Rapid in-field Nanopore-based identification of plant and animal pathogens

基于纳米孔的现场快速植物和动物病原体鉴定

基本信息

批准号：
BB/N023196/1
负责人：
Richard Leggett
金额：
$ 19.23万
依托单位：
Earlham Institute
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
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FN023196%2F1
关键词：
Rapid field Nanopore based identification

项目摘要

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.

空气中的作物疾病对粮食安全构成了严重威胁，并负责毁灭性的产量和过度依赖农药。早期发现使农民能够采取预防措施，大大降低了损害和成本。当前的检测方案通常依赖于植物损伤对病原体的专家识别。最近，出现了其他分子技术。但是，这些方法遇到了相同的问题 - 特定于单个物种，需要相对较大的病原材料。最近，TGAC一直在研究一种称为空气序列的方法，该方法旨在通过对空气中存在的生物颗粒的测序来鉴定病原体。这克服了与当前技术相关的两个问题，因为它是公正的（不受物种限制），并且需要少量的材料。我们的最终目的是将样本收集，测序和分析放在可以部署在现场的单个框中。成功的关键是一种紧凑的测序技术，最近以牛津纳米孔技术的形式出现了这一点（ONT）奴才。小兵是一种新的紧凑型，低成本的测序技术，可提供长期读取（成千上万的DNA基础）和一种流动的操作模式，从而使数据具有生成的数据分析。这些属性使其非常适合现场使用。但是，生成测序数据的一部分过程涉及将电信号从DNA传感孔转换为一系列碱基（字母），这是通过Internet“ BaseCalling”服务执行的。对于现场部署，这是不令人满意的，因为我们不能依靠高速，可靠的数据连接。我们认为，需要一种全新的方法，在该方法中，我们使用原始信号数据来识别物种，而不是针对基本序列进行搜索。在本项目中，我们将开发一种工具，以搜索纳米孔信号数据，以寻找感兴趣的病原体的特征信号痕迹，建立有关此过程中丰富水平的报告的特征信号。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples

DOI：
10.1101/2021.05.07.443191
发表时间：
2021-01-01
期刊：
bioRxiv
影响因子：
0
作者：
Martin, S.
通讯作者：
Martin, S.

Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples.

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

MinION Analysis and Reference Consortium: Phase 2 data release and analysis of R9.0 chemistry.

DOI：
10.12688/f1000research.11354.1
发表时间：
2017
期刊：
F1000Research
影响因子：
0
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