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• W102737505 abstract "A wide repertoire of bioinformatics applications exist for next-generation sequencing data analysis; however, certain requirements of the clinical molecular laboratory limit their use: i) comprehensive report generation, ii) compatibility with existing laboratory information systems and computer operating system, iii) knowledgebase development, iv) quality management, and v) data security. SeqReporter is a web-based application developed using ASP.NET framework version 4.0. The client-side was designed using HTML5, CSS3, and Javascript. The server-side processing (VB.NET) relied on interaction with a customized SQL server 2008 R2 database. Overall, 104 cases (1062 variant calls) were analyzed by SeqReporter. Each variant call was classified into one of five report levels: i) known clinical significance, ii) uncertain clinical significance, iii) pending pathologists’ review, iv) synonymous and deep intronic, and v) platform and panel-specific sequence errors. SeqReporter correctly annotated and classified 99.9% (859 of 860) of sequence variants, including 68.7% synonymous single-nucleotide variants, 28.3% nonsynonymous single-nucleotide variants, 1.7% insertions, and 1.3% deletions. One variant of potential clinical significance was re-classified after pathologist review. Laboratory information system–compatible clinical reports were generated automatically. SeqReporter also facilitated quality management activities. SeqReporter is an example of a customized and well-designed informatics solution to optimize and automate the downstream analysis of clinical next-generation sequencing data. We propose it as a model that may envisage the development of a comprehensive clinical informatics solution. A wide repertoire of bioinformatics applications exist for next-generation sequencing data analysis; however, certain requirements of the clinical molecular laboratory limit their use: i) comprehensive report generation, ii) compatibility with existing laboratory information systems and computer operating system, iii) knowledgebase development, iv) quality management, and v) data security. SeqReporter is a web-based application developed using ASP.NET framework version 4.0. The client-side was designed using HTML5, CSS3, and Javascript. The server-side processing (VB.NET) relied on interaction with a customized SQL server 2008 R2 database. Overall, 104 cases (1062 variant calls) were analyzed by SeqReporter. Each variant call was classified into one of five report levels: i) known clinical significance, ii) uncertain clinical significance, iii) pending pathologists’ review, iv) synonymous and deep intronic, and v) platform and panel-specific sequence errors. SeqReporter correctly annotated and classified 99.9% (859 of 860) of sequence variants, including 68.7% synonymous single-nucleotide variants, 28.3% nonsynonymous single-nucleotide variants, 1.7% insertions, and 1.3% deletions. One variant of potential clinical significance was re-classified after pathologist review. Laboratory information system–compatible clinical reports were generated automatically. SeqReporter also facilitated quality management activities. SeqReporter is an example of a customized and well-designed informatics solution to optimize and automate the downstream analysis of clinical next-generation sequencing data. We propose it as a model that may envisage the development of a comprehensive clinical informatics solution. Next-generation sequencing (NGS) technology has opened doors to innovative and personalized health care.1Schrijver I. Aziz N. Farkas D.H. Furtado M. Gonzalez A.F. Greiner T.C. Grody W.W. Hambuch T. Kalman L. Kant J.A. Klein R.D. Leonard D.G. Lubin I.M. Mao R. Nagan N. Pratt V.M. Sobel M.E. Voelkerding K.V. Gibson J.S. Opportunities and challenges associated with clinical diagnostic genome sequencing: a report of the Association for Molecular Pathology.J Mol Diagn. 2012; 14: 525-540Abstract Full Text Full Text PDF PubMed Scopus (122) Google Scholar In the past couple years, with the introduction of NGS platforms, there has been excitement and a trend toward clinical implementation of NGS technology for the diagnosis of genetic diseases and management of advanced cancer.2Gullapalli R.R. Desai K.V. Santana-Santos L. Kant J.A. Becich M.J. Next generation sequencing in clinical medicine: challenges and lessons for pathology and biomedical informatics.J Pathol Inform. 2012; 3: 40Crossref PubMed Google Scholar NGS is a rapidly evolving technology and is in its infancy in the clinical domain. Unlike prior molecular testing platforms, data output from an NGS system are of mammoth proportion, which makes bioinformatics solutions inevitable. Starting with targeted sequencing and moving toward whole exome and genome sequencing, the size and complexity of the generated data escalate.1Schrijver I. Aziz N. Farkas D.H. Furtado M. Gonzalez A.F. Greiner T.C. Grody W.W. Hambuch T. Kalman L. Kant J.A. Klein R.D. Leonard D.G. Lubin I.M. Mao R. Nagan N. Pratt V.M. Sobel M.E. Voelkerding K.V. Gibson J.S. Opportunities and challenges associated with clinical diagnostic genome sequencing: a report of the Association for Molecular Pathology.J Mol Diagn. 