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      Genetic map‐guided genome assembly reveals a virulence‐governing minichromosome in the lentil anthracnose pathogen Colletotrichum lentis

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          Summary

          • Colletotrichum lentis causes anthracnose, which is a serious disease on lentil and can account for up to 70% crop loss. Two pathogenic races, 0 and 1, have been described in the C. lentis population from lentil.

          • To unravel the genetic control of virulence, an isolate of the virulent race 0 was sequenced at 1481‐fold genomic coverage. The 56.10‐Mb genome assembly consists of 50 scaffolds with N 50 scaffold length of 4.89 Mb. A total of 11 436 protein‐coding gene models was predicted in the genome with 237 coding candidate effectors, 43 secondary metabolite biosynthetic enzymes and 229 carbohydrate‐active enzymes ( CAZymes), suggesting a contraction of the virulence gene repertoire in C. lentis.

          • Scaffolds were assigned to 10 core and two minichromosomes using a population (race 0 × race 1, =  94 progeny isolates) sequencing‐based, high‐density (14 312 single nucleotide polymorphisms) genetic map. Composite interval mapping revealed a single quantitative trait locus ( QTL), qClVIR‐11 , located on minichromosome 11, explaining 85% of the variability in virulence of the C. lentis population. The QTL covers a physical distance of 0.84 Mb with 98 genes, including seven candidate effector and two secondary metabolite genes.

          • Taken together, the study provides genetic and physical evidence for the existence of a minichromosome controlling the C. lentis virulence on lentil.

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          SMURF: Genomic mapping of fungal secondary metabolite clusters.

          Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters. 2010 Elsevier Inc. All rights reserved.
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            PredGPI: a GPI-anchor predictor

            Background Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called ω-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes. Results Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature. Conclusion PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes.
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              Automatic annotation of eukaryotic genes, pseudogenes and promoters

              Background The ENCODE gene prediction workshop (EGASP) has been organized to evaluate how well state-of-the-art automatic gene finding methods are able to reproduce the manual and experimental gene annotation of the human genome. We have used Softberry gene finding software to predict genes, pseudogenes and promoters in 44 selected ENCODE sequences representing approximately 1% (30 Mb) of the human genome. Predictions of gene finding programs were evaluated in terms of their ability to reproduce the ENCODE-HAVANA annotation. Results The Fgenesh++ gene prediction pipeline can identify 91% of coding nucleotides with a specificity of 90%. Our automatic pseudogene finder (PSF program) found 90% of the manually annotated pseudogenes and some new ones. The Fprom promoter prediction program identifies 80% of TATA promoters sequences with one false positive prediction per 2,000 base-pairs (bp) and 50% of TATA-less promoters with one false positive prediction per 650 bp. It can be used to identify transcription start sites upstream of annotated coding parts of genes found by gene prediction software. Conclusion We review our software and underlying methods for identifying these three important structural and functional genome components and discuss the accuracy of predictions, recent advances and open problems in annotating genomic sequences. We have demonstrated that our methods can be effectively used for initial automatic annotation of the eukaryotic genome.
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                Author and article information

                Contributors
                vijai.bhadauria@canada.ca
                sabine.banniza@usask.ca
                Journal
                New Phytol
                New Phytol
                10.1111/(ISSN)1469-8137
                NPH
                The New Phytologist
                John Wiley and Sons Inc. (Hoboken )
                0028-646X
                1469-8137
                04 August 2018
                January 2019
                : 221
                : 1 ( doiID: 10.1111/nph.2019.221.issue-1 )
                : 431-445
                Affiliations
                [ 1 ] Crop Development Centre/Department of Plant Sciences University of Saskatchewan Saskatoon SK S7N 5A8 Canada
                [ 2 ] Swift Current Research and Development Center Agriculture and Agri‐Food Canada Swift Current SK S9H 3X2 Canada
                Author notes
                [*] [* ] Authors for correspondence:

                Sabine Banniza

                Tel: +1 306 966 2619

                Email: sabine.banniza@ 123456usask.ca

                Vijai Bhadauria

                Tel: +1 306 770 4416

                Email: vijai.bhadauria@ 123456canada.ca

                Author information
                http://orcid.org/0000-0002-9311-049X
                Article
                NPH15369 2018-26980
                10.1111/nph.15369
                6668012
                30076781
                bda8904f-637e-4155-9cb3-2d415d347475
                © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 May 2018
                : 02 July 2018
                Page count
                Figures: 9, Tables: 3, Pages: 15, Words: 9881
                Funding
                Funded by: National Science and Engineering Research Council of Canada (NSERC) Discovery
                Funded by: Western Grains Research Foundation, Canada
                Funded by: Saskatchewan Pulse Growers
                Categories
                Full Paper
                Research
                Full Papers
                Custom metadata
                2.0
                nph15369
                January 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.6.2 mode:remove_FC converted:31.07.2019

                Plant science & Botany
                conditionally dispensable chromosomes,disease resistance,effectors,genomics,genotyping‐by‐whole‐genome shotgun sequencing,legumes,pathogens

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