Dataset Information

COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis.

ABSTRACT: Small RNA-Seq is a common means to interrogate the small RNA'ome or the full spectrum of small RNAs (<200 nucleotide length) of a biological system. A pivotal problem in NGS based small RNA analysis is identifying and quantifying the small RNA'ome constituent components. For example, small RNAs in the circulatory system (circulating RNAs) are potential disease biomarkers and their function is being actively investigated. Most existing NGS data analysis tools focus on the microRNA component and a few other small RNA types like piRNA, snRNA and snoRNA. A comprehensive platform is needed to interrogate the full small RNA'ome, a prerequisite for down-stream data analysis. We present COMPSRA, a comprehensive modular stand-alone platform for identifying and quantifying small RNAs from small RNA sequencing data. COMPSRA contains prebuilt customizable standard RNA databases and sequence processing tools to enable turnkey basic small RNA analysis. We evaluated COMPSRA against comparable existing tools on small RNA sequencing data set from serum samples of 12 healthy human controls, and COMPSRA identified a greater diversity and abundance of small RNA molecules. COMPSRA is modular, stand-alone and integrates multiple customizable RNA databases and sequence processing tool and is distributed under the GNU General Public License free to non-commercial registered users at https://github.com/cougarlj/COMPSRA.

SUBMITTER: Li J

PROVIDER: S-EPMC7067867 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis.

Li Jiang J Kho Alvin T AT Chase Robert P RP Pantano Lorena L Farnam Leanna L Amr Sami S SS Tantisira Kelan G KG

Scientific reports 20200312 1

Small RNA-Seq is a common means to interrogate the small RNA'ome or the full spectrum of small RNAs (<200 nucleotide length) of a biological system. A pivotal problem in NGS based small RNA analysis is identifying and quantifying the small RNA'ome constituent components. For example, small RNAs in the circulatory system (circulating RNAs) are potential disease biomarkers and their function is being actively investigated. Most existing NGS data analysis tools focus on the microRNA component and a ...[more]

PMID: 32165660

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Project description:BackgroundRNA-Seq has become one of the most widely used applications based on next-generation sequencing technology. However, raw RNA-Seq data may have quality issues, which can significantly distort analytical results and lead to erroneous conclusions. Therefore, the raw data must be subjected to vigorous quality control (QC) procedures before downstream analysis. Currently, an accurate and complete QC of RNA-Seq data requires of a suite of different QC tools used consecutively, which is inefficient in terms of usability, running time, file usage, and interpretability of the results.ResultsWe developed a comprehensive, fast and easy-to-use QC pipeline for RNA-Seq data, RNA-QC-Chain, which involves three steps: (1) sequencing-quality assessment and trimming; (2) internal (ribosomal RNAs) and external (reads from foreign species) contamination filtering; (3) alignment statistics reporting (such as read number, alignment coverage, sequencing depth and pair-end read mapping information). This package was developed based on our previously reported tool for general QC of next-generation sequencing (NGS) data called QC-Chain, with extensions specifically designed for RNA-Seq data. It has several features that are not available yet in other QC tools for RNA-Seq data, such as RNA sequence trimming, automatic rRNA detection and automatic contaminating species identification. The three QC steps can run either sequentially or independently, enabling RNA-QC-Chain as a comprehensive package with high flexibility and usability. Moreover, parallel computing and optimizations are embedded in most of the QC procedures, providing a superior efficiency. The performance of RNA-QC-Chain has been evaluated with different types of datasets, including an in-house sequencing data, a semi-simulated data, and two real datasets downloaded from public database. Comparisons of RNA-QC-Chain with other QC tools have manifested its superiorities in both function versatility and processing speed.ConclusionsWe present here a tool, RNA-QC-Chain, which can be used to comprehensively resolve the quality control processes of RNA-Seq data effectively and efficiently.

Project description:RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

Dataset Information

COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis.

Publications

COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis.

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