Oil palm is an important source of edible oil. The importance of the crop, as well as its long breeding cycle (10-12 years) has led to the sequencing of its genome in 2013 to pave the way for genomics-guided breeding. Nevertheless, the first set of gene predictions, although useful, had many fragmented genes. Classification and characterization of genes associated with traits of interest, such as those for fatty acid biosynthesis and disease resistance, were also limited. Lipid-, especially fatty acid (FA)-related genes are of particular interest for the oil palm as they specify oil yields and quality. This paper presents the characterization of the oil palm genome using different gene prediction methods and comparative genomics analysis, identification of FA biosynthesis and disease resistance genes, and the development of an annotation database and bioinformatics tools.
Using two independent gene-prediction pipelines, Fgenesh++ and Seqping, 26,059 oil palm genes with transcriptome and RefSeq support were identified from the oil palm genome. These coding regions of the genome have a characteristic broad distribution of GC3 (fraction of cytosine and guanine in the third position of a codon) with over half the GC3-rich genes (GC3 . 0.75286) being intronless. In comparison, only one-seventh of the oil palm genes identified are intronless. Using comparative genomics analysis, characterization of conserved domains and active sites, and expression analysis, 42 key genes involved in FA biosynthesis in oil palm were identified. For three of them, namely EgFABF, EgFABH and EgFAD3, segmental duplication events were detected. Our analysis also identified 210 candidate resistance genes in six classes, grouped by their protein domain structures.
We present an accurate and comprehensive annotation of the oil palm genome, focusing on analysis of important categories of genes (GC3-rich and intronless), as well as those associated with important functions, such as FA biosynthesis and disease resistance. The study demonstrated the advantages of having an integrated approach to gene prediction and developed a computational framework for combining multiple genome annotations. These results, available in the oil palm annotation database (http://palmxplore.mpob.gov.my), will provide important resources for studies on the genomes of oil palm and related crops.
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.
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.Read more ...