2012; 14: 525-540Abstract Full Text Full Text PDF PubMed Scopus (122) Google Scholar, 2Gullapalli R.R. Desai K.V. Santana-Santos L. Kant J.A. Becich M.J. Next generation sequencing in clinical medicine: challenges and lessons for pathology and biomedical informatics.J Pathol Inform. 2012; 3: 40Crossref PubMed Google Scholar There are two distinct, albeit tightly coupled, units in an NGS system: wet analysis (library preparation and sequencing reaction) and dry analysis (bioinformatics processes for data analysis). The bioinformatics workflow comprises several core elements that are executed in tandem to complete the analysis, often referred to as an analysis pipeline.2Gullapalli R.R. Desai K.V. Santana-Santos L. Kant J.A. Becich M.J. Next generation sequencing in clinical medicine: challenges and lessons for pathology and biomedical informatics.J Pathol Inform. 2012; 3: 40Crossref PubMed Google Scholar, 3Pabinger S. Dander A. Fischer M. Snajder R. Sperk M. Efremova M. Krabichler B. Speicher M.R. Zschocke J. Trajanoski Z. A survey of tools for variant analysis of next-generation genome sequencing data.Brief Bioinform. 2013; (Epub ahead of print)PubMed Google Scholar These core components are as follows: raw signal processing (electrical or optical), sequence quality control, base calling, mapping against a reference genome, mapping quality control, assembly of aligned reads, variant calling, variant annotation, and visualization. The repertoire of NGS-related bioinformatics application is constantly increasing and improving.3Pabinger S. Dander A. Fischer M. Snajder R. Sperk M. Efremova M. Krabichler B. Speicher M.R. Zschocke J. Trajanoski Z. A survey of tools for variant analysis of next-generation genome sequencing data.Brief Bioinform. 2013; (Epub ahead of print)PubMed Google Scholar, 4Bao S. Jiang R. Kwan W. Wang B. Ma X. Song Y.Q. Evaluation of next-generation sequencing software in mapping and assembly.J Hum Genet. 2011; 56: 406-414Crossref PubMed Scopus (100) Google Scholar, 5Nielsen R. Paul J.S. Albrechtsen A. Song Y.S. Genotype and SNP calling from next-generation sequencing data.Nat Rev Genet. 2011; 12: 443-451Crossref PubMed Scopus (934) Google Scholar By nature of the solutions rendered, the applications are either comprehensive pipelines or more often are focused on a specific aspect of the analysis (eg, alignment, variant calling, and so forth).2Gullapalli R.R. Desai K.V. Santana-Santos L. Kant J.A. Becich M.J. Next generation sequencing in clinical medicine: challenges and lessons for pathology and biomedical informatics.J Pathol Inform. 2012; 3: 40Crossref PubMed Google Scholar, 3Pabinger S. Dander A. Fischer M. Snajder R. Sperk M. Efremova M. Krabichler B. Speicher M.R. Zschocke J. Trajanoski Z. A survey of tools for variant analysis of next-generation genome sequencing data.Brief Bioinform. 2013; (Epub ahead of print)PubMed Google Scholar Although graphic user interface or web interface is available in some of the newer software, most solutions present a command line interface to execute an uncompiled or compiled script.2Gullapalli R.R. Desai K.V. Santana-Santos L. Kant J.A. Becich M.J. Next generation sequencing in clinical medicine: challenges and lessons for pathology and biomedical informatics.J Pathol Inform. 2012; 3: 40Crossref PubMed Google Scholar The command line tools are optimized to run on “–nix” based operating systems (OS) [eg, Unix (Burlington, MA), Linux (San Francisco, CA), and MacOSX (Cupertino, CA)] with little, if any, compatibility with Microsoft Windows OS (Santa Clara, CA). Comprehensive and meaningful clinical reporting of NGS data unfortunately has received little attention from vendors as well as end users involved in NGS development. In a clinical laboratory, accuracy and turnaround time for a test is crucial for effective patient care and test sustainability.2Gullapalli R.R. Desai K.V. Santana-Santos L. Kant J.A. Becich M.J. Next generation sequencing in clinical medicine: challenges and lessons for pathology and biomedical informatics.J Pathol Inform. 