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Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data
Publisher: BMC Bioinformatics
Date: 27 January 2017


Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion.
We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO.s plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

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Non-tenera contamination and the economic impact of SHELL genetic testing in the Malaysian independent oil palm industry
Publisher: Front. Plant Sci.
Date: 21 June 2016


Oil palm (Elaeis guineensis) is the most productive oil bearing crop worldwide. It has three fruit forms, namely dura (thick-shelled), pisifera (shell-less) and tenera (thin-shelled), which are controlled by the SHELL gene. The fruit forms exhibit monogenic co-dominant inheritance, where tenera is a hybrid obtained by crossing maternal dura and paternal pisifera palms. Commercial palm oil production is based on planting thin-shelled tenera palms, which typically yield 30% more oil than dura palms, while pisifera palms are female-sterile and have little to no palm oil yield. It is clear that tenera hybrids produce more oil than either parent due to single gene heterosis. The unintentional planting of dura or pisifera palms reduces overall yield and impacts land utilization that would otherwise be devoted to more productive tenera palms. Here, we identify three additional novel mutant alleles of the SHELL gene, which encode a type II MADS-box transcription factor, and determine oil yield via control of shell fruit form phenotype in a manner similar to two previously identified mutant SHELL alleles. Assays encompassing all five mutations account for all dura and pisifera palms analyzed. By assaying for these variants in 10,224 mature palms or seedlings, we report the first large scale accurate genotype-based determination of the fruit forms in independent oil palm planting sites and in the nurseries that supply them throughout Malaysia. The measured non-tenera contamination rate (10.9% overall on a weighted average basis) underscores the importance of SHELL genetic testing of seedlings prior to planting in production fields. By eliminating non-tenera contamination, comprehensive SHELL genetic testing can improve sustainability by increasing yield on existing planted lands. In addition, economic modeling demonstrates that SHELL gene testing will confer substantial annual economic gains to the oil palm industry, to Malaysian gross national income and to Malaysian government tax receipts.

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SureSawitTM SHELL - A diagnostic assay to predict fruit forms of oil palm
Publisher: Oil Palm Bulletin
Date: May 2015


Genetic studies in oil palm have been impeded by its long breeding cycle and requirement for large tracts of land for field planting. The application of genomics tools provides an opportunity to overcome these constraints and move forward from the phenotype-driven research that had been the focus previously. In order to develop these tools, the Malaysian Palm Oil Board (MPOB) and its partners achieved a significant scientific breakthrough by decoding the E. guineensis and E. oleifera genomes, in 2013. Obtaining high quality sequence assembly proved to be a valuable resource to identify genes linked to important agronomic traits of oil palm. In this respect, using a combination of classical genetics and whole genome sequence as a reference, the gene responsible for the three different fruit forms of E. guineensis -SHELL- was identified. The identification of the gene responsible for the fruit forms paved the way for the development of the SureSawitTM SHELL diagnostic assay kit. The kit advances the application of molecular diagnostic tools in both oil palm breeding and commercial seed production.

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