Gwybodaeth Modiwlau
Module Identifier
PGM2810
Module Title
Skills in Bioinformatics for Biologists
Academic Year
2021/2022
Co-ordinator
Semester
Semester 2 (Taught over 2 semesters)
Other Staff
Course Delivery
Assessment
Due to Covid-19 students should refer to the module Blackboard pages for assessment details
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | A written summary report of the module material (~ 2500 words) | 100% |
| Supplementary Assessment | Resubmission of the report. | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Confidently demonstrate an understanding of the concepts covered and apply the acquired critical analysis and practical computing skills to the appropriate design and bioinformatics analysis of Biological experiments within their field of research.
2. Critically evaluate the application of specific bioinformatics / statistical techniques to research problems in Biological sciences.
3. Demonstrate critical academic evaluation skills, including writing summaries of this topic in the context of the student’s research topic.
Brief description
Informatics skills such as programming and data handling have become increasingly important for postgraduate students. They have been highlighted by IBERS PG students as an area for which more training should be provided as these skills are not typically taught on undergraduate programmes. This module addresses this by providing PG students with a series of practical sessions where computing skills will be taught and practiced.
This module aims to provide PhD students, for whom bioinformatics and the analysis of high-throughput data sets feature significantly in their PhD research, with practical computing skills. As a result of taking this module students should be able to run R scripts to perform statistical analysis, and use Linux on a high performance computing cluster.
This module aims to provide PhD students, for whom bioinformatics and the analysis of high-throughput data sets feature significantly in their PhD research, with practical computing skills. As a result of taking this module students should be able to run R scripts to perform statistical analysis, and use Linux on a high performance computing cluster.
Aims
To impart further bioinformatics related skills to IBERS research postgraduate students, especially practical computing skills such as programming with R, the use of the Linux command line, and the use of High Performance Computing facilities.
Content
There are two types of content in this module, which are explained individually below. The first revolves around the use of the Linux computer operating system that is widely used for scientific applications, and the second revolves around the use of the R programming language in statistical analysis.
1. Introduction to Linux for Data Intensive Bioinformatics
Co-ordinator: Martin Swain
Format: 3 hour practical sessions in a computing lab using the Linux command line.
1. General Linux Introduction Part 1 : navigating the file system
2. General Linux Introduction Part 2 : using grep to interrogate data files
3. General Linux Introduction Part 3: writing simple shell scripts to automate analysis
4. Introduction to High Performance Computing: using a cluster job scheduling system, submitting and monitoring jobs
5. Genomic data processing: basic handling of DNA sequences using Linux and HPC e.g. running BLAST via the command line, read mapping with Bowtie and samtools.
Total time = 5*3 = 15 hours
2. MAM5120 Statistical Concepts, Methods and Tools
Co-ordinator: Kim Kenobi
Format: 8 * 1 hour lectures, plus 8 * 2 hour practical computing sessions using R.
Week 1: Introduction to Data; exploring data in R
Week 2: Introduction to probability; the graphical package ggplot2 and probability distributions
Week 3: Models, likelihood and confidence intervals; Pearson's chi-squared and likelihood plots
Week 4: Normal and t-distributions; Bayesian inference
Week 5: The normal distribution continued; Probability
Week 6: One-way ANOVA (lecture) ; Two-sample methods
Week 7: Two-way ANOVA (lecture) ; One-way ANOVA (R practical)
Week 8: Linear models; Two-way ANOVA (R practical)
Total time = 8 * 3 = 24 hours.
1. Introduction to Linux for Data Intensive Bioinformatics
Co-ordinator: Martin Swain
Format: 3 hour practical sessions in a computing lab using the Linux command line.
1. General Linux Introduction Part 1 : navigating the file system
2. General Linux Introduction Part 2 : using grep to interrogate data files
3. General Linux Introduction Part 3: writing simple shell scripts to automate analysis
4. Introduction to High Performance Computing: using a cluster job scheduling system, submitting and monitoring jobs
5. Genomic data processing: basic handling of DNA sequences using Linux and HPC e.g. running BLAST via the command line, read mapping with Bowtie and samtools.
Total time = 5*3 = 15 hours
2. MAM5120 Statistical Concepts, Methods and Tools
Co-ordinator: Kim Kenobi
Format: 8 * 1 hour lectures, plus 8 * 2 hour practical computing sessions using R.
Week 1: Introduction to Data; exploring data in R
Week 2: Introduction to probability; the graphical package ggplot2 and probability distributions
Week 3: Models, likelihood and confidence intervals; Pearson's chi-squared and likelihood plots
Week 4: Normal and t-distributions; Bayesian inference
Week 5: The normal distribution continued; Probability
Week 6: One-way ANOVA (lecture) ; Two-sample methods
Week 7: Two-way ANOVA (lecture) ; One-way ANOVA (R practical)
Week 8: Linear models; Two-way ANOVA (R practical)
Total time = 8 * 3 = 24 hours.
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Inherent to the subject of the module; ie statistical analysis |
| Communication | Students will develop effective listening skills for the lectures. Students will develop effective written communication skills by writing the module summary report, which will be assessed. Further communication and literacy problem solving skills developed will be module dependent. |
| Improving own Learning and Performance | Students will develop their ability to devise and monitor time management, learning and performance skills throughout the module via attending lectures, tutorials, practicals etc. Outside the formal contact hours, students will be expected to research materials. The coursework will provide an opportunity for students to explore their own learning styles and preferences, and identify their needs and barriers to learning. |
| Information Technology | Developing informatics skills is the focus of the module. |
| Personal Development and Career planning | The module conveys theoretical and applied skills in bioinformatics which are critical for a career in any area of biological science. |
| Problem solving | Students will develop skills and gain experience in identifying and using appropriate bioinformatics related analysis techniques in their research projects. Further problem solving skills developed will be module dependent. |
| Research skills | Key research skills are developed by training in the theory and application of bioinformatics analysis and the critical evaluation of data. Further problem solving skills developed will be module dependent. |
| Subject Specific Skills | Data analysis protocols applicable to student's research area |
| Team work | Developed indirectly by contribution to group discussions. |
Notes
This module is at CQFW Level 7