Affymetrix, Inc.

Eos Biotechnology, Inc.

Ghassan Ghandour

Robert J Lipshutz

Ronald J Sapolsky

Anthony J Berno

Mark S Chee

Lubert Stryer

Thomas R Gingeras

David H Mack

Ching Wang

Huu Minh Tran

Thomas B Ryder

Richard Glynne

Vincent E Phillips

The present invention generally provides a rapid efficient method for analyzing polymorphic or biallelic markers, and arrays for carrying out these analyses. In general, the methods of the present invention employ arrays of oligonucleotide probes that are complementary to target nucleic acids which correspond to the marker sequences of an individual. The probes are typically arranged in detection blocks, each block being capable of discriminating the three genotypes for a given marker, i.e., the heterozygote or either of the two homozygotes. The method allows for rapid, automatable analysis of genetic linkage to even complex polygenic traits.

Polymorphism detection

This invention provides oligonucleotide based arrays and methods for speciating and phenotyping organisms, for example, using oligonucleotide sequences based on therpoB gene. The groups or species to which an organism belongs may be determined by comparing hybridization patterns of target nucleic acid from the organism to hybridization patterns in a database.

Chip-based speciation and phenotypic characterization of microorganisms

Systems and methods for identifying, confirming, mapping, and categorizing sample polymers, such as nucleic acid sequences, are provided. An estimation of the fraction of first and second polymers in a sample of polymers can be calculated by inputting a first hybridization value indicative of hybridization affinity of the sample of polymers to polymers probes that are complementary to the first polymer and inputting a second hybridization value indicative of hybridization affinity of the sample of polymers to polymers probes that are complementary to the second polymer. The estimation of the fraction of the first and second polymers in the sample of polymers can then be calculated by dividing the first hybridization value by a sum of the first and second hybridization values. Estimations of the fractions of alleles in a sample can be clustered to form a fraction pattern usable for identifying, confirming, mapping, and genotyping sample nucleic acids.

Techniques for identifying confirming mapping and categorizing nucleic acids

The present invention generally provides a rapid efficient method for analyzing polymorphic or biallelic markers, and arrays for carrying out these analyses. In general, the methods of the present invention employ arrays of oligonucleotide probes that are complementary to target nucleic acids which correspond to the marker sequences of an individual. The probes are typically arranged in detection blocks, each block being capable of discriminating the three genotypes for a given marker, e.g., the heterozygote or either of the two homozygotes. The method allows for rapid, automatable analysis of genetic linkage to even complex polygenic traits.

This invention provides oligonucleotide based arrays and methods for speciating and phenotyping organisms, for example, using oligonucleotide sequences based on thegene. The groups or species to which an organism belongs may be determined by comparing hybridization patterns of target nucleic acid from the organism to hybridization patterns in a database.

Chip-based species identification and phenotypic characterization of microorganisms

Computer programs and computer-implemented methods are disclosed for extracting, analyzing and comparing data from biomolecular binding experiments. The invention receives a set of values corresponding to the interaction of one or more target samples with probes on one or more arrays of biomolecular probes, and uses the values to generate one or more probability values indicating the probability that a molecular species complementary to the probe set is not present in the target samples, or is present at levels greater than or less than one or more reference levels. The probability values are generated by comparing the values for individual probes in a probe set with values for a calibration set, which may include a null set, spiked probe sets, or housekeeping probe sets. Normalized experimental values are generated for sets of probes on the arrays using statistical methods. Data is output in the form of data files including normalized experimental values and probability values for probe sets in the arrays.

Method and apparatus for analysis of data from biomolecular arrays

This invention provides oligonucleotide based arrays and methods for speciating and phenotyping organisms, for example, using oligonucleotide sequences based on the,rpoB gene. The groups or species to which an organism belongs may be determined by comparing hybridization patterns of target nucleic acid from the organism to hybridization patterns in a database.

A genetic sample is analyzed by providing a gene probe array including a plurality of genetic probes having different receptors. The sample is processed to include at least one fluorescently tagged ligand. The array is hybridized by exposing the probes to the processed sample such that ligands can bind to complementary receptors. Composite data having a data dynamic range is obtained from the array using an optical scanner which has a scanner dynamic range that is smaller than the data dynamic range. The scanner optically irradiates and scans the probes and detects fluorescent emissions at a first wavelength which is selected such that the scanner produces valid first data in a low intensity portion of the data dynamic range and is in saturation in at least part of a high intensity portion of the data dynamic range. The scanner then optically irradiates and scans the probes with light and detects fluorescent emissions at a second wavelength which is selected such that the scanner produces valid second data in a high intensity portion of the data dynamic range and is in cutoff in at least part of a low intensity portion of the data dynamic range. A scale factor correlation function is calculated between the first data and the second data, and is applied to convert the second data to have a same scale factor as the first data. The first data for the first portion of the data dynamic range is combined with the converted second data for the second portion of the data dynamic range to obtain the composite data.

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Atherton, CA, United States of America

Ghassan Ghandour