scholarly journals P20-03. Identification of low frequency genetic variants during acute and early infection by parallel-allele specific sequencing

Retrovirology ◽  
2009 ◽  
Vol 6 (S3) ◽  
Author(s):  
JW Pavlicek ◽  
S Chen ◽  
J Hopper ◽  
J Kirchherr ◽  
F Gao
Keyword(s):  
Genetic Variants ◽  
Low Frequency ◽  
Early Infection ◽  
Allele Specific ◽  
Allele Specific Sequencing

scholarly journals DEEPGENTM—A Novel Variant Calling Assay for Low Frequency Variants

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Genetic Variants ◽  
Liquid Biopsy ◽  
Hot Spot ◽  
Treatment Success ◽  
Low Frequency ◽  
Variant Calling ◽  
Subsequent Treatment ◽  
Precision Oncology ◽  
Orthogonal Comparison

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

scholarly journals Analysis of Low-Frequency Mutations Associated with Drug Resistance to Raltegravir before Antiretroviral Treatment

2010 ◽  
Vol 55 (3) ◽  
pp. 1114-1119 ◽  
Author(s):  
Jia Liu ◽  
Michael D. Miller ◽  
Robert M. Danovich ◽  
Nathan Vandergrift ◽  
Fangping Cai ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Treatment Failure ◽  
Low Frequency ◽  
Hiv Treatment ◽  
Viral Population ◽  
Virologic Suppression ◽  
Resistance Mutations ◽  
The Difference ◽  
Allele Specific Sequencing ◽  
Hiv 1

ABSTRACTRaltegravir is highly efficacious in the treatment of HIV-1 infection. The prevalence and impact on virologic outcome of low-frequency resistant mutations among HIV-1-infected patients not previously treated with raltegravir have not been fully established. Samples from HIV treatment-experienced patients entering a clinical trial of raltegravir treatment were analyzed using a parallel allele-specific sequencing (PASS) assay that assessed six primary and six secondary integrase mutations. Patients who achieved and sustained virologic suppression (success patients,n= 36) and those who experienced virologic rebound (failure patients,n= 35) were compared. Patients who experienced treatment failure had twice as many raltegravir-associated resistance mutations prior to initiating treatment as those who achieved sustained virologic success, but the difference was not statistically significant. The frequency of nearly all detected resistance mutations was less than 1% of viral population, and the frequencies of mutations between the success and failure groups were similar. Expansion of pre-existing mutations (one primary and five secondary) was observed in 16 treatment failure patients in whom minority resistant mutations were detected at baseline, suggesting that they might play a role in the development of drug resistance. Two or more mutations were found in 13 patients (18.3%), but multiple mutations were not present in any single viral genome by linkage analysis. Our study demonstrates that low-frequency primary RAL-resistant mutations were uncommon, while minority secondary RAL-resistant mutations were more frequently detected in patients naïve to raltegravir. Additional studies in larger populations are warranted to fully understand the clinical implications of these mutations.

scholarly journals GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data

2021 ◽  
Author(s):  
Julia Markowski ◽  
Rieke Kempfer ◽  
Alexander Kukalev ◽  
Ibai Irastorza-Azcarate ◽  
Gesa Loof ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Embryonic Stem Cell Line ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cell ◽  
Supplementary Information ◽  
Model Organisms ◽  
Genome Architecture ◽  
Chromatin Conformation ◽  
Haplotype Reconstruction ◽  
Allele Specific

Abstract Motivation Genome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM’s ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. Results We present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. Availability GAMIBHEAR is available as an R package under the open source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear Maintainer [email protected] Supplementary information Supplementary information is available at Bioinformatics online.

scholarly journals Discovery of allele-specific protein-RNA interactions in human transcriptomes

2018 ◽  
Author(s):  
Emad Bahrami-Samani ◽  
Yi Xing
Keyword(s):  
Genetic Variants ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Specific Protein ◽  
Computational Method ◽  
Joint Analysis ◽  
Transcriptional Level ◽  
Rna Seq ◽  
Allele Specific ◽  
Rna Interaction

