scholarly journals Perturbation robustness analyses reveal important parameters in variant interpretation pipelines

2020 ◽  
Author(s):  
Yaqiong Wang ◽  
Aashish N. Adhikari ◽  
Uma Sunderam ◽  
Mark N. Kvale ◽  
Robert J. Currier ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Expert Knowledge ◽  
Variant Interpretation ◽  
Limited Data ◽  
Inborn Errors ◽  
General Application ◽  
Impact Prediction ◽  
Prediction Tools ◽  
Genome Interpretation ◽  
Pipeline Design

AbstractMotivationGenome sequencing is being used routinely in clinical and research applications, but subsequent variant interpretation pipelines can vary widely. A systematic approach for exploring parameter choices and selection plays an important role in designing robust pipelines for specific clinical applications.ResultsWe present a framework to be applied in scenarios with limited data whereby expert knowledge informs pipeline refinement. Starting from initial reference variant interpretation pipelines with commonly used parameters, we derived pipelines by perturbing the parameters one by one to determine which parameters can yield meaningful changes in a pipeline’s performance. We updated the reference pipeline by fixing the value of parameters which have small impact on the pipeline’s performance. Then we conducted new rounds of perturbation as the process converged, yielding a stable pipeline which is robust. We applied the framework for genetic disease prediction in de-identified exomes from a cohort of 138 individuals with rare Mendelian inborn errors of metabolism (IEMs) and systematically explored how perturbing different parameters affected the pipeline’s sensitivity and specificity. For this application, we perturbed commonly used parameters in variant interpretation pipelines, including choices of genes, variant callers, transcript models, databases of allele frequencies, databases of curated disease variants, and tools for variant impact prediction. Our analyses showed that choice of variant callers, variant impact prediction tools, MAF threshold, and MAF databases can meaningfully alter results from a pipeline. This work informs the development of exome analysis pipelines designed for newborn metabolic disorder screening and suggests the general application of perturbation analysis in genome interpretation pipeline design.

Retrospective evaluation of in silico prediction tools, REVEL and CADD, for supporting level evidence (PP3/BP4) of genomic variant interpretation

2021 ◽  
Vol 132 ◽  
pp. S260-S261
Author(s):  
Benjamin Edward Kang ◽  
Bryan Gall ◽  
Ezen Choo ◽  
Nina Sanapareddy ◽  
Irina Rakova ◽  
...  
Keyword(s):  
In Silico ◽  
Variant Interpretation ◽  
In Silico Prediction ◽  
Retrospective Evaluation ◽  
Prediction Tools ◽  
Genomic Variant

scholarly journals Biomarker Discovery, Disease Classification, and Similarity Query Processing on High-Throughput MS/MS Data of Inborn Errors of Metabolism

2005 ◽  
Vol 11 (1) ◽  
pp. 90-99 ◽  
Author(s):  
Christian Baumgartner ◽  
Daniela Baumgartner
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Biomarker Discovery ◽  
Expert Knowledge ◽  
Propionic Acidemia ◽  
Disease Classification ◽  
Therapeutic Interventions ◽  
Data Sets ◽  
Type I ◽  
Inborn Errors ◽  
Discriminatory Performance

In newborn errors of metabolism, biomarkers are urgently needed for disease screening, diagnosis, and monitoring of therapeutic interventions. This article describes a 2-step approach to discovermetabolic markers, which involves (1) the identification ofmarker candidates and (2) the prioritization of thembased on expert knowledge of diseasemetabolism. For step 1, the authors developed a new algorithm, the biomarker identifier (BMI), to identifymarkers fromquantified diseased versus normal tandemmass spectrometry data sets. BMI produces a ranked list ofmarker candidates and discards irrelevant metabolites based on a quality measure, taking into account the discriminatory performance, discriminatory space, and variance ofmetabolites’ concentrations at the state of disease. To determine the ability of identified markers to classify subjects, the authors compared the discriminatory performance of several machine-learning paradigms and described a retrieval technique that searches and classifies abnormal metabolic profiles from a screening database. Seven inborn errors of metabolism— phenylketonuria (PKU), glutaric acidemia type I (GA-I), 3-methylcrotonylglycinemia deficiency (3-MCCD), methylmalonic acidemia (MMA), propionic acidemia (PA), medium-chain acylCoAdehydrogenase deficiency (MCADD), and 3-OH longchain acyl CoA dehydrogenase deficiency (LCHADD)—were investigated. All primarily prioritized marker candidates could be confirmed by literature. Somenovel secondary candidateswere identified (i.e., C16:1 andC4DCfor PKU, C4DCfor GA-I, and C18:1 forMCADD), which require further validation to confirmtheir biochemical role during health and disease.

scholarly journals An active learning framework improves tumor variant interpretation

2021 ◽  
Author(s):  
Alexandra M Blee ◽  
Bian Li ◽  
Turner Pecen ◽  
Jens Meiler ◽  
Zachary D Nagel ◽  
...  
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Excision Repair ◽  
Complementation Group ◽  
Full Potential ◽  
Variant Interpretation ◽  
Validation Data ◽  
Protein Variant ◽  
Prediction Tools ◽  
Potential Biomarker

