Bioinformatics and Data Management

1. CTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCAAAGNTNNGNNNANNACNNTTGGCCGAGAACTTNGNNGGGGNTNANTNNNATATTCCNATTTTGCCTAATACNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAAACANTNNNCNANTNNCATCNTNTTNGNANAACNNGGGGGGGAATTTTTTNNCAAGGNGGNNCCAANGCGNNATTATCCANCNCNNCCNAGTTGTNNAAANNAGTNTNCCNCGAGGNTAAAAAAACTTTTNTCCGGCGGNGGCAGNTNGNGNAAATAACNGTTTCCCCNCCNTTGTGTTNGGGGGCNCCCCCCCCCCCCCTNCAAANAANANAAANGNNNGNCGGNNATTTTNACCGTCGCGGGGGGCCCNCACCNCACCCGNAGNAAATCNACCANATCAAGNGAGGANGGNGGGNGAGGCCTTTTTTTTTTTNNAAAATCCCANAAAAACTTNNNNCCGNNGGGGGGGCTAAAAAAAAAACCCCCCCCNCCCACCCNNCCNGGGGGGGNGNAGGTTTNTTGTTTTTTTTTCCANAANACTNGGTTNGNGGGAAGAGATNAANNAACACACCCCCCCNCNTGNGGTCCTTNTTTCCCCNAANGGGTGNGGGNGGNNNATTCCTCCTNCNTNCCACNAANAAAGGGGGNNTTATTAAAAACTTNNCCTCAGGTNCNCTNGNGGGGGGGGGGGGGGGGNGGNCCANAANTNTTNCNCCCGGGNCGGGGNNAATTNCCCNGGGTNAGGNATCCTTCNAANAGAGGTTTTTAAAANACCTTNNCNCCCGGGGGGAAATNCCTGNTCCCCCCTCTCNNNAAGANGAAAAATAAAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCAAAGNTNNGNNNANNACNNTTGGCCGAGAACTTNGNNGGGGNTNANTNNNATATTCCNATTTTGCCTAATACNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAAACANTNNNCNANTNNCATCNTNTTNGNANAACNNGGGGGGGAATTTTTTNNCAAGGNGGNNCCAANGCGNNATTATCCANCNCNNCCNAGTTGTNNAAANNAGTNTNCCNCGAGGNTAAAAAAACTTTTNTCCGGCGGNGGCAGNTNGNGNAAATAACNGTTTCCCCNCCNTTGTGTTNGGGGGCNCCCCCCCCCCCCCTNCAAANAANANAAANGNNNGNCGGNNATTTTNACCGTCGCGGGGGGCCCNCACCNCACCCGNAGNAAATCNACCANATCAAGNGAGGANGGNGGGNGAGGCCTTTTTTTTTTTNNAAAATCCCANAAAAACTTNNNNCCGNNGGGGGGGCTAAAAAAAAAACCCCCCCCNCCCACCCNNCCNGGGGGGGNGNAGGTTTNTTGTTTTTTTTTCCANAANACTNGGTTNGNGGGAAGAGATNAANNAACACACCCCCCCNCNTGNGGTCCTTNTTTCCCCNAANGGGTGNGGGNGGNNNATTCCTCCTNCNTNCCACNAANAAAGGGGGNNTTATTAAAAACTTNNCCTCAGGTNCNCTNGNGGGGGGGGGGGGGGGGNGGNCCANAANTNTTNCNCCCGGGNCGGGGNNAATTNCCCNGGGTNAGGNATCCTTCNAANAGAGGTTTTTAAAANACCTTNNCNCCCGGGGGGAAATNCCTGNTCCCCCCTCTCNNNAAGANGAAAAATAAAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCAAAGNTNNGNNNANNACNNTTGGCCGAGAACTTNGNNGGGGNTNANTNNNATATTCCNATTTTGCCTAATACNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAAACANTNNNCNANTNNCATCNTNTTNGNANAACNNGGGGGGGAATTTTTTNNCAAGGNGGNNCCAANGCGNNATTATCCANCNCNNCCNAGTTGTNNAAANNAGTNTNCCNCGAGGNTAAAAAAACTTTTNTCCGGCGGNGGCAGNTNGNGNAAATAACNGTTTCCCCNCCNTTGTGTTNGGGGGCNCCCCCCCCCCCCCTNCAAANAANANAAANGNNNGNCGGNNATTTTNACCGTCGCGGGGGGCCCNCACCNCACCCGNAGNAAATCNACCANATCAAGNGAGGANGGNGGGNGAGGCCTTTTTTTTTTTNNAAAATCCCANAAAAACTTNNNNCCGNNGGGGGGGCTAAAAAAAAAACCCCCCCCNCCCACCCNNCCNGGGGGGGNGNAGGTTTNTTGTTTTTTTTTCCANAANACTNGGTTNGNGGGAAGAGATNAANNAACACACCCCCCCNCNTGNGGTCCTTNTTTCCCCNAANGGGTGNGGGNGGNNNATTCCTCCTNCNTNCCACNAANAAAGGGGGNNTTATTAAAAACTTNNCCTCAGGTNCNCTNGNGGGGGGGGGGGGGGGGNGGNCCANAANTNTTNCNCCCGGGNCGGGGNNAATTNCCCNGGGTNAGGNATCCTTCNAANAGAGGTTTTTAAAANACCTTNNCNCCCGGGGGGAAATNCCTGNTCCCCCCTCTCNNNAAGANGAAAAATAAAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCAAAGNTNNGNNNANNACNNTTGGCCGAGAACTTNGNNGGGGNTNANTNNNATATTCCNATTTTGCCTAATACNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAAACANTNNNCNANTNNCATCNTNTTNGNANAACNNGGGGGGGAATTTTTTNNCAAGGNGGNNCCAANGCGNNATTATCCANCNCNNCCNAGTTGTNNAAANNAGTNTNCCNCGAGGNTAAAAAAACTTTTNTCCGGCGGNGGCAGNTNGNGNAAATAACNGTTTCCCCNCCNTTGTGTTNGGGGGCNCCCCCCCCCCCCCTNCAAANAANANAAANGNNNGNCGGNNATTTTNACCGTCGCGGGGGGCCCNCACCNCACCCGNAGNAAATCNACCANATCAAGNGAGGANGGNGGGNGAGGCCTTTTTTTTTTTNNAAAATCCCANAAAAACTTNNNNCCGNNGGGGGGGCTAAAAAAAAAACCCCCCCCNCCCACCCNNCCNGGGGGGGNGNAGGTTTNTTGTTTTTTTTTCCANAANACTNGGTTNGNGGGAAGAGATNAANNAACACACCCCCCCNCNTGNGGTCCTTNTTTCCCCNAANGGGTGNGGGNGGNNNATTCCTCCTNCNTNCCACNAANAAAGGGGGNNTTATTAAAAACTTNNCCTCAGGTNCNCTNGNGGGGGGGGGGGGGGGGNGGNCCANAANTNTTNCNCCCGGGNCGGGGNNAATTNCCCNGGGTNAGGNATCCTTCNAANAGAGGTTTTTAAAANACCTTNNCNCCCGGGGGGAAATNCCTGNTCCCCCCTCTCNNNAAGANGAAAAATAAAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAA Bioinformatics and Data Management Jeff LeJeune Lejeune.3@osu.edu 330-263-3739

