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Modeling Performance of Sample Collection Sites Using Whole Exome Sequencing Metrics
Author(s) -
Natallia Kalinava,
Abraham Apfel,
Robert Cartmell,
Sujaya Srinivasan,
MingShan Chien,
Kyung In Kim,
Ryan Golhar,
Kathryn E Bednarz,
Saumya Pant,
Joseph D. Szustakowski,
Scott D. Chasalow,
Ariella Sasson,
Stefan Kirov
Publication year - 2020
Publication title -
biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/btn-2020-0086
Subject(s) - exome sequencing , sample (material) , data collection , dna sequencing , computer science , exome , computational biology , data mining , statistics , biology , genetics , mathematics , gene , chemistry , chromatography , mutation
Although next-generation sequencing assays are routinely carried out using samples from cancer trials, the sequencing data are not always of the required quality. There is a need to evaluate the performance of tissue collection sites and provide feedback about the quality of next-generation sequencing data. This study used a modeling approach based on whole exome sequencing quality control (QC) metrics to evaluate the relative performance of sites participating in the Bristol Myers Squibb Immuno-Oncology clinical trials sample collection. We identified several events for the sample swap. Overall, most sites performed well and few showed poor performance. These findings can increase awareness of sample failure and improve the quality of samples.

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