@Article{catchen_stacks:_2013,
  title = {Stacks: An Analysis Tool Set for Population Genomics},
  volume = {22},
  issn = {0962-1083},
  url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3936987/},
  doi = {10.1111/mec.12354},
  shorttitle = {Stacks},
  abstract = {Massively parallel short-read sequencing technologies, coupled with powerful software platforms, are enabling investigators to analyse tens of thousands of genetic markers. This wealth of data is rapidly expanding and allowing biological questions to be addressed with unprecedented scope and precision. The sizes of the data sets are now posing significant data processing and analysis challenges. Here we describe an extension of the Stacks software package to efficiently use genotype-by-sequencing data for studies of populations of organisms. Stacks now produces core population genomic summary statistics and SNP-by-SNP statistical tests. These statistics can be analysed across a reference genome using a smoothed sliding window. Stacks also now provides several output formats for several commonly used downstream analysis packages. The expanded population genomics functions in Stacks will make it a useful tool to harness the newest generation of massively parallel genotyping data for ecological and evolutionary genetics.},
  timestamp = {2017-05-08T17:18:26Z},
  number = {11},
  journaltitle = {Molecular ecology},
  shortjournal = {Mol Ecol},
  author = {JULIAN CATCHEN and PAUL A. HOHENLOHE and SUSAN BASSHAM and ANGEL AMORES and WILLIAM A. CRESKO},
  urldate = {2017-05-08},
  date = {2013-06},
  pages = {3124--3140},
  file = {CATCHEN et al_2013_Stacks.pdf:/Users/han/Library/Application Support/Zotero/Profiles/n80u1dxx.default/zotero/storage/SP9QJFMT/CATCHEN et al_2013_Stacks.pdf:application/pdf},
  groups = {manzanita,manzanita},
  eprinttype = {pmid},
  eprint = {23701397},
  pmcid = {PMC3936987},
}
@Article{robinson_scaling_2010,
  title = {A Scaling Normalization Method for Differential Expression Analysis of {{RNA}}-Seq Data},
  volume = {11},
  issn = {1474-760X},
  url = {https://doi.org/10.1186/gb-2010-11-3-r25},
  doi = {10.1186/gb-2010-11-3-r25},
  abstract = {The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.},
  timestamp = {2017-11-17T12:57:45Z},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  author = {Mark D. Robinson and Alicia Oshlack},
  date = {2010-03-02},
  pages = {R25},
  file = {Robinson_Oshlack_2010_A scaling normalization method for differential expression analysis of RNA-seq.pdf:/Users/han/Library/Application Support/Zotero/Profiles/n80u1dxx.default/zotero/storage/CRM2EDP8/Robinson_Oshlack_2010_A scaling normalization method for differential expression analysis of RNA-seq.pdf:application/pdf;Snapshot:/Users/han/Library/Application Support/Zotero/Profiles/n80u1dxx.default/zotero/storage/FMPNVG7C/gb-2010-11-3-r25.html:text/html},
  groups = {dgctools,dgctools},
}
@Article{law_voom:_2014,
  title = {Voom: Precision Weights Unlock Linear Model Analysis Tools for {{RNA}}-Seq Read Counts},
  volume = {15},
  issn = {1474-760X},
  url = {https://doi.org/10.1186/gb-2014-15-2-r29},
  doi = {10.1186/gb-2014-15-2-r29},
  shorttitle = {Voom},
  abstract = {New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.},
  timestamp = {2017-11-17T15:25:06Z},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  author = {Charity W. Law and Yunshun Chen and Wei Shi and Gordon K. Smyth},
  date = {2014-02-03},
  pages = {R29},
  file = {Law et al_2014_voom.pdf:/Users/han/Library/Application Support/Zotero/Profiles/n80u1dxx.default/zotero/storage/855NCR6M/Law et al_2014_voom.pdf:application/pdf;Snapshot:/Users/han/Library/Application Support/Zotero/Profiles/n80u1dxx.default/zotero/storage/QAFKJVHM/gb-2014-15-2-r29.html:text/html},
  groups = {dgctools,dgctools},
}
@Article{rich_practical_2010,
