Evidence-based gene models for structural and functional annotations of the oil palm genome
This paper explores the oil palm genome, focusing on identifying and annotating genes that are important for plant health and oil production.
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- 1 In addition, the plot of CG3 skew increases in the proportion of cytosine in the GC3-rich group, consistent with the hypothesis of transcriptional optimization of GC3-rich genes.
- 2 Even then, it is mainly planted as a backcross to guineensis (interspecific hybrid) to raise its yield.
- 3 Individual components of the framework were trained on known genes of plants closely related to the oil palm, such as the date palm, to identify the most suitable parameters for gene prediction.
- 4 The dataset was used to train the Hidden Markov Model (HMM) for gene prediction, and as transcriptome evidence support.
Introduction
Oil palm belongs to the genus Elaeis of the family Arecaceae. The genus has two species -E. guineensis (African oil palm) and E. oleifera (American oil palm).
E. guineensis has three fruit forms that mainly vary in the thickness of their seed (or kernel) shell -dura (thick-shell), tenera (thin-shell) and pisifera (no shell).
The African oil palm is by far the most productive oil crop 1 in the world, with estimated production in year 2015/2016 of 61.68 million tonnes, of which the Malaysian share was 19.50 million tonnes 2 .
Research Question
In addition, the plot of CG3 skew increases in the proportion of cytosine in the GC3-rich group, consistent with the hypothesis of transcriptional optimization of GC3-rich genes.
Methodology
A major limitation of many of the current approaches is their relatively poor performance in organisms with atypical distribution of nucleotides. Accurate gene prediction and the discovery of regulatory elements in promoter sequences are two of the most important challenges in computational biology, for the prediction quality affects all aspects of genomics analysis. This paper presents a bioinformatics analysis of the oil palm genome, including comparative genomics analysis, database and.
Study Design
BLAST analysis of the predicted sequences was also carried out against the E. guineensis mRNA dataset, using an identify cutoff of >90%.
The predicted sequences were compared to the RefSeq 33 protein sequences and E. guineensis mRNA dataset via BLAST analysis.
A major limitation of many of the current approaches is their relatively poor performance in organisms with atypical distribution of nucleotides. Accurate gene prediction and the discovery of regulatory elements in promoter sequences are two of the most important challenges.
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Results & Findings
E. oleifera, is little planted because of its low yield (only 10 -20% of guineensis). Even then, it is mainly planted as a backcross to guineensis (interspecific hybrid) to raise its yield.
- E. oleifera, is little planted because of its low yield (only 10 -20% of guineensis).
- Even then, it is mainly planted as a backcross to guineensis (interspecific hybrid) to raise its yield.
- A few years later, the Genscan 9 software predicted multiple and partial genes on both strands, with improved accuracy.
- To overcome the lack of precision in many predictive models, we developed an integrated gene-finding framework, and applied it to identify high quality oil palm gene.
- Parameters of the framework were optimized to reliably identify the genes.
Even then, it is mainly planted as a backcross to guineensis (interspecific hybrid) to raise its yield.
Individual components of the framework were trained on known genes of plants closely related to the oil palm, such as the date palm, to identify the most suitable parameters for gene prediction.
Practical Applications
These proteins may have evolved, or been regained, only in plants, while other organisms have lost their ancestral genes during evolution.
Fgenesh++ Gene Prediction
Details the Fgenesh++ pipeline for gene prediction, which utilizes Hidden Markov Models and integrates various supporting data to enhance gene annotation accuracy.
Seqping Gene Prediction
Describes the Seqping pipeline, a customized approach based on MAKER2, for identifying full-length open reading frames and refining gene predictions through comparative analysis.
Frequently Asked Questions
Nevertheless, it has economically valuable traits which plant breeders drool over to introgress into guineensis, such as a more liquid oil with higher carotenoid and vitamin E contents, disease resistance and slow height increment. In addition, the plot of CG3 skew increases.
The predicted sequences were compared to the RefSeq 33 protein sequences and E. guineensis mRNA dataset via BLAST analysis. Different overlap thresholds, from 60% to 95% in 5% increments, were tested to determine the best threshold for further analysis, simultaneously maximizing the.
Even then, it is mainly planted as a backcross to guineensis (interspecific hybrid) to raise its yield. Individual components of the framework were trained on known genes of plants closely related to the oil palm, such as the date palm, to identify.
These proteins may have evolved, or been regained, only in plants, while other organisms have lost their ancestral genes during evolution. 99, 100 In conclusion, from our representative gene models, we estimate that about one-seventh of the genes in oil palm are.
A major limitation of many of the current approaches is their relatively poor performance in organisms with atypical distribution of nucleotides. Accurate gene prediction and the discovery of regulatory elements in promoter sequences are two of the most important challenges in computational.
This paper explores the oil palm genome, focusing on identifying and annotating genes that are important for plant health and oil production.