dataesr/scientific-paragraphs-categorization huggingface dataset:
Often, prior to gene by gene analysis, sequencing reads are assembled, typically using the de novo based approach, into longer contiguous sequences (called contigs) which constitute a draft genome (i. e. one that still contains gaps). To assign an MLST type, the assembled short reads are compared using BLAST to a reference allele database (MLST scheme) holding all known allelic variants for each locus defined for a specific species. Variations, including SNPs, indels (insertions and deletions) and recombinations in the same gene are considered as a single allele difference. In some MLST pipelines, allele calling is completed with assembly-free allele calling whereby raw sequencing reads are mapped to alleles in a database. The choice of assembly or assemblyfree allele calling usually depends upon whether a de novo assembly already exists or if reads have been mapped to a reference genome.
A valuable evaluation of different MLST software for NGS sequencing data has been conducted by  Page et al. (2017)  using a validated dataset which provides information on accuracy, limitations and computational performance. Traditional 7-gene MLST provides a broad phylogenetic relevant split of a species into sequence types (STs) and clonal complexes (CCs), whereas cgMLST provides highly detailed phylogenetically relevant information about the genetic relatedness of a species. wgMLST provides even more discrimination than cgMLST and this can be valuable for cluster investigations to discriminate between closely related isolates. However, because it includes sequence data possibly acquired by horizontal transfer, wgMLST analysis may not be as phylogenetically relevant when compared to cgMLST derived phylogeny.
Thus, whilst genes on mobile elements are usually included in wgMLST they are often, as in SNP analysis, filtered out in the final analysis. A public validated database with a shared nomenclature is recommended for comparisons, but ad hoc databases can also be created when a public reference is unavailable or insufficient. Examples of publicly available cgMLST schemes for common foodborne pathogens are provided in Table  2. There are currently no public cg/wg MLST schemes available for other foodborne bacteria, such as spoilage bacteria.
Comparison between SNP and cg/wgMLST- The choice of which comparative genomic approach to use depends on the needs of the end-user and the epidemiological context. While either SNP or gene-by-gene approaches can be used to investigate a fixed number of strains associated with a particular contamination event, cgMLST might be more appropriate if multiple users need to systematically analyse every new isolate added to a common database, e. g. , in an outbreak surveillance network, especially if the sequence information cannot be disclosed in the public domain. For investigating phylogeny, the use of either cgMLST or cgSNP may provide more robust analyses than wgMLST or wgSNP since it includes only regions of the genome present in all strains, however, the use of wgMLST or wgSNP can give higher resolution for strain discrimination.
SNP and gene-by-gene approaches assess genetic variation in slightly different ways and should be viewed as being complementary and both used when one method alone does not provide a clear-cut answer or for stronger support for an association between isolates e. g. to confirm the source of an outbreak and support regulatory action. To date, both methods, have been shown to be equally discriminatory when calling strain relatedness and epidemiologically concordant for outbreak investigations  (Chen et al. , 2017; Cunningham et al. , 2017; Katz et al. , 2017). However, comparisons of the two approaches using WGS data from a wider range of foodborne pathogens in a variety of outbreak settings would be valuable and are in progress. A major advantage of cg/wgMLST is that it can be standardized and harmonized by using an allele database with standardized allele calling and this approach is being adopted by PulseNet International  (Nadon et al.
, 2017)  to enable global strain comparisons for public health. The cg/wgMLST allele databases must be curated to maintain quality and, whilst most curation can be automated, manual curation by a subject matter expert in cg/wgMLST and microbiology is required if new alleles deviate from the quality thresholds defined for the automated curation. An important difference between SNP and gene-by-gene approaches is the level of computational support required. SNP analysis has traditionally been performed using open source software requiring expert bioinformatic support, whereas cg/wg MLST has been implemented on both command-line open-source software and commercial solutions with user-friendly interfaces. Maximal benefit from WGS of foodborne pathogens will be achieved if sequenced genomes are deposited in public databases in real time.
Whilst there is general agreement on this principle, at present, not all agencies, organisations and companies are able to share their sequencing data. Raw sequencing data can be submitted to the international public archival resource the 'Sequence Read Archive (SRA)' either through the National Center for Biotechnology Information (NCBI) (www. ncbi. nlm. nih. gov/sra), the European Bioinformatics Institute (EBI) (www. ebi. ac. uk/ena) or the DNA Data Bank of Japan (DDBJ) (trace. ddbj. nig. ac. jp) with data shared between all three  (Kodama et al. , 2012). The NCBI pathogen detection website, which provides daily SNP based phylogenetic trees for all publicly available data, is also available to those able to make their pathogen sequence data public since it is a requirement from NCBI that the users submit their sequences to their public repository before their tools can be used.
