AbstractSummaryThe ever-increasing number of sequenced genomes necessitates the development of pangenomic approaches for comparative genomics. Introduced in 2016, PanTools is a platform that allows pangenome construction, homology grouping and pangenomic read mapping. The use of graph database technology makes PanTools versatile, applicable from small viral genomes like SARS-CoV-2 up to large plant or animal genomes like tomato or human. Here, we present our third major update to PanTools that enables the integration of functional annotations and provides both gene-level analyses and phylogenetics.Availability and implementationPanTools is implemented in Java 8 and released under the GNU GPLv3 license. Software and documentation are available at https://git.wur.nl/bioinformatics/pantoolsSupplementary informationSupplementary data are available at Bioinformatics online.

In modern genomics, mapping reads to a single reference genome is common practice. However, a reference genome does not necessarily accurately represent a population or species and as a result a substantial percentage of reads often cannot be mapped. A number of graph-based variation-aware mapping methods have recently been proposed to remedy this. Here, we propose an alternative multi-reference approach, which aligns reads to large collections of genomes simultaneously. Our approach, an extension to our pangenomics suite PanTools (), is as accurate as state-of the-art tools but more efficient on large numbers of genomes. We successfully applied PanTools to map genomic and metagenomic reads to large collections of viral, archaeal, bacterial, fungal and plant genomes.

BACKGROUND:Identification of homologous genes is fundamental to comparative genomics, functional genomics and phylogenomics. Extensive public homology databases are of great value for investigating homology but need to be continually updated to incorporate new sequences. As new sequences are rapidly being generated, there is a need for efficient standalone tools to detect homologs in novel data.RESULTS:To address this, we present a fast method for detecting homology groups across a large number of individuals and/or species. We adopted a k-mer based approach which considerably reduces the number of pairwise protein alignments without sacrificing sensitivity. We demonstrate accuracy, scalability, efficiency and applicability of the presented method for detecting homology in large proteomes of bacteria, fungi, plants and Metazoa.CONCLUSIONS:We clearly observed the trade-off between recall and precision in our homology inference. Favoring recall or precision strongly depends on the application. The clustering behavior of our program can be optimized for particular applications by altering a few key parameters. The program is available for public use at https://github.com/sheikhizadeh/pantools as an extension to our pan-genomic analysis tool, PanTools.

MOTIVATION Next-generation sequencing technology is generating a wealth of highly similar genome sequences for many species, paving the way for a transition from single-genome to pan-genome analyses. Accordingly, genomics research is going to switch from reference-centric to pan-genomic approaches. We define the pan-genome as a comprehensive representation of multiple annotated genomes, facilitating analyses on the similarity and divergence of the constituent genomes at the nucleotide, gene and genome structure level. Current pan-genomic approaches do not thoroughly address scalability, functionality and usability. RESULTS We introduce a generalized De Bruijn graph as a pan-genome representation, as well as an online algorithm to construct it. This representation is stored in a Neo4j graph database, which makes our approach scalable to large eukaryotic genomes. Besides the construction algorithm, our software package, called PanTools, currently provides functionality for annotating pan-genomes, adding sequences, grouping genes, retrieving gene sequences or genomic regions, reconstructing genomes and comparing and querying pan-genomes. We demonstrate the performance of the tool using datasets of 62 E. coli genomes, 93 yeast genomes and 19 Arabidopsis thaliana genomes. AVAILABILITY AND IMPLEMENTATION The Java implementation of PanTools is publicly available at http://www.bif.wur.nl CONTACT sandra.smit@wur.nl.

Summary: The quality of high-throughput next-generation sequencing data significantly influences the performance and memory consumption of assembly and mapping algorithms. The most ubiquitous platform, Illumina, mainly suffers from substitution errors. We have developed a tool, ACE, based on K-mer tries to correct such errors. On real MiSeq and HiSeq Illumina archives, ACE yields higher gains in terms of coverage depth, outperforming state-of-the-art competitors in the majority of cases. Availability and implementation: ACE is licensed under the GPL license and can be freely obtained at https://github.com/sheikhizadeh/ACE/. The program is implemented in C++ and runs on most Unix-derived operating systems. Supplementary information: available at Bioinformatics online. Contact: siavash.sheikhizadehanari@wur.nl

UNLABELLED:The quality of high-throughput next-generation sequencing data significantly influences the performance and memory consumption of assembly and mapping algorithms. The most ubiquitous platform, Illumina, mainly suffers from substitution errors. We have developed a tool, ACE, based on K-mer tries to correct such errors. On real MiSeq and HiSeq Illumina archives, ACE yields higher gains in terms of coverage depth, outperforming state-of-the-art competitors in the majority of cases.AVAILABILITY AND IMPLEMENTATION:ACE is licensed under the GPL license and can be freely obtained at https://github.com/sheikhizadeh/ACE/. The program is implemented in C++ and runs on most Unix-derived operating systems.CONTACT:siavash.sheikhizadehanari@wur.nlSUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

In this paper, an exhaustive algorithm for extracting structured motifs has been presented. Structured motif is defined as an ordered set of highly-conserved over-presented patterns which occur near each other in a set of DNA sequences. The presented algorithm is based on an innovative data structure called l-mer trie. As opposed to other existing motif finders, this algorithm offers more flexibility in terms of the possibility of determining a range for length of the single patterns, their substitution rates and the spacing between them. The possibility of defining a minimum bound for substitution rates saves considerable time and space in the case of searching for weak motifs occurring with many mutations.Efficiency of the algorithm has been verified on some artificial sequences as well as real DNA sequences of some plant viruses. The results have been compared with those achieved by RISO, another tree-based algorithm, which is claimed to have notable time and space gains over the best known exact algorithms.

In core logging, each joint set intersects borehole into some segments. In this research, it has been shown that length of the borehole segments created by each joint set could be computed by trigonometrical relations. By realizing the lengths associated with joint sets, an algorithm has been designed to compute the length of borehole pieces (created by all joint sets) and to calculate RQD. Effect of some factors have been analyzed and applied to the abstract model of the rock mass to have the most similarity to a real rock mass. The program proposed in this study, is a robust platform to calculate the RQD in all directions inside a rock mass without having to deal with the labor of core logging and wrestling with difficulties and inaccuracies of the traditional processes. This is the first algorithmic method for estimating the rock quality which could be employed to develop a new and far more reliable measurement for the degree of jointing inside a rock mass.