Minimization of surprisal data through application of hierarchy filter pattern

• US 10,353,869 B2
• Filed: 06/08/2012
• Issued: 07/16/2019
• Est. Priority Date: 05/18/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Friedlander, Robert R., Kraemer, James R.
• Primary Examiner(s): Zeman, Mary K
• Application Number:
  US13/491,884
• Publication Number:
  US 20130311435A1
• Time in Patent Office: 2,594 Days
• CPC Class Codes: G06F 16/1744 using compression, e.g. sparse files
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/50 Compression of genetic data

Abstract

A method, computer product, and computer system of minimizing surprisal data comprising: at a source, reading and identifying characteristics of a genetic sequence of an organism; receiving an input of rank of at least two identified characteristics of the genetic sequence of the organism; generating a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics of the genetic sequence of the organism; comparing the hierarchy of ranked, identified characteristics to a repository of reference genomes; and if at least one reference genome from the repository matches the hierarchy of ranked, identified characteristics, breaking the matched reference genomes into pieces, combining pieces associated with the identified characteristics from at least one matched reference genome to form a filter pattern to be compared to the nucleotides of the genetic sequence of the organism, to obtain differences and create surprisal data.

Claims

1. A method of minimizing surprisal data representing an entire genome of an organism for compression and transmission, comprising a source computer having one or more processors and one or more computer-readable memories coupled to the one or more processors performing the steps of:

a) reading and identifying characteristics of the organism'"'"'s medical history and background associated with a genetic sequence of an organism;

b) receiving an input of rank of at least two identified characteristics associated with the genetic sequence of the organism;

c) generating a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics associated with the genetic sequence of the organism;

d) comparing the hierarchy of ranked, identified characteristics to a repository of reference genomes; and

e) if at least one reference genome from the repository matches the hierarchy of ranked, identified characteristics,

i) storing the at least one matched reference genome in a repository;

ii) breaking the at least one matched reference genome into pieces comprising nucleotides of the genetic sequence which comprises at least one gene, at least some of the pieces being associated with the identified characteristics;

iii) storing the pieces which are associated with the identified characteristics in the repository;

iv) combining the stored pieces of the at least one matched reference genome into a filter pattern;

v) comparing pieces of the nucleotides of the genetic sequence of the organism which comprises at least one gene which correspond to the stored pieces of the at least one matched reference genome to the nucleotides of the filter pattern of the pieces of the at least one matched reference genome, to find differences where nucleotides of the genetic sequence of the organism which are different from the nucleotides of the at least one matched reference genome;

vi) using the differences to create surprisal data representing an entire genome of the organism and storing the surprisal data in the repository, the surprisal data comprising a starting location of the differences within the reference genome, how the reference genomes were broken into pieces, a count of a number of differences at the location within the at least one matched reference genome and the nucleotides from the genetic sequence of the organism which are different from the nucleotides of the reference genome; and

vii) transmitting to a destination, a compressed, minimized genome representing an entire genome by sending the surprisal data, an indication of the at least one matched reference genome, and how the reference genome were broken into pieces, and not sending sequences of nucleotides that are the same in the genetic sequence of the organism and the at least one matched reference genome.

2. The method of claim 1, further comprising receiving the compressed genome of the organism comprising a destination computer having one or more processors and one or more computer-readable memories coupled to the one or more processors performing the steps of:

receiving the compressed genome from the source computer, the compressed genome comprising surprisal data, the indication of the at least one matched reference genome used to compress the genome, a count of a number of differences at the location within the at least one matched reference genome, and how the reference genomes were broken into pieces;

retrieving the at least one indicated matched reference genome from a repository;

breaking the at least one indicated matched reference genome from the repository into pieces associated with the identified characteristics and based on the surprisal data and storing the pieces associated with the identified characteristic in the repository; and

combining the pieces into the filter pattern;

altering the filter pattern comprised of pieces of at least one matched reference genome based on the surprisal data by replacing nucleotides at each location in the at least one matched reference genome specified by the surprisal data with the nucleotides from the genetic sequence of the organism in the surprisal data associated with the location;

resulting in an entire genome of the organism.

3. The method of claim 1, wherein the organism is an animal.

4. The method of claim 1, wherein the organism is a microorganism.

5. The method of claim 1, wherein the organism is a plant.

6. The method of claim 1, wherein the organism is a human.