dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, in the case of NGS, there should be an increased awareness of potential contamination (primer hopping) if samples are pooled in a single sequencing run. Although much progress has been made in NGS applications, some gaps and challenges still need to be addressed for using NGS in the regulatory context. For instance, appropriate viral and infected-cell standards need to be publicly available for evaluating the sensitivity and specificity of NGS for standardization and validation of the methodology. Furthermore, the development of international standards can help evaluate NGS results across different laboratories since protocols are still being developed and sequencing platforms continue to evolve. Additionally, the large volumes of data generated by NGS require extra computational and bioinformatics capacities to support the establishment of pipelines and processes for data analysis, storage and transfer, and comprehensive, annotated and publicly available reference virus databases are also needed.
Perspectives on NGS for adventitious virus detection Dr. Arifa Khan (U. S. FDA) gave an overview of the past and current efforts in the last decade by regulatory health authorities and industry toward use of NGS for adventitious virus detection of biologics. The outcomes of the early meetings, which are shown in Table  1 , identified challenges for using NGS for adventitious virus detection, that led to focussed discussions and scientific collaborations for addressing the technical and bioinformatics gaps for NGS applications in biologics. Dr. Khan outlined the internal and external efforts by the FDA and the Office of Vaccines Research and Review (OVRR). These included: evaluation and for method standardization and validation  [6]. • The development of a public database of all viral sequences to fa- cilitate detection of known and novel viruses  [7, 8].
Dr. Khan summarized the 2017 NGS meeting  [5]  and noted that considerable progress has been made since then. Some of the achievements, described in other sessions, included: • A spiking study with model viruses added in a background of dif- ferent matrices representing biological materials was performed to evaluate NGS virus detection across three different laboratories  [13]. • The publication of papers on upstream sample preparation [11] and downstream bioinformatics for optimisation of NGS virus detection  [12]. • The publication of a reference viral database (RVDB) [7]. • The development of five live, reference virus stocks for NGS eva- luation and standardization and completion of 24-month stability studies  [6]. • The use of NGS for the detection of active viral RNAs by tran- scriptomics analysis  [14, 15].
The adoption of NGS is subject to a number of challenges, most notably related to the diversity of viral targets and biological matrices (such as cell banks, viral seeds and raw materials), along with the complexity of the NGS technologies and associated bioinformatics, which makes method validation difficult. In order to address these challenges, the AVDTIG initially identified five priority areas, and five corresponding subgroups (A to E) were formed to specifically address these priority areas through discussions and conduct of collaborative studies: A) sample selection, preparation and processing; B) the development of virus standards and reference materials; C) the development of a correctly annotated viral reference database, complete with all viral sequences, excluding bacterial viruses; D) the analysis of bioinformatics pipelines; and E) the development of follow-up strategies to confirm the identity of a positive signal.
The results of the collaborative pilot virus spiking study and further discussions in Subgroup B led to the development of virus reference material comprising of five virus stocks, which can facilitate NGS performance evaluation, standardization, and comparison to conventional assays (presented by Dr. Arifa Khan). Discussions in Subgroup C resulted in the development of a reference virus database to reduce nonspecific cellular hits and to increase the likelihood of novel virus detection  [7, 8]. Subgroup D members have published a paper on considerations for optimisation of bioinformatics pipelines  [12]. Subgroup E members are preparing a manuscript regarding considerations for data interpretation, including the bioinformatics criteria and thresholds for a positive hit, sources of background signals and the effectiveness of laboratory follow-up procedures (presented by Dr.
Sources of noise include residuals within test reagents (for instance Escherichia coli bacteriophages), residuals within controls, human shedding during testing and environmental contamination in the testing laboratory. False positives can include sequences that are similar to the host sequences (when those segments of the host genome are not in the database), inaccurate records in public databases, records in public databases that pick-up host sequences, as well as sequencing errors or natural variation in a viral harvest dataset that lead to related viruses being misidentified. There are three scenarios that should be considered. Firstly, true positives where the viral nucleic acids are actually in the test article are detected. This can include both adventitious viral nucleic acids, as well as other background nucleic acids, and is best managed with a risk-based approach.
Bioinformatics pipeline optimisation can be achieved through the use of reference datasets, by clearly defining the quality criteria for an acceptable read, and by defining parameters for short read assembly and for hybrid assembly to correct the high error rate currently seen in long-read sequencing. Current, correctly annotated, publicly available reference virus databases are also needed. Other challenges include the development of an appropriate followup strategy for verifying a positive signal and assessing the biological relevance and significance of the hit. The FDA and CBER are continuing to prepare for the use of NGS and for the review of NGS in regulatory submissions. They are actively working to identify the challenges and to address the knowledge gaps and are working to maintain and advance the necessary in-house technology and bioinformatics infrastructure and expertise.
