Publications

@article{Wu20181,

title = {circlncRNAnet: An integrated web-based resource for mapping functional networks of long or circular forms of noncoding RNAs},

author = {S. -M. Wu and H. Liu and P. -J. Huang and I. Y. -F. Chang and C. -C. Lee and C. -Y. Yang and W. -S. Tsai and B. C. -M. Tan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043516778&doi=10.1093%2fgigascience%2fgix118&partnerID=40&md5=75661152f773bd306214b4b3c762a973},

doi = {10.1093/gigascience/gix118},

issn = {2047217X},

year  = {2018},

date = {2018-01-01},

journal = {GigaScience},

volume = {7},

number = {1},

pages = {1-10},

publisher = {Oxford University Press},

abstract = {Background: Despite their lack of protein-coding potential, long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation. Given their widespread target spectrum, as well as extensive modes of action, a complete understanding of their biological relevance will depend on integrative analyses of systems data at various levels. Findings: While a handful of publicly available databases have been reported, existing tools do not fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest. Through an integrated and streamlined design, circlncRNAnet aims to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions, on the basis of large-scale RNA-seq data. This web server is implemented with several features that represent advances in the bioinformatics of ncRNAs: (1) a flexible framework that accepts and processes user-defined next-generation sequencing-based expression data; (2) multiple analytic modules that assign and productively assess the regulatory networks of user-selected ncRNAs by cross-referencing extensively curated databases; (3) an all-purpose, information-rich workflow design that is tailored to all types of ncRNAs. Outputs on expression profiles, co-expression networks and pathways, and molecular interactomes, are dynamically and interactively displayed according to user-defined criteria. Conclusions: In short, users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on circRNAs/lncRNAs of their interest. In summary, circlncRNAnet provides a "one-stop" resource for in-depth analyses of ncRNA biology. circlncRNAnet is freely available at http://app.cgu.edu.tw/circlnc/. © The Author(s) 2017.},

note = {cited By 41},

keywords = {circular, circular RNA; long untranslated RNA; RNA binding protein; unclassified drug; untranslated RNA; messenger RNA; microRNA; RNA; RNA, Computational Biology; Databases, Genetic; Gene Regulatory Networks; High-Throughput Nucleotide Sequencing; Humans; Internet; MicroRNAs; RNA; RNA Processing, Genetic; User-Computer Interface, Long Noncoding; RNA, Messenger; Sequence Analysis, Nucleic Acid; Epigenesis, Post-Transcriptional; RNA, RNA; Transcription},

pubstate = {published},

tppubtype = {article}

}

@article{Huang20131340,

title = {CPAP: Cancer panel analysis pipeline},

author = {P. -J. Huang and Y. -M. Yeh and R. -C. Gan and C. -C. Lee and T. -W. Chen and C. -Y. Lee and H. Liu and S. -J. Chen and P. Tang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884532707&doi=10.1002%2fhumu.22386&partnerID=40&md5=76fe590e5c4e08ac19172d34d42dabba},

doi = {10.1002/humu.22386},

issn = {10597794},

year  = {2013},

date = {2013-01-01},

journal = {Human Mutation},

volume = {34},

number = {10},

pages = {1340-1346},

abstract = {Targeted sequencing using next-generation sequencing technologies is currently being rapidly adopted for clinical sequencing and cancer marker tests. However, no existing bioinformatics tool is available for the analysis and visualization of multiple targeted sequencing datasets. In the present study, we use cancer panel targeted sequencing datasets generated by the Life Technologies Ion Personal Genome Machine Sequencer as an example to illustrate how to develop an automated pipeline for the comparative analyses of multiple datasets. Cancer Panel Analysis Pipeline (CPAP) uses standard output files from variant calling software to generate a distribution map of SNPs among all of the samples in a circular diagram generated by Circos. The diagram is hyperlinked to a dynamic HTML table that allows the users to identify target SNPs by using different filters. CPAP also integrates additional information about the identified SNPs by linking to an integrated SQL database compiled from SNP-related databases, including dbSNP, 1000 Genomes Project, COSMIC, and dbNSFP. CPAP only takes 17 min to complete a comparative analysis of 500 datasets. CPAP not only provides an automated platform for the analysis of multiple cancer panel datasets but can also serve as a model for any customized targeted sequencing project. © 2013 Wiley Periodicals, Inc.},

note = {cited By 3},

keywords = {Biological; Web Browser, cancer panel; Circos; ion torrent; target sequencing, Computational Biology; Databases, Genetic; Humans; Neoplasms; Software; Tumor Markers},

pubstate = {published},

tppubtype = {article}

}