scholarly journals Detection of aberrant gene expression events in RNA sequencing data

2021 ◽  
Vol 16 (2) ◽  
pp. 1276-1296
Author(s):  
Vicente A. Yépez ◽  
Christian Mertes ◽  
Michaela F. Müller ◽  
Daniela Klaproth-Andrade ◽  
Leonhard Wachutka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

scholarly journals OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

2018 ◽  
Author(s):  
Felix Brechtmann ◽  
Agnė Matusevičiūtė ◽  
Christian Mertes ◽  
Vicente A Yépez ◽  
Žiga Avsec ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Negative Binomial ◽  
Statistical Significance ◽  
P Value ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Set ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

AbstractRNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (OUTlier in RNA-seq fInDER), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read count expectations according to the co-variation among genes resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best correction of artificially corrupted data. Precision–recall analyses using simulated outlier read counts demonstrated the importance of combining correction for co-variation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostic platforms.

scholarly journals 78 Novel RNA-seq platform improve patient outcome in clinical oncology and enable implementation of AI in the clinic

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A87-A87
Author(s):  
Vy Nguyen ◽  
Gitte Pedersen ◽  
Morten Pedersen
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Clinical Oncology ◽  
Individualized Treatment ◽  
Tissue Type ◽  
Clinical Samples ◽  
Aberrant Gene Expression ◽  
Approved Drugs ◽  
Aberrant Gene ◽  
Insight Into

BackgroundOnly 1 out of 4 cancer treatments prolongs life while expenditure for cancer treatment is greater than $100 billion/year. RNA-sequencing has allowed researchers to gain insight into the transcriptome of human cancers. However, RNA-sequencing remains widely unused in clinical oncology. We address this issue through the development and CLIA validation of OneRNA—an RNA-sequencing platform for cancer diagnostics and the design of new treatments. The development of OneRNA had to overcome the two main hurdles for implementation of RNA sequencing in the clinic: 1) clinical samples are typically embedded in FFPE which results in highly fragmented RNA making sequencing of these samples difficult. 2) how to interpret aberrant gene expression events and translate these results into clinical action. We demonstrate how OneRNA® would enable the design of sophisticated combinatorial clinical studies. An example is combining immune targeting agents such as checkpoint inhibitors with mRNA vaccines. OneRNA also supports the integration of gene expression algorithms because of its ability to interrogate the entire sample transcriptome. OneRNA® has been CLIA certified using FFPE, FF, blood, and saliva samples. Furthermore, the sample preparation method has demonstrated >95% concordance between FF and FFPE and 5–10X the sensitivity compared to Truseq.MethodsThis study aims to demonstrate the clinical utility of OneRNA in detecting aberrant gene expression events and connecting these to already approved drugs that targets these events to offer truly individualized treatment options.ResultsWe show that OneRNA has the ability to predict results for not only validated biomarkers used in standard of care such as ER, PR and HER2 in breast cancer, but also provide insight into biomarkers for response to already approved drugs independent of tissue type and with no standard test. Finally, we demonstrate the reproducibility of OneRNA in predicting IHC status in ER, PR and HER2.ConclusionsThese results demonstrate that OneRNA has applications in both cancer research, drug discovery and development, development of companion diagnostic algorithms and implementation of truly individualized treatment.

scholarly journals Diverse transcriptomic signatures across human tissues identify functional rare genetic variation

2019 ◽  
Author(s):  
Nicole M. Ferraro ◽  
Benjamin J. Strober ◽  
Jonah Einson ◽  
Xin Li ◽  
Francois Aguet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Rare Variants ◽  
Sequencing Data ◽  
Specific Expression ◽  
Aberrant Gene Expression ◽  
Rare Genetic Variation ◽  
Allele Specific ◽  
The Uk ◽  
Aberrant Gene

AbstractRare genetic variation is abundant in the human genome, yet identifying functional rare variants and their impact on traits remains challenging. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants. Here, we expand detection of genetically driven transcriptome abnormalities by evaluating and integrating gene expression, allele-specific expression, and alternative splicing from multi-tissue RNA-sequencing data. We demonstrate that each signal informs unique classes of rare variants. We further develop Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function. Assessing rare variants prioritized by Watershed in the UK Biobank and Million Veterans Program, we identify large effects across 34 traits, and 33 rare variant-trait combinations with both high Watershed scores and large trait effect sizes. Together, we provide a comprehensive analysis of the transcriptomic impact of rare variation and a framework to prioritize functional rare variants and assess their trait relevance.One-sentence summaryIntegrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

scholarly journals Transcriptomic signatures across human tissues identify functional rare genetic variation

