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Cason C, D’Accolti M, Soffritti I, Mazzacane S, Comar M, Caselli E. Next-generation sequencing and PCR technologies in monitoring the hospital microbiome and its drug resistance. Front Microbiol 2022; 13:969863. [PMID: 35966671 PMCID: PMC9370071 DOI: 10.3389/fmicb.2022.969863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
The hospital environment significantly contributes to the onset of healthcare-associated infections (HAIs), which represent one of the most frequent complications occurring in healthcare facilities worldwide. Moreover, the increased antimicrobial resistance (AMR) characterizing HAI-associated microbes is one of the human health’s main concerns, requiring the characterization of the contaminating microbial population in the hospital environment. The monitoring of surface microbiota in hospitals is generally addressed by microbial cultural isolation. However, this has some important limitations mainly relating to the inability to define the whole drug-resistance profile of the contaminating microbiota and to the long time period required to obtain the results. Hence, there is an urgent need to implement environmental surveillance systems using more effective methods. Molecular approaches, including next-generation sequencing and PCR assays, may be useful and effective tools to monitor microbial contamination, especially the growing AMR of HAI-associated pathogens. Herein, we summarize the results of our recent studies using culture-based and molecular analyses in 12 hospitals for adults and children over a 5-year period, highlighting the advantages and disadvantages of the techniques used.
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Affiliation(s)
- Carolina Cason
- Department of Advanced Translational Microbiology, Institute for Maternal and Child Health, IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Maria D’Accolti
- Department of Chemical, Pharmaceutical and Agricultural Sciences, Section of Microbiology and LTTA, University of Ferrara, Ferrara, Italy
- CIAS Research Centre, University of Ferrara, Ferrara, Italy
| | - Irene Soffritti
- Department of Chemical, Pharmaceutical and Agricultural Sciences, Section of Microbiology and LTTA, University of Ferrara, Ferrara, Italy
- CIAS Research Centre, University of Ferrara, Ferrara, Italy
| | | | - Manola Comar
- Department of Advanced Translational Microbiology, Institute for Maternal and Child Health, IRCCS “Burlo Garofolo”, Trieste, Italy
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Elisabetta Caselli
- Department of Chemical, Pharmaceutical and Agricultural Sciences, Section of Microbiology and LTTA, University of Ferrara, Ferrara, Italy
- CIAS Research Centre, University of Ferrara, Ferrara, Italy
- *Correspondence: Elisabetta Caselli,
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2
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Foerster H, Battey JND, Sierro N, Ivanov NV, Mueller LA. Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook. Brief Bioinform 2021; 22:bbaa136. [PMID: 32662816 PMCID: PMC8138835 DOI: 10.1093/bib/bbaa136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/19/2020] [Accepted: 06/04/2020] [Indexed: 01/09/2023] Open
Abstract
Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.
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3
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Batovska J, Piper AM, Valenzuela I, Cunningham JP, Blacket MJ. Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches. Sci Rep 2021; 11:7946. [PMID: 33846382 PMCID: PMC8041782 DOI: 10.1038/s41598-021-85855-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/02/2021] [Indexed: 01/04/2023] Open
Abstract
Metabarcoding has the potential to revolutionise insect surveillance by providing high-throughput and cost-effective species identification of all specimens within mixed trap catches. Nevertheless, incorporation of metabarcoding into insect diagnostic laboratories will first require the development and evaluation of protocols that adhere to the specialised regulatory requirements of invasive species surveillance. In this study, we develop a multi-locus non-destructive metabarcoding protocol that allows sensitive detection of agricultural pests, and subsequent confirmation using traditional diagnostic techniques. We validate this protocol for the detection of tomato potato psyllid (Bactericera cockerelli) and Russian wheat aphid (Diuraphis noxia) within mock communities and field survey traps. We find that metabarcoding can reliably detect target insects within mixed community samples, including specimens that morphological identification did not initially detect, but sensitivity appears inversely related to community size and is impacted by primer biases, target loci, and sample indexing strategy. While our multi-locus approach allowed independent validation of target detection, lack of reference sequences for 18S and 12S restricted its usefulness for estimating diversity in field samples. The non-destructive DNA extraction proved invaluable for resolving inconsistencies between morphological and metabarcoding identification results, and post-extraction specimens were suitable for both morphological re-examination and DNA re-extraction for confirmatory barcoding.
