scholarly journals Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA

2019 ◽  
Vol 47 (20) ◽  
pp. e130-e130 ◽  
Author(s):  
Jin Wang ◽  
Bing Liang Alvin Chew ◽  
Yong Lai ◽  
Hongping Dong ◽  
Luang Xu ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Rna Processing ◽  
Processing Conditions ◽  
Transcription Start ◽  
Purine Nucleotides ◽  
Eukaryotic Transcription ◽  
Transcription Start Sites ◽  
Mouse Tissues ◽  
Human Lymphoblast ◽  
Lymphoblast Cells

Abstract Chemical modification of transcripts with 5′ caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps—m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG—and 5 ‘metabolite’ caps—NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus, Escherichia coli, yeast, mouse tissues, and human cells, we discovered new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and high proportions of caps lacking 2′-O-methylation (m7Gpppm6A in mammals, m7GpppA in dengue virus). While substantial Dimroth-induced loss of m1A and m1Am arose with specific RNA processing conditions, human lymphoblast cells showed no detectable m1A or m1Am in caps. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps.

scholarly journals Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA

2019 ◽  
Author(s):  
Jin Wang ◽  
Bing Liang Alvin Chew ◽  
Yong Lai ◽  
Hongping Dong ◽  
Luang Xu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Escherichia Coli ◽  
Quantitative Analysis ◽  
Dengue Virus ◽  
Chemical Modification ◽  
Transcription Start ◽  
Purine Nucleotides ◽  
Eukaryotic Transcription ◽  
Transcription Start Sites ◽  
A Cell

ABSTRACTChemical modification of transcripts with 5’ caps occurs in all organisms. Here we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of the cap epitranscriptome in any organism. The method was piloted with 21 canonical caps – m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG – and 5 “metabolite” caps – NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus,Escherichia coli, yeast, mice, and humans, we discovered four new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and surprisingly high proportions of caps lacking 2’-O-methylation, such as m7Gpppm6A in mammals and m7GpppA in dengue virus, and we did not detect cap m1A/m1Am in humans. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps. The mystery around cap m1A/m1Am analysis remains unresolved.

scholarly journals ReCappable Seq: Comprehensive Determination of Transcription Start Sites derived from all RNA polymerases

2019 ◽  
Author(s):  
Bo Yan ◽  
George Tzertzinis ◽  
Ira Schildkraut ◽  
Laurence Ettwiller
Keyword(s):  
Rna Polymerases ◽  
Transcription Start ◽  
Eukaryotic Transcription ◽  
Pol Ii ◽  
Transcription Start Sites ◽  
Cell Functions ◽  
A Genome ◽  
Wide Scale ◽  
Nucleotide Resolution ◽  
Pol Iii

AbstractMethodologies for determining eukaryotic Transcription Start Sites (TSS) rely on the selection of the 5’ canonical cap structure of Pol-II transcripts and are consequently ignoring entire classes of TSS derived from other RNA polymerases which play critical roles in various cell functions. To overcome this limitation, we developed ReCappable-seq and identified TSS from Pol-ll and non-Pol-II transcripts at nucleotide resolution. Applied to the human transcriptome, ReCappable-seq identifies Pol-II TSS with higher specificity than CAGE and reveals a rich landscape of TSS associated notably with Pol-III transcripts which have been previously not possible to study on a genome-wide scale. Novel TSS consistent with non-Pol-II transcripts can be found in the nuclear and mitochondrial genomes. By identifying TSS derived from all RNA-polymerases, ReCappable-seq reveals distinct epigenetic marks among Pol-lI and non-Pol-II TSS and provides a unique opportunity to concurrently interrogate the regulatory landscape of coding and non-coding RNA.

scholarly journals TransPrise: a novel machine learning approach for eukaryotic promoter prediction

2019 ◽  
Author(s):  
Stepan Pachganov ◽  
Khalimat Murtazalieva ◽  
Alexei Zarubin ◽  
Dmitry Sokolov ◽  
Duane Chartier ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Source Code ◽  
Processing Unit ◽  
Promoter Prediction ◽  
Transcription Start ◽  
Eukaryotic Transcription ◽  
Transcription Start Sites ◽  
Eukaryotic Promoter ◽  
A Genome ◽  
Graphics Processing

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.

scholarly journals TransPrise: a novel machine learning approach for eukaryotic promoter prediction

2019 ◽  
Author(s):  
Stepan Pachganov ◽  
Khalimat Murtazalieva ◽  
Alexei Zarubin ◽  
Dmitry Sokolov ◽  
Duane Chartier ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Source Code ◽  
Processing Unit ◽  
Promoter Prediction ◽  
Transcription Start ◽  
Eukaryotic Transcription ◽  
Transcription Start Sites ◽  
Eukaryotic Promoter ◽  
A Genome ◽  
Graphics Processing

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.

scholarly journals Genome-Wide Identification of Transcription Start Sites, Promoters and Transcription Factor Binding Sites in E. coli

PLoS ONE ◽  
2009 ◽  
Vol 4 (10) ◽  
pp. e7526 ◽  
Author(s):  
Alfredo Mendoza-Vargas ◽  
Leticia Olvera ◽  
Maricela Olvera ◽  
Ricardo Grande ◽  
Leticia Vega-Alvarado ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
E Coli ◽  
Factor Binding ◽  
Genome Wide
Sign in / Sign up

Export Citation Format

Share Document