2012; 3: 40Crossref PubMed Google Scholar For instance, it is incomprehensible for a clinical NGS-based test to turnaround final results in several weeks because of the task of classification, interpretation, and reporting of hundreds to thousands of variants in a clinically meaningful fashion. In addition to these challenges in the clinical implementation of NGS, incompatibility of available bioinformatics tools with commonly used computer operating systems in a clinical laboratory and the inability to cross-talk with pre-existing (legacy) laboratory information systems (LIS)6Sepulveda J.L. Young D.S. The ideal laboratory information system.Arch Pathol Lab Med. 2013; 137: 1129-1140Crossref PubMed Scopus (44) Google Scholar are other significant bottlenecks in the workflow. In contrast to traditional molecular tests, highly complex and multiparameter NGS data make it impractical for drafting a clinical report manually into an existing LIS.6Sepulveda J.L. Young D.S. The ideal laboratory information system.Arch Pathol Lab Med. 2013; 137: 1129-1140Crossref PubMed Scopus (44) Google Scholar Not inconceivably, the probability of the introduction of human errors in such reports is expected to increase dramatically. Ongoing quality management/quality control (QM/QC) practice, as recommended by accrediting and regulatory organizations, is a vital aspect of any clinically offered laboratory test, including NGS.7Gargis A.S. Kalman L. Berry M.W. Bick D.P. Dimmock D.P. Hambuch T. et al.Assuring the quality of next-generation sequencing in clinical laboratory practice.Nat Biotechnol. 2012; 30: 1033-1036Crossref PubMed Scopus (362) Google Scholar, 8Schramm J: CAP checklist a first for next generation sequencing laboratory standards. College of American Pathologists, Northfield, IL, 2012Google Scholar Real-time capture and review of hundreds of data points are necessary to document test performance characteristics, which poses additional challenges in NGS implementation. Integration and automation of reporting solutions, variant knowledge base development and maintenance, and decision support systems are crucial for the success of NGS-based testing in a clinical environment. The current economic climate and changing molecular test reimbursement patterns add to the challenge. We describe a bioinformatics solution developed and validated in the Department of Molecular and Genomic Pathology at the University of Pittsburgh Medical Center, as a quality improvement initiative, with the intent to address the issues outlined earlier, improve laboratory practices, and achieve high standards of patient care. At the time of the conception and design of SeqReporter, there was no single application (commercial or open source) that was comprehensive enough to address all of the laboratory requirements outlined earlier. This study was reviewed and approved by the Quality Improvement subcommittee of the Institutional Review Board at the University of Pittsburgh Medical Center (Pittsburgh, PA). A typical analysis was performed on an Ion Personalized Genome Machine sequencer (Life Technologies, Carlsbad, CA) using the AmpliSeq (Life Technologies) cancer panel. The entire workflow of several NGS runs were critically examined from start (case accessioning) to finish (report sign-out) to identify bottlenecks and associated downstream probability of generating errors in the final result. The algorithm design and subsequent software development was aimed at addressing the following requirements by the laboratory for implementing NGS testing: i) establishing an informatics bridge between NGS analysis systems and existing LIS (Cerner CoPath Plus, Kansas City, MO) in a manner that will facilitate comprehensive review of all variant calls and transfer information with minimal human input; ii) streamlining variant annotation (technical, biological, and clinical) and classification based on clinical and biological relevance; iii) development of a comprehensive and highly customized knowledge base for efficient variant review process and educational purposes; iv) develop methods to automate laboratory workflow and minimize human errors arising from redundant data entry and manual report synthesis by laboratory staff; v) automate clinical report synthesis by incorporating multiple annotations and appropriate clinical comments relevant to the treating physician or oncologist; and vi) development of a database management system for managing clinical test information and results to establish ongoing QM/QC practices and improve overall laboratory workflow. The College of American Pathologists molecular pathology checklist, modified for NGS testing in July 2012, was reviewed to ensure compliance with the requirements.8Schramm J: CAP checklist a first for next generation sequencing laboratory standards. College of American Pathologists, Northfield, IL, 2012Google Scholar After the development of SeqReporter, it was deployed in a test environment for validation, debugging, and customization based on user feedback. A total of 46 test samples were used for validating the SeqReporter algorithm, which were sequenced on the Ion Personalized Genome Machine using the AmpliSeq cancer panel and analyzed using Torrent Suite (version 2.2 and subsequently version 3.4.2; Life Technologies). The FASTQ files of each sample also were analyzed by commercially available software, NextGENe version 2.3.1 (Softgenetics, State College, PA), using its own aligner and variant caller. The NGS