MX2017014659A - Metodos y sistemas para la deteccion de variantes del numero de copias. - Google Patents

Metodos y sistemas para la deteccion de variantes del numero de copias.

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Publication number
MX2017014659A
MX2017014659A MX2017014659A MX2017014659A MX2017014659A MX 2017014659 A MX2017014659 A MX 2017014659A MX 2017014659 A MX2017014659 A MX 2017014659A MX 2017014659 A MX2017014659 A MX 2017014659A MX 2017014659 A MX2017014659 A MX 2017014659A
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Mexico
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copy number
coverage data
systems
methods
example method
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MX2017014659A
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English (en)
Inventor
Reid Jeffrey
HABEGGER Lukas
Maxwell Evan
Packer Jonathan
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Regeneron Pharma
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Publication of MX2017014659A publication Critical patent/MX2017014659A/es

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medical Informatics (AREA)
  • Chemical & Material Sciences (AREA)
  • Biotechnology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Analytical Chemistry (AREA)
  • Molecular Biology (AREA)
  • Genetics & Genomics (AREA)
  • Data Mining & Analysis (AREA)
  • Organic Chemistry (AREA)
  • Artificial Intelligence (AREA)
  • Bioethics (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Evolutionary Computation (AREA)
  • Public Health (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Microbiology (AREA)
  • Immunology (AREA)
  • Biochemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Image Analysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Complex Calculations (AREA)

Abstract

Se describen métodos y sistemas para determinar variantes del número de copias. Un método ilustrativo puede comprender aplicar una técnica de agrupamiento de muestras para seleccionar datos de cobertura de referencia, normalizar los datos de cobertura de muestras que comprende una pluralidad de regiones genómicas, y ajustar un modelo de mezcla a los datos de cobertura de muestras normalizados en función de los datos de cobertura de referencia seleccionados. Un método ilustrativo puede comprender identificar una o más variantes del número de copias (CNV) de conformidad con un Modelo de Markov Oculto (HMM) en función de los datos de cobertura de muestras normalizados y el modelo de mezcla ajustado. Un método ilustrativo puede comprender producir la una o más de las variantes del número de copias.
MX2017014659A 2015-05-18 2016-05-13 Metodos y sistemas para la deteccion de variantes del numero de copias. MX2017014659A (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US14/714,949 US10395759B2 (en) 2015-05-18 2015-05-18 Methods and systems for copy number variant detection
PCT/US2016/032484 WO2016187051A1 (en) 2015-05-18 2016-05-13 Methods and systems for copy number variant detection

Publications (1)

Publication Number Publication Date
MX2017014659A true MX2017014659A (es) 2018-03-07

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
MX2017014659A MX2017014659A (es) 2015-05-18 2016-05-13 Metodos y sistemas para la deteccion de variantes del numero de copias.

Country Status (16)

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US (2) US10395759B2 (es)
EP (1) EP3298523B1 (es)
JP (1) JP6718885B2 (es)
KR (1) KR102307872B1 (es)
CN (1) CN107810502B (es)
AU (1) AU2016263192B2 (es)
BR (1) BR112017024742A2 (es)
CA (1) CA2986117C (es)
HK (1) HK1252103A1 (es)
IL (1) IL255458B (es)
MX (1) MX2017014659A (es)
NZ (1) NZ737005A (es)
RU (1) RU2746477C2 (es)
SG (1) SG11201708787TA (es)
WO (1) WO2016187051A1 (es)
ZA (1) ZA201707665B (es)

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