TarSeqQC: Quality control on targeted sequencing experiments in R

Targeted sequencing (TS) is growing as a screening methodology used in research and medical genetics to identify genomic alterations causing human diseases. In general, a list of possible genomic variants is derived from mapped reads through a variant calling step. This processing step is usually ba...

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Autores principales: Merino, G.A., Murua, Y.A., Fresno, C., Sendoya, J.M., Golubicki, M., Iseas, S., Coraglio, M., Podhajcer, O.L., Llera, A.S., Fernández, E.A.
Formato: JOUR
Materias:
Cancer panel
experiment performance
medical genetics
quality control
R package
targeted sequencing
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_10597794_v38_n5_p494_Merino
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_10597794_v38_n5_p494_Merino
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spelling todo:paper_10597794_v38_n5_p494_Merino2023-10-03T16:01:04Z TarSeqQC: Quality control on targeted sequencing experiments in R Merino, G.A. Murua, Y.A. Fresno, C. Sendoya, J.M. Golubicki, M. Iseas, S. Coraglio, M. Podhajcer, O.L. Llera, A.S. Fernández, E.A. Cancer panel experiment performance medical genetics quality control R package targeted sequencing Targeted sequencing (TS) is growing as a screening methodology used in research and medical genetics to identify genomic alterations causing human diseases. In general, a list of possible genomic variants is derived from mapped reads through a variant calling step. This processing step is usually based on variant coverage, although it may be affected by several factors. Therefore, undercovered relevant clinical variants may not be reported, affecting pathology diagnosis or treatment. Thus, a prior quality control of the experiment is critical to determine variant detection accuracy and to avoid erroneous medical conclusions. There are several quality control tools, but they are focused on issues related to whole-genome sequencing. However, in TS, quality control should assess experiment, gene, and genomic region performances based on achieved coverages. Here, we propose TarSeqQC R package for quality control in TS experiments. The tool is freely available at Bioconductor repository. TarSeqQC was used to analyze two datasets; low-performance primer pools and features were detected, enhancing the quality of experiment results. Read count profiles were also explored, showing TarSeqQC's effectiveness as an exploration tool. Our proposal may be a valuable bioinformatic tool for routinely TS experiments in both research and medical genetics. © 2017 Wiley Periodicals, Inc. Fil:Podhajcer, O.L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_10597794_v38_n5_p494_Merino
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Cancer panel
experiment performance
medical genetics
quality control
R package
targeted sequencing
spellingShingle Cancer panel
experiment performance
medical genetics
quality control
R package
targeted sequencing
Merino, G.A.
Murua, Y.A.
Fresno, C.
Sendoya, J.M.
Golubicki, M.
Iseas, S.
Coraglio, M.
Podhajcer, O.L.
Llera, A.S.
Fernández, E.A.
TarSeqQC: Quality control on targeted sequencing experiments in R
topic_facet Cancer panel
experiment performance
medical genetics
quality control
R package
targeted sequencing
description Targeted sequencing (TS) is growing as a screening methodology used in research and medical genetics to identify genomic alterations causing human diseases. In general, a list of possible genomic variants is derived from mapped reads through a variant calling step. This processing step is usually based on variant coverage, although it may be affected by several factors. Therefore, undercovered relevant clinical variants may not be reported, affecting pathology diagnosis or treatment. Thus, a prior quality control of the experiment is critical to determine variant detection accuracy and to avoid erroneous medical conclusions. There are several quality control tools, but they are focused on issues related to whole-genome sequencing. However, in TS, quality control should assess experiment, gene, and genomic region performances based on achieved coverages. Here, we propose TarSeqQC R package for quality control in TS experiments. The tool is freely available at Bioconductor repository. TarSeqQC was used to analyze two datasets; low-performance primer pools and features were detected, enhancing the quality of experiment results. Read count profiles were also explored, showing TarSeqQC's effectiveness as an exploration tool. Our proposal may be a valuable bioinformatic tool for routinely TS experiments in both research and medical genetics. © 2017 Wiley Periodicals, Inc.
format JOUR
author Merino, G.A.
Murua, Y.A.
Fresno, C.
Sendoya, J.M.
Golubicki, M.
Iseas, S.
Coraglio, M.
Podhajcer, O.L.
Llera, A.S.
Fernández, E.A.
author_facet Merino, G.A.
Murua, Y.A.
Fresno, C.
Sendoya, J.M.
Golubicki, M.
Iseas, S.
Coraglio, M.
Podhajcer, O.L.
Llera, A.S.
Fernández, E.A.
author_sort Merino, G.A.
title TarSeqQC: Quality control on targeted sequencing experiments in R
title_short TarSeqQC: Quality control on targeted sequencing experiments in R
title_full TarSeqQC: Quality control on targeted sequencing experiments in R
title_fullStr TarSeqQC: Quality control on targeted sequencing experiments in R
title_full_unstemmed TarSeqQC: Quality control on targeted sequencing experiments in R
title_sort tarseqqc: quality control on targeted sequencing experiments in r
url http://hdl.handle.net/20.500.12110/paper_10597794_v38_n5_p494_Merino
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