Clearly defining the types of standards needed for the various steps in the sequencing process was identified as a priority. It was also agreed that it will not be possible to make all standards for all methods and that the most common NGS procedures should be identified and that standards development should at least initially target those procedures. NIST is compiling a list of resources for standardization and reference materials. Dr. Arifa Khan (FDA) described FDA/CBER reference materials for NGS virus detection, which included well-characterized virus stocks and a reference viral database (RVDB). The FDA is supporting NGS standardization and validation efforts for adventitious virus detection in biological products through the development of reference materials including virus stocks and cell-based materials intended to be available publicly and by conducting spiking studies to evaluate different sequencing platforms.
Additionally, bioinformatics challenges are being addressed by development of a complete and correctly annotated viral database to facilitate known and novel adventitious virus detection. The results of a multicenter study to evaluate the performance of NGS for virus detection  [13] , and discussions in the AVDTIG prompted the initiation of efforts by the FDA to prepare reference virus stocks for NGS platform evaluation and standardization. Large-scale preparations were made of the five viruses that were used in the previous study. These live viruses can be used for evaluating the entire NGS workflow, from sample extraction through bioinformatics. They represent diverse virus families of potential safety concerns in human vaccines and consist of different genome lengths and types (double-strand and single strand; RNA and DNA; linear and circular), particle sizes, and include enveloped and non-enveloped viruses.
• To compare NGS with current nucleic acid-based assays or in- fectivity assays for adventitious virus detection, such as PCR, in vitro or in vivo assays. • To generate well-characterized NGS datasets for evaluating bioin- formatics pipelines. The virus stocks have been distributed since 2017 for collaborative spiking studies and also to others in industry with similar efforts toward NGS standardization and validation as an alternative assay to supplement or replace the conventional adventitious virus detection assays. FDA is currently participating in virus spiking studies in the AVDTIG to evaluate the limit of detection of different virus types by NGS. Additional FDA efforts are ongoing to evaluate virus detection using the Nanopore MinION and for developing well-characterized, cell-based reference materials for NGS virus detection.
In 2013, the FDA, in consultation with the AVDTIG and the National Center for Biotechnology Information (NCBI) at NIH, started work on developing a new and complete reference viral database, the RVDB  [7]. The RVDB includes all viral, viral-related and viral-like sequences, including endogenous viruses and retroelements, and it has a reduced cellular content. It contains sequences of viruses from all species relevant to biologics, except bacteria. These include viral sequences that are relevant to human and animal health as well as other disciplines. The RVDB is currently available from the Center for Bioinformatics and Computational Biology at the University of Delaware (https://rvdb. dbi. udel. edu/). A corresponding protein database was generated by the Institut Pasteur  [8] , which is available on the RVDB site and at the Institut Pasteur (https://rvdb-prot.
pasteur. fr/). Use of the RVDB is expected to enhance NGS investigations for product safety by increasing the specificity and sensitivity of virus detection and enhancing the efficiency of large NGS data analysis, in particular for the detection of novel viruses. The reduction in nonspecific cellular hits has resulted in a reduction in the volume of data for bioinformatics analysis, thus reducing the computational time. The scripts for generating the database are publicly available online (https://github. com/ArifaKhanLab/RVDB), along with an RVDB toolbox, which contains instructions and python scripts for generating the database. Annotation of the RVDB is ongoing to identify viral and non-viral sequences to facilitate follow-up strategies for NGS hits. Additional efforts include updating the RVDB with complete representation of diverse virus families, especially for host species used in cell substrates for the production of biologics, as well as accurately annotated entries indicating viral and non-viral regions, to provide rapid and accurate results for adventitious virus detection.
The regular updates of the RVDB to include new entries in Genbank would facilitate the detection of emerging viruses. It is also planned to develop machine learning approaches for adventitious and endogenous retrovirus detection. Dr. Edward Mee (NIBSC) gave an overview of joint NIBSC/WHO initiatives to develop standards and reference materials for use in NGS for biological products, including DNA and RNA detection assays and viral infectivity assays. These include standards for new methods that are under development and standards that can be used for NGS bridging and validation studies. The NIBSC is developing reference materials that could be used in bridging studies to compare the sensitivity and specificity of NGS for virus detection to that of in vivo methods. The NIBSC previously developed a highly multiplexed reference material that can be used for benchmarking NGS methods  [10].
