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网友点评-L and functional validationFigure two Overview of approaches for cancer-genome sequencing. A-线缆测高仪,超声波测高仪, 手持式测高仪-上海交通大学科技园上海野豹企业发展公司
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发布于:2018-11-5 13:53:03  访问:59 次 回复: 篇
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L and functional validationFigure two Overview of approaches for cancer-genome sequencing. A
L and functional validationFigure 2 Overview of get Resminostat (hydrochloride) techniques for cancer-genome sequencing. [51] Description Utilizes coverage data and genomic characteristics (e.g. DNA replication time) to estimate the background mutation price of a gene. Uses a per-gene background mutation rate; enables for user-defined regions of interest. Incorporates predicted impact on protein function in determining recurrent mutations.Sj lom et al. [52] Defines a cancer mutation prevalence score for every gene. DrGaP [139] CNA GISTIC2 [61], JISTIC [63] CMDS [62] ADMIRE [65] Functional influence prediction Common SIFT [72] Polyphen-2 [74] Utilizes Bayesian strategy to estimate background mutation rate; helpful for cancer sorts with low mutation rate. Utilizes `peel-off‘ approaches to locate smaller recurrent aberrations inside bigger aberrations. Identifies recurrent CNAs from unsegmented information. Multi-scale smoothing of copy number profiles. Utilizes conservation of amino acids to predict functional impact of a nonsynonymous PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28724915 amino-acid change. Infers functional influence of non-synonymous amino-acid alterations through alignments of associated peptide sequences in get 174635-69-9 addition to a machine-learning-based probabilistic classifier. Utilizes protein PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28878015 homologs to calculate a score determined by the divergence in conservation brought on by an amino-acid transform. Benchmarks favorably against MutationAssessor, Polyphen-2 and SIFT. Utilizes a machine-learning method to classify mutations as drivers or passengers according to sequence conservation, protein domains, and protein structure. Combines scores from SIFT, Polyphen-2, and MutationAccessor into a single ranking. Finds clusters of non-synonymous mutations across sufferers. Normally made use of with missense mutations to detect so-called `activating‘ mutations. Extends the NMC method to look for clusters of mutations in threedimensional space applying crystal structures of proteins. A basic technique for testing ranked lists of genes for enrichment in recognized gene sets. Is often applied on rankings derived from significance of observed mutations. Multi-scale smoothing of copy number profiles. Makes use of conservation of amino acids to predict functional influence of a nonsynonymous PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28724915 amino-acid adjust. Infers functional effect of non-synonymous amino-acid adjustments by way of alignments of associated peptide sequences and also a machine-learning-based probabilistic classifier. Uses protein PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28878015 homologs to calculate a score determined by the divergence in conservation triggered by an amino-acid transform. Benchmarks favorably against MutationAssessor, Polyphen-2 and SIFT. Uses a machine-learning approach to classify mutations as drivers or passengers depending on sequence conservation, protein domains, and protein structure. Combines scores from SIFT, Polyphen-2, and MutationAccessor into a single ranking. Finds clusters of non-synonymous mutations across sufferers. Normally utilised with missense mutations to detect so-called `activating‘ mutations. Extends the NMC method to look for clusters of mutations in threedimensional space working with crystal structures of proteins. A general method for testing ranked lists of genes for enrichment in recognized gene sets. Is often made use of on rankings derived from significance of observed mutations. Finds pathways with excess of mutations within a gene set (pathway), by combining P-values of enrichment across samples. Tests known pathways employing a binary indicator for any pathway in every patient.
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