CTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCAAAGNTNNGNNNANNACNNTTGGCCGAGAACTTNGNNGGGGNTNANTNNNATATTCCNATTTTGCCTAATACNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAAACANTNNNCNANTNNCATCNTNTTNGNANAACNNGGGGGGGAATTTTTTNNCAAGGNGGNNCCAANGCGNNATTATCCANCNCNNCCNAGTTGTNNAAANNAGTNTNCCNCGAGGNTAAAAAAACTTTTNTCCGGCGGNGGCAGNTNGNGNAAATAACNGTTTCCCCNCCNTTGTGTTNGGGGGCNCCCCCCCCCCCCCTNCAAANAANANAAANGNNNGNCGGNNATTTTNACCGTCGCGGGGGGCCCNCACCNCACCCGNAGNAAATCNACCANATCAAGNGAGGANGGNGGGNGAGGCCTTTTTTTTTTTNNAAAATCCCANAAAAACTTNNNNCCGNNGGGGGGGCTAAAAAAAAAACCCCCCCCNCCCACCCNNCCNGGGGGGGNGNAGGTTTNTTGTTTTTTTTTCCANAANACTNGGTTNGNGGGAAGAGATNAANNAACACACCCCCCCNCNTGNGGTCCTTNTTTCCCCNAANGGGTGNGGGNGGNNNATTCCTCCTNCNTNCCACNAANAAAGGGGGNNTTATTAAAAACTTNNCCTCAGGTNCNCTNGNGGGGGGGGGGGGGGGGNGGNCCANAANTNTTNCNCCCGGGNCGGGGNNAATTNCCCNGGGTNAGGNATCCTTCNAANAGAGGTTTTTAAAANACCTTNNCNCCCGGGGGGAAATNCCTGNTCCCCCCTCTCNNNAAGANGAAAAATAAAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCAAAGNTNNGNNNANNACNNTTGGCCGAGAACTTNGNNGGGGNTNANTNNNATATTCCNATTTTGCCTAATACNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAAACANTNNNCNANTNNCATCNTNTTNGNANAACNNGGGGGGGAATTTTTTNNCAAGGNGGNNCCAANGCGNNATTATCCANCNCNNCCNAGTTGTNNAAANNAGTNTNCCNCGAGGNTAAAAAAACTTTTNTCCGGCGGNGGCAGNTNGNGNAAATAACNGTTTCCCCNCCNTTGTGTTNGGGGGCNCCCCCCCCCCCCCTNCAAANAANANAAANGNNNGNCGGNNATTTTNACCGTCGCGGGGGGCCCNCACCNCACCCGNAGNAAATCNACCANATCAAGNGAGGANGGNGGGNGAGGCCTTTTTTTTTTTNNAAAATCCCANAAAAACTTNNNNCCGNNGGGGGGGCTAAAAAAAAAACCCCCCCCNCCCACCCNNCCNGGGGGGGNGNAGGTTTNTTGTTTTTTTTTCCANAANACTNGGTTNGNGGGAAGAGATNAANNAACACACCCCCCCNCNTGNGGTCCTTNTTTCCCCNAANGGGTGNGGGNGGNNNATTCCTCCTNCNTNCCACNAANAAAGGGGGNNTTATTAAAAACTTNNCCTCAGGTNCNCTNGNGGGGGGGGGGGGGGGGNGGNCCANAANTNTTNCNCCCGGGNCGGGGNNAATTNCCCNGGGTNAGGNATCCTTCNAANAGAGGTTTTTAAAANACCTTNNCNCCCGGGGGGAAATNCCTGNTCCCCCCTCTCNNNAAGANGAAAAATAAAACTCAAGGGGTNAGNNNTNTNAAAGNTGCCNTTCCNANGCTTGATANTTTCCGTTTNNTCNCACCTGGGNNCNNNTAATCGGATGNNGGACANANCAANGCGGGCCTTCACCCCATCNTGGNGGNCCNTNNGNCCNTTTNGCCANTCNCNTNCGCCCNNGGGGTNNTNCNTNGCAGGGGGNNTANCGGTTCCNGGGGGNCAAANNTNCCNCAATGGNTTTNGGANNGTGNCCCCCNCCNTGAGNANTTNAA

Jeff LeJeuneLejeune.3@osu.edu330-263-3739

Bioinformatics and

Data Management

Lecture Goals

• Define Bioinformatics

• Explore NCBI’s website

• Introduce some useful tools in available Microsoft Excel

• Review other computer software used in Molecular Epidemiology

Bioinformatics Defined

Systematic approach to

store and classify and analyze

data and information and metadata

that allows for the

acquisition of knowledge and

enhancement of the understanding

of biological systems.

Data

Numbers derived from observations, experiments or calculations

Examples: Positive/NegativeA, T, G, Cill/healthy

Information

Data in context.

Data with associated explanations and interpretations.

Examples: Publications, available sequence data, that which is freely available.

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.

Knowledge and Understanding

• This is what we’re all doing here!

• Advancement of Science

• Solving of problems

Bioinformatics DefinedData Information Metadata

Knowledge & Understanding

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

On-line Tools Available at NCBI

Blast Searches

Microsoft Excel

• Easily available

• Filters

• Pivot Table Reports and Charts

Other Software

• ClustalW multiple sequence alignments– www.ebi.ac.uk/clustalw/index.html

• BioNumerics

•Fingerprint types

•Character types

•Sequence types

•2-D gel types

•Matrix types

Geographic Information System (GIS) software

Cardinal Rules of Data Management

I. Save your data

II. Back-up your data

III. Record databases, and program versions used

IV. Write down sequence numbers

V. Record program parameters

VI. Use E-values

VII. Double check your results visually

VIII. In spreadsheets, one entry per column