scholarly journals Analysis and design of RNA sequencing experiments for identifying RNA editing and other single-nucleotide variants

RNA ◽  
2013 ◽  
Vol 19 (6) ◽  
pp. 725-732 ◽  
Author(s):  
J.-H. Lee ◽  
J. K. Ang ◽  
X. Xiao
Keyword(s):  
Rna Sequencing ◽  
Rna Editing ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Analysis And Design

scholarly journals Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

2021 ◽  
Author(s):  
Alex Rogozhnikov ◽  
Pavan Ramkumar ◽  
Saul Kato ◽  
Sean Escola
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Single Cell Sequencing ◽  
Additional Information ◽  
Genotype Information ◽  
Single Cell Rna Sequencing ◽  
Genotype Inference

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

scholarly journals Single-nucleotide variants in human RNA: RNA editing and beyond

2018 ◽  
Vol 18 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Yan Guo ◽  
Hui Yu ◽  
David C Samuels ◽  
Wei Yue ◽  
Scott Ness ◽  
...  
Keyword(s):  
Rna Editing ◽  
Rna Seq ◽  
Rna Modifications ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Genomic Variants ◽  
High Prevalence ◽  
History Of ◽  
Gene Expression Quantification

Abstract Through analysis of paired high-throughput DNA-Seq and RNA-Seq data, researchers quickly recognized that RNA-Seq can be used for more than just gene expression quantification. The alternative applications of RNA-Seq data are abundant, and we are particularly interested in its usefulness for detecting single-nucleotide variants, which arise from RNA editing, genomic variants and other RNA modifications. A stunning discovery made from RNA-Seq analyses is the unexpectedly high prevalence of RNA-editing events, many of which cannot be explained by known RNA-editing mechanisms. Over the past 6–7 years, substantial efforts have been made to maximize the potential of RNA-Seq data. In this review we describe the controversial history of mining RNA-editing events from RNA-Seq data and the corresponding development of methodologies to identify, predict, assess the quality of and catalog RNA-editing events as well as genomic variants.

Abstract 5173: Identification of single-nucleotide variants by high-throughput RNA sequencing in endemic Burkitt Lymphoma

2014 ◽  
Author(s):  
Maria Antonella Laginestra ◽  
Francesco Abate ◽  
Maryam Etebari ◽  
Giulia D. Falco ◽  
Fabio Fuligni ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
High Throughput ◽  
Burkitt Lymphoma ◽  
Single Nucleotide Variants ◽  
Single Nucleotide

scholarly journals Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant

2020 ◽  
Vol 31 (9) ◽  
pp. 1977-1986 ◽  
Author(s):  
Andrew F. Malone ◽  
Haojia Wu ◽  
Catrina Fronick ◽  
Robert Fulton ◽  
Joseph P. Gaut ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Kidney Transplant ◽  
Immune Cell ◽  
Solid Organ ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Transcriptional Profiles ◽  
Single Cell Rna Sequencing

BackgroundIn solid organ transplantation, donor-derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient from donor cells.MethodsWhole-exome sequencing of donor and recipient DNA and single-cell RNA sequencing (scRNA-seq) of five human kidney transplant biopsy cores distinguished immune cell contributions from both participants. DNA-sequence comparisons used single nucleotide variants (SNVs) identified in the exome sequences across all samples.ResultsAnalysis of expressed SNVs in the scRNA-seq data set distinguished recipient versus donor origin for all 81,139 cells examined. The leukocyte donor/recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages displayed inflammatory activation whereas donor macrophages demonstrated antigen presentation and complement signaling. Recipient-origin T cells expressed cytotoxic and proinflammatory genes consistent with an effector cell phenotype, whereas donor-origin T cells appeared quiescent, expressing oxidative phosphorylation genes. Finally, both donor and recipient T cell clones within the rejecting kidney suggested lymphoid aggregation. The results indicate that donor-origin macrophages and T cells have distinct transcriptional profiles compared with their recipient counterparts, and that donor macrophages can persist for years post-transplantation.ConclusionsAnalysis of single nucleotide variants and their expression in single cells provides a powerful novel approach to accurately define leukocyte chimerism in a complex organ such as a transplanted kidney, coupled with the ability to examine transcriptional profiles at single-cell resolution.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/JASN/2020_08_07_JASN2020030326.mp3

scholarly journals Differentiating Genomic SNPs Using Allele Depth and Predicted Genotype