laboratory staff manually looked up and annotated all variant calls for each sample, which subsequently was verified by a pathologist. After running the analysis on SeqReporter, all variant calls for all of the samples were compared one-on-one with the manual annotation and classification as well as with the annotated variant calls from NextGENe. If the comparison was congruent, a final report (LIS-compatible format) for each case was generated. These reports were assessed for the following parameters: accuracy of reportable information, accuracy of variant call (VC) annotation, accuracy of knowledge base and references, and appropriate report layout and formatting scheme. Any deviation from the expected results and system errors were critically reviewed and appropriate modifications were implemented in the source code. Each change in the software was tested and validated before final approval by the laboratory director. Subsequent to validation, SeqReporter was used for analysis of clinical cases, and we report the application’s performance characteristics with the first 58 consecutive cases, including the trend in variant classification efficiency over time. Statistical analysis was performed using GraphPad Prism version 6.0c (La Jolla, CA). The overall aim was to achieve optimal performance with a shorter processing time and to significantly reduce human errors. SeqReporter version 4.0 is an n-tier web application that was built on the .NET framework 4.0 using Microsoft Visual Studio 2010 (Microsoft, Santa Clara, CA). The front end (client side) was scripted using HTML 5, CSS 3.0, and JavaScript. Asynchronous JavaScript and XML were implemented using the open-source add-on AjaxControl Toolkit for partial page rendering to ensure seamless user experience. The back end (server side) was scripted using VB.NET. The SeqReporter database was implemented using Microsoft SQL Server 2008 R2 (Microsoft) and native SQL server client drivers enabled database queries from the application. The Integrative Genomics Viewer (Broad Institute, Cambridge, MA) was used for visualization of sequence read pile-ups.9Robinson J.T. Thorvaldsdottir H. Winckler W. Guttman M. Lander E.S. Getz G. Mesirov J.P. Integrative genomics viewer.Nat Biotechnol. 2011; 29: 24-26Crossref PubMed Scopus (7678) Google Scholar The Human Genome Variation Society (http://www.hgvs.org/mutnomen/index.html, last accessed June 2, 2013.) guidelines were followed for variant nomenclature.10den Dunnen J.T. Antonarakis S.E. Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion.Hum Mutat. 2000; 15: 7-12Crossref PubMed Scopus (1515) Google Scholar The Report Synthesis Module encompassed the following format conversion engines: OpenXML SDK version 2.0 (Microsoft, Santa Clara, CA), customized XML, and the PDF synthesis engine. Workstations running Microsoft Windows 7 Professional OS served as the application development and testing environment. This OS platform was targeted because of the institution’s available infrastructure and support. Secure socket layer/TLS protocols were implemented with the institution’s trusted digital certificate for securing protected health information transactions. An application-specific log-in module, using 512-bit SHA encryption protocol, was implemented to enable role-based, highly secure user access. SeqReporter’s database comprises three core components. This component stores all information pertaining to a sample, including sample identification (ID), case accession details, microdissection and DNA extraction information, test order details, sequencing run information (eg, sequencer and software version details, server locations and hyperlinks, gene-panel information, and panel-specific hotspot coverage distribution), variant calls, and all related annotations. This component of the database stores rich annotation information on a set of a large number of sequence variants. Records in this data table represent all variants that have been encountered by the laboratory at least once during NGS- or conventional Sanger sequencing–based testing. Detailed annotation information on a subset of these variants was available at the outset of deployment of SeqReporter based on the laboratory’s extensive experience with conventional Sanger sequencing for important cancer-related genes, namely EGFR, KRAS, NRAS, HRAS, BRAF, PIK3CA, IDH1, IDH2, and GNAS. The in-house variant knowledge base (IVKb) itself comprises several data tables, each of which contain certain parameters for a variant call, and contribute to the overall annotation process. Level 1 information includes several parameters, namely, genomic coordinates, reference and variant nucleotides, gene information, variant location, exon/intron numbers, cDNA sequence nucleotide position, details of predicted changes in amino acid sequence, and strand information. These parameters are stored in separate data tables for single-nucleotide variants (SNVs) and insertions and deletions. A third data table stores information for panel- and sequencer-specific false-positive variant calls. Some genes, for example, TP53, have been annotated with different transcript IDs corresponding to a specific cDNA/mRNA sequence. This often presents with challenges in annotating sequence variants. The RefSeq gene information in external variant databases (EVDs) provides information for all transcript IDs related to a specific gene. The decision to choose the appropriate transcript ID is based on the NGS team review of the provided list of transcript IDs from the vendor for each gene, review of th RefSeq gene database for the most recent version or longest transcript, and currently accepted sequence variant annotations in public databases. Level 2 comprises the most updated evidence for clinical and biological significance of a variant in the context of specific tumor types. Because several unique combinations of variants and tumor types exist in IVKb, the individual parameters, described later, are specific for that combination. The source of evidence is based on a thorough review of the current literature by the NGS staff. Parameters defining this information include data fields for clinical significance, report comments designated to be included in the final report, precomputed PolyPhen-2 and SIFT scores (imported from the dbNSFP data table in EVD), available targeted therapy and clinical trial information, and literature references (eg, PubMed ID). Based on pathologist assessment of all of the level 2 annotations, every variant tumor–type combination is assigned to one of the four report levels (1, 2, 4, or 5) (Table 1). Once a report level is assigned, SeqReporter is able to classify a given set of variant calls from a sample with extremely high precision. When the final report that is signed-off by the pathologist contains new variants, and new or updated knowledge base information, SeqReporter is able to capture that information and train the IVKb for subsequent analyses.Table 1Variant Classification Scheme for Cancer GenomicsReport levelDefinition/explanationClassified1Nonsynonymous SNVs, insertions and deletions, occurring in the coding, untranslated, and splice site regions, which are of known clinical significance2Nonsynonymous SNVs, insertions and deletions, occurring in the coding, untranslated, and splice site regions, which are of uncertain clinical significance4Synonymous sequence variants, sequence variants with established evidence of benign biological and/or clinical outcome, and intronic variants (except splice site)5Sequence variants, which have been proved to be gene panel– and platform-specific sequencing errors based on repeated experimental data, Sanger sequence confirmation, and thorough manual review of sequence reads for QC metrics (eg, base call quality score, mapping quality score)Unclassified3Variant(s) unable to be classified into levels 1, 2, 4, or 5 automatically by SeqReporter; pathologist is required to review and reclassify appropriately based on available evidence Open table in a new tab EVD houses direct imports from various publically available variant databases (COSMIC version 63 followed by version 64,11Forbes S.A. Bindal N. Bamford S. Cole C. Kok C.Y. Beare D. Jia M. Shepherd R. Leung K. Menzies A. Teague J.W. Campbell P.J. Stratton M.R. Futreal P.A. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer.Nucleic Acids Res. 2011; 39: D945-D950Crossref PubMed Scopus (1816) Google Scholar dbSNP build 137,12Sherry S.T. Ward M.H. Kholodov M. Baker J. Phan L. Smigielski E.M. Sirotkin K. dbSNP: the NCBI database of genetic variation.Nucleic Acids Res. 2001; 29: 308-311Crossref PubMed Scopus (4865) Google Scholar dbNSFP light version 1.3,13Liu X. Jian X. Boerwinkle E. dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions.Hum Mutat. 2011; 32: 894-899Crossref PubMed Scopus (514) Google Scholar PolyPhen-2,14Adzhubei I.A. Schmidt S. Peshkin L. Ramensky V.E. Gerasimova A. Bork P. Kondrashov A.S. Sunyaev S.R. A method and server for predicting damaging missense mutations.Nat Methods. 2010; 7: 248-249Crossref PubMed Scopus (9354) Google Scholar SIFT,15Kumar P. Henikoff S. Ng P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.Nat Protoc. 