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Affiliation(s)
- Jana Batovska
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia. .,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia.
| | - Alexander M Piper
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia
| | - Isabel Valenzuela
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia
| | - John Paul Cunningham
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia
| | - Mark J Blacket
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia
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4
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Martoni F, Valenzuela I, Blacket MJ. On the complementarity of DNA barcoding and morphology to distinguish benign endemic insects from possible pests: the case of Dirioxa pornia and the tribe Acanthonevrini (Diptera: Tephritidae: Phytalmiinae) in Australia. INSECT SCIENCE 2021; 28:261-270. [PMID: 32096585 PMCID: PMC7818419 DOI: 10.1111/1744-7917.12769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 02/20/2020] [Accepted: 02/22/2020] [Indexed: 06/10/2023]
Abstract
Fruit flies are considered economically important insects due to some species being agricultural pests. However, morphological identification of fruit fly adults and larvae can be difficult requiring a high level of taxonomic expertise, with misidentifications causing problematic false-positive/negative results. While destructive molecular techniques can assist with the identification process, these often cannot be applied where it is mandatory to retain a voucher reference specimen. In this work, we non-destructively (and partial-destructively) processed larvae and adults mostly belonging to the species Dirioxa pornia (Walker, 1849), of the poorly studied nonpest fruit fly tribe Acanthonevrini (Tephritidae) from Australia, to enable molecular identifications whilst retaining morphological vouchers. By retaining the morphological features of specimens, we confirmed useful characters for genus/species-level identification, contributing to improved accuracy for future diagnostics using both molecular and morphological approaches. We provide DNA barcode information for three species of Acanthonevrini known from Australia, which prior to our study was only available for a single species, D. pornia. Our specimen examinations provide new distribution records for three nonpest species: Acanthonevroides variegatus Permkam and Hancock, 1995 in South Australia, Acanthonevroides basalis (Walker, 1853) and D. pornia in Victoria, Australia; as well as new host plant records for D. pornia, from kangaroo apple, apricot and loquat.
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Affiliation(s)
- Francesco Martoni
- Agriculture Victoria ResearchAgriBio Centre for AgriBioscienceBundooraVictoriaAustralia
| | - Isabel Valenzuela
- Agriculture Victoria ResearchAgriBio Centre for AgriBioscienceBundooraVictoriaAustralia
| | - Mark J. Blacket
- Agriculture Victoria ResearchAgriBio Centre for AgriBioscienceBundooraVictoriaAustralia
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5
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Chiu JKH, Ong RTH. ARGDIT: a validation and integration toolkit for Antimicrobial Resistance Gene Databases. Bioinformatics 2020; 35:2466-2474. [PMID: 30520940 DOI: 10.1093/bioinformatics/bty987] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 11/10/2018] [Accepted: 12/04/2018] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Antimicrobial resistance is currently one of the main challenges in public health due to the excessive use of antimicrobials in medical treatments and agriculture. The advancements in high-throughput next-generation sequencing and development of bioinformatics tools allow simultaneous detection and identification of antimicrobial resistance genes (ARGs) from clinical, food and environment samples, to monitor the prevalence and track the dissemination of these ARGs. Such analyses are however reliant on a comprehensive database of ARGs with accurate sequence content and annotation. Most of the current ARG databases are therefore manually curated, but this is a time-consuming process and the resulting curation errors could be hard to detect. Several secondary ARG databases consolidate contents from different source ARG databases, and hence modifications in the primary databases might not be propagated and updated promptly in the secondary ARG databases. RESULTS To address these problems, a validation and integration toolkit called ARGDIT was developed to validate ARG database fidelity, and merge multiple primary ARG databases into a single consolidated secondary ARG database with optional automated sequence re-annotation. Experimental results demonstrated the effectiveness of this toolkit in identifying errors such as sequence annotation typos in current ARG databases and generating an integrated non-redundant ARG database with structured annotation. A toolkit-oriented workflow is also proposed to minimize the efforts in validating, curating and merging multiple ARG protein or coding sequence databases. Database developers therefore benefit from faster update cycles and lower costs for database maintenance, while ARG pipeline users can easily evaluate the reference ARG database quality. AVAILABILITY AND IMPLEMENTATION ARGDIT is available at https://github.com/phglab/ARGDIT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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6
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Treiber ML, Taft DH, Korf I, Mills DA, Lemay DG. Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes. BMC Bioinformatics 2020; 21:74. [PMID: 32093654 PMCID: PMC7041091 DOI: 10.1186/s12859-020-3416-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 02/17/2020] [Indexed: 01/04/2023] Open
Abstract
Background Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. Results Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. Conclusions Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.