Isolates do not always fall within the above SNP/allele thresholds and thus can appear to lie between being highly related and unrelated. For example, isolates in a food processing plant may cluster separately from all other isolates in a database but still be 30 SNPs/alleles from each other. This indicates that the isolates share a common ancestor and may have evolved from a resident strain in the premises and potentially persistent  (Elson et al. , in publication). This can happen when microbial populations experience frequent reduction in numbers (i. e. by cleaning and disinfection), as random mutations can lead to diversification of the original resident strain. In addition, a factory environment offers several different environmental niches that enable isolates therein to undergo genetic drift, again causing strain diversification.
g. the Global Microbial Identifier (GMI) has been providing PTs for microbial WGS since 2015 (http:// www. globalmicrobialidentifier. org/). Also, an end-user survey was published that provided information on capability, attitudes and practices of GMI community members  (Moran-Gilad et al. , 2015). This scheme provides bacterial strains for end to end testing, extracted DNA for sequencing and data analysis assessment and sequence data all from the same strain for bioinformatic analysis. Other quality initiatives include benchmarking activities in which well characterised sets of strains are available for evaluating the performance of bioinformatic pipelines. Recently, an outbreak benchmark dataset has been publicly released consisting of sequence data, sample metadata and corresponding known phylogenetic trees for L.
monocytogenes, S. enterica ser. Bareilly, Escherichia coli, and Campylobacter jejuni and one simulated dataset (https://github. com/WGS-standards-and-analysis/Datasets), for laboratories to use to assess their bioinformatic tools and pipelines  (Timme et al. , 2017). Work has also been carried out under the EFSA funded Engage project (http://www. engageeurope. eu) to benchmark specific bioinformatic tools. A standard set of sequencing data has been used to evaluate different de novo assembly tools for predicting Salmonella serotypes as well as antimicrobial resistance gene profiling tools. The results of these benchmarking studies demonstrate that serotyping and predicting antimicrobial resistance in Salmonella using WGS data is a very feasible option.
Public health and regulatory actions based on WGS results Increasingly, food regulators and public health scientists are monitoring sequence databases to identify indistinguishable isolates from patients, the food chain, and clustered clinical isolates which could indicate a foodborne outbreak. Such findings justify exploring the potential link between cases, and the food isolate(s). Using a WGS profile as part of the case definition in an outbreak investigation allows cases to be ruled in or out of the outbreak with a higher degree of resolution than previously possible. WGS evidence for isolates being the same strain is allowing cases to be attributable to outbreaks over longer time frames and to link cases from broader geographical areas than was possible with previous typing methods e.
g. L. monocytogenes isolates from cases of listeriosis occurring over several years can be shown to be the same strain  (Chen et al. , 2017; Gillesberg Lassen et al. , 2016; Kleta et al. , 2017; Wilson et al. , 2016) ; isolates of Salmonella Enteritidis from cases in different European countries have been demonstrated to be the same by SNP analysis and to have evolved from a common ancestor  (Dallman et al. , 2016). A more robust case definition gives increased power to subsequent epidemiological analyses such as case-control studies, as unrelated cases which may have been previously included as part of the outbreak, no longer confounds the analyses  (Lienau et al. , 2011). Sequence data from outbreak isolates can be compared to known sequence databases and may be found to match isolates associated with distinct geographical signals which may give indications to the possible original source of contamination and thus help to direct food chain and environmental investigations  (Hoffmann et al.
Outbreaks detected by WGS are investigated using similar approaches as used previously with cases being interviewed about their food exposures and case-control or case-case studies conducted as part of analytical epidemiological investigations to provide supporting evidence for the potential food source. Food authorities will conduct traceability investigations on implicated food products in order to confirm or refute links to the outbreak and if linked, to identify the root cause of the outbreak so that effective control measures can be implemented. In addition to the overwhelming evidence that WGS provides to outbreak investigations, it also provides support to prevent false positive association of a food to an outbreak. Where a pathogen has been identified in a food product or the food production environment, the isolate sequence can be compared with a database of human isolates to see if there are any matches.
The findings and resulting improvements in manufacturing and farming practices can then be shared with the entire food sector, not just the facility involved in the contamination event. Besides direct matching of environmental isolates relative to a contamination, it is also possible for industry to compare isolates with entries in public databases as used by public health authorities and food regulators. Depending on the database used for comparison, valuable information can be gleaned such as the identification of potential novel sources (which may provide an indication on initial route of entry into the food production premises), geographic signals about possible origin of contamination and association with human illness. WGS can also lead to valuable insights to refine the 'hazard identification' step in microbial risk assessment process.
