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      Orphan Crops Browser: a bridge between model and orphan crops

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          Abstract

          Many important crops have received little attention by the scientific community, either because they are not considered economically important or due to their large and complex genomes. De novo transcriptome assembly, using next-generation sequencing data, is an attractive option for the study of these orphan crops. In spite of the large amount of sequencing data that can be generated, there is currently a lack of tools which can effectively help molecular breeders and biologists to mine this type of information. Our goal was to develop a tool that enables molecular breeders, without extensive bioinformatics knowledge, to efficiently study de novo transcriptome data from any orphan crop ( http://www.bioinformatics.nl/denovobrowser/db/species/index). The Orphan Crops Browser has been designed to facilitate the following tasks (1) search and identification of candidate transcripts based on phylogenetic relationships between orthologous sequence data from a set of related species and (2) design specific and degenerate primers for expression studies in the orphan crop of interest. To demonstrate the usability and reliability of the browser, it was used to identify the putative orthologues of 17 known lignin biosynthetic genes from maize and sugarcane in the orphan crop Miscanthus sinensis. Expression studies in miscanthus stem internode tissue differing in maturation were subsequently carried out, to follow the expression of these genes during lignification. Our results showed a negative correlation between lignin content and gene expression. The present data are in agreement with recent findings in maize and other crops, and it is further discussed in this paper.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s11032-015-0430-2) contains supplementary material, which is available to authorized users.

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          Most cited references35

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets.

            TGICL is a pipeline for analysis of large Expressed Sequence Tags (EST) and mRNA databases in which the sequences are first clustered based on pairwise sequence similarity, and then assembled by individual clusters (optionally with quality values) to produce longer, more complete consensus sequences. The system can run on multi-CPU architectures including SMP and PVM.
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              Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle.

              The Minimum Evolution (ME) approach to phylogeny estimation has been shown to be statistically consistent when it is used in conjunction with ordinary least-squares (OLS) fitting of a metric to a tree structure. The traditional approach to using ME has been to start with the Neighbor Joining (NJ) topology for a given matrix and then do a topological search from that starting point. The first stage requires O(n(3)) time, where n is the number of taxa, while the current implementations of the second are in O(p n(3)) or more, where p is the number of swaps performed by the program. In this paper, we examine a greedy approach to minimum evolution which produces a starting topology in O(n(2)) time. Moreover, we provide an algorithm that searches for the best topology using nearest neighbor interchanges (NNIs), where the cost of doing p NNIs is O(n(2) + p n), i.e., O(n(2)) in practice because p is always much smaller than n. The Greedy Minimum Evolution (GME) algorithm, when used in combination with NNIs, produces trees which are fairly close to NJ trees in terms of topological accuracy. We also examine ME under a balanced weighting scheme, where sibling subtrees have equal weight, as opposed to the standard "unweighted" OLS, where all taxa have the same weight so that the weight of a subtree is equal to the number of its taxa. The balanced minimum evolution scheme (BME) runs slower than the OLS version, requiring O(n(2) x diam(T)) operations to build the starting tree and O(p n x diam(T)) to perform the NNIs, where diam(T) is the topological diameter of the output tree. In the usual Yule-Harding distribution on phylogenetic trees, the diameter expectation is in log(n), so our algorithms are in practice faster that NJ. Moreover, this BME scheme yields a very significant improvement over NJ and other distance-based algorithms, especially with large trees, in terms of topological accuracy.
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                Author and article information

                Contributors
                +31 317 482127 , luisa.trindade@wur.nl
                Journal
                Mol Breed
                Mol. Breed
                Molecular Breeding
                Springer Netherlands (Dordrecht )
                1380-3743
                1572-9788
                12 January 2016
                12 January 2016
                2016
                : 36
                : 9
                Affiliations
                [ ]Wageningen UR Plant Breeding, Wageningen University and Research Centre, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
                [ ]Laboratory of Genetics, Wageningen University and Research Centre, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
                [ ]Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
                [ ]Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
                Article
                430
                10.1007/s11032-015-0430-2
                4710642
                26798323
                410ce63d-0da2-4898-b022-38f9f6570761
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 3 September 2015
                : 23 December 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme (BE);
                Award ID: 251132
                Award ID: 251132
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media Dordrecht 2016

                Animal science & Zoology
                orthologous genes,bioinformatics tool,breeding targets,de novo transcriptome,orphan crops

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