Science ◽  
2020 ◽  
Vol 369 (6509) ◽  
pp. eaaz5900 ◽  
Author(s):  
Nicole M. Ferraro ◽  
Benjamin J. Strober ◽  
Jonah Einson ◽  
Nathan S. Abell ◽  
Francois Aguet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rare Variants ◽  
Sequencing Data ◽  
Specific Expression ◽  
Aberrant Gene Expression ◽  
Heart Study ◽  
Rare Genetic Variation ◽  
Allele Specific ◽  
The Uk ◽  
Aberrant Gene

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

scholarly journals Publisher Correction to: C11orf95-RELA fusion drives aberrant gene expression through the unique epigenetic regulation for ependymoma formation

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Tatsuya Ozawa ◽  
Syuzo Kaneko ◽  
Frank Szulzewsky ◽  
Zhiwei Qiao ◽  
Mutsumi Takadera ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epigenetic Regulation ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Transcriptome analysis identified aberrant gene expression in pollen developmental pathways leading to CGMS in cotton (Gossypium hirsutum L.)

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218381 ◽  
Author(s):  
Rasmieh Hamid ◽  
Hassan Marashi ◽  
Rukam S. Tomar ◽  
Saeid Malekzadeh Shafaroudi ◽  
Pritesh H. Sabara
Keyword(s):  
Gene Expression ◽  
Gossypium Hirsutum ◽  
Transcriptome Analysis ◽  
Developmental Pathways ◽  
Gossypium Hirsutum L ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

scholarly journals Receptor-driven invasion profiles in diffuse intrinsic pontine glioma (DIPG)

2021 ◽  
Author(s):  
Anju Karki ◽  
Noah E Berlow ◽  
Jin-Ah Kim ◽  
Esther Hulleman ◽  
Qianqian Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Factor ◽  
Protein Expression ◽  
Cell Viability ◽  
Rna Sequencing ◽  
Cell Invasion ◽  
Growth Factor Receptor ◽  
Diffuse Intrinsic Pontine Glioma ◽  
Sequencing Data ◽  
Pontine Glioma

Abstract Background Diffuse intrinsic pontine glioma (DIPG) is a devastating pediatric cancer with unmet clinical need. DIPG is invasive in nature, where tumor cells interweave into the fiber nerve tracts of the pons making the tumor unresectable. Accordingly, novel approaches in combating the disease is of utmost importance and receptor-driven cell invasion in the context of DIPG is under-researched area. Here we investigated the impact on cell invasion mediated by PLEXINB1, PLEXINB2, platelet growth factor receptor (PDGFR)α, PDGFRβ, epithelial growth factor receptor (EGFR), activin receptor 1 (ACVR1), chemokine receptor 4 (CXCR4) and NOTCH1. Methods We used previously published RNA-sequencing data to measure gene expression of selected receptors in DIPG tumor tissue versus matched normal tissue controls (n=18). We assessed protein expression of the corresponding genes using DIPG cell culture models. Then, we performed cell viability and cell invasion assays of DIPG cells stimulated with chemoattractants/ligands. Results RNA-sequencing data showed increased gene expression of receptor genes such as PLEXINB2, PDGFRα, EGFR, ACVR1, CXCR4 and NOTCH1 in DIPG tumors compared to the control tissues. Representative DIPG cell lines demonstrated correspondingly increased protein expression levels of these genes. Cell viability assays showed minimal effects of growth factors/chemokines on tumor cell growth in most instances. Recombinant SEMA4C, SEM4D, PDGF-AA, PDGF-BB, ACVA, CXCL12 and DLL4 ligand stimulation altered invasion in DIPG cells. Conclusions We show that no single growth factor-ligand pair universally induces DIPG cell invasion. However, our results reveal a potential to create a composite of cytokines or anti-cytokines to modulate DIPG cell invasion.

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