Data Oriented Genomics Techniques.pdf (3.9 MB)
Data Oriented Genomics Techniques
The book begins with a comparative genomics study of a gene superfamily among 10 genomes through an in silico deep sequence data analysis, which will help students to learn how to develop biological hypothesis from big data analysis. To test their hypotheses, the students will experience a series of hands on wet-bench genomics techniques, including molecular cloning, genomic DNA analysis, protein biochemistry, and RNA expression analysis at both single gene and transcriptomic levels. Upon finishing the book, the students are expected to acquire a comprehensive set of skills in functional genomics studies.
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Ohio University Startup Fund
The National Science Foundation CAREER Award
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- Bioinformatics and computational biology not elsewhere classified
- Bioinformatic methods development
- Other biological sciences not elsewhere classified
- Gene expression (incl. microarray and other genome-wide approaches)
- Genomics
- Plant cell and molecular biology
- Animal cell and molecular biology
- Biochemistry and cell biology not elsewhere classified
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