Dataset Information

Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis.

ABSTRACT: The use of RNA-seq as a generalized tool to measure the differential expression of genes has essentially replaced the use of the microarray. Despite the acknowledged technical advantages to this approach, RNA-seq library preparation remains mostly conducted by core facilities rather than in the laboratory due to the infrastructure, expertise and time required per sample. We have recently described two 'click-chemistry' based library construction methods termed ClickSeq and Poly(A)-ClickSeq (PAC-seq) as alternatives to conventional RNA-seq that are both cost effective and rely on straightforward reagents readily available to most labs. ClickSeq is random-primed and can sequence any (unfragmented) RNA template, while PAC-seq is targeted to poly(A) tails of mRNAs. Here, we further develop PAC-seq as a platform that allows for simultaneous mapping of poly(A) sites and the measurement of differential expression of genes. We provide a detailed protocol, descriptions of appropriate computational pipelines, and a proof-of-principle dataset to illustrate the technique. PAC-seq offers a unique advantage over other 3' end mapping protocols in that it does not require additional purification, selection, or fragmentation steps allowing sample preparation directly from crude total cellular RNA. We have shown that PAC-seq is able to accurately and sensitively count transcripts for differential gene expression analysis, as well as identify alternative poly(A) sites and determine the precise nucleotides of the poly(A) tail boundaries.

SUBMITTER: Elrod ND

PROVIDER: S-EPMC7291597 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis.

Elrod Nathan D ND Jaworski Elizabeth A EA Ji Ping P Wagner Eric J EJ Routh Andrew A

Methods (San Diego, Calif.) 20190106

The use of RNA-seq as a generalized tool to measure the differential expression of genes has essentially replaced the use of the microarray. Despite the acknowledged technical advantages to this approach, RNA-seq library preparation remains mostly conducted by core facilities rather than in the laboratory due to the infrastructure, expertise and time required per sample. We have recently described two 'click-chemistry' based library construction methods termed ClickSeq and Poly(A)-ClickSeq (PAC- ...[more]

PMID: 30625385

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Project description:UnlabelledBackgroundThe amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central - a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data.ResultsA pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications.ConclusionsWe present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.

Dataset Information

Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis.

Publications

Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis.

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