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Probe design and data analysis for g...

Warren, Liling Li.

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Probe design and data analysis for gene expression microarrays.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probe design and data analysis for gene expression microarrays./
作者:	Warren, Liling Li.
面頁冊數:	106 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0530.
Contained By:	Dissertation Abstracts International64-02B.
標題:	Biology, Genetics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3081752

Probe design and data analysis for gene expression microarrays.
Warren, Liling Li.

Probe design and data analysis for gene expression microarrays. - 106 p.

Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0530.

Thesis (Ph.D.)--North Carolina State University, 2003.

In Chapter one, a novel algorithm to design gene specific probes is described. When gene specific oligos are used as probes, it is crucial to select a set of probes that have desirable properties in order for many hybridization reactions to take place in parallel on an array. Detailed steps on how to implement the algorithm are outlined and examples are given. With some modifications, the algorithm can also be applied to design allele specific probes for SNP genotyping or point mutation detections.Subjects--Topical Terms:

1017730
Biology, Genetics.

Probe design and data analysis for gene expression microarrays.
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245 1 0 $a Probe design and data analysis for gene expression microarrays.
300 $a 106 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0530.
500 $a Chair: Ben H. Liu.
502 $a Thesis (Ph.D.)--North Carolina State University, 2003.
520 $a In Chapter one, a novel algorithm to design gene specific probes is described. When gene specific oligos are used as probes, it is crucial to select a set of probes that have desirable properties in order for many hybridization reactions to take place in parallel on an array. Detailed steps on how to implement the algorithm are outlined and examples are given. With some modifications, the algorithm can also be applied to design allele specific probes for SNP genotyping or point mutation detections.
520 $a In Chapter two, five normalization methods are compared with each other and also compared with analysis skipping the normalization step. Overall, performing normalization can reduce systematic variations and identify more genes as differentially expressed than without the normalization step. Among different normalization methods being compared, ANOVA based normalization method has the most power to detect differentially expressed genes. When the same normalization and analysis methods are used, ratio based method has more power than the one based on absolute signal intensity values. Insights from this study on how to incorporate biological variation into future experimental designs are also discussed.
520 $a In Chapter three, we present methods to choose a set of short oligos to design a genome or tissue specific biochip and then to solve a set of equations for gene expression levels to determine genes that are differentially expressed between samples. The process of mining probe sets depends on knowing gene sequence information in a specific genome or tissue. As more genomes are being sequenced, this method holds great promise towards enabling more accurate and less expensive microarray experiments.
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