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Affiliation(s)
- Michelle L Treiber
- USDA ARS Western Human Nutrition Research Center, Davis, CA, 95616, USA.,Department of Food Science and Technology, Robert Mondavi Institute for Wine and Food Science, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA
| | - Diana H Taft
- Department of Food Science and Technology, Robert Mondavi Institute for Wine and Food Science, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA
| | - Ian Korf
- Genome Center, University of California, Davis, CA, 95616, USA
| | - David A Mills
- Department of Food Science and Technology, Robert Mondavi Institute for Wine and Food Science, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA
| | - Danielle G Lemay
- USDA ARS Western Human Nutrition Research Center, Davis, CA, 95616, USA. .,Genome Center, University of California, Davis, CA, 95616, USA. .,Department of Nutrition, University of California, Davis, CA, 95616, USA.
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7
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Sahruzaini NA, Rejab NA, Harikrishna JA, Khairul Ikram NK, Ismail I, Kugan HM, Cheng A. Pulse Crop Genetics for a Sustainable Future: Where We Are Now and Where We Should Be Heading. FRONTIERS IN PLANT SCIENCE 2020; 11:531. [PMID: 32431724 PMCID: PMC7212832 DOI: 10.3389/fpls.2020.00531] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 04/07/2020] [Indexed: 05/12/2023]
Abstract
The last decade has witnessed dramatic changes in global food consumption patterns mainly because of population growth and economic development. Food substitutions for healthier eating, such as swapping regular servings of meat for protein-rich crops, is an emerging diet trend that may shape the future of food systems and the environment worldwide. To meet the erratic consumer demand in a rapidly changing world where resources become increasingly scarce due largely to anthropogenic activity, the need to develop crops that benefit both human health and the environment has become urgent. Legumes are often considered to be affordable plant-based sources of dietary proteins. Growing legumes provides significant benefits to cropping systems and the environment because of their natural ability to perform symbiotic nitrogen fixation, which enhances both soil fertility and water-use efficiency. In recent years, the focus in legume research has seen a transition from merely improving economically important species such as soybeans to increasingly turning attention to some promising underutilized species whose genetic resources hold the potential to address global challenges such as food security and climate change. Pulse crops have gained in popularity as an affordable source of food or feed; in fact, the United Nations designated 2016 as the International Year of Pulses, proclaiming their critical role in enhancing global food security. Given that many studies have been conducted on numerous underutilized pulse crops across the world, we provide a systematic review of the related literature to identify gaps and opportunities in pulse crop genetics research. We then discuss plausible strategies for developing and using pulse crops to strengthen food and nutrition security in the face of climate and anthropogenic changes.
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Affiliation(s)
- Nurul Amylia Sahruzaini
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Nur Ardiyana Rejab
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
- Centre for Research in Biotechnology for Agriculture (CEBAR), University of Malaya, Kuala Lumpur, Malaysia
| | - Jennifer Ann Harikrishna
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
- Centre for Research in Biotechnology for Agriculture (CEBAR), University of Malaya, Kuala Lumpur, Malaysia
| | - Nur Kusaira Khairul Ikram
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
- Centre for Research in Biotechnology for Agriculture (CEBAR), University of Malaya, Kuala Lumpur, Malaysia
| | - Ismanizan Ismail
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Hazel Marie Kugan
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Acga Cheng
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Acga Cheng,
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Bengtsson-Palme J, Richardson RT, Meola M, Wurzbacher C, Tremblay ÉD, Thorell K, Kanger K, Eriksson KM, Bilodeau GJ, Johnson RM, Hartmann M, Nilsson RH. Metaxa2 Database Builder: enabling taxonomic identification from metagenomic or metabarcoding data using any genetic marker. Bioinformatics 2019; 34:4027-4033. [PMID: 29912385 PMCID: PMC6247927 DOI: 10.1093/bioinformatics/bty482] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 06/12/2018] [Indexed: 11/25/2022] Open
Abstract
Motivation Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data. Results We evaluated the Metaxa2 Database Builder on 11 commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality. Availability and implementation Freely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Johan Bengtsson-Palme
