massive Key Sets

Minimal perfect hash functions provide space-efficient and collision-free hashing on static sets. Existing algorithms and implementations that build such functions have practical limitations on the number of input elements they can process, due to high construction time, RAM or external memory usage. We revisit a simple algorithm and show that it is highly competitive with the state of the art, especially in terms of construction time and memory usage. We provide a parallel C++ implementation called BBhash. It is capable of creating a minimal perfect hash function of 10^{10} elements in less than 7 minutes using 8 threads and 5 GB of memory, and the resulting function uses 3.7 bits/element. To the best of our knowledge, this is also the first implementation that has been successfully tested on an input of cardinality 10^{12}. Source code: https://github.com/rizkg/BBHas