Browsing by Subject "High-Throughput Nucleotide Sequencing"

Now showing 1 - 7 of 7
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    Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes
    (Springer Nature, 2018-02-26) Numanagić, Ibrahim; Malikić, Salem; Ford, Michael; Qin, Xiang; Toji, Lorraine; Radovich, Milan; Skaar, Todd C.; Pratt, Victoria M.; Berger, Bonnie; Scherer, Steve; Sahinalp, S. Cenk; Medicine, School of Medicine
    High-throughput sequencing provides the means to determine the allelic decomposition for any gene of interest-the number of copies and the exact sequence content of each copy of a gene. Although many clinically and functionally important genes are highly polymorphic and have undergone structural alterations, no high-throughput sequencing data analysis tool has yet been designed to effectively solve the full allelic decomposition problem. Here we introduce a combinatorial optimization framework that successfully resolves this challenging problem, including for genes with structural alterations. We provide an associated computational tool Aldy that performs allelic decomposition of highly polymorphic, multi-copy genes through using whole or targeted genome sequencing data. For a large diverse sequencing data set, Aldy identifies multiple rare and novel alleles for several important pharmacogenes, significantly improving upon the accuracy and utility of current genotyping assays. As more data sets become available, we expect Aldy to become an essential component of genotyping toolkits.
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    Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing
    (Elsevier, 2022) Ly, Reynold C.; Shugg, Tyler; Ratcliff, Ryan; Osei, Wilberforce; Lynnes, Ty C.; Pratt, Victoria M.; Schneider, Bryan P.; Radovich, Milan; Bray, Steven M.; Salisbury, Benjamin A.; Parikh, Baiju; Sahinalp, S. Cenk; Numanagić, Ibrahim; Skaar, Todd C.; Medicine, School of Medicine
    Germline whole exome sequencing from molecular tumor boards has the potential to be repurposed to support clinical pharmacogenomics. However, accurately calling pharmacogenomics-relevant genotypes from exome sequencing data remains challenging. Accordingly, this study assessed the analytical validity of the computational tool, Aldy, in calling pharmacogenomics-relevant genotypes from exome sequencing data for 13 major pharmacogenes. Germline DNA from whole blood was obtained for 164 subjects seen at an institutional molecular solid tumor board. All subjects had whole exome sequencing from Ashion Analytics and panel-based genotyping from an institutional pharmacogenomics laboratory. Aldy version 3.3 was operationalized on the LifeOmic Precision Health Cloud with copy number fixed to two copies per gene. Aldy results were compared with those from genotyping for 56 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, and TPMT. Read depth was >100× for all variants except CYP3A4∗22. For 75 subjects in the validation cohort, all 3393 Aldy variant calls were concordant with genotyping. Aldy calls for 736 diplotypes containing alleles assessed by both platforms were also concordant. Aldy identified additional star alleles not covered by targeted genotyping for 139 diplotypes. Aldy accurately called variants and diplotypes for 13 major pharmacogenes, except for CYP2D6 variants involving copy number variations, thus allowing repurposing of whole exome sequencing to support clinical pharmacogenomics.
