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Bioinformatics Program

St. Edward’s University Genomics Education Partnership (GEP) Genomics Consortium for Active Teaching (GCAT). Bioinformatics Program. Curriculum . Genomics Track (11-12hrs): Evolution Biochemistry I, II Cell, Micro, Neuro. Bio-Math Track Track (11-12hrs ): Linear Algebra

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Bioinformatics Program

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

  2. Curriculum 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 Bioinformatics Senior Seminar Research (3x) Y3,4 Alg. & Data Struct. Applied Stats Genomics Calculus III Java II Y2 Perl, Python, R Discrete Molecular Organic I Java I Organisms/Pop Analytic Chem Calculus II Y1 Gen. Chem Calculus I Cells/Org. Sys. Intro BINF

  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 • Other Projects: • Smith-Waterman • Multivariate Analysis (PCoA) • RNASeq Analysis (Tophat/Bowtie) Construct simple hidden Markov model Membrane Proteins: LILWLVIAVVLMSVFQSFGP PSLLASIFISWFPMLLLIGVWIFFM YFVIQTYLPCIMTVILSQVSFW Soluble Proteins: MAKN RQMQGGGGKGAMSFGKSKARMLTEDQIKTTFADVAGCDEAKEEVAELVEYLREPSRFQKLGGKIPKGVLMVGPPGTGKTLLAKAIAGEAKVPF State Sequences: >FTSH_ECOLI iiiiMMMMMMMMMMMMMMMMMMMMooooooooooooooooooooooooooooooooo oooooooooooooooooooooooooooooooooooMMMMMMMMMMMMMMMMMMMMM Iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii

  5. Senior Research Projects

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

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