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.,Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-41346 Gothenburg, Sweden.,Centre for Antibiotic Resistance Research (CARe) at University of Gothenburg, SE-40530 Gothenburg, Sweden
| | - Rodney T Richardson
- Department of Entomology, The Ohio State University-Ohio Agricultural Research and Development Center, Wooster, OH, USA
| | - Marco Meola
- Fermentation Organisms, Methods Development and Analytics, Agroscope, CH-3003 Bern, Switzerland
| | - Christian Wurzbacher
- Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.,Chair of Urban Water Systems Engineering, Technical University of Munich, Garching, Germany
| | - Émilie D Tremblay
- Canadian Food Inspection Agency, Ottawa Laboratory Fallowfield, Ottawa, ON, Canada
| | - Kaisa Thorell
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Kärt Kanger
- Department of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia
| | - K Martin Eriksson
- Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Guillaume J Bilodeau
- Canadian Food Inspection Agency, Ottawa Laboratory Fallowfield, Ottawa, ON, Canada
| | - Reed M Johnson
- Department of Entomology, The Ohio State University-Ohio Agricultural Research and Development Center, Wooster, OH, USA
| | - Martin Hartmann
- Forest Soils and Biogeochemistry, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.,Sustainable Agroecosystems, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - R Henrik Nilsson
- Department of Biological and Environmental Sciences, University of Gothenburg, SE-40530 Gothenburg, Sweden.,Gothenburg Global Biodiversity Centre, SE-405 30 Göteborg, Sweden
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9
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Piper AM, Batovska J, Cogan NOI, Weiss J, Cunningham JP, Rodoni BC, Blacket MJ. Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. Gigascience 2019; 8:giz092. [PMID: 31363753 PMCID: PMC6667344 DOI: 10.1093/gigascience/giz092] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 06/25/2019] [Accepted: 07/09/2019] [Indexed: 12/21/2022] Open
Abstract
Trap-based surveillance strategies are widely used for monitoring of invasive insect species, aiming to detect newly arrived exotic taxa as well as track the population levels of established or endemic pests. Where these surveillance traps have low specificity and capture non-target endemic species in excess of the target pests, the need for extensive specimen sorting and identification creates a major diagnostic bottleneck. While the recent development of standardized molecular diagnostics has partly alleviated this requirement, the single specimen per reaction nature of these methods does not readily scale to the sheer number of insects trapped in surveillance programmes. Consequently, target lists are often restricted to a few high-priority pests, allowing unanticipated species to avoid detection and potentially establish populations. DNA metabarcoding has recently emerged as a method for conducting simultaneous, multi-species identification of complex mixed communities and may lend itself ideally to rapid diagnostics of bulk insect trap samples. Moreover, the high-throughput nature of recent sequencing platforms could enable the multiplexing of hundreds of diverse trap samples on a single flow cell, thereby providing the means to dramatically scale up insect surveillance in terms of both the quantity of traps that can be processed concurrently and number of pest species that can be targeted. In this review of the metabarcoding literature, we explore how DNA metabarcoding could be tailored to the detection of invasive insects in a surveillance context and highlight the unique technical and regulatory challenges that must be considered when implementing high-throughput sequencing technologies into sensitive diagnostic applications.
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Affiliation(s)
- Alexander M Piper
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Jana Batovska
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Noel O I Cogan
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - John Weiss
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
| | - John Paul Cunningham
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
| | - Brendan C Rodoni
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Mark J Blacket
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
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10
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Griffin PC, Khadake J, LeMay KS, Lewis SE, Orchard S, Pask A, Pope B, Roessner U, Russell K, Seemann T, Treloar A, Tyagi S, Christiansen JH, Dayalan S, Gladman S, Hangartner SB, Hayden HL, Ho WWH, Keeble-Gagnère G, Korhonen PK, Neish P, Prestes PR, Richardson MF, Watson-Haigh NS, Wyres KL, Young ND, Schneider MV. Best practice data life cycle approaches for the life sciences. F1000Res 2018; 6:1618. [PMID: 30109017 DOI: 10.12688/f1000research.12344.1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/17/2017] [Indexed: 11/20/2022] Open
Abstract
Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.