  title = {A {{PRACTICAL GUIDE TO UNDERSTANDING KAPLAN}}-{{MEIER CURVES}}},
  volume = {143},
  issn = {0194-5998},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932959/},
  doi = {10.1016/j.otohns.2010.05.007},
  abstract = {In 1958, Edward L. Kaplan and Paul Meier collaborated to publish a seminal paper on how to deal with incomplete observations. Subsequently, the Kaplan-Meier curves and estimates of survival data have become a familiar way of dealing with differing survival times (times-to-event), especially when not all the subjects continue in the study. “Survival” times need not relate to actual survival with death being the event; the “event” may be any event of interest. Kaplan-Meier analyses are also used in non-medical disciplines., The purpose of this paper is to explain how Kaplan-Meier curves are generated and analyzed. Throughout this article we will discuss Kaplan-Meier (K-M) estimates in the context of “survival” before the event of interest. Two small groups of hypothetical data are used as examples in order for the reader to clearly see how the process works. These examples also illustrate the crucially important point that comparative analysis depends upon the whole curve and not upon isolated points.},
  timestamp = {2017-11-17T18:47:56Z},
  number = {3},
  journaltitle = {Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery},
  shortjournal = {Otolaryngol Head Neck Surg},
  author = {JASON T. RICH and J. GAIL NEELY and RANDAL C. PANIELLO and COURTNEY C. J. VOELKER and BRIAN NUSSENBAUM and ERIC W. WANG},
  date = {2010-09},
  pages = {331--336},
  file = {RICH et al_2010_A PRACTICAL GUIDE TO UNDERSTANDING KAPLAN-MEIER CURVES.pdf:/Users/han/Library/Application Support/Zotero/Profiles/n80u1dxx.default/zotero/storage/DF6TFF55/RICH et al_2010_A PRACTICAL GUIDE TO UNDERSTANDING KAPLAN-MEIER CURVES.pdf:application/pdf},
  groups = {dgctools,dgctools},
  eprinttype = {pmid},
  eprint = {20723767},
  pmcid = {PMC3932959},
}
@Article{Ritchielimmapowersdifferential2015a,
  langid = {english},
  title = {Limma Powers Differential Expression Analyses for {{RNA}}-Sequencing and Microarray Studies},
  volume = {43},
  issn = {1362-4962},
  doi = {10.1093/nar/gkv007},
  abstract = {limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.},
  number = {7},
  journaltitle = {Nucleic Acids Research},
  shortjournal = {Nucleic Acids Res.},
  author = {Matthew E. Ritchie and Belinda Phipson and Di Wu and Yifang Hu and Charity W. Law and Wei Shi and Gordon K. Smyth},
  date = {2015-04-20},
  pages = {e47},
  keywords = {Software,Gene Expression Regulation,Oligonucleotide Array Sequence Analysis,Sequence Analysis; RNA},
  eprinttype = {pmid},
  eprint = {25605792},
  pmcid = {PMC4402510},
}
@Article{RobinsonedgeRBioconductorpackage2010,
  title = {{{edgeR}}: A {{Bioconductor}} Package for Differential Expression Analysis of Digital Gene Expression Data},
  volume = {26},
  issn = {1367-4803},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/},
  doi = {10.1093/bioinformatics/btp616},
  shorttitle = {{{edgeR}}},
  abstract = {Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data., Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org)., Contact: mrobinson@wehi.edu.au},
  number = {1},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  author = {Mark D. Robinson and Davis J. McCarthy and Gordon K. Smyth},
  urldate = {2017-11-29},
  date = {2010-01-01},
  pages = {139--140},
  file = {/Users/han/Zotero/storage/9LKFJ7QB/Robinson et al. - 2010 - edgeR a Bioconductor package for differential exp.pdf},
  eprinttype = {pmid},
  eprint = {19910308},
  pmcid = {PMC2796818},
}
@Article{LoveModeratedestimationfold2014,
  title = {Moderated Estimation of Fold Change and Dispersion for {{RNA}}-Seq Data with {{DESeq2}}},
  volume = {15},
  issn = {1474-760X},
  url = {https://doi.org/10.1186/s13059-014-0550-8},
