Processing

Please wait...

Settings

Settings

Goto Application

1. WO2019016353 - CLASSIFYING SOMATIC MUTATIONS FROM HETEROGENEOUS SAMPLE

Publication Number WO/2019/016353
Publication Date 24.01.2019
International Application No. PCT/EP2018/069720
International Filing Date 20.07.2018
IPC
G06F 19/22 2011.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
22for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or Single-Nucleotide Polymorphism discovery or sequence alignment
C12Q 1/6869 2018.1
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING 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
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
68involving nucleic acids
6869Methods for sequencing
CPC
C12Q 1/6869
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms
68involving nucleic acids
6869Methods for sequencing
G16B 20/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
20Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 30/10
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
30ICT specially adapted for sequence analysis involving nucleotides or amino acids
10Sequence alignment; Homology search
G16B 40/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
20Supervised data analysis
Applicants
  • F. HOFFMANN-LA ROCHE AG [CH]/[CH] (AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BE, BF, BG, BH, BJ, BN, BR, BW, BY, BZ, CA, CF, CG, CH, CI, CL, CM, CN, CO, CR, CU, CY, CZ, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, FR, GA, GB, GD, GE, GH, GM, GN, GQ, GR, GT, GW, HN, HR, HU, ID, IE, IL, IN, IR, IS, IT, JO, JP, KE, KG, KH, KM, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LT, LU, LV, LY, MA, MC, MD, ME, MG, MK, ML, MN, MR, MT, MW, MX, MY, MZ, NA, NE, NG, NI, NL, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SI, SK, SL, SM, SN, ST, SV, SY, SZ, TD, TG, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, UZ, VC, VN, ZA, ZM, ZW)
  • ROCHE DIAGNOSTICS GMBH [DE]/[DE] (DE)
  • ROCHE SEQUENCING SOLUTIONS, INC. [US]/[US] (US)
Inventors
  • LAL, Preeti
  • LAM Y. K., Hugo
  • LEE, John
  • LOVEJOY, Alex
  • PALMA F., Johm
  • ROSENTHAL, Andre
  • YAO, Lijing
Agents
  • HILDEBRANDT, Martin
  • GOEHRING, Frank
  • BURGER, Alexander
  • JENNI, Wolfgang
  • MISERA, Simon
  • NASER, Werner
  • SKOLAUT, Alexander
  • EISENMANN, Ulrike
  • MERTES, Maria
  • HEINEMEYER, Thomas
  • WOLTERS, Brit
  • HEYSE, Gero
Priority Data
62/535,72721.07.2017US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) CLASSIFYING SOMATIC MUTATIONS FROM HETEROGENEOUS SAMPLE
(FR) CLASSIFICATION DE MUTATIONS SOMATIQUES À PARTIR D’UN ÉCHANTILLON HÉTÉROGÈNE
Abstract
(EN) In order to determine somatic mutations from germline mutations when matched normal sequences may not be available, adaboost machine learning algorithms are developed to classify germline mutations and somatic mutations. Three models were built based on different types of samples. The types samples can include, for example, fresh frozen samples, formalin-fixed paraffin-embedded (FFPE) samples, and plasma samples. The performances of the algorithms are evaluated with either ten-fold cross-validation or tested on independent set of samples.
(FR) Afin de déterminer des mutations somatiques à partir de mutations de lignée germinale lorsque des séquences normales appariées peuvent ne pas être disponibles, on développe des algorithmes d’apprentissage machine Adaboost pour classifier des mutations de lignée germinale et des mutations somatiques. Trois modèles ont été élaborés à partir de différents types d’échantillons. Les échantillons de types peuvent comprendre, par exemple, des échantillons congelés frais, des échantillons renfermés dans de la paraffine fixée à la formaline (FFPE) et des échantillons de plasma. Les performances des algorithmes sont évaluées par une validation croisée à dix fois ou testées sur un ensemble d’échantillons indépendant.
Latest bibliographic data on file with the International Bureau