International Business Machines Corporation

Agency for Science, Technology and Research

Mars, Incorporated

Mark Kunitomi

Matthew Aaron Davis

James Harvey Kaufman

Simone Bianco

James L Hedrick

Yi Yan Yang

Chuan Yang

Daniel Waddington

Xin Ding

Zhen Chang Liang

Paola Florez De Sessions

Balamurugan Periaswamy

David Darden Chambliss

Kenneth Lee Clarkson

Dmitry Zubarev

Vito Paolo Pastore

Kun Hu

Bart C Weimer

Sarathkrishna Swaminathan

Forest Dussault

Amir Abboud

Samyukta Satish Rao

Provided is a deep learning algorithm that analyzes fragments of biological sequences. The input for the deep learning algorithm is a biological sequence fragment of unknown origin and the output is the closest known biological genome that could share phenotypic properties with the biological species of unknown origin. The workflow thus has application for genomic classification, identification of mutations within known genomes, and the identification of the closest class for unknown species.

Identification of unknown genomes and closest known genomes

Information received from a graphical user interface (GUI) and a list of user-curated command line patterns are received by an auto-wrapper system, wherein the auto-wrapper system is associated with an analytics workflow service. A module including a parameter space having one or more parameters and options used in the list of user-curated command line patterns is generated, by the auto-wrapper system, wherein content for each parameter is derived from the parameter's presence in the list of user-curated command line patterns combined with the information received from a GUI.

Auto-wrappering tools with guidance from exemplar commands

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

A bioinformatics method is provided for identifying candidate biological sequences, such as DNA, RNA, and proteins, with high sensitivity and specificity for application in procedures such as PCR and gene and protein sequencing. The method involves categorizing a collection of biological sequences within an out-group and an in-group, identifying the intersection between the in-group and the out-group, the union of the out-group, and a relative complement of sequences that are members of the in-group, but not the out-group. A biological signature for a species of interest with high sensitivity and specificity will be a member of the relative complement that has an out-group frequency of zero.

Discovery of biological signatures of optimized sensitivity and specificity

Techniques regarding treating one or more microbe infections with combination therapy are provided. For example, one or more embodiments described herein can comprise a method, which can comprise enhancing an antimicrobial activity of an antibiotic by a combination therapy. The combination therapy can comprise the antibiotic and a polycarbonate polymer functionalized with a guanidinium functional group.

Utilizing polymers and antibiotics to enhance antimicrobial activity and inhibit antibiotic resistance

A method is described that utilizes non-negative matrix factorization to predict susceptibility of a microorganism to an antimicrobial drug. A sparse adjacency matrix is constructed from existing ground truth datasets that include antibiogram data and other data associated with microorganisms. The rows of the adjacency matrix correspond to biosamples, and the columns correspond to instances of metadata and drugs associated with one or more of the biosamples. The elements of the adjacency matrix are assigned non-zero numerical values or zero depending on whether a known association exists. The adjacency matrix is then factored using a selected number of latent factors, thereby producing a reconstruction matrix approximating the adjacency matrix. The values of the reconstruction matrix are used to predict antimicrobial susceptibility of a biosample ID to a drug when antibiogram data are lacking.

Matrix factorization of antibiogram metadata

A computer-implemented method includes receiving genomic data associated with a plurality of genomes and identifying k-mer sets within the genomic data. The method includes constructing a k-mer subset tree according to the following process: performing iterative pairwise comparisons on the k-mer sets, wherein the iterative pairwise comparisons identify fragments with the most shared k-mers, merging the identified fragments into non-leaf nodes of the k-mer subset tree, and placing each remaining k-mer into a leaf node of the k-mer subset tree. The method includes storing the k-mer subset tree. A computer program product for data compression includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the compute to perform the foregoing method. A system includes a processor and logic. The logic is configured to perform the foregoing method.

K-mer based genomic reference data compression

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.

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San Francisco, CA, United States of America

Mark Kunitomi