  doi = {10.1186/s13059-014-0550-8},
  abstract = {In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at                   http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html                                  .},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  author = {Michael I. Love and Wolfgang Huber and Simon Anders},
  urldate = {2017-11-29},
  date = {2014-12-05},
  pages = {550},
  file = {/Users/han/Zotero/storage/8RNUC8UB/Love et al. - 2014 - Moderated estimation of fold change and dispersion.pdf;/Users/han/Zotero/storage/8GZAHL86/s13059-014-0550-8.html},
}
@Article{PaciComputationalanalysisidentifies2014,
  title = {Computational Analysis Identifies a Sponge Interaction Network between Long Non-Coding {{RNAs}} and Messenger {{RNAs}} in Human Breast Cancer},
  volume = {8},
  issn = {1752-0509},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4113672/},
  doi = {10.1186/1752-0509-8-83},
  abstract = {Background
Non-coding RNAs (ncRNAs) are emerging as key regulators of many cellular processes in both physiological and pathological states. Moreover, the constant discovery of new non-coding RNA species suggests that the study of their complex functions is still in its very early stages. This variegated class of RNA species encompasses the well-known microRNAs (miRNAs) and the most recently acknowledged long non-coding RNAs (lncRNAs). Interestingly, in the last couple of years, a few studies have shown that some lncRNAs can act as miRNA sponges, i.e. as competing endogenous RNAs (ceRNAs), able to reduce the amount of miRNAs available to target messenger RNAs (mRNAs).

Results
We propose a computational approach to explore the ability of lncRNAs to act as ceRNAs by protecting mRNAs from miRNA repression. A seed match analysis was performed to validate the underlying regression model. We built normal and cancer networks of miRNA-mediated sponge interactions (MMI-networks) using breast cancer expression data provided by The Cancer Genome Atlas.

Conclusions
Our study highlights a marked rewiring in the ceRNA program between normal and pathological breast tissue, documented by its “on/off” switch from normal to cancer, and vice-versa. This mutually exclusive activation confers an interesting character to ceRNAs as potential oncosuppressive, or oncogenic, protagonists in cancer. At the heart of this phenomenon is the lncRNA PVT1, as illustrated by both the width of its antagonist mRNAs in normal-MMI-network, and the relevance of the latter in breast cancer. Interestingly, PVT1 revealed a net binding preference towards the mir-200 family as the bone of contention with its rival mRNAs.},
  journaltitle = {BMC Systems Biology},
  shortjournal = {BMC Syst Biol},
  author = {Paola Paci and Teresa Colombo and Lorenzo Farina},
  urldate = {2017-11-29},
  date = {2014-07-17},
  pages = {83},
  file = {/Users/han/Zotero/storage/ZWPQP4LF/Paci et al. - 2014 - Computational analysis identifies a sponge interac.pdf},
  eprinttype = {pmid},
  eprint = {25033876},
  pmcid = {PMC4113672},
}
@Article{LangfelderWGCNApackageweighted2008,
  title = {{{WGCNA}}: An {{R}} Package for Weighted Correlation Network Analysis},
  volume = {9},
  issn = {1471-2105},
  url = {https://doi.org/10.1186/1471-2105-9-559},
  doi = {10.1186/1471-2105-9-559},
  shorttitle = {{{WGCNA}}},
  abstract = {Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.},
  journaltitle = {BMC Bioinformatics},
  shortjournal = {BMC Bioinformatics},
  author = {Peter Langfelder and Steve Horvath},
  urldate = {2017-11-29},
  date = {2008-12-29},
  pages = {559},
  file = {/Users/han/Zotero/storage/FXYXK9US/Langfelder and Horvath - 2008 - WGCNA an R package for weighted correlation netwo.pdf;/Users/han/Zotero/storage/QRW3GTMV/1471-2105-9-559.html},
}
@Article{ColapricoTCGAbiolinksBioconductorpackage2016,