For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed that characterize variants of unknown significance (VUS) in a patient's genome with respect to their likelihood to influence treatment response and outcomes. However, the performance of most variant prediction tools is limited by the difficulty of acquiring sufficient training and validation data. To overcome these limitations, we applied an iterative active learning approach starting from available biochemical, evolutionary, and functional annotations. The potential of active learning to improve variant interpretation was first demonstrated by applying it to synthetic and deep mutational scanning (DMS) datasets for four cancer-relevant proteins. We then probed its utility to guide interpretation and functional validation of tumor VUS in a potential biomarker for cancer therapy sensitivity, the nucleotide excision repair (NER) protein Xeroderma Pigmentosum Complementation Group A (XPA). A quantitative high-throughput cell-based NER activity assay, fluorescence-based multiplex flow-cytometric host cell reactivation (FM-HCR), was used to validate XPA VUS selected by the active learning strategy. In all cases, selecting VUS for validation by active learning yielded an improvement in performance over traditional learning. These analyses suggest that active learning is well-suited to significantly improve interpretation of VUS and cancer patient genomes.

Congenital Absence of the Pericardium: A Systematic Approach to Diagnosis and Management

Cardiology ◽  
2016 ◽  
Vol 136 (4) ◽  
pp. 270-278 ◽  
Author(s):  
Bo Xu ◽  
Jorge Betancor ◽  
Craig Asher ◽  
Adriana Rosario ◽  
Allan Klein
Keyword(s):  
Clinical Features ◽  
Systematic Approach ◽  
Delayed Diagnosis ◽  
Rare Condition ◽  
Cardiovascular Imaging ◽  
Congenital Absence ◽  
Limited Data ◽  
Diagnosis And Management ◽  
Optimal Approach

Congenital absence of the pericardium (CAP) is a rare condition. Failure to recognize the clinical features of this condition can lead to incorrect and delayed diagnosis. Limited data are available regarding the optimal approach to diagnose and manage patients with suspected CAP. Due to the rare nature of CAP, this condition can present diagnostic and management dilemmas for clinicians. Using 3 cases of CAP as a framework, a clinically focused review on the diagnosis and management of CAP is presented. Clinicians will be provided with a systematic approach to evaluating patients with suspected CAP, incorporating key history, examination, and multimodality cardiovascular imaging investigations.

Abstract LB-250: The Critical Assessment of Genome Interpretation: A community experiment that informs use of methods for germline cancer variant impact prediction

2020 ◽  
Author(s):  
Constantina Bakolitsa ◽  
Gaia Andreoletti ◽  
Roger Hoskins ◽  
Predrag Radivojac ◽  
John Moult ◽  
...  
Keyword(s):  
Critical Assessment ◽  
Impact Prediction ◽  
Genome Interpretation

scholarly journals Hyperglycemia During Childhood Cancer Therapy: Incidence, Implications, and Impact on Outcomes

2019 ◽  
Vol 2019 (54) ◽  
pp. 132-138 ◽  
Author(s):  
Allison Grimes ◽  
Ashraf Mohamed ◽  
Jenna Sopfe ◽  
Rachel Hill ◽  
Jane Lynch
Keyword(s):  
Risk Factors ◽  
Cancer Therapy ◽  
Adverse Effects ◽  
Cancer Treatment ◽  
Childhood Cancer ◽  
Pediatric Cancer ◽  
Immune Checkpoint Inhibitors ◽  
Systematic Approach ◽  
Checkpoint Inhibitors ◽  
Limited Data

Abstract Hyperglycemia is a known complication of therapies used in the treatment of childhood cancer, particularly glucocorticoids and asparaginase. It has been linked to increased infection and reduced survival. With more limited data on hyperglycemia during childhood cancer treatment compared with adult cancer, impact on outcomes is less clear in this population. As additional glycemic-altering cancer agents including immune checkpoint inhibitors and targeted therapies make their way into pediatric cancer treatment, there is a more pressing need to better understand the mechanisms, risk factors, and adverse effects of hyperglycemia on the child with cancer. Thus, we utilized a systematic approach to review the current understanding of the incidence, implications, and outcomes of hyperglycemia during childhood cancer therapy.

Surface-Exposure Chronology Using in Situ Cosmogenic 3He in Antarctic Quartz Sandstone Boulders

1993 ◽  
Vol 39 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Edward J. Brook ◽  
Mark D. Kurz
Keyword(s):  
Isotopic Composition ◽  
Dry Valleys ◽  
Limited Data ◽  
Production Rates ◽  
General Application ◽  
Size Fractions ◽  
Diffusion Data ◽  
Taylor Glacier ◽  
Geomorphic Surfaces

AbstractIn situ-produced cosmogenic helium (3Hec) provides a new tool for constraining histories of Quaternary geomorphic surfaces. Before general application of the technique, however, the systematics and production rates of 3Hec must be well understood. In a companion study, 3He and 10Be data from sandstone and granite boulders in the Dry Valleys region of Antarctica have been used to constrain the ages of an important moraine sequence formed by the Taylor Glacier. Data from these deposits also provide information about the systematics of 3He in quartz that has important implications for geochronology based on 3Hec. In contrast to previous results from olivine and clinopyroxene, crushing quartz in vacuo releases helium with high 3He/4He ratios (up to 148 × Ra, where Ra is the atmospheric 3He/4He ratio), indicating that crushing cannot be used to determine the isotopic composition of trapped (i.e., noncosmogenic) helium in quartz. Analysis of 3He in different size fractions of the same samples indicates significant 3 He loss not predicted by existing 3He diffusion data for quartz. The origin of the discrepancy is not clear, but loss from these samples is not as significant as suggested by the limited data of previous studies.

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