scholarly journals All-FIT: allele-frequency-based imputation of tumor purity from high-depth sequencing data

2019 ◽  
Vol 36 (7) ◽  
pp. 2173-2180 ◽  
Author(s):  
Jui Wan Loh ◽  
Caitlin Guccione ◽  
Frances Di Clemente ◽  
Gregory Riedlinger ◽  
Shridar Ganesan ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Least Square Method ◽  
Least Square ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Cell Content ◽  
Clinical Sequencing ◽  
Tumor Purity ◽  
Matched Data ◽  
High Depth

Abstract Summary Clinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal–tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations. Allele-frequency-based imputation of tumor (All-FIT) is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data. Using simulated and clinical data, we demonstrate All-FIT’s accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors. Availability and implementation Freely available at http://software.khiabanian-lab.org. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals All-FIT: Allele-Frequency-based Imputation of Tumor Purity from High-Depth Sequencing Data

2019 ◽  
Author(s):  
Jui Wan Loh ◽  
Caitlin Guccione ◽  
Frances Di Clemente ◽  
Gregory Riedlinger ◽  
Shridar Ganesan ◽  
...  
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Sequencing Data ◽  
Cell Content ◽  
Clinical Sequencing ◽  
Tumor Purity ◽  
Improved Performance ◽  
Matched Data ◽  
Computational Analyses ◽  
High Depth

AbstractMotivationClinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal-tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations.MethodsAll-FIT is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data.ResultsUsing simulated and clinical data, we demonstrate All-FIT’s accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors.Availability and ImplementationFreely available at http://software.khiabanian-lab.org.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

2019 ◽  
Vol 36 (7) ◽  
pp. 2017-2024
Author(s):  
Weiwei Zhang ◽  
Ziyi Li ◽  
Nana Wei ◽  
Hua-Jun Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Real Data ◽  
R Package ◽  
Differential Methylation ◽  
Least Square ◽  
Epigenetic Mechanism ◽  
Supplementary Information ◽  
Cpg Sites ◽  
Tumor Purity ◽  
Different Sources ◽  
Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals JuLI: accurate detection of DNA fusions in clinical sequencing for precision oncology

2019 ◽  
Author(s):  
Hyun-Tae Shin ◽  
Nayoung K. D. Kim ◽  
Jae Won Yun ◽  
Boram Lee ◽  
Sungkyu Kyung ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
False Negative ◽  
Detection Algorithm ◽  
Clinical Samples ◽  
Whole Genome Sequencing Data ◽  
Precision Oncology ◽  
Sequencing Data ◽  
Clinical Sequencing ◽  
Accurate Detection ◽  
High Depth

ABSTRACTAccurate detection of genomic fusions by high-throughput sequencing in clinical samples with inadequate tumor purity and formalin-fixed paraffin embedded (FFPE) tissue is an essential task in precise oncology. We developed the fusion detection algorithm Junction Location Identifier (JuLI) for optimization of high-depth clinical sequencing. We implemented novel filtering steps to minimize false positives and a joint calling function to increase sensitivity in clinical setting. We comprehensively validated the algorithm using high-depth sequencing data from cancer cell lines and clinical samples and whole genome sequencing data from NA12878. We showed that JuLI outperformed state-of-the-art fusion callers in cases with high-depth clinical sequencing and rescued a driver fusion from false negative in plasma cell-free DNA. JuLI is freely available via GitHub (https://github.com/sgilab/JuLI).

scholarly journals Inference of Germline Mutational Status and Evaluation of Loss of Heterozygosity in High-Depth, Tumor-Only Sequencing Data

2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hossein Khiabanian ◽  
Kim M. Hirshfield ◽  
Mendel Goldfinger ◽  
Simon Bird ◽  
Mark Stein ◽  
...  
Keyword(s):  
Loss Of Heterozygosity ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Familial Cancer ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Tumor Purity ◽  
Statistical Model Selection ◽  
Mutational Status ◽  
High Depth

