MOTIVATION: The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. RESULTS: Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. AVAILABILITYAND IMPLEMENTATION: polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2018) doi.org/10.1093/bioinformatics/bty371, 34(20): 3496-3502. In the original article, there was an incorrect formula. The formula appears in section 2.2.2, ‘Linkage analysis in the presence of preferential chromosomal pairing’, on page 3498. The correct formula is below in the context in which it appears. This has been corrected.

SUMMARY: Haplotype assembly of polyploids is an open issue in plant genomics. Recent experimental studies on highly heterozygous autotetraploid potato have shown that available methods do not deliver satisfying results in practice. We propose an optimal method to assemble haplotypes of highly heterozygous polyploids from Illumina short-sequencing reads. Our method is based on a generalization of the existing minimum fragment removal model to the polyploid case and on new integer linear programs to reconstruct optimal haplotypes. We validate our methods experimentally by means of a combined evaluation on simulated and experimental data based on 83 previously sequenced autotetraploid potato cultivars. Results on simulated data show that our methods produce highly accurate haplotype assemblies, while results on experimental data confirm a sensible improvement over the state of the art. AVAILABILITY AND IMPLEMENTATION: Executables for Linux at http://github.com/Computational Genomics/HaplotypeAssembler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2019) doi.10.1093/bioinformatics/btz060 The author apologises for the error.

Motivation: Polyploid species carry more than two copies of each chromosome, a condition found in many of the world's most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realize all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops. Results: polymapR is an R package for genetic linkage analysis and integrated genetic map construction from bi-parental populations of outcrossing autopolyploids. It can currently analyse triploid, tetraploid and hexaploid marker datasets and is applicable to various crops including potato, leek, alfalfa, blueberry, chrysanthemum, sweet potato or kiwifruit. It can detect, estimate and correct for preferential chromosome pairing, and has been tested on high-density marker datasets from potato, rose and chrysanthemum, generating high-density integrated linkage maps in all of these crops. Availability and implementation: polymapR is freely available under the general public license from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polymapR. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. RESULTS: The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. CONCLUSION: QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

BACKGROUND: In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest. RESULTS: We used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine. CONCLUSIONS: In this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.

UNLABELLED: Marker2sequence (M2S) aims at mining quantitative trait loci (QTLs) for candidate genes. For each gene, within the QTL region, M2S uses data integration technology to integrate putative gene function with associated gene ontology terms, proteins, pathways and literature. As a typical QTL region easily contains several hundreds of genes, this gene list can then be further filtered using a keyword-based query on the aggregated annotations. M2S will help breeders to identify potential candidate genes for their traits of interest. AVAILABILITY: Marker2sequence is freely accessible at http://www.plantbreeding.wur.nl/BreeDB/marker2seq/. The source code can be obtained at https://github.com/PBR/Marker2Sequence. CONTACT: [email protected]