3 research outputs found

    Red Panda: A Novel Method for Detecting Variation in Single-Cell RNA Sequencing

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    Single-cell sequencing enables the rapid acquisition of genomic and transcriptomic data from individual cells to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. However, generating high quality data is vital to the study of the aforementioned diseases, among others. Our goal is to create such a tool and use in-house sequencing to validate its effectiveness. Our software employs the unique information found in scRNA-seq data to more accurately identify variants in ways not possible with software designed for bulk sequencing. We intentionally isolate variants based on three different classes: homozygous-looking, heterozygous, and bimodally-distributed heterozygous, the last of which can only be identified in scRNA-seq. To properly validate the results from this method, variants were called on: scRNA-seq and exome sequencing jointly performed on human articular chondrocytes, scRNA-seq from mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. The chondrocyte exome sequencing was used to validate the chondrocyte scRNA-seq results. For Red Panda, on average, 913 variants were shared with the exome and had a Positive Predictive Value (PPV) of 45.0%. Other tools—FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, and Platypus—ranged from 65-705 variants and 5.8%-31.7% PPV. Sanger sequencing was performed on a subset of the variants identified in the MEFs, and simulated data was generated to assess the sensitivity of each tools. From the latter, Red Panda had the highest sensitivity at 72.44%. The other tools ranged from 18.22% to 39.09%. We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently-existing software

    Red Panda: A Novel Method for Detecting Variants in Single-Cell RNA Sequencing

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    BACKGROUND: Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. RESULTS: In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools-FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus-ranged from 5.8-41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. CONCLUSIONS: We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved

    Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature

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    BACKGROUND: We previously associated HIST1H1E mutations causing Rahman syndrome with a specific genome-wide methylation pattern. RESULTS: Methylome analysis from peripheral blood samples of six affected subjects led us to identify a specific hypomethylated profile. This "episignature" was enriched for genes involved in neuronal system development and function. A computational classifier yielded full sensitivity and specificity in detecting subjects with Rahman syndrome. Applying this model to a cohort of undiagnosed probands allowed us to reach diagnosis in one subject. CONCLUSIONS: We demonstrate an epigenetic signature in subjects with Rahman syndrome that can be used to reach molecular diagnosis
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