Dataset Information

RNA splicing analysis using heterogeneous and large RNA-seq datasets.

ABSTRACT: 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 assess the advantages of MAJIQ v2 compared to 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 insights into brain subregion-specific splicing regulation.

SUBMITTER: Vaquero-Garcia J

PROVIDER: S-EPMC9984406 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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RNA splicing analysis using heterogeneous and large RNA-seq datasets.

Vaquero-Garcia Jorge J Aicher Joseph K JK Jewell San S Gazzara Matthew R MR Radens Caleb M CM Jha Anupama A Norton Scott S SS Lahens Nicholas F NF Grant Gregory R GR Barash Yoseph Y

Nature communications 20230303 1

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 t ...[more]

PMID: 36869033

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Project description:BackgroundThe development of a safe, effective, reversible, non-hormonal contraceptive method for men has been an ongoing effort for the past few decades. However, despite significant progress on elucidating the function of key proteins involved in reproduction, understanding male reproductive physiology is limited by incomplete information on the genes expressed in reproductive tissues, and no contraceptive targets have so far reached clinical trials. To advance product development, further identification of novel reproductive tract-specific genes leading to potentially druggable protein targets is imperative.ResultsIn this study, we expand on previous single tissue, single species studies by integrating analysis of publicly available human and mouse RNA-seq datasets whose initial published purpose was not focused on identifying male reproductive tract-specific targets. We also incorporate analysis of additional newly acquired human and mouse testis and epididymis samples to increase the number of targets identified. We detected a combined total of 1178 genes for which no previous evidence of male reproductive tract-specific expression was annotated, many of which are potentially druggable targets. Through RT-PCR, we confirmed the reproductive tract-specific expression of 51 novel orthologous human and mouse genes without a reported mouse model. Of these, we ablated four epididymis-specific genes (Spint3, Spint4, Spint5, and Ces5a) and two testis-specific genes (Pp2d1 and Saxo1) in individual or double knockout mice generated through the CRISPR/Cas9 system. Our results validate a functional requirement for Spint4/5 and Ces5a in male mouse fertility, while demonstrating that Spint3, Pp2d1, and Saxo1 are each individually dispensable for male mouse fertility.ConclusionsOur work provides a plethora of novel testis- and epididymis-specific genes and elucidates the functional requirement of several of these genes, which is essential towards understanding the etiology of male infertility and the development of male contraceptives.

Dataset Information

RNA splicing analysis using heterogeneous and large RNA-seq datasets.

Publications

RNA splicing analysis using heterogeneous and large RNA-seq datasets.

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