AbstractGene expression is tightly regulated at the post-transcriptional level through splicing, transport, translation, and decay. RNA-binding proteins (RBPs) play key roles in post-transcriptional gene regulation, and genetic variants that alter RBP-RNA interactions can affect gene products and functions. We developed a computational method ASPRIN (Allele-Specific Protein-RNA Interaction), that uses a joint analysis of CLIP-seq (cross-linking and immunoprecipitation followed by high-throughput sequencing) and RNA-seq data to identify genetic variants that alter RBP-RNA interactions by directly observing the allelic preference of RBP from CLIP-seq experiments as compared to RNA-seq. We used ASPRIN to systematically analyze CLIP-seq and RNA-seq data for 166 RBPs in two ENCODE (Encyclopedia of DNA Elements) cell lines. ASPRIN identified genetic variants that alter RBP-RNA interactions by modifying RBP binding motifs within RNA. Moreover, through an integrative ASPRIN analysis with population-scale RNA-seq data, we showed that ASPRIN can help reveal potential causal variants that affect alternative splicing via allele-specific protein-RNA interactions.

scholarly journals Evaluation of the Influence of Genetic Variants of SLC2A9 (GLUT9) and SLC22A12 (URAT1) on the Development of Hyperuricemia and Gout

2020 ◽  
Vol 9 (8) ◽  
pp. 2510
Author(s):  
Katerina Pavelcova ◽  
Jana Bohata ◽  
Marketa Pavlikova ◽  
Eliska Bubenikova ◽  
Karel Pavelka ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Uric Acid ◽  
Genetic Variants ◽  
Biochemical Parameters ◽  
Previous Analysis ◽  
Direct Sequencing ◽  
Low Frequency ◽  
European Population ◽  
The Body ◽  
Genes Encoding

Urate transporters, which are located in the kidneys, significantly affect the level of uric acid in the body. We looked at genetic variants of genes encoding the major reabsorption proteins GLUT9 (SLC2A9) and URAT1 (SLC22A12) and their association with hyperuricemia and gout. In a cohort of 250 individuals with primary hyperuricemia and gout, we used direct sequencing to examine the SLC22A12 and SLC2A9 genes. Identified variants were evaluated in relation to clinical data, biochemical parameters, metabolic syndrome criteria, and our previous analysis of the major secretory urate transporter ABCG2. We detected seven nonsynonymous variants of SLC2A9. There were no nonsynonymous variants of SLC22A12. Eleven variants of SLC2A9 and two variants of SLC22A12 were significantly more common in our cohort than in the European population (p = 0), while variants p.V282I and c.1002+78A>G had a low frequency in our cohort (p = 0). Since the association between variants and the level of uric acid was not demonstrated, the influence of variants on the development of hyperuricemia and gout should be evaluated with caution. However, consistent with the findings of other studies, our data suggest that p.V282I and c.1002+78A>G (SLC2A9) reduce the risk of gout, while p.N82N (SLC22A12) increases the risk.

scholarly journals Allele-specific methylation of imprinted genes in fetal cord blood is influenced by cis-acting genetic variants and parental factors

Epigenomics ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1315-1326 ◽  
Author(s):  
Ramya Potabattula ◽  
Marcus Dittrich ◽  
Julia Böck ◽  
Larissa Haertle ◽  
Tobias Müller ◽  
...  
Keyword(s):  
Cord Blood ◽  
Genetic Variants ◽  
Imprinted Genes ◽  
Parental Factors ◽  
Cis Acting ◽  
Fetal Cord Blood ◽  
Allele Specific ◽  
Allele Specific Methylation

scholarly journals Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily Berger ◽  
Deniz Yorukoglu ◽  
Lillian Zhang ◽  
Sarah K. Nyquist ◽  
Alex K. Shalek ◽  
...  
Keyword(s):  
Long Range ◽  
Genetic Variants ◽  
Read Length ◽  
Rna Seq ◽  
Sequencing Data ◽  
Specific Expression ◽  
Integrative Framework ◽  
Whole Exome ◽  
Allele Specific ◽  
Diverse Data

Abstract Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X’s feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10×  faster than other tools. The advantage of HapTree-X’s ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.

scholarly journals Polygenic analysis of the effect of common and low-frequency genetic variants on serum uric acid levels in Korean individuals

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sung Kweon Cho ◽  
Beomsu Kim ◽  
Woojae Myung ◽  
Yoosoo Chang ◽  
Seungho Ryu ◽  
...  
Keyword(s):  
Uric Acid ◽  
Serum Uric Acid ◽  
Genetic Variants ◽  
Low Frequency ◽  
Polygenic Analysis
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