Automated quality control for a molecular surveillance system
Abstract Background Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon seq...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-10-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2329-5 |
| _version_ | 1818941087698386944 |
|---|---|
| author | Seth Sims Atkinson G. Longmire David S. Campo Sumathi Ramachandran Magdalena Medrzycki Lilia Ganova-Raeva Yulin Lin Amanda Sue Hong Thai Alexander Zelikovsky Yury Khudyakov |
| author_facet | Seth Sims Atkinson G. Longmire David S. Campo Sumathi Ramachandran Magdalena Medrzycki Lilia Ganova-Raeva Yulin Lin Amanda Sue Hong Thai Alexander Zelikovsky Yury Khudyakov |
| author_sort | Seth Sims |
| collection | DOAJ |
| description | Abstract Background Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. Results The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of “chordless” cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. Conclusions Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users. |
| first_indexed | 2024-12-20T06:49:58Z |
| format | Article |
| id | doaj.art-0123f253243749639ba01dfc5fb62ee5 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-20T06:49:58Z |
| publishDate | 2018-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0123f253243749639ba01dfc5fb62ee52022-12-21T19:49:33ZengBMCBMC Bioinformatics1471-21052018-10-0119S1111510.1186/s12859-018-2329-5Automated quality control for a molecular surveillance systemSeth Sims0Atkinson G. Longmire1David S. Campo2Sumathi Ramachandran3Magdalena Medrzycki4Lilia Ganova-Raeva5Yulin Lin6Amanda Sue7Hong Thai8Alexander Zelikovsky9Yury Khudyakov10Division of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDivision of Viral Hepatitis, Centers for Disease Control and PreventionDepartment of Computer Science, Georgia State UniversityDivision of Viral Hepatitis, Centers for Disease Control and PreventionAbstract Background Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. Results The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of “chordless” cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. Conclusions Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users.http://link.springer.com/article/10.1186/s12859-018-2329-5HVR1HCVTransmissionOutbreak detectionMolecular surveillanceQuality control |
| spellingShingle | Seth Sims Atkinson G. Longmire David S. Campo Sumathi Ramachandran Magdalena Medrzycki Lilia Ganova-Raeva Yulin Lin Amanda Sue Hong Thai Alexander Zelikovsky Yury Khudyakov Automated quality control for a molecular surveillance system BMC Bioinformatics HVR1 HCV Transmission Outbreak detection Molecular surveillance Quality control |
| title | Automated quality control for a molecular surveillance system |
| title_full | Automated quality control for a molecular surveillance system |
| title_fullStr | Automated quality control for a molecular surveillance system |
| title_full_unstemmed | Automated quality control for a molecular surveillance system |
| title_short | Automated quality control for a molecular surveillance system |
| title_sort | automated quality control for a molecular surveillance system |
| topic | HVR1 HCV Transmission Outbreak detection Molecular surveillance Quality control |
| url | http://link.springer.com/article/10.1186/s12859-018-2329-5 |
| work_keys_str_mv | AT sethsims automatedqualitycontrolforamolecularsurveillancesystem AT atkinsonglongmire automatedqualitycontrolforamolecularsurveillancesystem AT davidscampo automatedqualitycontrolforamolecularsurveillancesystem AT sumathiramachandran automatedqualitycontrolforamolecularsurveillancesystem AT magdalenamedrzycki automatedqualitycontrolforamolecularsurveillancesystem AT liliaganovaraeva automatedqualitycontrolforamolecularsurveillancesystem AT yulinlin automatedqualitycontrolforamolecularsurveillancesystem AT amandasue automatedqualitycontrolforamolecularsurveillancesystem AT hongthai automatedqualitycontrolforamolecularsurveillancesystem AT alexanderzelikovsky automatedqualitycontrolforamolecularsurveillancesystem AT yurykhudyakov automatedqualitycontrolforamolecularsurveillancesystem |