  title = {{{TCGAbiolinks}}: An {{R}}/{{Bioconductor}} Package for Integrative Analysis of {{TCGA}} Data},
  volume = {44},
  issn = {0305-1048},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856967/},
  doi = {10.1093/nar/gkv1507},
  shorttitle = {{{TCGAbiolinks}}},
  abstract = {The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.},
  number = {8},
  journaltitle = {Nucleic Acids Research},
  shortjournal = {Nucleic Acids Res},
  author = {Antonio Colaprico and Tiago C. Silva and Catharina Olsen and Luciano Garofano and Claudia Cava and Davide Garolini and Thais S. Sabedot and Tathiane M. Malta and Stefano M. Pagnotta and Isabella Castiglioni and Michele Ceccarelli and Gianluca Bontempi and Houtan Noushmehr},
  urldate = {2017-11-30},
  date = {2016-05-05},
  pages = {e71},
  file = {/Users/han/Zotero/storage/I45WBTP4/Colaprico et al. - 2016 - TCGAbiolinks an RBioconductor package for integr.pdf},
  eprinttype = {pmid},
  eprint = {26704973},
  pmcid = {PMC4856967},
}
@Article{ZhuTCGAAssemblerOpenSourcePipeline2014,
  title = {{{TCGA}}-{{Assembler}}: {{An Open}}-{{Source Pipeline}} for {{TCGA Data Downloading}}, {{Assembling}}, and {{Processing}}},
  volume = {11},
  issn = {1548-7091},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387197/},
  doi = {10.1038/nmeth.2956},
  shorttitle = {{{TCGA}}-{{Assembler}}},
  number = {6},
  journaltitle = {Nature methods},
  shortjournal = {Nat Methods},
  author = {Yitan Zhu and Peng Qiu and Yuan Ji},
  urldate = {2017-11-30},
  date = {2014-06},
  pages = {599--600},
  file = {/Users/han/Zotero/storage/UGBNYA2Q/Zhu et al. - 2014 - TCGA-Assembler An Open-Source Pipeline for TCGA D.pdf},
  eprinttype = {pmid},
  eprint = {24874569},
  pmcid = {PMC4387197},
}
@Article{Robinsonscalingnormalizationmethod2010,
  title = {A Scaling Normalization Method for Differential Expression Analysis of {{RNA}}-Seq Data},
  volume = {11},
  issn = {1474-760X},
  url = {https://doi.org/10.1186/gb-2010-11-3-r25},
  doi = {10.1186/gb-2010-11-3-r25},
  abstract = {The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  author = {Mark D. Robinson and Alicia Oshlack},
  date = {2010-03-02},
  pages = {R25},
  file = {/Users/han/Zotero/storage/CRM2EDP8/Robinson_Oshlack_2010_A scaling normalization method for differential expression analysis of RNA-seq.pdf;/Users/han/Zotero/storage/FMPNVG7C/gb-2010-11-3-r25.html},
}
@Article{Lawvoomprecisionweights2014,
  title = {Voom: Precision Weights Unlock Linear Model Analysis Tools for {{RNA}}-Seq Read Counts},
  volume = {15},
  issn = {1474-760X},
  url = {https://doi.org/10.1186/gb-2014-15-2-r29},
  doi = {10.1186/gb-2014-15-2-r29},
  shorttitle = {Voom},
  abstract = {New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  author = {Charity W. Law and Yunshun Chen and Wei Shi and Gordon K. Smyth},
  date = {2014-02-03},
  pages = {R29},
  file = {/Users/han/Zotero/storage/855NCR6M/Law et al_2014_voom.pdf;/Users/han/Zotero/storage/QAFKJVHM/gb-2014-15-2-r29.html},
}
@Article{Furio-TarispongeScanwebdetecting2016,
  title = {{{spongeScan}}: {{A}} Web for Detecting {{microRNA}} Binding Elements in {{lncRNA}} Sequences},
  volume = {44},
  issn = {0305-1048},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987953/},
  doi = {10.1093/nar/gkw443},
  shorttitle = {{{spongeScan}}},
  abstract = {Non-coding RNA transcripts such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are important genetic regulators. However, the functions of many of these transcripts are still not clearly understood. Recently, it has become apparent that there is significant crosstalk between miRNAs and lncRNAs and that this creates competition for binding between the miRNA, a lncRNA and other regulatory targets. Indeed, various competitive endogenous RNAs (ceRNAs) have already been identified where a lncRNA acts by sequestering miRNAs. This implies the down-regulation in the interaction of the miRNAs with their mRNA targets, what has been called a sponge effect. Multiple approaches exist for the prediction of miRNA targets in mRNAs. However, few methods exist for the prediction of miRNA response elements (MREs) in lncRNAs acting as ceRNAs (sponges). Here, we present spongeScan (http://spongescan.rc.ufl.edu), a graphical web tool to compute and visualize putative MREs in lncRNAs, along with different measures to assess their likely behavior as ceRNAs.},
