CPSS: A computational platform for the analysis of small RNA deep sequencing data

Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.
Bioinformatics (Impact Factor: 4.98). 05/2012; 28(14):1925-7. DOI: 10.1093/bioinformatics/bts282
Source: PubMed


Next generation sequencing (NGS) techniques have been widely used to document the small ribonucleic acids (RNAs) implicated in a variety of biological, physiological and pathological processes. An integrated computational tool is needed for handling and analysing the enormous datasets from small RNA deep sequencing approach. Herein, we present a novel web server, CPSS (a computational platform for the analysis of small RNA deep sequencing data), designed to completely annotate and functionally analyse microRNAs (miRNAs) from NGS data on one platform with a single data submission. Small RNA NGS data can be submitted to this server with analysis results being returned in two parts: (i) annotation analysis, which provides the most comprehensive analysis for small RNA transcriptome, including length distribution and genome mapping of sequencing reads, small RNA quantification, prediction of novel miRNAs, identification of differentially expressed miRNAs, piwi-interacting RNAs and other non-coding small RNAs between paired samples and detection of miRNA editing and modifications and (ii) functional analysis, including prediction of miRNA targeted genes by multiple tools, enrichment of gene ontology terms, signalling pathway involvement and protein-protein interaction analysis for the predicted genes. CPSS, a ready-to-use web server that integrates most functions of currently available bioinformatics tools, provides all the information wanted by the majority of users from small RNA deep sequencing datasets. AVAILABILITY: CPSS is implemented in PHP/PERL+MySQL+R and can be freely accessed at or

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Available from: Qinghua Shi
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    • "). CPSS (Zhang et al., 2012) and Chimira (Vitsios & Enright, 2015) servers processes datasets for limited number of species only. Another tool, isomiRID (De Oliveira, Christoff & Margis, 2013), reports modified miRNA list without distinguishing templated and non-templated modifications. "
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    ABSTRACT: In the past decade, the microRNAs (miRNAs) have emerged to be important regulators of gene expression across various species. Several studies have confirmed different types of post-transcriptional modifications at terminal ends of miRNAs. The reports indicate that miRNA modifications are conserved and functionally significant as it may affect miRNA stability and ability to bind mRNA targets, hence affecting target gene repression. Next Generation Sequencing (NGS) of the small RNA (sRNA) provides an efficient and reliable method to explore miRNA modifications. The need for dedicated software, especially for users with little knowledge of computers, to determine and analyze miRNA modifications in sRNA NGS data, motivated us to develop miRMOD. miRMOD is a user-friendly, Microsoft Windows and Graphical User Interface (GUI) based tool for identification and analysis of 5′ and 3′ miRNA modifications (non-templated nucleotide additions and trimming) in sRNA NGS data. In addition to identification of miRNA modifications, the tool also predicts and compares the targets of query and modified miRNAs. In order to compare binding affinities for the same target, miRMOD utilizes minimum free energies of the miRNA:target and modified-miRNA:target interactions. Comparisons of the binding energies may guide experimental exploration of miRNA post-transcriptional modifications. The tool is available as a stand-alone package to overcome large data transfer problems commonly faced in web-based high-throughput (HT) sequencing data analysis tools. miRMOD package is freely available at .
    Full-text · Article · Oct 2015 · PeerJ
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    • "For GO analysis of the predicted miRNA target genes from CRCs and COCs, the predicted target genes of differentially expressed and selected miRNAs were subjected to analysis of gene ontology terms [34]. The target genes were mapped to the GO annotation dataset, and the enriched biological processes were extracted using the hypergeometric test according our previous reports [29]. A GO term was identified as a key term in this study when its ratio of enrichment was >2 and the p-value was <0.05. "
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    ABSTRACT: During folliculogenesis, cumulus cells surrounding the oocyte differentiate into corona radiata cells (CRCs) and cumulus oophorus cells (COCs), which are involved in gonadal steroidogenesis and the development of germ cells. Several studies suggested that microRNAs (miRNAs) play an important regulatory role at the post-transcriptional level in cumulus cells. However, comparative miRNA profiles and associated processes in human CRCs and COCs have not been reported before. In this study, miRNA profiles were obtained from CRCs and COCs using next generation sequencing in women undergoing controlled ovarian stimulation for IVF. A total of 785 and 799 annotated miRNAs were identified in CRCs and COCs, while high expression levels of six novel miRNAs were detected both in CRCs and in COCs. In addition, different expression patterns in CRCs and COCs were detected in 72 annotated miRNAs. To confirm the miRNA profile in COCs and CRCs, quantitative real-time PCR was used to validate the expression of annotated miRNAs, differentially expressed miRNAs, and novel miRNAs. The miRNAs in the let-7 family were found to be involved in the regulation of a broad range of biological processes in both cumulus cell populations, which was accompanied by a large amount of miRNA editing. Bioinformatics analysis showed that amino acid and energy metabolism were targeted significantly by miRNAs that were differentially expressed between CRCs and COCs. Our work extends the current knowledge of the regulatory role of miRNAs and their targeted pathways in folliculogenesis, and provides novel candidates for molecular biomarkers in the research of female infertility.
    Full-text · Article · Sep 2014 · PLoS ONE
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    • "There are several web services for miRNA-seq data analysis targeting the needs of non-technical users. Deep-sequencing Small RNA analysis Pipeline (DSAP) (Huang et al. 2010) quantifies known miRNAs, while miRAnalyzer (Hackenberg et al., 2011), Computational Platform analysis of Small RNA deep Sequencing data (CPSS) (Zhang et al., 2012) and wapRNA (Zhao et al., 2011) perform novel miRNA prediction and target prediction. mirTools (Zhu et al., 2010) add functional annotation, while omiRas (Muller et al., 2013) allows for upload of raw FASTQ files. "
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    ABSTRACT: Summary: MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours—>600% end-to-end performance improvement over state of the art. MAGI’s salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication.Availability and implementation: MAGI relies on the Node.js JavaScript framework, along with NVIDIA CUDA C, PHP: Hypertext Preprocessor (PHP), Perl and R. It is freely available at j5kim@ucsd.eduSupplementary information: Supplementary data are available at Bioinformatics online.
    Full-text · Article · Jun 2014 · Bioinformatics
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