Dataset Information

Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing.

ABSTRACT: RNA editing is a widespread post-transcriptional molecular phenomenon that can increase proteomic diversity, by modifying the sequence of completely or partially non-functional primary transcripts, through a variety of mechanistically and evolutionarily unrelated pathways. Editing by base substitution has been investigated in both animals and plants. However, conventional strategies based on directed Sanger sequencing are time-consuming and effectively preclude genome wide identification of RNA editing and assessment of partial and tissue-specific editing sites. In contrast, the high-throughput RNA-Seq approach allows the generation of a comprehensive landscape of RNA editing at the genome level. Short reads from Solexa/Illumina GA and ABI SOLiD platforms have been used to investigate the editing pattern in mitochondria of Vitis vinifera providing significant support for 401 C-to-U conversions in coding regions and an additional 44 modifications in non-coding RNAs. Moreover, 76% of all C-to-U conversions in coding genes represent partial RNA editing events and 28% of them were shown to be significantly tissue specific. Solexa/Illumina and SOLiD platforms showed different characteristics with respect to the specific issue of large-scale editing analysis, and the combined approach presented here reduces the false positive rate of discovery of editing events.

SUBMITTER: Picardi E

PROVIDER: S-EPMC2919710 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing.

Picardi Ernesto E Horner David S DS Chiara Matteo M Schiavon Riccardo R Valle Giorgio G Pesole Graziano G

Nucleic acids research 20100412 14

RNA editing is a widespread post-transcriptional molecular phenomenon that can increase proteomic diversity, by modifying the sequence of completely or partially non-functional primary transcripts, through a variety of mechanistically and evolutionarily unrelated pathways. Editing by base substitution has been investigated in both animals and plants. However, conventional strategies based on directed Sanger sequencing are time-consuming and effectively preclude genome wide identification of RNA ...[more]

PMID: 20385587

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Project description:BackgroundSingle-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.ResultsIn order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing.ConclusionsTaken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.

Dataset Information

Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing.

Publications

Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing.

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