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Affiliation(s)
- Philippa C Griffin
- EMBL Australia Bioinformatics Resource, The University of Melbourne, Parkville, VIC, 3010, Australia.,Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Jyoti Khadake
- NIHR BioResource, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust Hills Road, Cambridge , CB2 0QQ, UK
| | - Kate S LeMay
- Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia
| | - Suzanna E Lewis
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, 94720, USA
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK
| | - Andrew Pask
- School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Bernard Pope
- Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ute Roessner
- Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Keith Russell
- Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia
| | - Torsten Seemann
- Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Andrew Treloar
- Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia
| | - Sonika Tyagi
- Australian Genome Research Facility Ltd, Parkville, VIC, 3052, Australia.,Monash Bioinformatics Platform, Monash University, Clayton, VIC, 3800, Australia
| | - Jeffrey H Christiansen
- Queensland Cyber Infrastructure Foundation and the University of Queensland Research Computing Centre, St Lucia, QLD, 4072, Australia
| | - Saravanan Dayalan
- Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Simon Gladman
- EMBL Australia Bioinformatics Resource, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Sandra B Hangartner
- School of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia
| | - Helen L Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, VIC, 3083, Australia
| | - William W H Ho
- School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Gabriel Keeble-Gagnère
- School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, VIC, 3083, Australia
| | - Pasi K Korhonen
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Peter Neish
- The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Priscilla R Prestes
- Faculty of Science and Engineering, Federation University Australia, Mt Helen , VIC, 3350, Australia
| | - Mark F Richardson
- Bioinformatics Core Research Group & Centre for Integrative Ecology, Deakin University, Geelong, VIC, 3220, Australia
| | - Nathan S Watson-Haigh
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | - Kelly L Wyres
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Neil D Young
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Maria Victoria Schneider
- Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia.,The University of Melbourne, Parkville, VIC, 3010, Australia
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11
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Poux S, Arighi CN, Magrane M, Bateman A, Wei CH, Lu Z, Boutet E, Bye-A-Jee H, Famiglietti ML, Roechert B, UniProt Consortium T. On expert curation and scalability: UniProtKB/Swiss-Prot as a case study. Bioinformatics 2018; 33:3454-3460. [PMID: 29036270 PMCID: PMC5860168 DOI: 10.1093/bioinformatics/btx439] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 07/10/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Biological knowledgebases, such as UniProtKB/Swiss-Prot, constitute an essential component of daily scientific research by offering distilled, summarized and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, their ability to keep up with the growth of biomedical literature is under scrutiny. Using UniProtKB/Swiss-Prot as a case study, we address this concern via multiple literature triage approaches. Results With the assistance of the PubTator text-mining tool, we tagged more than 10 000 articles to assess the ratio of papers relevant for curation. We first show that curators read and evaluate many more papers than they curate, and that measuring the number of curated publications is insufficient to provide a complete picture as demonstrated by the fact that 8000–10 000 papers are curated in UniProt each year while curators evaluate 50 000–70 000 papers per year. We show that 90% of the papers in PubMed are out of the scope of UniProt, that a maximum of 2–3% of the papers indexed in PubMed each year are relevant for UniProt curation, and that, despite appearances, expert curation in UniProt is scalable. Availability and implementation UniProt is freely available at http://www.uniprot.org/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sylvain Poux
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Cecilia N Arighi
- Protein Information Resource, University of Delaware, Newark, DE 19711, USA
| | - Michele Magrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), US National Library of Medicine, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), US National Library of Medicine, Bethesda, MD 20894, USA
| | - Emmanuel Boutet
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Hema Bye-A-Jee
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Maria Livia Famiglietti
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Bernd Roechert
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - The UniProt Consortium
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland.,Protein Information Resource, University of Delaware, Newark, DE 19711, USA.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.,Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA
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12
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Bengtsson-Palme J. The diversity of uncharacterized antibiotic resistance genes can be predicted from known gene variants-but not always. MICROBIOME 2018; 6:125. [PMID: 29981578 PMCID: PMC6035801 DOI: 10.1186/s40168-018-0508-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 06/25/2018] [Indexed: 05/21/2023]
Abstract
BACKGROUND Antibiotic resistance is considered one of the most urgent threats to modern healthcare, and the role of the environment in resistance development is increasingly recognized. It is often assumed that the abundance and diversity of known resistance genes are representative also for the non-characterized fraction of the resistome in a given environment, but this assumption has not been verified. In this study, this hypothesis is tested, using the resistance gene profiles of 1109 metagenomes from various environments. RESULTS This study shows that the diversity and abundance of known antibiotic resistance genes can generally predict the diversity and abundance of undescribed resistance genes. However, the extent of this predictability is dependent on the type of environment investigated. Furthermore, it is shown that carefully selected small sets of resistance genes can describe total resistance gene diversity remarkably well. CONCLUSIONS The results of this study suggest that knowledge gained from large-scale quantifications of known resistance genes can be utilized as a proxy for unknown resistance factors. This is important for current and proposed monitoring efforts for environmental antibiotic resistance and has implications for the design of risk ranking strategies and the choices of measures and methods for describing resistance gene abundance and diversity in the environment.