Purpose Inherited germline defects are implicated in up to 10% of human tumors, with particularly well-known roles in breast and ovarian cancers that harbor BRCA1/2-mutated genes. There is also increasing evidence for the role of germline alterations in other malignancies such as colon and pancreatic cancers. Mutations in familial cancer genes can be detected by high-throughput sequencing, when applied to formalin-fixed paraffin-embedded tumor specimens. However, because of the frequent lack of patient-matched control normal DNA and/or low tumor purity, there is limited ability to determine the genomic status of these alterations (germline v somatic) and to assess the presence of loss of heterozygosity (LOH). These analyses, especially when applied to genes such as BRCA1/2, can have significant clinical implications for patient care. Materials and Methods LOHGIC (LOH-germline inference calculator) is a statistical model selection method to determine somatic versus germline status and predict LOH for mutations identified via clinical grade, high-depth, hybrid capture, tumor-only sequencing. LOHGIC incorporates statistical uncertainties inherent to high-throughput sequencing as well as specimen biases in tumor purity estimates, which we used to assess BRCA1/2 mutations in 1,636 specimens sequenced at Rutgers Cancer Institute of New Jersey. Results Evaluation of LOHGIC with available germline sequencing from BRCA1/2 testing demonstrates 93% accuracy, 100% precision, and 96% recall. This analysis highlights a differential tumor spectrum associated with BRCA1/2 mutations. Conclusion LOHGIC can assess LOH status for both germline and somatic mutations. It also can be applied to any gene with candidate, inherited mutations. This approach demonstrates the clinical utility of targeted sequencing in both identifying patients with potential germline alterations in tumor suppressor genes as well as estimating LOH occurrence in cancer cells, which may confer therapeutic relevance.

scholarly journals unCOVERApp: an interactive graphical application for clinical assessment of sequence coverage at the base-pair level

2020 ◽  
Author(s):  
Emanuela Iovino ◽  
Marco Seri ◽  
Tommaso Pippucci
Keyword(s):  
Allele Frequency ◽  
Base Pair ◽  
Quality Parameters ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Sequence Coverage ◽  
Bioinformatic Tools ◽  
Clinical Annotation ◽  
Clinical Consequences ◽  
Pair Level

Abstract Motivation Next-generation sequencing is increasingly adopted in the clinical practice largely thanks to concurrent advancements in bioinformatic tools for variant detection and annotation. However, the need to assess sequencing quality at the base-pair level still poses challenges for diagnostic accuracy. One of the most popular quality parameters is the percentage of targeted bases characterized by low depth of coverage (DoC). These regions potentially ‘hide’ clinically relevant variants, but no annotation is usually returned with them. However, visualizing low-DoC data with their potential functional and clinical consequences may be useful to prioritize inspection of specific regions before re-sequencing all coverage gaps or making assertions about completeness of the diagnostic test. To meet this need, we have developed unCOVERApp, an interactive application for graphical inspection and clinical annotation of low-DoC genomic regions containing genes. Results unCOVERApp interactive plots allow to display gene sequence coverage down to the base-pair level, and functional and clinical annotations of sites below a user-defined DoC threshold can be downloaded in a user-friendly spreadsheet format. Moreover, unCOVERApp provides a simple statistical framework to evaluate if DoC is sufficient for the detection of somatic variants. A maximum credible allele frequency calculator is also available allowing users to set allele frequency cut-offs based on assumptions about the genetic architecture of the disease. In conclusion, unCOVERApp is an original tool designed to identify sites of potential clinical interest that may be ‘hidden’ in diagnostic sequencing data. Availabilityand implementation unCOVERApp is a free application developed with Shiny packages and available in GitHub (https://github.com/Manuelaio/uncoverappLib). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TPES: tumor purity estimation from SNVs

2019 ◽  
Vol 35 (21) ◽  
pp. 4433-4435 ◽  
Author(s):  
Alessio Locallo ◽  
Davide Prandi ◽  
Tarcisio Fedrizzi ◽  
Francesca Demichelis
Keyword(s):  
R Package ◽  
Computational Method ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Tumor Purity ◽  
Whole Exome ◽  
Whole Exome Sequencing Data ◽  
Fraction Distribution ◽  
Tumor Genome

