Software Lab in Bioinformatics 2019

Bioinformatics Software Lab 2019

Location: MTZ Seminar Room, Pauwelstr, 19; 3rd Floor, Corridor B room 3.04.

Dates: Monday 9:30-12:30 (starting 16.04.2017)

Language: English

Prerequisite (desirable): Introduction to Bioinformatics

Credits: 7 (10 with extra work for Media M.Sc. Students)

Lecturers: Ivan G. Costa

Evaluation: 20% prototypes / 60% final project / 20% presentation

RWTH online descriptionBioinformatik Praktikum

Description:

Next-generation sequencing (NGS) allows the measurement of molecular characteristics of individuals on a genome-wide scale. The application of NGS methods to large patient groups enables precise medicine, i.e. finding genetic features to guide medical treatment. The low level analysis of NGS data imposes large computational and statistical challenges. NGS data are typically large (1 to 100 GB per sample/patient) requiring efficient computational strategies for data analysis and storage. Moreover, NGS data contains artifacts and noise, which affects the reliability of predictions and leads to errors.

In this software lab, we will explore computational problems associated to the analysis of nanopore sequencing data. Nanopore represent a novel sequencing technology allowing sequencing experiments to be done at home and with a laptop.  However, the high error rates requires robust computational methods to perform base calling detection and align the reads to genomes. Students will implement strategies based on machine learning and statistical methods to analyze nanopore sequencing data. We will use the high-performance cluster and GPUs from the ITC RWTH Aachen as the computational platform for this course.

Schedule:

29.04.2019 –Introduction to Bioinformatics and Next Generation Sequencing

06.05.2018 – Practical Course in NGS data analysis

07.05.2018 – Project Description

13.05.2018 – Introduction to HPC clusters and GPUs

20.05.2018 – Project Development

14.05.2018 to 8.07.2018 – Project Development

 15.07.2019 – Project Presentation

Literature & Videos

Pavel A. Pevzner and Phillip Compeau, Bioinformatics Algorithms: An Active Learning Approach.

Check for Chapter 9 for some algorithms on short read alignment http://bioinformaticsalgorithms.com/videos.htm

 

 

 

 

 

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