Dataset Information

PlantAPA: A Portal for Visualization and Analysis of Alternative Polyadenylation in Plants.

ABSTRACT: Alternative polyadenylation (APA) is an important layer of gene regulation that produces mRNAs that have different 3' ends and/or encode diverse protein isoforms. Up to 70% of annotated genes in plants undergo APA. Increasing numbers of poly(A) sites collected in various plant species demand new methods and tools to access and mine these data. We have created an open-access web service called PlantAPA (http://bmi.xmu.edu.cn/plantapa) to visualize and analyze genome-wide poly(A) sites in plants. PlantAPA provides various interactive and dynamic graphics and seamlessly integrates a genome browser that can profile heterogeneous cleavage sites and quantify expression patterns of poly(A) sites across different conditions. Particularly, through PlantAPA, users can analyze poly(A) sites in extended 3' UTR regions, intergenic regions, and ambiguous regions owing to alternative transcription or RNA processing. In addition, it also provides tools for analyzing poly(A) site selections, 3' UTR lengthening or shortening, non-canonical APA site switching, and differential gene expression between conditions, making it more powerful for the study of APA-mediated gene expression regulation. More importantly, PlantAPA offers a bioinformatics pipeline that allows users to upload their own short reads or ESTs for poly(A) site extraction, enabling users to further explore poly(A) site selection using stored PlantAPA poly(A) sites together with their own poly(A) site datasets. To date, PlantAPA hosts the largest database of APA sites in plants, including Oryza sativa, Arabidopsis thaliana, Medicago truncatula, and Chlamydomonas reinhardtii. As a user-friendly web service, PlantAPA will be a valuable addition to the community of biologists studying APA mechanisms and gene expression regulation in plants.

SUBMITTER: Wu X

PROVIDER: S-EPMC4914594 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PlantAPA: A Portal for Visualization and Analysis of Alternative Polyadenylation in Plants.

Wu Xiaohui X Zhang Yumin Y Li Qingshun Q QQ

Frontiers in plant science 20160621

Alternative polyadenylation (APA) is an important layer of gene regulation that produces mRNAs that have different 3' ends and/or encode diverse protein isoforms. Up to 70% of annotated genes in plants undergo APA. Increasing numbers of poly(A) sites collected in various plant species demand new methods and tools to access and mine these data. We have created an open-access web service called PlantAPA (http://bmi.xmu.edu.cn/plantapa) to visualize and analyze genome-wide poly(A) sites in plants. ...[more]

PMID: 27446120

Similar Datasets

Project description:BackgroundAlternative polyadenylation (APA) has emerged as a pervasive mechanism that contributes to the transcriptome complexity and dynamics of gene regulation. The current tsunami of whole genome poly(A) site data from various conditions generated by 3' end sequencing provides a valuable data source for the study of APA-related gene expression. Cluster analysis is a powerful technique for investigating the association structure among genes, however, conventional gene clustering methods are not suitable for APA-related data as they fail to consider the information of poly(A) sites (e.g., location, abundance, number, etc.) within each gene or measure the association among poly(A) sites between two genes.ResultsHere we proposed a computational framework, named PASCCA, for clustering genes from replicated or unreplicated poly(A) site data using canonical correlation analysis (CCA). PASCCA incorporates multiple layers of gene expression data from both the poly(A) site level and gene level and takes into account the number of replicates and the variability within each experimental group. Moreover, PASCCA characterizes poly(A) sites in various ways including the abundance and relative usage, which can exploit the advantages of 3' end deep sequencing in quantifying APA sites. Using both real and synthetic poly(A) site data sets, the cluster analysis demonstrates that PASCCA outperforms other widely-used distance measures under five performance metrics including connectivity, the Dunn index, average distance, average distance between means, and the biological homogeneity index. We also used PASCCA to infer APA-specific gene modules from recently published poly(A) site data of rice and discovered some distinct functional gene modules. We have made PASCCA an easy-to-use R package for APA-related gene expression analyses, including the characterization of poly(A) sites, quantification of association between genes, and clustering of genes.ConclusionsBy providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data. PASCCA could be used to elucidate the dynamic interplay of genes and their APA sites among various biological conditions from emerging 3' end sequencing data to address the complex biological phenomenon.

Dataset Information

PlantAPA: A Portal for Visualization and Analysis of Alternative Polyadenylation in Plants.

Publications

PlantAPA: A Portal for Visualization and Analysis of Alternative Polyadenylation in Plants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets