컴퓨터 게놈학 II

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페이스북 트위터 카카오톡 네이버밴드 링크복사

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자연과학 >생물ㆍ화학ㆍ환경 >생명공학
강의학기

2010년 2학기

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3,620

This course covers advanced methods for analysis of genomic sequence data, including whole genome analysis, especially next-generation sequence data; microarray analysis; and applied machine learning in genome research. Some familiarity with computer programming is preferred, but not absolutely necessary.

수강안내 및 수강신청
※ 수강확인증 발급을 위해서는 수강신청이 필요합니다

Ab initio assembly with short reads.

차시별 강의

1.	Ab initio assembly with short reads.	Uses a hashing technique to index the genome.It can find alignments with as many as 2 mismatches Uses Mate-pairs Also takes advantage of the Qscore information to only use reads which are likely to be correct.
2.	Detecting variation with short readsAlgorithms: PolyBayes, PyroBayes, SHRimp	1.De novo assembly for short reads: review 2.Probability that read starts at a position: Uniform distribution 3.Overlap fraction and relationship to the Lander-waterman equations
3.	Structural variation in clone-end dataStructural variations in the human genomeMethods for detecting structural variations.	1. Focus of the paper is detecting large-scale structural variations of genes. 2. Critical questions I had initially: - What about variations outside of the gene context? - Can these methods be used to find small rearrangements / indels? - What is the lower size limit?
4.	microRNAs.Next generation data storage issuesCompressed sequence alignment	1. Recall how reads are stored 2. Multiple Users of NGS data? 3. Compressing read data
4.	microRNAs.Next generation data storage issuesCompressed sequence alignment	1. Recall how reads are stored 2. Multiple Users of NGS data? 3. Compressing read data
5.	The transcriptomeGene Expression ProfilingDe novo transcriptome sequencing	1. To catalog all species of RNA transcripts 2. To determine the transcriptional structure of genes, including: - 5’ end, 3’ end, splicing patterns, and modifications 3. To quantify the changes in expression levels of individual transcripts during cellular development and under different conditions.
6.	Basics of designing a microarrayImage analysisNormalizationClusteringMicroarray databases	1. Competitive hybridization 2. Indirect measurements
7.	R for analyzing microarray data.Microarray methods for copy number variation.	1. Installing Bioconductor 2. Affymetrix arrays 3. Getting microarray data into bioconductor