Unexpected Structure of Plant Promoters

Eckardt, Nancy A.

doi:10.1105/tpc.114.129239

“…Determination of the precise location of TSS is an essential preparatory step for motif discovery and reconstruction of gene regulatory networks [8]. The interaction of a vast number of proteins, multi-subunit complexes, and DNA binding sites make eukaryotic transcriptional regulation an extremely convoluted process [9]. Therefore, it is vitally important to have reliable methods for promoter prediction and analysis of regulatory elements if we are to enhance our capacity to engineer crops or to select therapeutic targets.…”

Section: Thousandsmentioning

confidence: 99%

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

Pachganov¹,

Murtazalieva

²

,

Zarubin

³

et al. 2019

Preprint

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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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“…Determination of the precise location of TSS is an essential preparatory step for motif discovery and reconstruction of gene regulatory networks (Triska et al, 2017a; Troukhan et al, 2009). The interaction of a vast number of proteins, multi-subunit complexes, and DNA binding sites make eukaryotic transcriptional regulation an extremely convoluted process (Eckardt, 2014). Therefore, it is vitally important to have reliable methods for promoter prediction and analysis of regulatory elements if we are to enhance our capacity to engineer crops or to select therapeutic targets.…”

Section: Introductionmentioning

confidence: 99%

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

Pachganov¹,

Murtazalieva

²

,

Zarubin

³

et al. 2019

PeerJ

View full text Add to dashboard Cite

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.

show abstract

“…Determination of the precise location of TSS is an essential preparatory step for motif discovery and reconstruction of gene regulatory networks (Triska et al 2017a;Troukhan et al 2009). The interaction of a vast number of proteins, multi-subunit complexes, and DNA binding sites make eukaryotic transcriptional regulation an extremely convoluted process (Eckardt 2014 Manuscript to be reviewed promoter prediction and analysis of regulatory elements if we are to enhance our capacity to engineer crops or to select therapeutic targets.…”

Section: Thousandsmentioning

confidence: 99%

Peer Review #1 of "TransPrise: a novel machine learning approach for eukaryotic promoter prediction (v0.2)"

2019

0

View full text Add to dashboard Cite

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 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.

show abstract

Unexpected Structure of Plant Promoters

Cited by 4 publications

References 5 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

Peer Review #1 of "TransPrise: a novel machine learning approach for eukaryotic promoter prediction (v0.2)"

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