Rapid in-field Nanopore-based identification of plant and animal pathogens
Rapid in-field Nanopore-based identification of plant and animal pathogens
批准号:
BB/N023196/1
负责人:
Richard Leggett
金额:
$19.23万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --
中文摘要
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英文摘要
Airborne crop diseases pose a serious threat to food security and are responsible for devastating loss of yield and over-reliance on pesticides. Early detection enables farmers to take preventative action, drastically reducing damage and cost. Current detection regimes often rely on expert identification of the pathogen from plant damage. More recently, other molecular techniques have emerged. However, these methods suffer the same problems - being specific for a single species and a need for relatively large quantities of pathogenic material. Recently, TGAC has been working on an approach dubbed Air-seq that seeks to identify pathogens through sequencing of biological particles present in air. This overcomes both problems associated with current techniques as it is unbiased (not limited by species) and requires very small quantities of material. Our ultimate aim is to put sample collection, sequencing and analysis in a single box that can be deployed in the field. Key to success is a compact sequencing technology and this has recently emerged in the form of Oxford Nanopore Technologies' (ONT) MinION.The MinION is a new compact, low-cost sequencing technology that offers long reads (thousands of bases of DNA) and a streamed mode of operation enabling analysis of data as it is generated. These attributes make it ideally suited to in-field use. However, part of the process of generating sequencing data involves converting an electrical signal from the DNA sensing pore into a sequence of bases (letters) and this is performed via an internet 'basecalling' service. For in-field deployment, this is unsatisfactory, as we cannot rely on high speed, reliable data connections. We believe a completely new approach is required in which we utilise the raw signal data in order to identify species, instead of searching against basecalled sequence.In this project, we will develop a tool that searches Nanopore signal data looking for the characteristic signal traces of pathogens of interest, building up a report on abundance levels in the process.
期刊论文(10)
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DOI:
10.1186/s13059-021-02582-x
发表时间:
2022-01-24
期刊:
Genome biology
影响因子:
12.3
作者:
[Martin S, Heavens D, Lan Y, Horsfield S, Clark MD, Leggett RM]
通讯作者:
Leggett RM
DOI:
10.12688/f1000research.11354.1
发表时间:
2017
期刊:
F1000Research
影响因子:
--
作者:
[Jain M, Tyson JR, Loose M, Ip CLC, Eccles DA, O'Grady J, Malla S, Leggett RM, Wallerman O, Jansen HJ, Zalunin V, Birney E, Brown BL, Snutch TP, Olsen HE, MinION Analysis and Reference Consortium]
通讯作者:
MinION Analysis and Reference Consortium
Additional file 3 of Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples
Nanopore自适应采样的附加文件3:宏基因组样本中低丰度物种富集的工具
DOI:
10.6084/m9.figshare.18968217
发表时间:
2022
期刊:
影响因子:
--
作者:
[Martin S]
通讯作者:
Martin S
Additional file 2 of Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples
Nanopore自适应采样的附加文件2:宏基因组样本中低丰度物种富集的工具
DOI:
10.6084/m9.figshare.18968214
发表时间:
2022
期刊:
影响因子:
--
作者:
[Martin S]
通讯作者:
Martin S
DOI:
10.1111/2041-210x.13265
发表时间:
2019-10-01
期刊:
METHODS IN ECOLOGY AND EVOLUTION
影响因子:
6.6
作者:
[Peel, Ned, Dicks, Lynn V., Yu, Douglas W.]
通讯作者:
Yu, Douglas W.
Algebraic Invariants for Phylogenetic Network Inference
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-
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-
财政年份:2022
-
负责人:Richard Leggett
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Algorithms for Phylogenetic Network Inference from DNA Sequence Data
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依托单位:
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