Computational Methods in BioinformaticsLaajuus (3 ECTS)

Objective

The student knows typical genome wide gene expression methods in RNA level and in protein level. The student is able to preprocess expression data. The student can use statistical methods to data mine multidimensional gene expression data for example to find statistically significantly differentially expressed genes of proteins. The student can also cluster and annotate gene expression data. The student is able to plan wet lab experimental designs suitable for bioinformatic analysis. The student will be able to interpret sequencing results and understands the algorithms used in sequence comparison and alignment.

Content

1. Genome wide gene expression measurement methods
2. Tools in bioinformatics
3. Data mining: Data formats and preprocessing of data. Determining statistically significant alterations in expression and clustering
4. Experimental design in genome wide gene expression tests
5. Sequence analysis and alignment algorithms

Assessment criteria, satisfactory (1)

1. The student is familiar with various gene expression measuring methods.
2. The student has some basic skills in a statistical analysis program designed for bioinformatics
3. The student is familiar with typical file formats and data structures and is able to determine what the data presents. The student is able to cluster simple gene expression data.
4. The student understands how experimental design affects the data analysis.
5. The student understands what it means that there are signals in sequence.

Assessment criteria, good (3)

1. The student is well aware of the pitfalls in gene expression measurements.
2. The student can write simple scripts to automate the steps in the data analysis.
3. The student can analyze clustered expression data by adding some functional annotation.
4. The student can evaluate the validity of analysis results by reviewing the raw data and experimental setup.
5. The student can search for regulation signals from DNA sequences and understands how sequence alignment algorithms work.

Assessment criteria, excellent (5)

1. The student realizes how cumulative and changing biological information affects the analysis of gene expression data.
2. The student is able to use various tools to visualize analysis results for comprehensive presentations.
3. The student is able to make biological conclusions from analysis results.
4. To present gene expression data in a more convincing manner the student is able to add dimensions to data like integrating functional annotations or protein-protein interaction data.
5. The student is familiar with various signals found from sequences such as promoter and enhancer signals and those signals in amino acid sequences