Dataset Information

ShapeShifter: a novel approach for identifying and quantifying stable lariat intronic species in RNAseq data.

ABSTRACT: Background:Most intronic lariats are rapidly turned over after splicing. However, new research suggests that some introns may have additional post-splicing functions. Current bioinformatics methods used to identify lariats require a sequencing read that traverses the lariat branchpoint. This method provides precise branchpoint sequence and position information, but is limited in its ability to quantify abundance of stabilized lariat species in a given RNAseq sample. Bioinformatic tools are needed to better address these emerging biological questions. Methods:We used an unsupervised machine learning approach on sequencing reads from publicly available ENCODE data to learn to identify and quantify lariats based on RNAseq read coverage shape. Results:We developed ShapeShifter, a novel approach for identifying and quantifying stable lariat species in RNAseq datasets. We learned a characteristic "lariat" curve from ENCODE RNAseq data and were able to estimate abundances for introns based on read coverage. Using this method we discovered new stable introns in these samples that were not represented using the older, branchpoint-traversing read method. Conclusions:ShapeShifter provides a robust approach towards detecting and quantifying stable lariat species.

SUBMITTER: Taggart AJ

PROVIDER: S-EPMC6688498 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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ShapeShifter: a novel approach for identifying and quantifying stable lariat intronic species in RNAseq data.

Taggart Allison J AJ Fairbrother William G WG

Quantitative biology (Beijing, China) 20180725 3

<h4>Background</h4>Most intronic lariats are rapidly turned over after splicing. However, new research suggests that some introns may have additional post-splicing functions. Current bioinformatics methods used to identify lariats require a sequencing read that traverses the lariat branchpoint. This method provides precise branchpoint sequence and position information, but is limited in its ability to quantify abundance of stabilized lariat species in a given RNAseq sample. Bioinformatic tools a ...[more]

PMID: 31404415

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Project description:Genes that are constitutively expressed across multiple environmental stimuli are crucial to quantifying differentially expressed genes, particularly when employing quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) assays. However, the identification of these potential reference genes in non-model organisms is challenging and is often guided by expression patterns in distantly related organisms. Here, transcriptome datasets from the diatom Thalassiosira pseudonana grown under replete, phosphorus-limited, iron-limited, and phosphorus and iron co-limited nutrient regimes were analyzed through literature-based searches for homologous reference genes, k-means clustering, and analysis of sequence counts (ASC) to identify putative reference genes. A total of 9759 genes were identified and screened for stable expression. Literature-based searches surveyed 18 generally accepted reference genes, revealing 101 homologs in T. pseudonana with variable expression and a wide range of mean tags per million. k-means analysis parsed the whole transcriptome into 15 clusters. The two most stable clusters contained 709 genes, but still had distinct patterns in expression. ASC analyses identified 179 genes that were stably expressed (posterior probability < 0.1 for 1.25 fold change). Genes known to have a stable expression pattern across the test treatments, like actin, were identified in this pool of 179 candidate genes. ASC can be employed on data without biological replicates and was more robust than the k-means approach in isolating genes with stable expression. The intersection of the genes identified through ASC with commonly used reference genes from the literature suggests that actin and ubiquitin ligase may be useful reference genes for T. pseudonana and potentially other diatoms. With the wealth of transcriptome sequence data becoming available, ASC can be easily applied to transcriptome datasets from other phytoplankton to identify reference genes.

Project description:Genes that are constitutively expressed across multiple environmental stimuli are crucial to quantifying differentially expressed genes, particularly when employing quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) assays. However, the identification of these potential reference genes in non-model organisms is challenging and is often guided by expression patterns in distantly related organisms. Here, transcriptome datasets from the diatom Thalassiosira pseudonana grown under replete, phosphorus-limited, iron-limited, and phosphorus and iron co-limited nutrient regimes were analyzed through literature-based searches for homologous reference genes, k-means clustering, and Analysis of Sequence Counts (ASC) to identify putative reference genes. A total of 9759 genes were identified and screened for stable expression. Literature-based searches surveyed 18 generally accepted reference genes, revealing 101 homologs in T. pseudonana with variable expression and a wide range of mean tags per million. K-means analysis parsed the whole transcriptome into 15 clusters. The two most stable clusters contained 709 genes but still had distinct patterns in expression. ASC analyses identified 179 genes that were stably expressed (posterior probability < 0.1 for 1.25 fold change). Genes known to have a stable expression pattern across the test treatments, like actin, were identified in this pool of 179 candidate genes. ASC can be employed on data without biological replicates and was more robust than the k-means approach in isolating genes with stable expression. The intersection of the genes identified through ASC with commonly used reference genes from the literature suggests that actin and ubiquitin ligase may be useful reference genes for T. pseudonana and potentially other diatoms. With the wealth of transcriptome sequence data becoming available, ASC can be easily applied to transcriptome datasets from other phytoplankton to identify reference genes.

Dataset Information

ShapeShifter: a novel approach for identifying and quantifying stable lariat intronic species in RNAseq data.

Publications

ShapeShifter: a novel approach for identifying and quantifying stable lariat intronic species in RNAseq data.

Similar Datasets

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