  issue = {Web Server issue},
  journaltitle = {Nucleic Acids Research},
  shortjournal = {Nucleic Acids Res},
  author = {Pedro Furi{\a'o}-Tar{\a'\i} and Sonia Tarazona and Toni Gabald{\a'o}n and Anton J. Enright and Ana Conesa},
  urldate = {2017-11-30},
  date = {2016-07-08},
  pages = {W176--W180},
  file = {/Users/han/Zotero/storage/PAPLDBWB/Furió-Tarí et al. - 2016 - spongeScan A web for detecting microRNA binding e.pdf},
  eprinttype = {pmid},
  eprint = {27198221},
  pmcid = {PMC4987953},
}
@Article{ListarBasev2decoding2014,
  title = {{{starBase}} v2.0: Decoding {{miRNA}}-{{ceRNA}}, {{miRNA}}-{{ncRNA}} and Protein–{{RNA}} Interaction Networks from Large-Scale {{CLIP}}-{{Seq}} Data},
  volume = {42},
  issn = {0305-1048},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964941/},
  doi = {10.1093/nar/gkt1248},
  shorttitle = {{{starBase}} v2.0},
  abstract = {Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA–RNA and protein–RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285 000 protein–RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10 000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.},
  issue = {Database issue},
  journaltitle = {Nucleic Acids Research},
  shortjournal = {Nucleic Acids Res},
  author = {Jun-Hao Li and Shun Liu and Hui Zhou and Liang-Hu Qu and Jian-Hua Yang},
  urldate = {2017-11-30},
  date = {2014-01-01},
  pages = {D92--D97},
  file = {/Users/han/Zotero/storage/2MQ8JZXL/Li et al. - 2014 - starBase v2.0 decoding miRNA-ceRNA, miRNA-ncRNA a.pdf},
  eprinttype = {pmid},
  eprint = {24297251},
  pmcid = {PMC3964941},
}
@Article{JeggarimiRcodemapputative2012,
  title = {{{miRcode}}: A Map of Putative {{microRNA}} Target Sites in the Long Non-Coding Transcriptome},
  volume = {28},
  issn = {1367-4803},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3400968/},
  doi = {10.1093/bioinformatics/bts344},
  shorttitle = {{{miRcode}}},
  abstract = {Summary: Although small non-coding RNAs, such as microRNAs, have well-established functions in the cell, long non-coding RNAs (lncRNAs) have only recently started to emerge as abundant regulators of cell physiology, and their functions may be diverse. A small number of studies describe interactions between small and lncRNAs, with lncRNAs acting either as inhibitory decoys or as regulatory targets of microRNAs, but such interactions are still poorly explored. To facilitate the study of microRNA–lncRNA interactions, we implemented miRcode: a comprehensive searchable map of putative microRNA target sites across the complete GENCODE annotated transcriptome, including 10 419 lncRNA genes in the current version., Availability:
http://www.mircode.org, Contact:
erik.larsson@gu.se, Supplementary Information:
Supplementary data are available at Bioinformatics online.},
  number = {15},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  author = {Ashwini Jeggari and Debora S Marks and Erik Larsson},
  urldate = {2017-11-30},
  date = {2012-08-01},
  pages = {2062--2063},
  file = {/Users/han/Zotero/storage/IYEATQHR/Jeggari et al. - 2012 - miRcode a map of putative microRNA target sites i.pdf},
  eprinttype = {pmid},
  eprint = {22718787},
  pmcid = {PMC3400968},
}
@Article{YuclusterProfilerPackageComparing2012,
  title = {{{clusterProfiler}}: An {{R Package}} for {{Comparing Biological Themes Among Gene Clusters}}},