Many disciplines including predictive food microbiology and thermal processing are likely to benefit from the use of WGS data for phenotypic prediction. There are a range of webbased tools and publicly available databases for local use for this purpose, a selection of which are listed in Table  3. These tools identify the genes of interest by aligning draft genomes to a gene database. For example, the genome data obtained through the routine sequencing of every day isolates can be queried to predict traits such as the virulence profile, heat resistance, stress response, biofilm formation, resistance against antimicrobials and biocides by studying their phenotypic characteristics in parallel  (Rantsiou et al. , 2017). It is important to recognize that detailed genomic information does not necessarily translate into knowledge of gene expression.
In addition, predictions of these tools vary widely in sensitivity and precision  (Weiss et al. , 2016). Various pipelines for the pre-processing, assembly, clustering and analyses are available for genomic/metatranscriptomic bioinformatic analyses (Table  4 ), such as MOCAT2  (Kultima et al. , 2016) , MetAMOS  (Treangen et al. , 2013)  and IMP  (Narayanasamy et al. , 2016)  as standalone frameworks and MG-RAST  (Wilke et al. , 2016)  and Anvi'o  (Eren et al. , 2015b)  as web-based platforms. For the functional annotations of meta-omics data, the most commonly used databases remain KEGG  (Kanehisa et al. , 2017) , COG  (Huerta-Cepas et al. , 2016)  and Pfam  (Finn et al. , 2016)  for functional classifications. Last but not least, bioinformatic platforms implementing complete workflows such as Galaxy  (Afgan et al.
Applications of metagenomics in food safety The absence of a well-curated and high-quality standard database of genomic sequence for pathogenic, probiotic, and functional microbes is a significant hindrance to the implementation of metagenomic-based methods for food safety management  (Weimer et al. , 2016). Groups such as the Consortium for Sequencing the Food Supply Chain (CSFSC), founded by IBM and Mars Incorporated, are putting efforts into collecting genome information on pathogenic bacteria across the food supply chain, as well as characterising and quantifying the microbiome before and after processing to use genomic and metagenomic data to assure food safety, authenticity and traceability  (IBM, 2015; Mars, 2015; Weimer et al. , 2016; Welser, 2015). DNA and RNA sequence information collected from food samples by the CSFSC will be used to describe a microbial baseline representing normal microbe communities, which can be applied to track the source of contamination and for food authentication  (IBM, 2015; Mars, 2015).
Using data from CSFSC's research, IBM is developing a scalable web-based bioinformatic workbench, the Metagenomics Computation and Analytics Workbench (MCAW), designed to analyse metagenomic and metatranscriptomic sequence data for assessing microbiological hazards and for food authentication in the supply chain. It also provides a service for the storage and management of raw genomic sequences and analysis results  (Edlund et al. , 2016). The work done to date within the CSFSC and its related MCAW bioinformatic tool offer a model of high-quality genomic and metagenomic database collection, as well as a bioinformatic workbench that can eventually apply NGS to food safety. Similar approaches are being applied by smaller service providers, who are aiming to use NGS to characterise pathogens in food ingredients and products.
Issues and challenges The evaluation of the complete functional repertoire of a microbial population remains difficult due to the incomplete nature of the functional annotation of individual genes or proteins in public databases. As an example, a recent global ocean reference gene catalogue has been annotated at roughly 50% using the eggNOG orthologous genes database  (Huerta-Cepas et al. , 2016 ) and only at roughly 30% using the KEGG metabolic pathways database  (Kanehisa and Goto, 2000). In recent years, detailed functional categories present in the KEGG  (Kanehisa et al. , 2017)  and SEED  (Aziz et al. , 2012; Overbeek et al. , 2005)  databases have been used to annotate and compare genomes and metagenomes using the KEGG Automatic Annotation Server (KAAS)  (Moriya et al. , 2007) , Metagenomics Rapid Annotation using Subsystem Technology  (Wilke et al.
, 2016) , and Metagenome Analyzer systems  (Huson et al. , 2007 (Huson et al. , , 2016)). However, these functional categories often remain broad and do not allow the distinguishing of metabolic and physiological features. New tools are required to characterise potential physiological and metabolic pathways  (De Filippo et al. , 2012)  such as the MAPLE system  (Takami et al. , 2016)  which uses KEGG module annotations and permits the estimation of functional abundance and indicates the working probability of the KEGG module based on completion ratio results. As with traditional microbiological methods, sampling is an extremely important first step in collecting relevant microbiological information from the food processing environment and final products (International Commission on Microbiological Specifications for Foods and  Christian and Roberts, 1986; Ni et al.
, 2017) , the DNA/RNA extraction  (Knudsen et al. , 2016; Yuan et al. , 2012) , the library preparation  (Jones et al. , 2015; Schirmer et al. , 2015) , the sequencing platform used  (Tremblay et al. , 2015)  and the bioinformatic approach applied  (Siegwald et al. , 2017). Nevertheless, standardization in the field is still in its infancy and the comparison and validation of these protocols and tools are essential to gain meaningful information and to make intra-and inter laboratory exchange of information effective  (Costea et al. , 2017). With respect to bioinformatic analyses, state of the art pipelines exist that include crucial steps such as adaptor removal, matrix genome sequence removal (meat, vegetables, fruit etc. ), low-quality read filtering, contig assembly and finally perform searches against regularly updated databases  (Olson et al.
