NGS Data Analysis Course – Berlin

NGS Data Analysis Course – Berlin, Learn Bioinformatics analysis and pick up your career in In-silico analysis. June 2020

  • Learn the essential computing skills for NGS bioinformatics
  • Understand NGS analysis algorithms (e.g. read alignment) and data formats
  • Use bioinformatics tools for handling NGS data
  • Perform first downstream analyses for studying genetic variation
  • Compare different approaches for differential expression analysis

Overview

The purpose of this intense one-week summer course is to get a deep understanding of Next-Generation Sequencing (NGS) with a special focus on bioinformatics issues. The advantages and disadvantages of current sequencing technologies and their implications for data analysis will be discovered. You will be trained on understanding NGS data formats and handling potential problems/errors therein. In the summer school, we will use a real-life RNA-seq dataset from the current market leader Illumina.

All students will be enabled to perform important first tasks of NGS data analysis themselves. The layout of the summer school has been adapted to the needs of beginners in the field of NGS bioinformatics and allows scientists with no or little background in computer science to get a first hands-on experience in this new and fast-evolving research topic.

In the evenings there will be social events, like a conference dinner, or a guided city tour through Berlin. These are always great networking possibilities.

Read our detailed course program.

Agenda

  1. Linux for Bioinformatics:
    This module will introduce the essential tools and file formats required for NGS data analysis. It helps to overcome the first hurdles when entering this (for NGS analyses) unavoidable operating system.
  2. Introduction to NGS data analysis:
    Different methods of NGS will be explained, the most important notations are given and first analyses be performed. This course covers essential knowledge for analyzing data of many different NGS applications.
  3. DNA Variant Calling: In this module different bioinformatics tools for variant calling will be described. We then apply various methods for variant calling and filtering using DNA-Seq data.
  4. RNA-seq Data Analyses: In this module different bioinformatics tools for RNA-seq alignment will be described and tested. We then apply and compare the various approaches for differential expression analysis using RNA-Seq.

Detailed Course Program

Linux for Bioinformatics

  • Introduction to the command line and important commands
  • Combining commands by piping and redirection
  • Introduction to bioinformatics file formats and databases (e.g. UCSC, ENSEMBL)
  • Usage of important bioinformatics toolkits

Introduction to NGS data analysis

  • Introduction to sequencing technologies from a data analysts view
  • Raw sequence files (FASTQ format)
  • Preprocessing of raw reads: quality control (FastQC), adapter clipping, quality trimming
  • Introduction to read mapping (Alignment methods, Mapping heuristics)
  • Read mapping (BWA, BWA-MEM, Bowtie2, STAR, segemehl)
  • Mapping output (SAM/BAM format)
  • Usage of important NGS toolkits (samtools, BEDtools)
  • Mapping statistics
  • Visualization of mapped reads (IGV, UCSC)

Analysis of genetic variation

  • DNA variant calling: methods and tools
  • Variant Call File Format (VCF)
  • Filtering and annotation of genetic variants

RNA-seq Data Analyses

  • Understand split-read mapping
  • Run different split-read mappers (tophat, STAR)
  • Run pseudo-alignment tool salmon
  • Understand the Tuxedo Suite (cufflinks, cuffcompare, cuffmerge, cuffdiff, etc.)
  • Understand the statistics behind DEseq2 and DIEGO
  • Quantify exons/genes/transcripts
  • Predict
    • Differential splicing using DIEGO
    • Differential gene expression using DEseq2
    • Differential isoform expression using cuffdiff
  • Create extensive diagnostic graphics with R
  • Apply your new skills by working on challenging exercises

NOTE: We will NOT cover single-cell RNA-Seq

Speakers

Invited Speakers (short talks)

University
Prof. Dr. Manja Marz (Friedrich Schiller University Jena)

Research Facility
Dr. Kristin Reiche (Fraunhofer Institute for Cell Therapy and Immunology)

Company
Niroshan Nadarajah (MLL Münchner Leukämielabor GmbH)

Your summer school trainers

Dr. Gero Doose (ecSeq Bioinformatics GmbH)
Gero found and published several circularized RNAs in various RNA-Seq experiments. He specialized in split-read analysis some years ago and has strong expertise in downstream analyses.  Publications

Dr. Christian Otto (ecSeq Bioinformatics GmbH)
Christian is one of the developers of the read mapping tool segemehl and is an expert on implementing efficient algorithms for NGS data analyses.  Publications

Dr. David Langenberger (ecSeq Bioinformatics GmbH)
David started working with small non-coding RNAs in 2006. Since 2009 he uses NGS technologies to investigate these short regulatory RNAs as well as other targets. He has been part of several large NGS projects, for example, the International Cancer Genome Consortium (ICGC).  Publications

Included in the Summer School

  •   Course materials
  •   Catering during the workshop
  •   High-performance workstations (no laptop needed)
  •   USB-Stick for taking home results and analysis
  •   Conference Dinner
  •   Guided city tour
  •   Networking opportunities

Attendance

Location: PC-College, Stresemannstraße 78, 10963 Berlin, Germany
Language: English
Available seats: 30 (first-come, first-served)
Registration Fee: 1,449 EUR (excluding VAT)

Travel expenses and accommodation are not covered by the registration fee.

Travel Information – Berlin

Key dates

Opening Date of Registration: November 14, 2019
Closing Date of Registration: June 1, 2020
Workshop: June 15-19, 2020 (9 am – 6 pm)

Downloads Flyer

Register using this link.

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NGS Data Analysis Course – Berlin