Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

Johnson, Nia; Dhroso, Andi; Hughes, Katelyn; Korkin, Dmitry

doi:10.1261/rna.062802.117

Cited by 41 publications

(33 citation statements)

References 70 publications

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“…S1 A and B). This result is consistent with prior observations that the exon-level splicing analysis is more robust against batch effects and other confounding factors in large-scale RNA-Seq datasets (35)(36)(37).…”

Section: Resultssupporting

confidence: 92%

“…Metaanalyses of RNA-Seq data with gene-or isoform-level counts are subject to confounding batch effects and rely on existing isoform annotation (33). Exon-level analysis, however, uses a ratio-based methodology to estimate exon incorporation, which may be more robust against batch effects and confounding factors in large-scale RNA-Seq datasets (34)(35)(36)(37). In addition, exon-level analysis can detect novel exon-exon junctions and is thus independent of previous annotation.…”

Section: Resultsmentioning

confidence: 99%

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Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers

Phillips

Pan

Tsai

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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We sought to define the landscape of alternative pre-mRNA splicing in prostate cancers and the relationship of exon choice to known cancer driver alterations. To do so, we compiled a metadataset composed of 876 RNA-sequencing (RNA-Seq) samples from five publicly available sources representing a range of prostate phenotypes from normal tissue to drug-resistant metastases. We subjected these samples to exon-level analysis with rMATS-turbo, purposebuilt software designed for large-scale analyses of splicing, and identified 13,149 high-confidence cassette exon events with variable incorporation across samples. We then developed a computational framework, pathway enrichment-guided activity study of alternative splicing (PEGASAS), to correlate transcriptional signatures of 50 different cancer driver pathways with these alternative splicing events. We discovered that Myc signaling was correlated with incorporation of a set of 1,039 cassette exons enriched in genes encoding RNA binding proteins. Using a human prostate epithelial transformation assay, we confirmed the Myc regulation of 147 of these exons, many of which introduced frameshifts or encoded premature stop codons. Our results connect changes in alternative pre-mRNA splicing to oncogenic alterations common in prostate and many other cancers. We also establish a role for Myc in regulating RNA splicing by controlling the incorporation of nonsense-mediated decay-determinant exons in genes encoding RNA binding proteins.alternative splicing | prostate cancer | Myc | rMATS | PEGASAS A lternative pre-mRNA splicing is a regulated process that governs exon choice and greatly diversifies the proteome. It is an essential process that contributes to development, tissue specification, and homeostasis and is often dysregulated in disease states (1). In cancer, this includes growth signaling, epithelial-tomesenchymal transition, resistance to apoptosis, and treatment resistance (2). In prostate cancer, our area of interest, the most notable splicing change is the emergence of the ligand-independent androgen receptor ARV7 isoform in response to hormone deprivation (3). Other examples include proangiogenic splice variants of VEGFA (4), tumorigenic variants of the transcription factors ERG and KLF6 (5, 6), and antiapoptotic splicing of BCL2L2 (7, 8). However, the intersection of upstream oncogenic signaling, pre-mRNA splicing, and the biological processes affected by those splicing events has not been defined at a global level.Prostate cancers progress from hormone-responsive, localized disease to hormone-independent, metastatic disease accompanied by changes in gene expression and mutations that confer cellautonomous growth and therapeutic resistance (9). The study of disease progression from primary prostate adenocarcinoma (PrAd) to metastatic, castration-resistant prostate cancer (mCRPC) and treatment-related neuroendocrine prostate cancer (NEPC) has been aided by large-scale genomic and transcriptomic studies of patient samples representing each form of the disease (10-13). E...

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Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers

Phillips

Pan

Tsai

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Gene expression can be simply defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA-Seq technology has become a prevalent approach to quantify gene expression that is expected to gain better insights to a number of biological and biomedical questions, compared to DNA microarray technology (Johnson, Dhroso, Hughes, & Korkin, 2018). The processing of RNA-Seq gene expression includes many stages to obtain data matrix (RNASeqV2 level 3 expression data) (MIT and Harvard, 2016).…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data

Huynh

Nguyen

2019

Journal of Information and Telecommunication

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In recent years, cancer is one of the leading causes of death worldwide. Therefore, there are more and more studies that have been conducted to find effective solutions to diagnose and treat cancer. However, there are still many challenges in cancer treatment because possible causes of cancer are genetic disorders or epigenetic alterations in the cells. RNA sequencing is a powerful technique for gene expression profiling in model organisms and it is able to produce information for diagnosing cancer at the biomolecular level. Gene expression data are used to build a classification model which supports treatment of cancer. Nevertheless, its characteristic is very-high-dimensional data which lead to over-fitting issue of classifying model. In this paper, we propose a new gene expression classification model of support vector machines (SVM) using features extracted by deep convolutional neural network (DCNN). In our approach, the DCNN extracts latent features from gene expression data, then they are used in conjunction with SVM that efficiently classify RNA-Seq gene expression data. Numerical test results on RNA-Seq gene expression datasets from The Cancer Genome Atlas (TCGA) illustrate that our proposed algorithm is more accurate than state-of-the-art classifying models including DCNN, SVM and random forests.

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“…The emergence of high throughput sequencing of large disease cohorts [19][20][21] , and the remarkable efforts to aggregate and annotate these mutations in an accessible infrastructure such as ClinVar 22 , now provides an unprecedented opportunity to apply novel deep learning approaches to predict mutations that affect pre-mRNA splicing 23 . The potential of developing such models will continue to increase as next generation transcriptome sequencing (RNASeq) data are amassed and curation of the associated mutational processes matures [23][24][25][26] .…”

mentioning

confidence: 99%

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

Gao

Morini

Salani

et al. 2020

Preprint

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Pre-mRNA splicing is a key control point in human gene expression. Disturbances in splicing due to mutation or aberrant splicing regulatory networks lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of active and selective splicing modulator compounds have been recently identified, thus proving that pre-mRNA splicing is a viable target for therapy. We describe herein the identification of BPN-15477, a novel splicing modulator compound, that restores correct splicing of exon 20 in the Elongator complex protein 1 (ELP1) gene carrying the major IVS20+6T>C mutation responsible for familial dysautonomia. We then developed a machine learning approach to evaluate the therapeutic potential of BPN-15477 to correct splicing in other human genetic diseases. Using transcriptome sequencing from compound-treated fibroblast cells, we identified treatment responsive sequence signatures, the majority of which center at the 5' splice site of exons whose inclusion or exclusion is modulated by SMC treatment. We then leveraged this model to identify 155 human disease genes that harbor ClinVar mutations predicted to alter pre-mRNA splicing as potential targets for BPN-15477 treatment. Using in vitro splicing assays, we validated representative predictions by demonstrating successful correction of splicing defects caused by mutations in genes responsible for cystic fibrosis (CFTR), cholesterol ester storage disease (LIPA), Lynch syndrome (MLH1) and familial frontotemporal dementia (MAPT). Our study shows that deep learning techniques can identify a complex set of sequence signatures and predict response to pharmacological modulation, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate splicing.

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Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

Cited by 41 publications

References 70 publications

Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers

Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers

Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

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