Dataset Information

Using Small RNA-seq Data to Detect siRNA Duplexes Induced by Plant Viruses.

ABSTRACT: Small interfering RNA (siRNA) duplexes are short (usually 21 to 24 bp) double-stranded RNAs (dsRNAs) with several overhanging nucleotides at both 5'- and 3'-ends. It has been found that siRNA duplexes bind the RNA-induced silencing complex (RISC) and cleave the sense strands with endonucleases. In this study, for the first time, we detected siRNA duplexes induced by plant viruses on a large scale using next-generation sequencing (NGS) data. In addition, we used the detected 21 nucleotide (nt) siRNA duplexes with 2 nt overhangs to construct a dataset for future data mining. The analytical results of the features in the detected siRNA duplexes were consistent with those from previous studies. The investigation of siRNA duplexes is useful for a better understanding of the RNA interference (RNAi) mechanism. It can also help to improve the virus detection based on the small RNA sequencing (sRNA-seq) technologies and to rationally design siRNAs for RNAi experiments.

SUBMITTER: Niu X

PROVIDER: S-EPMC5485527 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Using Small RNA-seq Data to Detect siRNA Duplexes Induced by Plant Viruses.

Niu Xiaoran X Sun Yu Y Chen Ze Z Li Rugang R Padmanabhan Chellappan C Ruan Jishou J Kreuze Jan F JF Ling KaiShu K Fei ZhangJun Z Gao Shan S

Genes 20170616 6

Small interfering RNA (siRNA) duplexes are short (usually 21 to 24 bp) double-stranded RNAs (dsRNAs) with several overhanging nucleotides at both 5'- and 3'-ends. It has been found that siRNA duplexes bind the RNA-induced silencing complex (RISC) and cleave the sense strands with endonucleases. In this study, for the first time, we detected siRNA duplexes induced by plant viruses on a large scale using next-generation sequencing (NGS) data. In addition, we used the detected 21 nucleotide (nt) si ...[more]

PMID: 28621718

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Project description:MotivationIn eukaryotic cells, alternative splicing expands the diversity of RNA transcripts and plays an important role in tissue-specific differentiation, and can be misregulated in disease. To understand these processes, there is a great need for methods to detect differential transcription between samples. Our focus is on samples observed using short-read RNA sequencing (RNA-seq).MethodsWe characterize differential transcription between two samples as the difference in the relative abundance of the transcript isoforms present in the samples. The magnitude of differential transcription of a gene between two samples can be measured by the square root of the Jensen Shannon Divergence (JSD*) between the gene's transcript abundance vectors in each sample. We define a weighted splice-graph representation of RNA-seq data, summarizing in compact form the alignment of RNA-seq reads to a reference genome. The flow difference metric (FDM) identifies regions of differential RNA transcript expression between pairs of splice graphs, without need for an underlying gene model or catalog of transcripts. We present a novel non-parametric statistical test between splice graphs to assess the significance of differential transcription, and extend it to group-wise comparison incorporating sample replicates.ResultsUsing simulated RNA-seq data consisting of four technical replicates of two samples with varying transcription between genes, we show that (i) the FDM is highly correlated with JSD* (r=0.82) when average RNA-seq coverage of the transcripts is sufficiently deep; and (ii) the FDM is able to identify 90% of genes with differential transcription when JSD* >0.28 and coverage >7. This represents higher sensitivity than Cufflinks (without annotations) and rDiff (MMD), which respectively identified 69 and 49% of the genes in this region as differential transcribed. Using annotations identifying the transcripts, Cufflinks was able to identify 86% of the genes in this region as differentially transcribed. Using experimental data consisting of four replicates each for two cancer cell lines (MCF7 and SUM102), FDM identified 1425 genes as significantly different in transcription. Subsequent study of the samples using quantitative real time polymerase chain reaction (qRT-PCR) of several differential transcription sites identified by FDM, confirmed significant differences at these sites.Availabilityhttp://csbio-linux001.cs.unc.edu/nextgen/software/FDM CONTACT: darshan@email.unc.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:BackgroundAs sequencing costs are being lowered continuously, RNA-seq has gradually been adopted as the first choice for comparative transcriptome studies with bacteria. Unlike microarrays, RNA-seq can directly detect cDNA derived from mRNA transcripts at a single nucleotide resolution. Not only does this allow researchers to determine the absolute expression level of genes, but it also conveys information about transcript structure. Few automatic software tools have yet been established to investigate large-scale RNA-seq data for bacterial transcript structure analysis.ResultsIn this study, 54 directional RNA-seq libraries from Salmonella serovar Typhimurium (S. Typhimurium) 14028s were examined for potential relationships between read mapping patterns and transcript structure. We developed an empirical method, combined with statistical tests, to automatically detect key transcript features, including transcriptional start sites (TSSs), transcriptional termination sites (TTSs) and operon organization. Using our method, we obtained 2,764 TSSs and 1,467 TTSs for 1331 and 844 different genes, respectively. Identification of TSSs facilitated further discrimination of 215 putative sigma 38 regulons and 863 potential sigma 70 regulons. Combining the TSSs and TTSs with intergenic distance and co-expression information, we comprehensively annotated the operon organization in S. Typhimurium 14028s.ConclusionsOur results show that directional RNA-seq can be used to detect transcriptional borders at an acceptable resolution of ±10-20 nucleotides. Technical limitations of the RNA-seq procedure may prevent single nucleotide resolution. The automatic transcript border detection methods, statistical models and operon organization pipeline that we have described could be widely applied to RNA-seq studies in other bacteria. Furthermore, the TSSs, TTSs, operons, promoters and unstranslated regions that we have defined for S. Typhimurium 14028s may constitute valuable resources that can be used for comparative analyses with other Salmonella serotypes.

Dataset Information

Using Small RNA-seq Data to Detect siRNA Duplexes Induced by Plant Viruses.

Publications

Using Small RNA-seq Data to Detect siRNA Duplexes Induced by Plant Viruses.

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