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US 2019/0318807 A1Published application
Systems and methods for ultra-fast identification and abundance estimates of microorganisms using a kmer-depth based approach and privacy-preserving protocols
AssigneeThe Joan and Irwin Jacobs Technion-Cornell Institute
InventorsNiamh B. O'Hara, Rachid Ounit
Life of this patent
Estimating the term…
Abstract
The present disclosure relates to the use of next generation technologies and analysis using a k-mer based approach which is depth-informed to classify microorganism and estimate abundance in single or mixed microorganismal populations in a rapid manner. The disclosure relates to methods for identifying taxa from environmentally or patient collected samples and processing these samples in a privacy-preserving manner.
Claims27
A method of identifying microorganisms, comprising:
- obtaining a sample including one or more microorganism populations;
- generating, by a control unit including a memory and a processor, nucleic acid sequence data for the one or more microorganism populations;
- determining, by the control unit, a set of k-mers of one or more nucleic acid regions from the one or more microorganism populations;
- comparing, by the control unit, the set of k-mers to a reference database;
- filtering, by the control unit, the nucleic acid sequence data that do not map unambiguously to one and only one organism, wherein the filtering does not cause loss of identification performance or accuracy;
- determining, by the control unit, depth of sequence coverage of taxa specific sequences to identify one or more taxa from the one or more microorganism populations;
- modeling, by the control unit, the frequency of k-mers in the sequenced sample that match the database using one or several probabilistic models per taxon;
- and
- modeling, by the control unit, the distribution of the frequency of the k-mers matching the database using one or more probabilistic models to estimate abundance of each identified one or more taxa.
6 more elements
A method of characterizing pathogenicity of one or more microorganism populations, comprising:
- obtaining a sample at a location including one or more microorganism populations at one or more time points;
- generating, by a control unit including a memory and a processor, nucleic acid sequence data for the one or more microorganism populations at each of the one or more time points;
- determining, by the control unit, a set of k-mers of one or more nucleic acid regions from the one or more microorganism populations;
- comparing, by the control unit, the set of k-mers to a reference database;
- filtering, by the control unit, the nucleic acid sequence data that do not map unambiguously to one and only one organism, wherein the filtering does not cause loss of identification performance or accuracy;
- determining, by the control unit, depth of sequence coverage of taxa specific sequences to identify one or more taxa from the one or more microorganism populations;
- modeling, by the control unit, the frequency of k-mers in the sequenced sample that match the database using one or several probabilistic models per taxon;
- and
- modeling, by the control unit, the distribution of the frequency of the k-mers matching the database using one or more probabilistic models to estimate abundance of each identified one or more taxa;
- determining, based on the identified taxa, the pathogenicity of one or more microorganism populations;
- and
- implementing a protocol for eliminating microorganisms based on the pathogenicity assessment.
9 more elements
A tangible, non-transitory, computer-readable media having software encoded thereon, the software, when executed by a processor, operable to:
- receive nucleic acid sequence data for one or more microorganism populations from an obtained sample;
- determine a set of k-mers of one or more nucleic acid regions from the one or more microorganism populations;
- compare the set of k-mers to a reference database;
- filter the nucleic acid sequence data that do not map unambiguously to one and only one organism, wherein the filtering does not cause loss of identification performance or accuracy;
- determine depth of sequence coverage of taxa specific sequences to identify one or more taxa from the one or more microorganism populations;
- model the frequency of k-mers in the sequenced sample that match the database using one or several probabilistic models per taxon;
- and
- model the distribution of the frequency of the k-mers matching the database using one or more probabilistic models to estimate abundance of each identified one or more taxa.
5 more elements
Dependencies are inferred from the claim text.