A Design Method of DNA chips for SNP Analysis Using Self Organizing Maps Author ： Honjoy Saga Graduate ： Chien-Ming Hsiao.

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Presentation transcript:

A Design Method of DNA chips for SNP Analysis Using Self Organizing Maps Author ： Honjoy Saga Graduate ： Chien-Ming Hsiao

Outline  Motivation  Objective  Introduction  DNA Chip and Sequence Analyses  Feature Mapping of DNA sequence by SOM  Experimental results  Conclusions  Opinion

Motivation  Conventional DNA chips are showing tendency to be comprised of longer probes and larger in size to achieve a higher resolution.  To shrink the size of DNA chips.

Objective  Applied SOM to obtain common features of DNA sequences with small number of probes which efficiently cover the target sequence with sufficient resolution for finding the correct position of SNPs.

Introduction  DNA chip are powerful tools for sequencings SNP analyses of DNA sequences.  SNPs can be detected by cDNA micro array as the lacks of cDNA sequences in target sequences.  Exact positions of SNPs can known with conventional sequencings.

DNA Chip and Sequence Analyses  DNA chip is a biochemical chip on which probes are printed as an array of short sub-sequences.  DNA is represented by symbols (A,G,T,C) and each DNA hybridizes with its complement DNA. Such as A with T and G with C  SNPs are variations of DNAs which are found in every nucleotides, and they correspond to the effectiveness and side-effect of the medicines.

DNA Chip and Sequence Analyses

Feature Mapping of DNA sequence by SOM  algorithms Step-1 : Initialize the map of probes using random sequences of specified length. Step-2 : Select a position of reference sequence (RS) randomly and find the sub-sequence which starts from that position. Step-3 : Update the closest probe Pr found in Step-2 as follows. Step-4 : Update the probe Pr’ whose distance is closer than M-Dist from Pr geometrically using the same procedure in Step-3. Repeat Step-2 to Step-4, changing the value Th-U and M- Dist.

Experimental results

Conclusions  This algorithm can select a set of small amount of probes that represent the feature of DNA sequence for SNP analyses.  This algorithm shows better results according to the covering rates, but shows worse results for detection rates of SNPs.  The organize probes of 9bp can not sufficiently cover the reference sequence.

Opinion  can consider to use another cluster algorithms to obtain common features of DNA sequences with small number of probes which efficiently cover the target sequence with sufficient resolution for finding the correct position of SNPs.