Abstract Motivation Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. ‘non-aberrant’ in terms of identifiable SCNAs. Results We introduce a computational method, tumor purity estimation from single-nucleotide variants (SNVs), which derives TP from the allelic fraction distribution of SNVs. On more than 7800 whole-exome sequencing data of TCGA tumor samples, it showed high concordance with a range of TP tools (Spearman’s correlation between 0.68 and 0.82; >9 SNVs) and rescued TP estimates of 1, 194 samples (15%) pan-cancer. Availability and implementation TPES is available as an R package on CRAN and at https://bitbucket.org/l0ka/tpes.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Global copy number profiling of cancer genomes

2015 ◽  
Vol 32 (6) ◽  
pp. 926-928 ◽  
Author(s):  
Xuefeng Wang ◽  
Mengjie Chen ◽  
Xiaoqing Yu ◽  
Natapol Pornputtapong ◽  
Hao Chen ◽  
...  
Keyword(s):  
Copy Number ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Tumor Purity ◽  
Whole Exome ◽  
Cancer Genomes ◽  
Whole Exome Sequencing Data ◽  
Allele Specific

Abstract Summary: In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity. Availability and implementation: https://github.com/xfwang/CLOSE Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Direct Assessment of Effective Renal Plasma Flow from Renoscintigraphy with a Gamma Camera and an On-Line Computer

1981 ◽  
Vol 20 (06) ◽  
pp. 274-278
Author(s):  
J. Liniecki ◽  
J. Bialobrzeski ◽  
Ewa Mlodkowska ◽  
M. J. Surma
Keyword(s):  
Count Rate ◽  
Renal Plasma Flow ◽  
Early Period ◽  
Least Square Method ◽  
Least Square ◽  
Uptake Coefficient ◽  
Positive Linear Correlation ◽  
On Line ◽  
Best Fit ◽  
Plasma Activity

A concept of a kidney uptake coefficient (UC) of 131I-o-hippurate was developed by analogy from the corresponding kidney clearance of blood plasma in the early period after injection of the hippurate. The UC for each kidney was defined as the count-rate over its ROI at a time shorter than the peak in the renoscintigraphic curve divided by the integral of the count-rate curve over the "blood"-ROI. A procedure for normalization of both curves against each other was also developed. The total kidney clearance of the hippurate was determined from the function of plasma activity concentration vs. time after a single injection; the determinations were made at 5, 10, 15, 20, 30, 45, 60, 75 and 90 min after intravenous administration of 131I-o-hippurate and the best-fit curve was obtained by means of the least-square method. When the UC was related to the absolute value of the clearance a positive linear correlation was found (r = 0.922, ρ > 0.99). Using this regression equation the clearance could be estimated in reverse from the uptake coefficient calculated solely on the basis of the renoscintigraphic curves without blood sampling. The errors of the estimate are compatible with the requirement of a fast appraisal of renal function for purposes of clinical diagknosis.

PENGARUH KNOWLEDGE MANAGEMENT, KOMPETENSI, MOTIVASI TERHADAP KINERJA KARYAWAN

2015 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Miftahol Arifin
Keyword(s):  
Knowledge Management ◽  
Linear Regression ◽  
Significant Influence ◽  
Employee Performance ◽  
Least Square Method ◽  
Least Square ◽  
Analysis Tool ◽  
Ordinary Least Square ◽  
Competence Motivation ◽  
Performance Analyze

The purpose of this research is to analyze the influence of knowledge management on employee performance, analyze the effect of competence on employee performance, analyze the influence of motivation on employee performance). In this study, samples taken are structural employees PT.centris Kingdom Taxi Yogyakarta. The analysis tool in this study using multiple linear regression with Ordinary Least Square method (OLS). The conclusion of this study showed that the variables of knowledge management has a significant influence on employee performance, competence variables have an influence on employee performance, motivation variables have an influence on employee performance, The analysis showed that the variables of knowledge management, competence, motivation on employee performance.Keywords: knowledge management, competence, motivation, employee performance.

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