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BIOINFORMATICS PROGRAM St. Edward’s University Genomics Education Partnership (GEP) Genomics Consortium for Active Teaching (GCAT)

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Presentation on theme: "BIOINFORMATICS PROGRAM St. Edward’s University Genomics Education Partnership (GEP) Genomics Consortium for Active Teaching (GCAT)"— Presentation transcript:

1 BIOINFORMATICS PROGRAM St. Edward’s University Genomics Education Partnership (GEP) Genomics Consortium for Active Teaching (GCAT)

2 Curriculum Genomics Track (11-12hrs): Evolution Biochemistry I, II Cell, Micro, Neuro Genomics Track (11-12hrs): Evolution Biochemistry I, II Cell, Micro, Neuro Bio-Math Track Track (11-12hrs): Linear Algebra Differential Equation Prob/Theory Stats. Cell, Micro, Neuro Bio-Math Track Track (11-12hrs): Linear Algebra Differential Equation Prob/Theory Stats. Cell, Micro, Neuro Cells/Org. Sys. Organisms/Pop Gen. Chem Analytic Chem Calculus I Calculus II Intro BINF Y1 Molecular Organic I Java I Calculus III Discrete Java II Genomics Perl, Python, R Y2 Applied Stats Alg. & Data Struct. Alg. & Data Struct. Senior Seminar Research (3x) Bioinformatics Y3,4

3 Biological Programming Data structures: scalars, arrays, hashes Control Structures Blast: principles, parsing (BioPerl) Distance matrices: dissimilarity (Jaccard) Phylogenetic Profiles Protein conservation/annotation Phylogenetic Profiles

4 Bioinformatics Construct simple hidden Markov model Membrane Proteins: LILWLVIAVVLMSVFQSFGP PSLLASIFISWFPMLLLIGVWIFFM YFVIQTYLPCIMTVILSQVSFW Soluble Proteins: MAKN RQMQGGGGKGAMSFGKSKARMLTEDQI KTTFADVAGCDEAKEEVAELVEYLREPS RFQKLGGKIPKGVLMVGPPGTGKTLLAK AIAGEAKVPF State Sequences: >FTSH_ECOLI iiiiMMMMMMMMMMMMMMMMMMMMoooooooooooooooooooooooooooooo ooo oooooooooooooooooooooooooooooooooooMMMMMMMMMMMMMMMMMM MMM Iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii Other Projects: Smith-Waterman Multivariate Analysis (PCoA) RNASeq Analysis (Tophat/Bowtie)

5 Senior Research Projects

6 No Burn Light Burn High Burn Unweighted (rare species) Isolate DNA PCR 454 Sequencing Primer Sets 454 Sequencing Primer Sets Soil samples QIIME Sequence Filtering depleted of barcodes/ primers < 200 removed Ave. quality score <25 Ambiguous base calls Homopolymer runs (>6x) Chimeras depleted of barcodes/ primers < 200 removed Ave. quality score <25 Ambiguous base calls Homopolymer runs (>6x) Chimeras OTU Identification Clustering at 3% divergence (97% similarity) Clustering at 3% divergence (97% similarity) OTU Classification Sequences were aligned to the Silva database using the PyNAST algorithm (minimum percent identity was set at 80%)

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