The chapter also provides guidance on specimens and sample preparation, including that precautions should be taken to ensure the integrity and quality of nucleic acids, that positive and negative controls should be included to verify each step of the procedure and that confirmation of results would require resampling of the original specimen, so the original specimen must be stored appropriately and protected from cross-contamination. It also provides guidance on the generation of sequence data, including the information on approaches to ensuring the quality control of sequencing. WAHIS includes the World Animal Health Information Database and web-based interface. The system disseminates information on animal health and sends real-time alerts on animal disease events. A new version of this system OIE-WAHIS will be launched in 2020.
OIE-WAHIS will include a pathogen genomic notification system. This webbased platform aims to collect digital whole-genome sequence data and to link these data with the corresponding epidemiological and outbreak data in OIE-WAHIS. It will provide a permanent record of the genetic sequences of pathogens that have been the subject of country reports to the OIE. It will also include quality information so that the reliability of the data can be considered when interpreting the data. This will be an open information system, so as to facilitate transparent sharing of information on the global animal health situation.
Validation and applications of NGS for adventitious virus testing NGS is a promising alternative method to the traditional in vivo and in vitro adventitious virus detection assays. Validation of NGS is needed to demonstrate that the assay is fit-for-purpose and may involve a multistep strategy. Validation using panels of model viruses allows the sensitivity, specificity and breadth of detection of the assay to be confirmed. Furthermore, the strategy for follow-up of positive signals needs to be defined. The viral database used for validation studies can impact on the number of false positive and false negative results. False positive results may also be associated with the reagents and components used in the assay, in particular those that are animal-derived. In the event of the detection of a viral contaminant, the significance of this finding will depend on whether the virus is infectious A risk assessment and management approach is also advocated for preventing contamination of vaccines with extraneous viral adventitious agents.
The steps include: 1. Nucleic acid extraction from a sample, using an extraction protocol that covers the extraction of different types of DNA and RNA and that is based on a comparison of eleven different extraction methods tested using EBV, RSV, Reo and FeLV  [34]. 2. Reverse transcription for RNA viruses and second strand synthesis for single stranded DNA viruses. 3. Sequencing using the Illumina HiSeq1500 Sequencing System platform 4. Data processing and analysis using in-house PhyloID software, which is an automated pipeline developed by Sanofi Pasteur for adventitious agent detection from NGS data  [35].
For the PhyloID analysis, quality control steps implemented on the data include removing the adaptors and filtering out low quality reads to remove sequencing or PCR errors that might lead to misidentifications. This helps to strengthen the robustness of the assembly step and subsequent identification. A validated panel of model viruses is used to assess the performance of the Sanofi Pasteur NGS assay for adventitious virus detection. This panel is based on viruses previously developed by an NIH study that demonstrated in vitro assays have greater sensitivity for virus detection than in vivo tests  [19]. The 16 viruses were representative of potential contaminants that could be introduced during vaccine production. The viruses are: bovine viral diarrhea virus, coxsackie A16, coxsackie B3, echovirus 11, rhinovirus 12, rubella, bovine parainfluenza type 3, influenza A, measles, mumps, vesicular stomatitis virus, adenovirus 5, adenovirus 41, herpes simplex virus type 1, simian cytomegalovirus, and simian virus 40.
As expected CPV, PCV2 and HVT were detected by DNA Seq whereas RD114, CDV, and BVDV by RNA Seq. However, no PRRSV was detected. out of seven viruses were observed by NGS, and the sensitivity was close to that achieved by cell culture (which is between 50 and 0. 1 CCID50/ml depending on the virus type and cell culture system). To understand why PRRSV was not detected by the NGS assay, the eight spiked samples and the corresponding libraries were analysed by specific digital PCR to verify the presence of the seven viruses in the master seed after RNA and DNA extraction and during the NGS process. The investigation showed that the root-cause for the absence of PRRSV was probably the RefSeq database used for the bioinformatics analysis. The RefSeq 10 K seq database was used because it has few redundancies, but the database is not exhaustive and appears to contain insufficient data to enable the detection of highly variable RNA viruses such as PRRSV.
The RefSeq database, although reliable in the event of positive results, has poor reliability in the event of negative results, leading to false negative results. The RefSeq database was compared to the Genbank database, which contains the largest number of sequences (about 3 million). The RefSeq has few redundancies, but is not exhaustive, whereas the Genbank is exhaustive but has redundancies and as a result is associated with an increase in false positive results, and a decrease in false negative results; however, the strong redundancies resulted in a signal drop, which may impact coverage. The analysis was partly repeated using the RVDB database  [7]. The use of the RVDB database resulted in better PRRSV detection, with little difference in the detection of the other viruses, demonstrating the importance of the database used, especially for highly variable RNA viruses such as the PRRSV.