  volume = {16},
  issn = {1536-2310},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339379/},
  doi = {10.1089/omi.2011.0118},
  shorttitle = {{{clusterProfiler}}},
  abstract = {Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html},
  number = {5},
  journaltitle = {OMICS : a Journal of Integrative Biology},
  shortjournal = {OMICS},
  author = {Guangchuang Yu and Li-Gen Wang and Yanyan Han and Qing-Yu He},
  urldate = {2017-11-30},
  date = {2012-05},
  pages = {284--287},
  file = {/Users/han/Zotero/storage/23GCWMQX/Yu et al. - 2012 - clusterProfiler an R Package for Comparing Biolog.pdf},
  eprinttype = {pmid},
  eprint = {22455463},
  pmcid = {PMC3339379},
}
@Article{YuDOSEBioconductorpackage2015,
  title = {{{DOSE}}: An {{R}}/{{Bioconductor}} Package for Disease Ontology Semantic and Enrichment Analysis},
  volume = {31},
  issn = {1367-4803},
  url = {https://academic.oup.com/bioinformatics/article/31/4/608/2748221},
  doi = {10.1093/bioinformatics/btu684},
  shorttitle = {{{DOSE}}},
  abstract = {Summary: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computations among DO terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported.Availability and implementation: DOSE is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/DOSE.html).Supplementary information:Supplementary Data are available at Bioinformatics online.Contact:gcyu@connect.hku.hk or tqyhe@jnu.edu.cn},
  number = {4},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  author = {Guangchuang Yu and Li-Gen Wang and Guang-Rong Yan and Qing-Yu He},
  urldate = {2017-11-30},
  date = {2015-02-15},
  pages = {608--609},
  file = {/Users/han/Zotero/storage/7FHQ5PUB/Yu et al. - 2015 - DOSE an RBioconductor package for disease ontolo.pdf},
}
@Article{LuoPathviewBioconductorpackage2013,
  title = {Pathview: An {{R}}/{{Bioconductor}} Package for Pathway-Based Data Integration and Visualization},
  volume = {29},
  issn = {1367-4803},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702256/},
  doi = {10.1093/bioinformatics/btt285},
  shorttitle = {Pathview},
  abstract = {Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines., Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/., Contact:
luo\_weijun@yahoo.com, Supplementary information:
Supplementary data are available at Bioinformatics online.},
  number = {14},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  author = {Weijun Luo and Cory Brouwer},
  urldate = {2017-11-30},
  date = {2013-07-15},
  pages = {1830--1831},
  file = {/Users/han/Zotero/storage/CAF4GJGM/Luo and Brouwer - 2013 - Pathview an RBioconductor package for pathway-ba.pdf},
  eprinttype = {pmid},
  eprint = {23740750},
  pmcid = {PMC3702256},
}
@Article{ChoumiRTarBaseupdate20182017,
  title = {{{miRTarBase}} Update 2018: A Resource for Experimentally Validated {{microRNA}}-Target Interactions},
  issn = {0305-1048},
  url = {http://dx.doi.org/10.1093/nar/gkx1067},
  doi = {10.1093/nar/gkx1067},
  abstract = {MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.},
  journaltitle = {Nucleic Acids Research},
  shortjournal = {Nucleic Acids Research},
  author = {Chih-Hung Chou and Sirjana Shrestha and Chi-Dung Yang and Nai-Wen Chang and Yu-Ling Lin and Kuang-Wen Liao and Wei-Chi Huang and Ting-Hsuan Sun and Siang-Jyun Tu and Wei-Hsiang Lee and Men-Yee Chiew and Chun-San Tai and Ting-Yen Wei and Tzi-Ren Tsai and Hsin-Tzu Huang and Chung-Yu Wang and Hsin-Yi Wu and Shu-Yi Ho and Pin-Rong Chen and Cheng-Hsun Chuang and Pei-Jung Hsieh and Yi-Shin Wu and Wen-Liang Chen and Meng-Ju Li and Yu-Chun Wu and Xin-Yi Huang and Fung Ling Ng and Waradee Buddhakosai and Pei-Chun Huang and Kuan-Chun Lan and Chia-Yen Huang and Shun-Long Weng and Yeong-Nan Cheng and Chao Liang and Wen-Lian Hsu and Hsien-Da Huang},
  date = {2017-11-06},
  pages = {gkx1067--gkx1067},
}
