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

ABSTRACT 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, Sep 27, 2015
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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.
    PLoS ONE 09/2014; 9(9):e106706. DOI:10.1371/journal.pone.0106706 · 3.23 Impact Factor
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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.
    Bioinformatics 06/2014; 30(19). DOI:10.1093/bioinformatics/btu377 · 4.98 Impact Factor
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    • "Some published tools have the function performing differential miRNA expression analysis between samples [3, 4, 7, 24]. However, miRanalyzer, CPSS and miRNAKey only allow a pair of samples using Chi Square or Fisher’s exact on raw read counts. "
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    ABSTRACT: Background miRNAs play a key role in normal physiology and various diseases. miRNA profiling through next generation sequencing (miRNA-seq) has become the main platform for biological research and biomarker discovery. However, analyzing miRNA sequencing data is challenging as it needs significant amount of computational resources and bioinformatics expertise. Several web based analytical tools have been developed but they are limited to processing one or a pair of samples at time and are not suitable for a large scale study. Lack of flexibility and reliability of these web applications are also common issues. Results We developed a Comprehensive Analysis Pipeline for microRNA Sequencing data (CAP-miRSeq) that integrates read pre-processing, alignment, mature/precursor/novel miRNA detection and quantification, data visualization, variant detection in miRNA coding region, and more flexible differential expression analysis between experimental conditions. According to computational infrastructure, users can install the package locally or deploy it in Amazon Cloud to run samples sequentially or in parallel for a large number of samples for speedy analyses. In either case, summary and expression reports for all samples are generated for easier quality assessment and downstream analyses. Using well characterized data, we demonstrated the pipeline’s superior performances, flexibility, and practical use in research and biomarker discovery. Conclusions CAP-miRSeq is a powerful and flexible tool for users to process and analyze miRNA-seq data scalable from a few to hundreds of samples. The results are presented in the convenient way for investigators or analysts to conduct further investigation and discovery. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-423) contains supplementary material, which is available to authorized users.
    BMC Genomics 06/2014; 15(1):423. DOI:10.1186/1471-2164-15-423 · 3.99 Impact Factor
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