BACKGROUND: A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required. RESULTS: Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools. CONCLUSIONS: SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.

SUMMARY: Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48; (ii) improved strategies applied to detect ncRNAs; (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA; (iv) identification of differentially expressed ncRNAs among multiple samples; (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research. AVAILABILITY AND IMPLEMENTATION: CPSS 2.0 is implemented in PHP + Perl + R and can be freely accessed at http://114.214.166.79/cpss2.0/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. RESULTS: To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. CONCLUSION: Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/ .

UNLABELLED: : Next-Generation Sequencing (NGS) technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent differences from their corresponding mature reference sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). These isomiRs mainly originate via the imprecise and alternative cleavage during the pre-miRNA processing and post-transcriptional modifications that influence miRNA stability, their sub-cellular localization and target selection. Although several tools for the identification of isomiR have been reported, no bioinformatics resource dedicated to gather isomiRs from public NGS data and to provide functional analysis of these isomiRs is available to date. Thus, a free online database, IsomiR Bank has been created to integrate isomiRs detected by our previously published algorithm CPSS. In total, 2727 samples (Small RNA NGS data downloaded from ArrayExpress) from eight species (Arabidopsis thaliana, Drosophila melanogaster, Danio rerio, Homo sapiens, Mus musculus, Oryza sativa, Solanum lycopersicum and Zea mays) are analyzed. At present, 308 919 isomiRs from 4706 mature miRNAs are collected into IsomiR Bank. In addition, IsomiR Bank provides target prediction and enrichment analysis to evaluate the effects of isomiRs on target selection. AVAILABILITY AND IMPLEMENTATION: IsomiR Bank is implemented in PHP/PERL + MySQL + R format and can be freely accessed at http://mcg.ustc.edu.cn/bsc/isomir/ CONTACTS: : [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: 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 http://mcg.ustc.edu.cn/db/cpss/index.html or http://mcg.ustc.edu.cn/sdap1/cpss/index.html.