2018 ◽  
Author(s):  
Saam Hasan
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Rna Editing ◽  
Editing Site ◽  
Biological Processes ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide Variants ◽  
Accuracy Rate ◽  
Single Nucleotide

AbstractDifferentiating between genomic SNPs and other types of single nucleotide variants becomes a key issue in research aimed at studying the importance of these variants of a particular type in biological processes. Here we present an R based method for differentiating between genomic single nucleotide polymorphisms (SNPs) and RNA editing sites. We use data from an earlier study of ours and target only the known dbsnp SNPs that we found in our study. Our method involves calculating the ratio of allele depth for ref and alt alleles and comparing that to the predicted genotype. We use the concept that editing levels should be different for each allele and thus should not reflect the ratio predicted by the genotype. The study yielded an accuracy rate ranging from 86 to over 90 percent at successfully predicted dbsnp entries as SNPs. Albeit this is in the absence of known RNA editing site vcf data to compare as a reference.

scholarly journals Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fenglin Liu ◽  
Yuanyuan Zhang ◽  
Lei Zhang ◽  
Ziyi Li ◽  
Qiao Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Allele Frequencies ◽  
Cellular Heterogeneity ◽  
Variant Allele ◽  
Detection Methods ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Single Cell Rna Sequencing

Abstract Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Asymptomatic Multiple Myeloma - Background of Progression, Evolution, and Prognosis

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 235-235 ◽  
Author(s):  
Anja Seckinger ◽  
Anna Jauch ◽  
Martina Emde ◽  
Susanne Beck ◽  
Marcel Mohr ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Plasma Cell ◽  
Research Funding ◽  
De Novo ◽  
Accumulation Rate ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Paired Samples ◽  
Whole Exome ◽  
Cell Accumulation

Abstract BACKGROUND. Asymptomatic multiple myeloma (AMM) evolves from monoclonal gammopathy of unknown significance (MGUS) and progresses to symptomatic myeloma characterized by end-organ damage. Aim of our study was to address the determinants of evolution and progression of AMM, their molecular background, and whether they are present upfront or evolve de novoin a multistep process on the background of an ongoing genetic instability. METHODS . CD138-purified plasma cell samples of 2369 consecutive patients with MGUS, asymptomatic, and symptomatic myeloma were investigated by fluorescence-in-situ-hybridization (n=304/432/1633), 951 (n=62/259/630) by gene expression profiling. Sixty-five paired samples at AMM and disease progression were assessed by iFISH, 28 of these were further assessed by array-comparative-genomic-hybridization, as well as whole exome- (WES), and RNA-sequencing. Serum/urine samples (n=8398) allowed modelling of plasma cell accumulation in AMM and MGUS, respectively (n=322/196). RESULTS . Up-front tumor mass, plasma cell accumulation rate and molecular characteristics, including alterations in gene expression and presence of progression-associated chromosomal aberrations, i.e. t(4;14), deletions of 13q14, 17p13, 8p21, gains of 1q21, as well as hyperdiploidy, drive and predict evolution and progression of AMM. But for hyperdiploidy, the same factors drive progression from symptomatic to relapsed myeloma and also in AMM rather their number than the specific single aberration impact on time to progression. This means that the mechanisms driving progression to symptomatic myeloma are (at least in part) the same driving progression under treatment. Molecularly, all chromosomal aberrations, most transcriptomic changes, and most frequent mutations detected in symptomatic myeloma including NRAS, KRAS, DIS3, HIST1H1E are already present in MGUS or AMM. In paired AMM/MM samples, 22/27 (81%) show a stable clonal pattern, 5/27 (19%) the de novo appearance of expressed clones, including KRAS or FAM46C. No significant transcriptomic differences are found by RNA-sequencing. (Sub-)Clonal complexity with 4-5 discernable clusters of 103-363 single nucleotide variants with an allele frequency of ≥10% remains fairly constant during disease progression with most being detectable in both AMM and MM, incompatible with clonal outgrowth to any reason in these patients. In CONCLUSION, evolution and progression of AMM are driven and can be well predicted by factors being present upfront, i.e. tumor mass, plasma cell accumulation rate, and the set of molecular alterations. Progression is, contrary to current thinking, in the vast majority of patients not driven by de novo acquired expressed clonal alterations. This is proven in our set of paired samples on the level of chromosomal numeric or structural alterations (as per iFISH and aCGH), expressed clonal single nucleotide variants (as per whole exome- and RNA-sequencing), and remaining subclonal complexity. This in turn disproves other de novo alterations (e.g. methylation), as the subclone harboring these would then need to become clonal. Disclosures Hillengass: Sanofi: Research Funding; Amgen: Consultancy, Honoraria; Celgene: Honoraria; BMS: Honoraria; Novartis: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Hose:Takeda: Other: Travel grant; EngMab: Research Funding; Sanofi: Research Funding.

