Software Lab in Bioinformatics 2020

Location: ZOOM Channel / TBA

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

Dates: Monday 9:30-12:30 (starting 20.04.2020)

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 single cell sequencing data. Students will implement strategies based on machine learning and statistical methods to analyze single cell sequencing data. We will use the high-performance cluster and GPUs from the ITC RWTH Aachen as the computational platform for this course.

Schedule:

20.04.2020 – Introduction to Bioinformatics and Single Cell Sequencing

27.04.2020 – Practical Course in single cell RNA-seq

4.05.2020 – Project Proposal / Introduction to HPC clusters and GPUs

11.05.2020 – 29.6.2020 – Project development

6.07.2020 – Project Presentation

Literature & Videos

Clustering methods:

  • Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Chapter 14
  • Bishop, Pattern Recognition and Machine Learning, Chapter 9

Advanced Single Cell Analysis

Short Read Aliginment

  • Pavel A. Pevzner and Phillip Compeau, Bioinformatics Algorithms: An Active Learning Approach / Chapter 9 | video

 

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