VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data.

Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

Standardizing laboratory data by mapping to LOINC.

The authors describe a pilot project to standardize local laboratory data at five Indian Health Service (IHS) medical facilities by mapping laboratory test names to Logical Observation Identifier Names and Codes (LOINC). An automated mapping tool was developed to assign LOINC codes. At these sites, they were able to map from 63% to 76% of the local active laboratory tests to LOINC using the mapping tool. Eleven percent to 27% of the tests were mapped manually. They could not assign LOINC codes to 6% to 19% of the laboratory tests due to incomplete or incorrect information about these tests. The results achieved approximate other similar efforts. Mapping of laboratory test names to LOINC codes will allow IHS to aggregate laboratory data more easily for disease surveillance and clinical and administrative reporting efforts. This project may provide a model for standardization efforts in other health systems.

Assessing a web-based approach to collect complex and sensitive surveillance data: the sexually transmitted diseases- laboratory tests methods survey.

The Centers for Disease Control and Prevention (CDC) is looking for improved ways to collect complex, and highly sensitive, public health surveillance data. The Sexually Transmitted Diseases - Laboratory Test Methods Survey (STD-LMS) was developed to replace the traditional STD mail survey. The web-based STD-LMS offered distinct advantages over the traditional mail survey technique including,reduced time and cost of conducting the survey and avoiding the often error prone and tedious task of data entry.

The map to LOINC project.

We describe a pilot project to standardize local laboratory test names to Logical Observation Identifier Names and Codes (LOINC) at five Indian Health Service (IHS) medical facilities. An automated mapping tool was developed to assign LOINC codes. The laboratory test names not mapped to LOINC by the mapping tool were assigned LOINC codes manually. The results achieved matched current benchmarks.