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    Bioinformatics Methods and Biological Interpretation for Next-Generation Sequencing Data
    (Hindawi, 2015-09-07) Wang, Guohua; Liu, Yunlong; Zhu, Dongxiao; Klau, Gunnar W.; Feng, Weixing; Department of Medical & Molecular Genetics, IU School of Medicine
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    Evaluation of the Genetic Basis of Familial Aggregation of Pacemaker Implantation by a Large Next Generation Sequencing Panel
    (Public Library of Science (PloS), 2015) Celestino-Soper, Patrícia B. S.; Doytchinova, Anisiia; Steiner, Hillel A.; Uradu, Andrea; Lynnes, Ty C.; Groh, William J.; Miller, John M.; Lin, Hai; Gao, Hongyu; Wang, Zhiping; Liu, Yunlong; Chen, Peng-Sheng; Vatta, Matteo; Department of Medical and Molecular Genetics, IU School of Medicine
    BACKGROUND: The etiology of conduction disturbances necessitating permanent pacemaker (PPM) implantation is often unknown, although familial aggregation of PPM (faPPM) suggests a possible genetic basis. We developed a pan-cardiovascular next generation sequencing (NGS) panel to genetically characterize a selected cohort of faPPM. MATERIALS AND METHODS: We designed and validated a custom NGS panel targeting the coding and splicing regions of 246 genes with involvement in cardiac pathogenicity. We enrolled 112 PPM patients and selected nine (8%) with faPPM to be analyzed by NGS. RESULTS: Our NGS panel covers 95% of the intended target with an average of 229x read depth at a minimum of 15-fold depth, reaching a SNP true positive rate of 98%. The faPPM patients presented with isolated cardiac conduction disease (ICCD) or sick sinus syndrome (SSS) without overt structural heart disease or identifiable secondary etiology. Three patients (33.3%) had heterozygous deleterious variants previously reported in autosomal dominant cardiac diseases including CCD: LDB3 (p.D117N) and TRPM4 (p.G844D) variants in patient 4; TRPM4 (p.G844D) and ABCC9 (p.V734I) variants in patient 6; and SCN5A (p.T220I) and APOB (p.R3527Q) variants in patient 7. CONCLUSION: FaPPM occurred in 8% of our PPM clinic population. The employment of massive parallel sequencing for a large selected panel of cardiovascular genes identified a high percentage (33.3%) of the faPPM patients with deleterious variants previously reported in autosomal dominant cardiac diseases, suggesting that genetic variants may play a role in faPPM.
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    LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
    (Oxford University Press, 2019-10-10) Wan, Changlin; Chang, Wennan; Zhang, Yu; Shah, Fenil; Lu, Xiaoyu; Zang, Yong; Zhang, Anru; Cao, Sha; Fishel, Melissa L.; Ma, Qin; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.
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    RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants
    (Mary Ann Liebert, Inc., 2017-07) Hao, Yangyang; Xuei, Xiaoling; Li, Lang; Nakshatri, Harikrishna; Edenberg, Howard J.; Liu, Yunlong; Medical and Molecular Genetics, School of Medicine
    Accurate identification of low-frequency somatic point mutations in tumor samples has important clinical utilities. Although high-throughput sequencing technology enables capturing such variants while sequencing primary tumor samples, our ability for accurate detection is compromised when the variant frequency is close to the sequencer error rate. Most current experimental and bioinformatic strategies target mutations with ≥5% allele frequency, which limits our ability to understand the cancer etiology and tumor evolution. We present an experimental and computational modeling framework, RareVar, to reliably identify low-frequency single-nucleotide variants from high-throughput sequencing data under standard experimental protocols. RareVar protocol includes a benchmark design by pooling DNAs from already sequenced individuals at various concentrations to target variants at desired frequencies, 0.5%-3% in our case. By applying a generalized, linear model-based, position-specific error model, followed by machine-learning-based variant calibration, our approach outperforms existing methods. Our method can be applied on most capture and sequencing platforms without modifying the experimental protocol.
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    Whole exome sequencing to identify genetic causes of short stature
    (S. Karger AG, 2014) Guo, Michael H.; Shen, Yiping; Walvoord, Emily C.; Miller, Timothy C.; Moon, Jennifer E.; Hirschhorn, Joel N.; Dauber, Andrew; Department of Pediatrics, IU School of Medicine
    BACKGROUND/AIMS: Short stature is a common reason for presentation to pediatric endocrinology clinics. However, for most patients, no cause for the short stature can be identified. As genetics plays a strong role in height, we sought to identify known and novel genetic causes of short stature. METHODS: We recruited 14 children with severe short stature of unknown etiology. We conducted whole exome sequencing of the patients and their family members. We used an analysis pipeline to identify rare non-synonymous genetic variants that cause the short stature. RESULTS: We identified a genetic cause of short stature in 5 of the 14 patients. This included cases of floating-harbor syndrome, Kenny-Caffey syndrome, the progeroid form of Ehlers-Danlos syndrome, as well as 2 cases of the 3-M syndrome. For the remaining patients, we have generated lists of candidate variants. CONCLUSIONS: Whole exome sequencing can help identify genetic causes of short stature in the context of defined genetic syndromes, but may be less effective in identifying novel genetic causes of short stature in individual families. Utilized in the clinic, whole exome sequencing can provide clinically relevant diagnoses for these patients. Rare syndromic causes of short stature may be underrecognized and underdiagnosed in pediatric endocrinology clinics.