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      An integrated and comparative approach towards identification, characterization and functional annotation of candidate genes for drought tolerance in sorghum ( Sorghum bicolor (L.) Moench)

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          Abstract

          Background

          Drought is the most disastrous abiotic stress that severely affects agricultural productivity worldwide. Understanding the biological basis of drought-regulated traits, requires identification and an in-depth characterization of genetic determinants using model organisms and high-throughput technologies. However, studies on drought tolerance have generally been limited to traditional candidate gene approach that targets only a single gene in a pathway that is related to a trait. In this study, we used sorghum, one of the model crops that is well adapted to arid regions, to mine genes and define determinants for drought tolerance using drought expression libraries and RNA-seq data.

          Results

          We provide an integrated and comparative in silico candidate gene identification, characterization and annotation approach, with an emphasis on genes playing a prominent role in conferring drought tolerance in sorghum. A total of 470 non-redundant functionally annotated drought responsive genes (DRGs) were identified using experimental data from drought responses by employing pairwise sequence similarity searches, pathway and interpro-domain analysis, expression profiling and orthology relation. Comparison of the genomic locations between these genes and sorghum quantitative trait loci (QTLs) showed that 40% of these genes were co-localized with QTLs known for drought tolerance. The genome reannotation conducted using the Program to Assemble Spliced Alignment (PASA), resulted in 9.6% of existing single gene models being updated. In addition, 210 putative novel genes were identified using AUGUSTUS and PASA based analysis on expression dataset. Among these, 50% were single exonic, 69.5% represented drought responsive and 5.7% were complete gene structure models. Analysis of biochemical metabolism revealed 14 metabolic pathways that are related to drought tolerance and also had a strong biological network, among categories of genes involved. Identification of these pathways, signifies the interplay of biochemical reactions that make up the metabolic network, constituting fundamental interface for sorghum defence mechanism against drought stress.

          Conclusions

          This study suggests untapped natural variability in sorghum that could be used for developing drought tolerance. The data presented here, may be regarded as an initial reference point in functional and comparative genomics in the Gramineae family.

          Electronic supplementary material

          The online version of this article (10.1186/s12863-017-0584-5) contains supplementary material, which is available to authorized users.

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          Most cited references55

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          The Sorghum bicolor genome and the diversification of grasses.

          Sorghum, an African grass related to sugar cane and maize, is grown for food, feed, fibre and fuel. We present an initial analysis of the approximately 730-megabase Sorghum bicolor (L.) Moench genome, placing approximately 98% of genes in their chromosomal context using whole-genome shotgun sequence validated by genetic, physical and syntenic information. Genetic recombination is largely confined to about one-third of the sorghum genome with gene order and density similar to those of rice. Retrotransposon accumulation in recombinationally recalcitrant heterochromatin explains the approximately 75% larger genome size of sorghum compared with rice. Although gene and repetitive DNA distributions have been preserved since palaeopolyploidization approximately 70 million years ago, most duplicated gene sets lost one member before the sorghum-rice divergence. Concerted evolution makes one duplicated chromosomal segment appear to be only a few million years old. About 24% of genes are grass-specific and 7% are sorghum-specific. Recent gene and microRNA duplications may contribute to sorghum's drought tolerance.
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            Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

            Background In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. Results We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. Conclusion Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.
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              Alternative splicing and evolution: diversification, exon definition and function.

              Over the past decade, it has been shown that alternative splicing (AS) is a major mechanism for the enhancement of transcriptome and proteome diversity, particularly in mammals. Splicing can be found in species from bacteria to humans, but its prevalence and characteristics vary considerably. Evolutionary studies are helping to address questions that are fundamental to understanding this important process: how and when did AS evolve? Which AS events are functional? What are the evolutionary forces that shaped, and continue to shape, AS? And what determines whether an exon is spliced in a constitutive or alternative manner? In this Review, we summarize the current knowledge of AS and evolution and provide insights into some of these unresolved questions.
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                Author and article information

                Contributors
                adugnaabdi@gmail.com , woldeaa@unisa.ac.za
                Journal
                BMC Genet
                BMC Genet
                BMC Genetics
                BioMed Central (London )
                1471-2156
                22 December 2017
                22 December 2017
                2017
                : 18
                : 119
                Affiliations
                [1 ]ISNI 0000 0001 2156 8226, GRID grid.8974.2, South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, , University of the Western Cape, ; Private Bag X17, Belleville, 7535 South Africa
                [2 ]ISNI 0000 0004 0610 3238, GRID grid.412801.e, Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, , University of South Africa, ; UNISA Science Campus, Corner of Christiaan De Wet Road and Pioneer Avenue, Johannesburg, Florida 1710 South Africa
                [3 ]ISNI 0000 0001 2156 8226, GRID grid.8974.2, Department of Biotechnology, , University of the Western Cape, ; Private Bag X17, Belleville, Cape Town, 7535 South Africa
                [4 ]Agricultural Research Council, Infruitech-Nietvoorbij, Private Bag X5026, Stellenbosch, 7599 South Africa
                Article
                584
                10.1186/s12863-017-0584-5
                5741957
                29273003
                e5d1363a-a77f-4612-a7c6-fe56c6b21d18
                © The Author(s). 2017

                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. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 8 May 2017
                : 6 December 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001321, National Research Foundation;
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                candidate gene identification,drought tolerance,functional genomics,integrated in silico approach,genome annotation,sorghum bicolor (l.) moench

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