International Business Machines Corporation

Mars, Incorporated

Matthew Aaron Davis

James Harvey Kaufman

Mark Kunitomi

Sondra Ronee Renly

Bart C Weimer

Rahil Garnavi

Rajib Chakravorty

Stefan Bengt Edlund

Kenneth Lee Clarkson

Mani Abedini

Stefan Von Cavallar

John T E Timm

Kun Hu

Hu Kun

Daniel Dörr

A large collection of sample genomes containing misclassified k-mers and metadata errors from a reference taxonomy was converted to a self-consistent k-mer database comprising a self-consistent taxonomy. The self-consistent taxonomy was based on genetic distances calculated using the MinHash method or the Meier-Koltoff method. An agglomerative clustering algorithm was used to calculate the self-consistent taxonomy. Each k-mer of the sample genomes was assigned to only one node of the self-consistent taxonomy. In another step, each node of the self-consistent taxonomy was mapped to the reference taxonomy, thereby preserving in the self-consistent taxonomy links to the reference taxonomy while correcting for the misclassification errors therein. The self-consistent k-mer database can be used to taxonomically profile sequenced nucleic acids with greater specificity compared to systems relying on the reference taxonomy.

K-mer database for organism identification

An iterative process for optimizing one or more parameters used by a k-mer based de novo genome assembler program to assemble a set of sequenced nucleic acids is described. The method utilizes quality metrics whose desired values are initially specified. Computed values of the quality metrics are calculated during the assembly process and compared to the desired values. The assembly process stops when the computed values are not desired values. After modification of one or more of the parameters (e.g., k-mer value), the assembly process re-initiates using the modified parameter set. This process repeats until the computed values of the quality metrics meet the desired values. The final parameter set is then used to generate or complete one or more final assembled genomes.

Methods of optimizing genome assembly parameters

Methods are disclosed for reducing the size of a k-mer reference database used for queries and/or taxonomic classifications when available computer storage and/or memory are inadequate. The k-mers of the reference database have been previously classified to a taxonomy, preferably based on genetic distances. In one method, the k-mers are separated into one or more groups followed by removing k-mers common to the groups. In another method, k-mers are removed based on a selected taxonomic threshold level. A third method combines the features of the previous two methods. The methods are adaptable to machine learning.

Optimizing k-mer databases by k-mer subtraction

One embodiment provides for a method including determining, by at least one processor, sequence-sequence distances for a biological sequence collection. The at least one processor generates a matrix Mof the sequence-sequence distances, where i and j are positive integers. The at least one processor further generates clusters for the matrix Mby performing hierarchical clustering. A self-consistent taxonomy is created from the clusters. A visual heat map display of the matrix Mis selectively controlled using metadata, zoom input and opacity input.

Biological sequence distance explorer system providing user visualization of genomic distance between a set of genomes in a dynamic zoomable fashion

A method is described for automatically correcting metadata errors in a k-mer database. A k-mer database having a self-consistent taxonomy based on genome-genome distance was constructed from a set of sample and reference genomes whose metadata included taxonomic labeling from a reference taxonomy (the standard NCBI taxonomy), which is not based on genetic distance. As a result, genomes of a given taxonomic ID of the self-consistent taxonomy could be separated into clusters based on the differences in the metadata. Genomes of the clusters less than a minimum cluster size Cmin were removed and profiled against the remaining genomes, correcting metadata automatically for those genomes that could be mapped back. The resulting k-mer database showed improved specificity for genetic profiling. Another method is described for identifying and handling chimeric genomes using the self-consistent taxonomy. Another method is described for correcting a classification database.

Methods of automatically and self-consistently correcting genome databases

For each unique pair of a complete set of data items, a computing device determines a distance between the data items of the unique pair. The computing device repeats the following until no data items remain in the complete set. For each data item remaining in the complete set, the computing device determines a similarity subset including each other data item that the distance between the data item and the other data item is less than a target difference threshold. The computing device moves a selected data item from a largest similarity subset to a reference database that is a subset of the complete set. The computing device removes each data item from the complete set that the distance between the selected data item and the data item is less than the threshold. A new data item can be classified using the reference database.

Construction of reference database accurately representing complete set of data items for faster and tractable classification usage

Embodiments of the present invention relate to proactive computer simulation of portable product failures, and more specifically, to determining the likely cause of an outbreak of foodborne disease or other geographically distributed symptoms of a failure or contamination of a portable product. In one embodiment, a method of and computer program product for simulating portable product failures is provided. Data regarding the locations of consumers of a portable product within a geographic region is received from a data store. A probability density map is determined from the data, indicating where the portable product is likely to be consumed within the geographical region. For each of a plurality of simulated failures of the portable product, the locations of a plurality of simulated incidents arising from the simulated failure are determined. From the locations of the plurality of simulated incidents and the probability density map, the number of incidents necessary to identify the portable product to a predetermined certainty is determined.

Proactive simulation and detection of outbreaks based on product data

A method for a cloud-based feedback-driven image training and recognition includes receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image, training one or more classification models from the set of expert annotations, testing the one or more classification models on a plurality of test images that are different from the training images, wherein each classification model yields a clinical diagnosis for each image and a confidence score for that diagnosis, and receiving expert classification result feedback regarding the clinical diagnosis for each image and a confidence score yielded by each classification model.

Cloud-based infrastructure for feedback-driven training and image recognition

A system facilitates management of public health data. The system includes an input interface, a policy data structure, a notification compliance engine, and a data translator. The input interface receives input data content descriptive of a public health event. The policy data structure stores reporting conditions for a plurality of public health jurisdictions. The notification compliance engine reviews at least some of the reporting conditions of the policy data structure and identifies a relevant reporting condition for the input data content. The data translator derives and arranges output data content based on the input data content according to the relevant reporting condition identified by the notification compliance engine.

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San Jose, CA, United States of America

Matthew Aaron Davis