Location: ZOOM Channel / TBA

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

Dates: Monday 9:30-12:30 (starting 25.04.2022)

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 description:  Bioinformatik 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 artefacts and noise, which affects the reliability of predictions and leads to errors. In this software lab, we will explore computational problems associated with 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:

Schedule:

25.04.2022 – Introduction to Bioinformatics and Single Cell Sequencing [slides sequencing | slides single cell]

2.05.2022 – Practical Course in single cell RNA-seq [slides theory |data1|data2|data3|ifnb|scripts|installation manual]

9.05.2022 – Intro. and Practical Course in ATAC-seq / Introduction to HPC clusters and GPU / Project Proposal [slides | practical hpc | practical epigenomics | material ]

16.05.2022 – 4.7.2022 – Project development

11.07.2022 – 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

• Hemberg Lab / Seurat
• Single cell and machine learning / Krishnaswamy Lab / lectures 1-6
• scWorkshop – https://github.com/broadinstitute/2020_scWorkshop

Short Read Aliginment

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