2009; 4: 1073-1081Crossref PubMed Scopus (5027) Google Scholar and RefSeq gene information table from the UCSC genome browser16Meyer L.R. Zweig A.S. Hinrichs A.S. Karolchik D. Kuhn R.M. Wong M. Sloan C.A. Rosenbloom K.R. Roe G. Rhead B. Raney B.J. Pohl A. Malladi V.S. Li C.H. Lee B.T. Learned K. Kirkup V. Hsu F. Heitner S. Harte R.A. Haeussler M. Guruvadoo L. Goldman M. Giardine B.M. Fujita P.A. Dreszer T.R. Diekhans M. Cline M.S. Clawson H. Barber G.P. Haussler D. Kent W.J. The UCSC Genome Browser database: extensions and updates 2013.Nucleic Acids Res. 2013; 41: D64-D69Crossref PubMed Scopus (602) Google Scholar without any modifications. After this, a few fields (eg, binID and variant type) are added to improve queries across the large databases. EVD is a much larger database and was designed to perform second-look queries when a given variant call is not found in IVKb. It also serves to provide useful information for newly encountered variants (variant position, exon-intron boundary, exon/intron number). Separation of the smaller, albeit richly annotated, IVKb from the larger EVD facilitates faster query execution. The EVD is updated periodically based on the availability of new versions of publicly available databases. To ensure data integrity of IVKb during the EVD update process, both the databases reside in physically separate files. Subsequent to the update process, the VCs in previously signed-off specimens are not updated automatically. Information on previously signed-off cases is never changed unless a physician requests a re-review of the case because it is treated as a permanent record. The variant annotation information in IVKb is updated during the subsequent sign-off process. The workflow of clinical mutational profiling of solid tumors, using semiconductor-based NGS technology, is summarized in Figure 1. Briefly, after receipt of the test order for the NGS panel, patient-, sample-, and test-specific details were accessioned into an in-house laboratory database for sample tracking. After DNA extraction from microdissected target(s), genomic DNA was amplified using the Ion Ampliseq cancer panel multiplex primer pool (Life Technologies). After successful library preparation, sequencing was performed using the Ion 316 and 318 chip on the Ion Personalized Genome Machine system. After a successful sequencing reaction, the raw signal data were analyzed using Torrent Suite software version 3.4.2 (Life Technologies). The pipeline includes signal processing, base calling, quality score assignment, adapter trimming, PCR duplicate removal, read alignment to human genome 19 reference, mapping QC, coverage analysis, and variant calling. After completion of the primary data analysis, a list of detected sequence variants (SNVs and insertions and deletions) with several analysis metrics were compiled in a variant call file (VCF) (format version 4.1) and presented via the web-based user interface. Although the VCF files contain the basic information necessary to interpret the variants, it is not comprehensible enough to incorporate into routine workflow or import into a LIS. SeqReporter acted as a bridge between the sequencer and the LIS by importing the variant information into a relational database model and automating various processes described later. The algorithm incorporates five core modules, each addressing a specific workflow step (Figures 2 and 3).Figure 3SeqReporter variant review and knowledge base training algorithm. The SIRM extracts the case details and the variant call management module (VMM) and the report synthesis module (RSM) present a preliminary but detailed report of all of the variant calls of the sample in an interactive HTML format when a pathologist receives the case for sign-out. The report (Supplemental Figure S1) enables the pathologist to make any changes and reclassify the variants if necessary. When all of the changes are made and the report is signed-off, the changes are saved to the sample information database. Simultaneously, the knowledge base training module is initiated, which iterates through every single data field of all variants to scan for changes. If any changes are detected, the appropriate data field(s) in the appropriate data table is modified, therefore enriching the knowledge base with the most updated information. DB, database; SRIM, sample and run information module.View Large Image Figure ViewerDownload Hi-res image Download (PPT) This module functions to import predefined data fields from the laboratory’s case tracking database into the SeqReporter database. Additional sample-specific information (eg, microdissection details, NGS sequence run information, and hotspot coverage details) is added to the metadata of the specimen. During variant call processing and case look-up, the sample and run information module keeps track of patient, sample, and run information to avoid redundancy, alerts users of historic testing on a patient, and records QA/QC data for all testing parameters. This module performs the following functions: first, it imports the VCs from the primary analysis server (Torrent server). Second, by using binary logic (yes/no), the variant call classifier module performs multiple database queries on error profile, SNV, and insertion and deletion data tables in IVKb for level 1 annotation (variant type, variant site, exon/intron number, strand information, CDS position, codon number, and predicted amino acid change). Subsequently, level 1 annotated VCs are queried against variant curation information" @default.
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