Preparing peptide spectra for identification

• US 7,512,524 B2
• Filed: 03/18/2005
• Issued: 03/31/2009
• Est. Priority Date: 03/18/2005
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Barnea, Eilon, Be'er, Ilan, Gofman, Emanuel
• Primary Examiner(s): Raymond; Edward
• Assistant Examiner(s): Charioui; Mohamed
• Application Number:
  US11/084,435
• Publication Number:
  US 20060208185A1
• Time in Patent Office: 1,474 Days
• Field of Search: 702/19, 702/21, 702/22, 702/23, 702/27, 702/30, 702/183, 702/193, 702/196, 435/6
• US Class Current: 702/196
• CPC Class Codes: G01N 33/6848 Methods of protein analysis involving mass spectrometry
• CPC Class Codes: H01J 49/04 Arrangements for introducing or extracting samples to be analysed, e.g. vacuum locks; Arrangements for external adjustment of electron- or ion-optical components

Abstract

A method for preparing peptide spectra for identification, the method including constructing a symmetric distance matrix from a plurality of peptide spectra, where a cluster of at least one of the spectra is represented in a row of the matrix, and where the cluster is also represented in a column of the matrix, finding the minimum of each of the clusters in the matrix, constructing a vector from the minima where each element in the vector corresponds to one of the clusters, finding the global minimum of the matrix as being the minimum of the vector, merging two of the clusters identified by the global minimum into a merged cluster, and providing the merged cluster for identification of at least one peptide associated with the merged cluster.

Claims

1. A method for preparing peptide spectra for identification, the method comprising:

a) populating a matrix with a plurality of clusters of a plurality of peptide spectra, wherein each of said clusters is represented in both a different row of said matrix and a different column of said matrix, thereby resulting in a symmetric matrix;

b) finding the minimum of each of said clusters in said matrix;

c) populating a vector with said minima of said clusters wherein each element in said vector corresponds to one of said clusters;

d) finding the global minimum of said matrix as being the minimum of said vector;

e) merging two of said clusters identified by said global minimum into a merged cluster; and

f) providing said merged cluster for identification of at least one peptide associated with said merged cluster.

2. A method according to claim 1 wherein said populating step a) comprises:

representing said plurality of peptide spectra as a set of multidimensional vectors;

ordering said multidimensional vectors;

determining the closeness between any two of said ordered vectors in accordance with a measure of closeness;

determining the distance between any two of said ordered vectors using a distance function where said vectors are close to each other in accordance with said measure of closeness; and

constructing said matrix from said distances.

3. A method according to claim 2 wherein said ordering step comprises ordering said vectors according to their precursor (parent) mass (PM) of their associated peptide.

4. A method according to claim 3 wherein said determining closeness step comprises determining that said two vectors are close where their masses are within 2 Daltons of each other.

5. A method according to claim 1 wherein said finding step d) comprises:

ordering said elements in said vector in hierarchical order; and

identifying the root of said hierarchy as said global minimum.

6. A method according to claim 5 and further comprising:

g) finding the minimum of any of said clusters in said matrix where said distance between said cluster and either of said merged clusters was the smallest relative to the distance between said cluster and any other of said clusters;

h) updating any of said elements in said vector for which a minimum was found in step g) for said cluster corresponding to said element; and

i) reordering said updated elements in said vector in hierarchical order.

7. A method according to claim 1 and further comprising:

g) finding the minimum of any of said clusters in said matrix where said distance between said cluster and either of said merged clusters was the smallest relative to the distance between said cluster and any other of said clusters; and

h) updating any of said elements in said vector for which a minimum was found in step g) for said cluster corresponding to said element.

8. A method according to claim 1 wherein each of said vector elements is associated with an index of any of said clusters for which said minimum was found with respect to said cluster to which said vector element corresponds.