Dataset Information

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.

ABSTRACT: BACKGROUND:Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step. RESULTS:In this paper, we introduce a transcriptome segmentation approach to decouple these two tasks. We propose an efficient algorithm to generate maximal disjoint segments given a transcriptome reference library on which ultra-fast pseudo-alignment can be used to produce per-sample segment counts. We show how to apply these maximally unambiguous count statistics in two specific expression analyses - alternative splicing and gene differential expression - without the need of a transcript quantification step. Our experiments based on simulated and experimental data showed that the use of segment counts, like other methods that rely on local coverage statistics, provides an advantage over approaches that rely on transcript quantification in detecting and correctly estimating local splicing in the case of incomplete transcript annotations. CONCLUSIONS:The transcriptome segmentation approach implemented in Yanagi exploits the computational and space efficiency of pseudo-alignment approaches. It significantly expands their applicability and interpretability in a variety of RNA-seq analyses by providing the means to model and capture local coverage variation in these analyses.

SUBMITTER: Gunady MK

PROVIDER: S-EPMC6693274 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.

Gunady Mohamed K MK Mount Stephen M SM Corrada Bravo Héctor H

BMC bioinformatics 20190813 1

<h4>Background</h4>Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step.<h4>Results</h4>In this paper, we intr ...[more]

PMID: 31409274

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Project description:BackgroundMultiple Sclerosis (MS) is an autoimmune neurodegenerative disease affecting approximately 3 million people globally. Despite rigorous research on MS, aspects of its development and progression remain unclear. Understanding molecular mechanisms underlying MS is crucial to providing insights into disease pathways, identifying potential biomarkers for early diagnosis, and revealing novel therapeutic targets for improved patient outcomes.MethodsWe utilized publicly available RNA-seq data (GSE138614) from post-mortem white matter tissues of five donors without any neurological disorder and ten MS patient donors. This data was interrogated for differential gene expression, alternative splicing and single nucleotide variants as well as for functional enrichments in the resulting datasets.ResultsA comparison of non-MS white matter (WM) to MS samples yielded differentially expressed genes involved in adaptive immune response, cell communication, and developmental processes. Genes with expression changes positively correlated with tissue inflammation were enriched in the immune system and receptor interaction pathways. Negatively correlated genes were enriched in neurogenesis, nervous system development, and metabolic pathways. Alternatively spliced transcripts between WM and MS lesions included genes that play roles in neurogenesis, myelination, and oligodendrocyte differentiation, such as brain enriched myelin associated protein (BCAS1), discs large MAGUK scaffold protein 1 (DLG1), KH domain containing RNA binding (QKI), and myelin basic protein (MBP). Our approach to comparing normal appearing WM (NAWM) and active lesion (AL) from one donor and NAWM and chronic active (CA) tissues from two donors, showed that different IgH and IgK gene subfamilies were differentially expressed. We also identified pathways involved in white matter injury repair and remyelination in these tissues. Differentially spliced genes between these lesions were involved in axon and dendrite structure stability. We also identified exon skipping events and spontaneous single nucleotide polymorphisms in membrane associated ring-CH-type finger 1 (MARCHF1), UDP glycosyltransferase 8 (UGT8), and other genes important in autoimmunity and neurodegeneration.ConclusionOverall, we identified unique genes, pathways, and novel splicing events affecting disease progression that can be further investigated as potential novel drug targets for MS treatment.

Dataset Information

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.

Publications

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.

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