2. Lecture Goals • Define Bioinformatics • Explore NCBI’s website • Introduce some useful tools in available Microsoft Excel • Review other computer software used in Molecular Epidemiology

3. Bioinformatics Defined Systematic approach to store and classify and analyze data and informationand metadata that allows for the acquisition of knowledge and enhancement of the understanding of biological systems.

4. Data Numbers derived from observations, experiments or calculations Examples: Positive/Negative A, T, G, C ill/healthy

5. Information Data in context. Data with associated explanations and interpretations. Examples: Publications, available sequence data, that which is freely available.

6. Metadata • Data about data • Context in which information is used. • One application's metadata is another application's data Examples:Descriptive studies, reference lists, sources, Genbank accession numbers.

7. Knowledge and Understanding • This is what we’re all doing here! • Advancement of Science • Solving of problems

8. Bioinformatics Defined • Data • Information • Metadata • Knowledge & Understanding

9. Data Analysis Tools • NCBI • Bibliographic information • Sequence analysis (nucleotide, protein) • Other • Data scanning using Microsoft Excel • Other tools • Gel comparisons • Spatial data-GIS • Temporal data • Statistical considerations

10. On-line Tools Available at NCBI

11. Blast Searches

12. Microsoft Excel • Easily available • Filters • Pivot Table Reports and Charts

13. Other Software • ClustalW multiple sequence alignments • www.ebi.ac.uk/clustalw/index.html • BioNumerics

14. Fingerprint types • Character types • Sequence types • 2-D gel types • Matrix types

15. Geographic Information System (GIS) software

16. Cardinal Rules of Data Management • Save your data • Back-up your data • Record databases, and program versions used • Write down sequence numbers • Record program parameters • Use E-values • Double check your results visually • In spreadsheets, one entry per column