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SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories

Bioinformatics analysis is a key element in the development of in-house next-generation sequencing assays for tumor genetic profiling that can include both tumor DNA and RNA with comparisons to matched-normal DNA in select cases. Bioinformatics analysis encompasses a computationally heavy component...

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Autores principales: Raulerson, Chelsea K., Villa, Erika C., Mathews, Jeremy A., Wakeland, Benjamin, Xu, Yan, Gagan, Jeffrey, Cantarel, Brandi L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794024/
https://www.ncbi.nlm.nih.gov/pubmed/35136669
http://dx.doi.org/10.4103/jpi.jpi_20_21
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author Raulerson, Chelsea K.
Villa, Erika C.
Mathews, Jeremy A.
Wakeland, Benjamin
Xu, Yan
Gagan, Jeffrey
Cantarel, Brandi L.
author_facet Raulerson, Chelsea K.
Villa, Erika C.
Mathews, Jeremy A.
Wakeland, Benjamin
Xu, Yan
Gagan, Jeffrey
Cantarel, Brandi L.
author_sort Raulerson, Chelsea K.
collection PubMed
description Bioinformatics analysis is a key element in the development of in-house next-generation sequencing assays for tumor genetic profiling that can include both tumor DNA and RNA with comparisons to matched-normal DNA in select cases. Bioinformatics analysis encompasses a computationally heavy component that requires a high-performance computing component and an assay-dependent quality assessment, aggregation, and data cleaning component. Although there are free, open-source solutions and fee-for-use commercial services for the computationally heavy component, these solutions and services can lack the options commonly utilized in increasingly complex genomic assays. Additionally, the cost to purchase commercial solutions or implement and maintain open-source solutions can be out of reach for many small clinical laboratories. Here, we present Software for Clinical Health in Oncology for Omics Laboratories (SCHOOL), a collection of genomics analysis workflows that (i) can be easily installed on any platform; (ii) run on the cloud with a user-friendly interface; and (iii) include the detection of single nucleotide variants, insertions/deletions, copy number variants (CNVs), and translocations from RNA and DNA sequencing. These workflows contain elements for customization based on target panel and assay design, including somatic mutational analysis with a matched-normal, microsatellite stability analysis, and CNV analysis with a single nucleotide polymorphism backbone. All of the features of SCHOOL have been designed to run on any computer system, where software dependencies have been containerized. SCHOOL has been built into apps with workflows that can be run on a cloud platform such as DNANexus using their point-and-click graphical interface, which could be automated for high-throughput laboratories.
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spelling pubmed-87940242022-02-07 SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories Raulerson, Chelsea K. Villa, Erika C. Mathews, Jeremy A. Wakeland, Benjamin Xu, Yan Gagan, Jeffrey Cantarel, Brandi L. J Pathol Inform Technical Note Bioinformatics analysis is a key element in the development of in-house next-generation sequencing assays for tumor genetic profiling that can include both tumor DNA and RNA with comparisons to matched-normal DNA in select cases. Bioinformatics analysis encompasses a computationally heavy component that requires a high-performance computing component and an assay-dependent quality assessment, aggregation, and data cleaning component. Although there are free, open-source solutions and fee-for-use commercial services for the computationally heavy component, these solutions and services can lack the options commonly utilized in increasingly complex genomic assays. Additionally, the cost to purchase commercial solutions or implement and maintain open-source solutions can be out of reach for many small clinical laboratories. Here, we present Software for Clinical Health in Oncology for Omics Laboratories (SCHOOL), a collection of genomics analysis workflows that (i) can be easily installed on any platform; (ii) run on the cloud with a user-friendly interface; and (iii) include the detection of single nucleotide variants, insertions/deletions, copy number variants (CNVs), and translocations from RNA and DNA sequencing. These workflows contain elements for customization based on target panel and assay design, including somatic mutational analysis with a matched-normal, microsatellite stability analysis, and CNV analysis with a single nucleotide polymorphism backbone. All of the features of SCHOOL have been designed to run on any computer system, where software dependencies have been containerized. SCHOOL has been built into apps with workflows that can be run on a cloud platform such as DNANexus using their point-and-click graphical interface, which could be automated for high-throughput laboratories. Elsevier 2022-12-23 /pmc/articles/PMC8794024/ /pubmed/35136669 http://dx.doi.org/10.4103/jpi.jpi_20_21 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Technical Note
Raulerson, Chelsea K.
Villa, Erika C.
Mathews, Jeremy A.
Wakeland, Benjamin
Xu, Yan
Gagan, Jeffrey
Cantarel, Brandi L.
SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories
title SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories
title_full SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories
title_fullStr SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories
title_full_unstemmed SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories
title_short SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories
title_sort school: software for clinical health in oncology for omics laboratories
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794024/
https://www.ncbi.nlm.nih.gov/pubmed/35136669
http://dx.doi.org/10.4103/jpi.jpi_20_21
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