Computer-implemented method and computer system for rank normalization for differential expression analysis of transcriptome sequencing data

A technology for transcriptome sequencing and differential expression, which is used in computing, electrical digital data processing, special data processing applications, etc.

Inactive Publication Date: 2013-10-30
IBM CORP
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  • Abstract
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Problems solved by technology

Although error models can be used to account for platform-specific inaccuracies, scaling errors can be inherent in many transcriptome data analysis methods

Method used

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  • Computer-implemented method and computer system for rank normalization for differential expression analysis of transcriptome sequencing data
  • Computer-implemented method and computer system for rank normalization for differential expression analysis of transcriptome sequencing data
  • Computer-implemented method and computer system for rank normalization for differential expression analysis of transcriptome sequencing data

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Embodiment Construction

[0014] Embodiments of systems and methods for rank normalization for differential expression analysis of transcriptome sequencing data are provided, and exemplary embodiments are discussed in detail below. Normalization of transcriptome sequencing data can be based on the relative position of genes in the dataset relative to other genes in the dataset. As used herein, the term gene may also refer to any transcriptome sequencing data, including transcripts or mRNA in various embodiments. Ordinal normalization of genetic data yields a unitless number for each gene that can be used for comparison between datasets. Rankings can be determined for individual genes in a dataset, and then gene-specific ranking differences can be determined between datasets. The two data sets being compared may include transcriptome sequencing data from two different samples in some embodiments, or may include transcriptome sequencing data from a single sample at two different time points in other emb...

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Abstract

A computer-implemented method for rank normalization for differential expression analysis of transcriptome sequencing data includes receiving, by a computer, a first dataset comprising transcriptome sequencing data, the first dataset comprising a plurality of genes, and further comprising a respective ranking value associated with each of the plurality of genes; assigning a rank to each of the genes of the plurality of genes based on the ranking value to produce a first rank normalized dataset; determining a change between a first rank of a particular gene in the first rank normalized dataset, and a second rank of the particular gene in a second rank normalized dataset, the second rank normalized dataset being based on a second dataset comprising transcriptome sequencing data; and determining whether the particular gene is differentially expressed between the first dataset and the second dataset based on the determined change in rank.

Description

technical field [0001] This disclosure relates generally to the field of messenger riboneucleic acid sequencing, and more specifically to differential expression (DE) analysis of transcriptome sequencing data based on rank normalization. Background technique [0002] Transcriptome data, including messenger ribonucleic acid (mRNA) data, can be generated from genes and more specifically gene transcripts. A gene can have multiple differently spliced ​​transcripts that produce mRNA, and mRNA can also be produced from other regions on the genome. Sequencing technology can provide data for a wide range of biological applications and is a powerful tool for investigating and understanding mRNA expression profiles. The number of mRNAs that can be measured by sequencing is not limited. Sequencing can be non-target specific, so the genes examined do not have to be pre-selected, thereby providing dynamic large-scale data and also allowing the possibility of discovering new sequence va...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/18G06F19/22
Inventor N·S·海米宁L·P·帕里达
Owner IBM CORP
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