Further analyses using other databases are planned, as is a study comparing different bioinformatics pipelines. Boehringer Ingelheim are also considering the creation of a complementary database based on the list of viruses listed in the Ph. Eur. The overall intention is to define the best bioinformatics solution including the database pipelines to unambiguously replace the classical cell detection system assays in the future. Dr. Dawid Walas (Merck Group, UK) presented a case associated with the investigation of a bovine polyomavirus 3 (BPV3) signal in a viral seed stock. Initial investigation included a qPCR endpoint assay. The extraction was performed using QIAGEN chemistry including carrier RNA. The extraction included plus and minus DNase steps (in separate reactions). Carrier RNA (cRNA) was included in the extractions -cRNA is routinely included in extraction of nucleic acids for many molecular assays like PCR in order to increase the yield of extracted nucleic acid, however, it is typically not used for NGS-based assays.
The NGS analysis was split into two tests. The first test was designed to mimic the original qPCR conditions. For the first test, nucleic acid extraction with cRNA was conducted, no DNase was used, and the library was prepared without whole genome amplification (WGA). The assay used 100 times less starting material (5 ng). The second test was identical except that cRNA was not included in the extraction step. The samples were sequenced on both MiSeq and NextSeq and bioinformatics was performed with the aid of an in-house proprietary viral database and an adventitious agent algorithm. For the first test, MiSeq sequencing generated over 35 million reads and the NextSeq sequencing generated over 400 million reads. No BPV3 sequence (reads) were detected in either data set. For test two, in which nucleic acids were extracted without cRNA, the MiSeq sequencing generated almost 60 million reads, and three BPV3 reads, corresponding to two different GenBank accession numbers, were detected.
The vendor confirmed that the master mix contained bovine serum albumin. Sequencing of the amplicons demonstrated 99% identity to the BPV3 reference sequence (accession number AF406967), as opposed to 96% similarity observed for the original BPV3 fragment detected by NGS (i. e. derived from sequence analysis of the original viral seed stock). This indicates that care must be taken when qualifying the reads and when deciding how to conduct further investigations of positive signals. Master mixes that contain bovine albumin may be unsuitable for the detection of bovine adventitious agents, such as BPV3, and may be problematic for the downstream detection of true hits. It is important to understand whether the reagents and components used in an assay impact the results, especially when trying to confirm a positive signal.
In both samples the cells were separated from the growth medium. The growth medium samples were treated with ultracentrifugation or filtration, and both DNA and RNA were extracted. For the cell samples, total RNA was isolated, this was followed by ribosomal RNA depletion. All samples were shotgun sequenced using the Illumina MiSeq. Processed media, fresh media and nuclease free water were used as negative controls. The bioinformatics pipeline for the metagenomic analysis involved an initial quality control step using the CLC Genomics Workbench, followed by direct similarity search of sequencing reads on protein level (against nr database) and subsequent taxonomic classification and visualization. Virallike reads were extracted from all the sequencing files and further analysed to possibly confirm/reject that they correspond to viral genomes.
Dr. Steven Van Borm (Sciensano) presented on the OHEJP-METASTAVA project, which is a project to standardize and validate metagenomics methods for the detection of foodborne zoonoses, antimicrobial resistance and emerging threats. This project is being implemented by Onehealth EJP, a consortium of 41 public organizations covering human and animal health and food. It is co-funded by the European Joint Programme Horizon 2020 Work programme for 2017. The project is running between 2018 and 2020 and there are six partners from Belgium, France, Germany, the Netherlands and Sweden. As part of the Metastava project a landscape analysis is being conducted to identify the existing data generation protocols, molecular diagnostic guidelines and data analysis protocols for NGS, with a view to using these to develop: i) fit for purpose standardized protocols for the model pathogens: hepatitis E, norovirus, antibiotic resistant bacteria, zoonotic pox viruses and Shigatoxigenic Escherichia coli; ii) quality metrics for diagnostic metagenomics, internal and external controls; and iii) analytical validation data for the model pathogens on sensitivity, specificity, repeatability, proficiency testing and interlaboratory reproducibility.
The parvo-like sequence was traced to the nucleic acid extraction spin columns and represented background contamination  [38, 39]. The next case study focused on avoiding false negatives through the evaluation of quality controls for RNA virus detection. As previously mentioned, the animal health diagnostic sector deals with a large variety of sample types. The Metastava project wanted to develop follow-up strategies for individual samples. In addition to the external controls monitoring run performance, a sample level spike-in control was evaluated (mengovirus extraction control, Biomérieux). The wetlab workflow purposely avoided any enrichment strategies. The data analysis workflow used a fast mapping approach to the mengovirus genome (accession number DQ294633. 1), as well as read classification against a custom database using Kraken v1.