UMUC

Graduate School of Management Technology

Course Descriptions - BIFS (Bioinformatics)

BIFS 613 Statistical Processes for Biotechnology (3)

(Formerly BIOT 613.) Prerequisite: Knowledge of basic statistics. A study of statistical tools—such as Bayesian statistics, Markov processes and information theoric indices—and how they can be used to analyze sequence homology, the presence of motifs in sequences, gene expression and gene regulation. Topics include information content, mutual information, long-range correlation, repeats, Fourier analysis and linguistic methods.

BIFS 614 Data Structures and Algorithms (3)

(Formerly CSMN 614.) An introduction to the definitions, implementations and applications of the most basic data structures used in computer science, including abstract data types. Basic formalism and concepts used in algorithm design and the analysis of algorithms are also introduced. The relative efficiency of the algorithms studied is estimated by informal application of these concepts. Algorithms and data structures discussed include those for sorting, searching, solving graph problems and dynamic programming.

BIFS 617 Advanced Bioinformatics (3)

(Formerly BIOT 617.) An overview of the basic programming tools for performing bioinformatic analyses in both the UNIX and MS DOS/Window environments. Focus is on the use of Perl and BioPerl as the basic programming tools. Basic programming skills are developed and practiced on such problems as codon usage/bias, open reading frame, CpG islands detection and gene identification.

BIFS 618 Java for Biotechnology Applications (3)

A study of basic concepts in Java and object-oriented programming in bioinformatics application development. Emphasis is on Web-based, graphical and database-driven application design. Review covers the function and design of some Javabased bioinformatics tools. Some commonly used libraries in the BioJava project are introduced, and developments of reusable modular application objects are examined. Basic problem-solving skills in the field of biotechnology using Java programming are developed through practical projects.

BIFS 619 Gene Expression Data Analysis (3)

A study of high-throughput technologies for transcriptome and genomic aberration profiling. Topics include statistical theories, algorithms and data analysis tools for microarray experiments, array comparative genome hybridization, SNP array experiments, and supervised and unsupervised machine learning technologies for class discovery and classifier identifications. Practice is provided in the preprocess of empirical gene expression profiling and the postprocess of microarray data analysis for identifying differentially regulated genes related to biological functions. Several legacy databases and data integration strategies in gene expression profiling are explored through data mining and functional annotation of interesting genes; statistical principles and theories are illustrated.