Correct data interpretation WGS data amenable to gross misinterpretation at the hands of poorly-trained personnel can pose serious risks to the food industry, especially in the age of social media. Mechanisms to prevent and tackle these concerns must be addressed for industry to engage with an open data model  (FAO, 2016; Taboada et al. , 2017). This was highlighted recently by the Technical University of Denmark, where a preliminary analysis reported the presence of monkey DNA in burgers. Following further analysis, this was shown to be cattle DNA (Sep 2016 http://www. food. dtu. dk/english/news/2016/08/mapping-foods-dna-can-reveal-fraud? id=800739d1-f72d-4c57-bab1-4376e0a87bc7). Database limitations and short reads used for data comparison were identified as reasons for the erroneous interpretation of the sequence results, highlighting the critical importance of specialised knowledge for analysing and interpreting WGS data.
Legal clarity/due diligence In the majority of WGS source tracking investigations, sequence data from closely related strains are included in the analysis to precisely understand the relatedness of the isolates being studied. This is usually achieved by querying the sequences of interest against a public sequence database comprising strains isolated from multiple sources. This can potentially result in the clustering of a food/environmental isolate being analysed with a clinical isolate. The situation becomes complicated when a link is found between a historical patient and a recent in-house isolate and vice-versa with regards to the subsequent steps which must be taken by the food processor from a due diligence perspective. In the USA, all foodborne pathogens obtained through surveillance and inspection are sequenced and the sequences are uploaded into the public domain where they will reside for the life of the database.
Data ownership There are concerns that the use of publicly available WGS data could result in trade barriers and even lead to local legal actions due to countries operating within different legal frameworks. Thus, there is a strong desire to establish and agree on a global, harmonised legal framework to facilitate open sharing  (FAO, 2016). Potential solutions to some of the issues could be agreed defined delays in data sharing or even a 'grace period' without legal consequences in order to promote active data sharing. Considerable effort in terms of cooperation and coordination in this area is required to achieve the aim open sharing of WGS data. It is important for industry to develop mechanisms to both share and protect sensitive information so can contribute to WGS databases more comfortably.
Furthermore, data generated from WGS and metagenomics are likely to be integrated with predictive microbiology for greater control of food safety and quality along the food chain. In the future, genomic databases may be linked to websites dealing with predictive microbiology such as ComBase (http://www. combase. cc/index. php/en/). Maximal food safety benefit from WGS depends on data sharing and it is anticipated that industry will develop a mechanism to both share and protect sensitive information, so it can contribute to the WGS databases more comfortably. The further development of easy to use bioinformatic pipelines and the harmonization of the methods are also required.
, 2014). These limitations are being addressed by high-throughput NGS technologies including metagenomic approaches, which provide more information on the microbial community populations and biological ingredients of a food product, as well as allowing cultureindependent testing. Metagenome prediction software has also been used to understand the impact of modified atmospheres on metabolic pathways, to aid the design of preservation systems  (Ferrocino and Cocolin, 2017). These metagenomic approaches, when combined with other 'omics technologies such as proteomics and metabolomics have the potential to link particular species in a community with functional characteristics, such as flavour production or production of harmful metabolites such as biogenic amines in rice wine  (Liu et al. , 2016).
Acknowledgements The authors would like to thank  Ms Lilou van Lieshout ,  Dr Belén Márquez-García  and  Dr Tobias Recker , Scientific Project Managers at  ILSI Europe who  facilitated scientific meetings and coordinated the overall project management and administrative tasks related to the completion of this work. The authors would like to thank  Prof. Frank Aarestrup ,  Prof. Dag Harmsen ,  Dr Robèr Kempermann ,  Dr Trevor Phister  and  Dr Masami Takeuchi , for their contributions and suggestions when developing the publication. The authors would like to thank  ILSI Europe's Microbiological Food Safety Task Force , the  ILSI North America Food Microbiology Committee ,  ILSI Japan Next Generation Sequencing Project , and  ILSI Southeast Asia Region  for their support as specified in the funding sources section.
funded by its member companies. In addition, ILSI Europe receives funding from the  European Union -funded projects they partner with and from projects initiated by  Member States ' national authorities. About ILSI North America ILSI profit foundation that provides a forum to advance understanding of scientific issues related to the nutritional quality and safety of the food supply by sponsoring research programs, educational seminars and workshops, and publications. ILSI North America receives support primarily from its industry membership. About ILSI Japan ILSI  was supported by  ILSI Japan ,  ILSI North America or ILSI Southeast Asia Region.