LnCompare: gene set feature analysis for human long non-coding RNAs

Abstract

Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets.

Figure 1.

Gene Set Feature Comparison module workflow. (A) LnCompare is based on a dataset of quantitative and categorical features of lncRNAs. For a given analysis, LnCompare divides input lncRNAs (yellow) and background lncRNAs (green). (B) For each quantitative feature, the distribution of input and background lists are compared by Wilcoxon test. Below are shown the results, displayed in the web server as a barplot. Each bar represents a feature, and length in the x-axis denotes the ratio of the mean input value and the mean background value. (C) For every categorical feature, LnCompare performs a hypergeometric test (upper panel), which is summarized in a bubble plot (lower panel). Features are distributed across the y-axis, while the x-axis displays the odds ratio obtained from the hypergeometric test. Circle radius reflects the P-value from the hypergeometric test.

Figure 2.

Similar Gene Discovery module workflow. The module compares either one gene or several input genes versus a list of background genes (‘1-to-N’ and ‘M-to-N’, respectively). The yellow box represents the input genes; the green box represents the background genes. Similarity comparisons are based on the distance between the two gene feature vectors, calculated by two different methods: cosine similarity and mutual rank.

Figure 3.

Results from Gene Set Feature Comparison analysis of CLC (A) Graphical results displaying features that are significantly different between CLC and background lncRNAs (FDR < 0.05). Feature labels are shown on the y-axis and grey boxes on the left summarize their content. The x-axis indicates the ratio between the mean of CLC genes (input) and background genes for each feature. (B) Table obtained from the same analysis for categorical features. ‘Feature’ and ‘Name’ indicate and describe the feature tested, ‘List’ and ‘background’ show the number of CLC and background genes associated with the feature, respectively. ‘P-value’, ‘FDR’ and ‘Odds Ratio’ from hypergeometric test are also shown in the table.