scholarly journals Corrigendum to: Single-nucleotide variants in human RNA: RNA editing and beyond

2018 ◽  
Vol 18 (1) ◽  
pp. 40-40
Author(s):  
Yan Guo ◽  
Hui Yu ◽  
David C Samuels ◽  
Wei Yue ◽  
Scott Ness ◽  
...  
Keyword(s):  
Rna Editing ◽  
Single Nucleotide Variants ◽  
Single Nucleotide

scholarly journals Targeted RNA-Sequencing Enables Detection of Relevant Translocations and Single Nucleotide Variants and Provides a Method for Classification of Hematological Malignancies–RANKING

2020 ◽  
Vol 66 (12) ◽  
pp. 1521-1530
Author(s):  
Kim de Lange ◽  
Eddy N de Boer ◽  
Anneke Bosga ◽  
Mohamed Z Alimohamed ◽  
Lennart F Johansson ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Hematological Malignancies ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
Primary Diagnosis ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Molecular Abnormalities ◽  
Wide Range

Abstract Background Patients with hematological malignancies (HMs) carry a wide range of chromosomal and molecular abnormalities that impact their prognosis and treatment. Since no current technique can detect all relevant abnormalities, technique(s) are chosen depending on the reason for referral, and abnormalities can be missed. We tested targeted transcriptome sequencing as a single platform to detect all relevant abnormalities and compared it to current techniques. Material and Methods We performed RNA-sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in bone marrow from 136 patients with a primary diagnosis of HM. We then applied machine learning to expression profile data to perform leukemia classification, a method we named RANKING. Gene fusions for all the genes in the panel were detected, and overexpression of the genes EVI1, CCND1, and BCL2 was quantified. Single nucleotide variants/indels were analyzed in acute myeloid leukemia (AML), myelodysplastic syndrome and patients with acute lymphoblastic leukemia (ALL) using a virtual myeloid (54 genes) or lymphoid panel (72 genes). Results RANKING correctly predicted the leukemia classification of all AML and ALL samples and improved classification in 3 patients. Compared to current methods, only one variant was missed, c.2447A>T in KIT (RT-PCR at 10−4), and BCL2 overexpression was not seen due to a t(14; 18)(q32; q21) in 2% of the cells. Our RNA-sequencing method also identified 6 additional fusion genes and overexpression of CCND1 due to a t(11; 14)(q13; q32) in 2 samples. Conclusions Our combination of targeted RNA-sequencing and data analysis workflow can improve the detection of relevant variants, and expression patterns can assist in establishing HM classification.

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