PalmXplore: oil palm gene database

Sanusi, Nik Shazana Nik Mohd; Rosli, Rozana; Halim, Mohd Amin Ab; Chan, Kuang‐Lim; Nagappan, Jayanthi; Azizi, Norazah; Amiruddin, Nadzirah; Tatarinova, Tatiana V.; Low, Eng‐Ti Leslie

doi:10.1093/database/bay095

“…We used Fgenesh mRNA-based gene prediction models, since Fgenesh-annotated promoters have a more pronounced nucleotide consensus as compared to the promoters annotated by MSU (Triska et al, 2017b). Fgenesh was successfully used to annotate several plant genomes (Chan et al, 2017a; Chan et al, 2017b; Davis et al, 2010; Ito et al, 2005; Jiang et al, 2015; Nasiri et al, 2013; Sanusi et al, 2018; Sheshadri et al, 2018; Yao et al, 2005). Therefore, we selected the Fgenesh annotation as the gold standard for our analysis.…”

Section: Methodsmentioning

confidence: 99%

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

Pachganov¹,

Murtazalieva

²

,

Zarubin

³

et al. 2019

PeerJ

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As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise—an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. Our pipeline consists of two parts: the binary classifier operates the first, and if a sequence is classified as TSS-containing the regression step follows, where the precise location of TSS is being identified. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. The Matthews correlation coefficient value for TransPrise is 0.79, more than two times larger than the 0.31 for TSSPlant classification models. This represents a high level of prediction accuracy. Additionally, the mean absolute error for the regression model is 29.19 nt, allowing for accurate prediction of TSS location. TransPrise was also tested in Homo sapiens, where mean absolute error of the regression model was 47.986 nt. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at (http://compubioverne.group/). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.

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“…We used Fgenesh mRNA-based gene prediction models, since Fgeneshannotated promoters have a more pronounced nucleotide consensus as compared to the promoters annotated by MSU [4]. Fgenesh was successfully used to annotate several plant genomes [38,[43][44][45][46][47][48][49]. Therefore, we selected the Fgenesh annotation as the gold standard for our analysis.…”

Section: Selection Of Genome Annotation Versionmentioning

confidence: 99%

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

Pachganov¹,

Murtazalieva

²

,

Zarubin

³

et al. 2019

Preprint

Self Cite

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As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise - an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at ( http://compubioverne.group /). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.

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“…Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) accessions were retrieved for each DEG, according to the PalmXplore database of oil palm [55]. We rstly clustered all the samples using both principal component analysis (PCA) and heatmap approaches with the program ClustVis [56], based on the relative expression of DEGs, to investigate the overall expression patterns between drought treatment and control groups.…”

Section: Functional Annotation Of Degsmentioning

confidence: 99%

Genes, pathways and networks responding to drought stress in oil palm roots

Wang

¹

,

Lee

²

,

Ye

³

et al. 2020

Preprint

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Background: Palm oil is an important feedstock for biofuel. Palm oil yield is seriously affected by drought stress. However, not much is known about the molecular responses of oil palm to drought stress.Results: We studied the root transcriptomic responses of oil palm seedlings under normal culture and 14-day drought stress using RNA-seq and bioinformatics analysis. We identified 1293 differentially expressed genes (DEGs), involved in several molecular processes, such as cell wall biogenesis and functions, phenylpropanoid biosynthesis and metabolisms, ion transport and homeostasis and cellular ketone metabolic process, as well as small molecule biosynthetic process. We observed that DEGs were significantly enriched into the two categories: hormone regulation and metabolism, as well as ABC transporters. In addition, we identified three protein-protein interaction networks involved in the response to drought stress, including ion transport, reactive nitrogen species metabolic process and nitrate assimilation. Finally, 96 differentially expressed transcription factors were detected to be associated with drought stress response, which were classified into 28 families.Conclusions : The transcriptomic responses of oil palm seedlings to drought stress were systematically analysed, revealing important genes, pathways, networks and transcription factors involved in drought stress responses. These results provide new insights into the mechanisms of drought stress responses in economic crops. The genes and pathways identified in this study provide valuable genomic resources to improve drought tolerance of oil palm by both genetic modification and selective breeding.

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PalmXplore: oil palm gene database

Cited by 23 publications

References 30 publications

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

Genes, pathways and networks responding to drought stress in oil palm roots

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