Biology subjects

Jewell, P.

Publications and source records attributed to Jewell, P..

4 recordsLinked to original sources

bioRxiv2021

RNA splicing analysis using heterogeneous and large RNA-seq1datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we demonstrate that the approaches in MAJIQ v2 outperform existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer new insights into brain subregion-specific splicing regulation.

bioinformatics↗

bioRxiv2020

MOCCASIN: A method for correcting for known and unknown confounders in RNA splicing analysis

While the effects of confounders on gene expression analysis have been extensively studied there is a lack of equivalent analysis and tools for RNA splicing analysis. Here we assess the effect of confounders in two large public RNA-Seq datasets (TARGET, ENCODE), develop a new method, MOCCASIN, to correct the effect of both known and unknown confounders on RNA splicing quantification, and demonstrate MOCCASINs effectiveness on both synthetic and real data.

genomics↗

bioRxiv2019

Meta-Analysis of Transcriptomic Variation in T cell Populations Reveals Novel Signatures of Gene Expression and Splicing

Distinct T cell subtypes are typically defined by the expression of distinct gene repertoires. However, there is variability between studies regarding the markers used to define each T cell subtype. Moreover, previous analysis of gene expression in T cell subsets has largely focused on gene expression rather than alternative splicing. Here we take a meta-analysis approach, comparing eleven independent RNA-Seq studies of human Th1, Th2, Th17 and/or Treg cells to identify transcriptomic features that correlate consistently with subtype. We find that known master-regulators are consistently enriched in the appropriate subtype, however, cytokines and other genes often used as markers are more variable. Importantly, we also identify previously unknown transcriptomic markers that consistently differentiate between subsets, including a few Treg-specific splicing patterns. Together this work highlights the heterogeneity in gene expression between isolates of the same subtype, but also suggests additional markers that can be used to define functional groupings.

immunology↗

bioRxiv2019

Mapping RNA splicing variations in clinically-accessible and non-accessible tissues to facilitate Mendelian disease diagnosis using RNA-seq

PurposeRNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically-accessible tissues (CATs) from blood and skin. RNA tissue-specificity makes it difficult to identify aberrations in relevant but non-accessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs.\n\nMethodsWe quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from GTEx and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues.\n\nResultsIn non-CATs, 39.7% of genes have splicing that is inadequately represented by at least one CAT. 6.2% of genes have splicing inadequately represented by all CATs. A majority (52.8%) of inadequately represented genes are lowly expressed in CATs (TPM < 1), but 6.2% are inadequately represented despite being well expressed (TPM > 10).\n\nConclusionMany splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

genomics↗