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Affiliation(s)
- Johan Bengtsson-Palme
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, 330 North Orchard Street, Madison, WI, 53715, USA.
- Centre for Antibiotic Resistance research (CARe) at University of Gothenburg, Gothenburg, Sweden.
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46, Gothenburg, Sweden.
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13
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Bengtsson-Palme J, Larsson DGJ, Kristiansson E. Using metagenomics to investigate human and environmental resistomes. J Antimicrob Chemother 2018; 72:2690-2703. [PMID: 28673041 DOI: 10.1093/jac/dkx199] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Antibiotic resistance is a global health concern declared by the WHO as one of the largest threats to modern healthcare. In recent years, metagenomic DNA sequencing has started to be applied as a tool to study antibiotic resistance in different environments, including the human microbiota. However, a multitude of methods exist for metagenomic data analysis, and not all methods are suitable for the investigation of resistance genes, particularly if the desired outcome is an assessment of risks to human health. In this review, we outline the current state of methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. In addition, we provide an overview of important considerations related to the analysis of resistance genes, and recommend some of the currently used tools and methods that are best equipped to inform research and clinical practice related to antibiotic resistance.
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Affiliation(s)
- Johan Bengtsson-Palme
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-41346, Gothenburg, Sweden.,Centre for Antibiotic Resistance Research (CARe) at University of Gothenburg, Box 440, SE-40530, Gothenburg, Sweden
| | - D G Joakim Larsson
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-41346, Gothenburg, Sweden.,Centre for Antibiotic Resistance Research (CARe) at University of Gothenburg, Box 440, SE-40530, Gothenburg, Sweden
| | - Erik Kristiansson
- Centre for Antibiotic Resistance Research (CARe) at University of Gothenburg, Box 440, SE-40530, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
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14
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Griffin PC, Khadake J, LeMay KS, Lewis SE, Orchard S, Pask A, Pope B, Roessner U, Russell K, Seemann T, Treloar A, Tyagi S, Christiansen JH, Dayalan S, Gladman S, Hangartner SB, Hayden HL, Ho WWH, Keeble-Gagnère G, Korhonen PK, Neish P, Prestes PR, Richardson MF, Watson-Haigh NS, Wyres KL, Young ND, Schneider MV. Best practice data life cycle approaches for the life sciences. F1000Res 2017; 6:1618. [PMID: 30109017 PMCID: PMC6069748 DOI: 10.12688/f1000research.12344.2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/29/2018] [Indexed: 11/20/2022] Open
Abstract
Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.
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Affiliation(s)
- Philippa C Griffin
- EMBL Australia Bioinformatics Resource, The University of Melbourne, Parkville, VIC, 3010, Australia.,Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Jyoti Khadake
- NIHR BioResource, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust Hills Road, Cambridge , CB2 0QQ, UK
| | - Kate S LeMay
- Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia
| | - Suzanna E Lewis
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, 94720, USA
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK
| | - Andrew Pask
- School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Bernard Pope
- Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ute Roessner
- Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Keith Russell
- Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia
| | - Torsten Seemann
- Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Andrew Treloar
- Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia
| | - Sonika Tyagi
- Australian Genome Research Facility Ltd, Parkville, VIC, 3052, Australia.,Monash Bioinformatics Platform, Monash University, Clayton, VIC, 3800, Australia
| | - Jeffrey H Christiansen
- Queensland Cyber Infrastructure Foundation and the University of Queensland Research Computing Centre, St Lucia, QLD, 4072, Australia
| | - Saravanan Dayalan
- Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Simon Gladman
- EMBL Australia Bioinformatics Resource, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Sandra B Hangartner
- School of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia
| | - Helen L Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, VIC, 3083, Australia
| | - William W H Ho
- School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Gabriel Keeble-Gagnère
- School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, VIC, 3083, Australia
| | - Pasi K Korhonen
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Peter Neish
- The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Priscilla R Prestes
- Faculty of Science and Engineering, Federation University Australia, Mt Helen , VIC, 3350, Australia
| | - Mark F Richardson
- Bioinformatics Core Research Group & Centre for Integrative Ecology, Deakin University, Geelong, VIC, 3220, Australia
| | - Nathan S Watson-Haigh
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | - Kelly L Wyres
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Neil D Young
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Maria Victoria Schneider
- Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia.,The University of Melbourne, Parkville, VIC, 3010, Australia
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