A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data

Chen, Moliang; Ji, Guoli; Fu, Hongjuan; Lin, Quan; Ye, Congting; Fraser, Michael; Su, Yangfeng; Wu, Xiaohui

doi:10.1093/bib/bbz068

Cited by 32 publications

(65 citation statements)

References 91 publications

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“…Future work will involve adding APA sites from more species and more diverse cell/tissue types upon the availability of new 39 seq data. Recently, a wide range of computational tools for identifying APA sites from RNA-seq data have continued to emerge, and an increasing number of APA sites have been found beyond annotated sites from 39 seq data (Chen et al, 2019). APA sites were also found from single-molecule sequencing (Wang et al, 2017(Wang et al, , 2018a.…”

Section: Discussionmentioning

confidence: 99%

PlantAPAdb: A Comprehensive Database for Alternative Polyadenylation Sites in Plants

Sheng

Fraser

et al. 2019

Plant Physiol.

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Alternative cleavage and polyadenylation (APA) is increasingly recognized as an important regulatory mechanism in eukaryotic gene expression and is dynamically modulated in a developmental, tissue-specific, or environmentally responsive manner. Given the functional importance of APA and the rapid accumulation of APA sites in plants, a comprehensive and easily accessible APA site database is necessary for improved understanding of APA-mediated gene expression regulation. We present a database called PlantAPAdb that catalogs the most comprehensive APA site data derived from sequences from diverse 39 sequencing protocols and biological samples in plants. Currently, PlantAPAdb contains APA sites in six species, Oryza sativa (japonica and indica), Arabidopsis (Arabidopsis thaliana), Medicago truncatula, Trifolium pratense, Phyllostachys edulis, and Chlamydomonas reinhardtii. APA sites in PlantAPAdb are available for bulk download and can be queried in a Google-like manner. PlantAPAdb provides rich information of the whole-genome APA sites, including genomic locations, heterogeneous cleavage sites, expression levels, and sample information. It also provides comprehensive poly(A) signals for APA sites in different genomic regions according to distinct profiles of cis-elements in plants. In addition, PlantAPAdb contains events of 39 untranslated region shortening/lengthening resulting from APA, which helps to understand the mechanisms underlying systematic changes in 39 untranslated region lengths. Additional information about conservation of APA sites in plants is also available, providing insights into the evolutionary polyadenylation configuration across species. As a user-friendly database, PlantAPAdb is a large and extendable resource for elucidating APA mechanisms, APA conservation, and gene expression regulation.

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Section: Discussionmentioning

confidence: 99%

PlantAPAdb: A Comprehensive Database for Alternative Polyadenylation Sites in Plants

Sheng

Fraser

et al. 2019

Plant Physiol.

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“…Extensive bioinformatics efforts have been made to identify poly(A) sites or mRNA 3 tails using RNA-seq data. These previous bioinformatic tools have been classified into four groups based on their principles of the algorithms (Chen et al, 2020). Some of the programs have been compared using benchmarking datasets from model species (i.e., human, mouse, and Arabidopsis).…”

Section: Discussionmentioning

confidence: 99%

“…In particular, PolyA-Tag sequencing has been extensively used for genome-wide APA studies in higher plants, including Arabidopsis (Shen et al, 2011;Wu et al, 2011Wu et al, , 2016bHong et al, 2018), Chlamydomonas reinhardtii (Bell et al, 2016), rice (Fu et al, 2016;Ye et al, 2019;Zhou et al, 2019), and Medicago (Wu et al, 2014). In contrast to the experimental approaches targeting poly(A) tails and/or 3 UTRs, many bioinformatic methods and pipelines have been developed to identify poly(A) sites and/or detect differential APA usage between regular RNA-seq samples [reviewed by Chen et al (2020)]. These bioinformatics tools fall into four major types: type 1 tools include QAPA (Ha et al, 2018) and PAQR (Gruber et al, 2018) and rely on a pre-existed annotation of poly(A) sites, such as PolyA_DB (Lee et al, 2007), Polysite (Gruber et al, 2016), APASdb (You et al, 2014), and…”

Section: Introductionmentioning

confidence: 99%

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Profiling Alternative 3′ Untranslated Regions in Sorghum using RNA-seq Data

2020

Front. Genet.

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Sorghum is an important crop widely used for food, feed, and fuel. Transcriptomewide studies of 3 untranslated regions (3 UTR) using regular RNA-seq remain scarce in sorghum, while transcriptomes have been characterized extensively using Illumina short-read sequencing platforms for many sorghum varieties under various conditions or developmental contexts. 3 UTR is a critical regulatory component of genes, controlling the translation, transport, and stability of messenger RNAs. In the present study, we profiled the alternative 3 UTRs at the transcriptome level in three genetically related but phenotypically contrasting lines of sorghum: Rio, BTx406, and R9188. A total of 1,197 transcripts with alternative 3 UTRs were detected using RNA-seq data. Their categorization identified 612 high-confidence alternative 3 UTRs. Importantly, the highconfidence alternative 3 UTR genes significantly overlapped with the genesets that are associated with RNA N 6-methyladenosine (m 6 A) modification, suggesting a clear indication between alternative 3 UTR and m 6 A methylation in sorghum. Moreover, taking advantage of sorghum genetics, we provided evidence of genotype specificity of alternative 3 UTR usage. In summary, our work exemplifies a transcriptome-wide profiling of alternative 3 UTRs using regular RNA-seq data in non-model crops and gains insights into alternative 3 UTRs and their genotype specificity.

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“…Among multiple methods for bulk RNA-Seq data, we employ the approaches of DaPars 14 due to the following reasons. First, DaPars maintains highest sensitivity and specificity compared to other methods in benchmark tests on biological and simulation data 15 . Second, DaPars identifies APA genes without any assumption on the RNA-Seq signal density by determining 3'UTR isoforms such that the difference between the sum of the isoforms and the input RNA-Seq signal density is minimized (step 2 in Fig.…”

Section: Alternative Polyadenylation Identification Across Multiple Cmentioning

confidence: 99%

Cell-type-specific alternative polyadenylation (APA) genes reveal the function of dynamic APA in complex tissues

Bai

Qin

Fan

et al. 2020

Preprint

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Alternative polyadenylation (APA) causes shortening or lengthening of the 3ʹ-untranslated region (3ʹ-UTR), widespread in complex tissues. Bioinformatic tools have been developed to identify dynamic APA in single cell RNA-Seq (scRNA-Seq) data, but with relatively low power and the lack of interpretability for multiple cell types. To address these limitations, we developed a model-based method, scMAPA. scMAPA increases power by building a regression model based on a sensitive quantification of 3ʹ-UTR short and long isoforms. By developing a de novo simulation platform, we demonstrated that scMAPA shows a markedly better sensitivity (9.5% on average) than the previous method with a negligible loss in specificity (0.3% on average). scMAPA improves interpretability by modeling the direction and the degree of dynamic APA for each cell type for each gene, while allowing flexibility to control potential confounders. scMAPA enables to bring a systematic understanding of APA dynamics in complex tissues in human Peripheral Blood Monocellular Cells and mouse brain data.

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A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data

Cited by 32 publications

References 91 publications

PlantAPAdb: A Comprehensive Database for Alternative Polyadenylation Sites in Plants

PlantAPAdb: A Comprehensive Database for Alternative Polyadenylation Sites in Plants

Profiling Alternative 3′ Untranslated Regions in Sorghum using RNA-seq Data

Cell-type-specific alternative polyadenylation (APA) genes reveal the function of dynamic APA in complex tissues

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