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Bioinformatics for Next-Generation Sequencing

26 Aug, 2019 @ 9:30 am 30 Aug, 2019 @ 6:00 pm



26 Aug, 2019 @ 9:30 am
30 Aug, 2019 @ 6:00 pm

San Nicolò ed Erasmo

Corso Regina Margherita
Modica, 97015 Italy
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Next-Generation Sequencing (NGS) has become an essential tool in genetic and genomic analysis. It is increasingly important for experimental scientists to gain the bioinformatics skills required to assess and analyze the large volumes of sequencing data produced by next-generation sequencers.

Advantages and disadvantages of current sequencing technologies and their implications on data analysis will be discovered.

This course will provide an introduction to the technology, analysis workflows, tools and resources for Next Generation Sequencing data analysis. The content will provide insights into how biological knowledge can be derived from genomics experiments and explain different approaches to analyzing such data.


  • NGS ABC’s
  • R-python workflow
  • data visualization
  • from code to interpretation


Day 1:

Linux for Bioinformatics:

  • Introduction to the command line and important commands
  • Combining commands by piping and redirection
  • Introduction to R and Python
  • Introduction to bioinformatics file formats and databases
  • Usage of important bioinformatics toolkits

Day 2:

Introduction to NGS data analysis:

  • Introduction to sequencing technologies
  • Raw sequence files (FASTQ format)
  • Preprocessing of raw reads: quality control – adapter clipping – quality trimming
  • Introduction to read mapping
  • Read mapping (Bowtie – BWA – STAR)

Day 3:

Introduction to NGS data analysis :

  • Mapping output (SAM/BAM format)
  • Usage of NGS toolkit (samtools – BEDtools – