Development of large-scale sequencing technology has yielded extensive small RNA (sRNA) sequencing data. Many small RNA analysis tools have been developed accordingly, such as Chimira (Vitsios and Enright, 2015), WapRNA (Zhao, et al., 2011), Oasis (Capece, et al., 2015), sRNAtoobox (Rueda, et al., 2015), mirTool 2.0 (Wu, et al., 2013), MAGI (Kim, et al., 2014), ISRNA (Luo, et al., 2014) and our published work, CPSS 1.0 (Zhang, et al., 2012).
With increasing evidence showing that small RNAs function in the context of complex regulatory network (Bracken, et al., 2016), a systematic interpretation platform of sRNA data is still in great demand. However, current tools provide simple small RNA profiling rather than a systematic analysis. Their main limitations are as followed: 1) Their analysis cannot provide comprehensiveness and profoundness at the same time. For instance, Chimira is specified in detecting miRNA modification. Although many tools such as sRNAtoolbox and Oasis are equipped with multiple modules, they fail to integrate each other. Thus, users should conduct unnecessary intermediate submission. 2) Most of existing methods are short of graph presentations of the results. In some cases, even they provide plenty of graphs, lack of clear illustration and appropriate layout does not help to improve their popularity. 3) Owing to the fixed analysis report, users are not able to modulate parameters after the completion of analysis.
To meet the urgent demand, we updated CPSS 1.0 to CPSS 2.0, including the following improvements: 1) Within a single submission, CPSS 2.0 is able to deliver analysis report from ncRNA quantification to miRNA target prediction and annotation of single and multiple datasets. With lncRNA and circRNA added to the system, CPSS 2.0 assembles the most abundant ncRNA modules. The number of supported species is also substantially increased from 10 to 48. All databases and software integrated in CPSS 2.0 are updated to the latest version. 2) CPSS 2.0 classifies all results into two main categories “General Results” and “Functional Analysis”. Each has several subcategories, presenting results in graphs and charts, which is very helpful for users with an intuitive understanding of statistic data. 3) On each detailed result page, CPSS 2.0 provides search function for user to search specific terms or values. On GO, Pathway and Protein domain detailed pages, user could modify default parameters, P-value and enrichment fold. Taken together, CPSS 2.0 is the most comprehensive webserver so far among all available tools. Detailed comparison in specific modules we deemed essential or important is provided in the supplementary table 1. We believe that CPSS 2.0 could assist users in a comprehensive and effective manner.
Changlin Wan, Jianing Gao, Huan Zhang, Xiaohua Jiang, Qiguang Zang2, Rongjun Ban1, Yuanwei Zhang,* and Qinghua Shi,*. CPSS 2.0: a computational platform update for the analysis of small RNA sequencing data. Bioinformatics. 2017 Feb 8. doi: 10.1093/bioinformatics/btx066.
Vitsios, D.M. and Enright, A.J. Chimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics 2015:btv380.
Zhao, W., et al. wapRNA: a web-based application for the processing of RNA sequences. Bioinformatics 2011;27(21):3076-3077.
Capece, V., et al. Oasis: online analysis of small RNA deep sequencing data. Bioinformatics 2015;31(13):2205-2207.
Rueda, A., et al. sRNAtoolbox: an integrated collection of small RNA research tools. Nucleic acids research 2015;43(W1):W467-W473.
Wu, J., et al. mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA biology 2013;10(7):1087-1092.
Kim, J., et al. MAGI: a Node. js web service for fast microRNA-Seq analysis in a GPU infrastructure. Bioinformatics 2014;30(19):2826-2827.
Luo, G.-Z., et al. ISRNA: an integrative online toolkit for short reads from high-throughput sequencing data. Bioinformatics 2014;30(3):434-436.
Zhang, Y., et al. CPSS: a computational platform for the analysis of small RNA deep sequencing data. Bioinformatics 2012;28(14):1925-1927.
Bracken, C.P., Scott, H.S. and Goodall, G.J. A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet 2016;17(12):719-732.