Real-time field-based nanopore sequencing of Arabidopsis spp.
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB22018 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR2088730_1.fastq.gz | Fastqsanger.gz | |||
| ERR2088730_2.fastq.gz | Fastqsanger.gz | |||
| ERR2088740.fastq.gz | Fastqsanger.gz | |||
| ERR2088733.fastq.gz | Fastqsanger.gz |
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Project description:A growing number of new technologies are supported by a single- or multi-nanopore architecture for capture, sensing, and delivery of polymeric biomolecules. Nanopore-based single-molecule DNA sequencing is the premier example. This method relies on the uniform linear charge density of DNA, so that each DNA strand is overwhelmingly likely to pass through the nanopore and across the separating membrane. For disordered peptides, folded proteins, or block copolymers with heterogeneous charge densities, by contrast, translocation is not assured, and additional strategies to monitor the progress of the polymer molecule through a nanopore are required. Here, we demonstrate a single-molecule method for direct, model-free, real-time monitoring of the translocation of a disordered, heterogeneously charged polypeptide through a nanopore. The crucial elements are two "selectivity tags"-regions of different but uniform charge density-at the ends of the polypeptide. These affect the selectivity of the nanopore differently and enable discrimination between polypeptide translocation and retraction. Our results demonstrate exquisite sensitivity of polypeptide translocation to applied transmembrane potential and prove the principle that nanopore selectivity reports on biopolymer substructure. We anticipate that the selectivity tag technique will be broadly applicable to nanopore-based protein detection, analysis, and separation technologies, and to the elucidation of protein translocation processes in normal cellular function and in disease.
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Project description:SummaryMinoTour offers a LIMS system for Oxford Nanopore Technology sequencers, with real-time metrics and analysis available permanently for review. Integration of unique real-time automated analysis can reduce the time required to answer biological questions, including mapping and classification of sequence whilst a run is in progress. Real-time sequence data requires new methods of analysis which do not wait for the completion of a run and MinoTour provides a framework to allow users to exploit these features.Availability and implementationSource code and documentation are available at https://github.com/LooseLab/minotourcli and https://github.com/LooseLab/minotourapp. Docker images are available from https://hub.docker.com/r/adoni5/, and can be installed using a preconfigured docker-compose script at https://github.com/LooseLab/minotour-docker. An example server is available at http://137.44.59.170.Supplementary informationSupplementary data are available at Bioinformatics online.
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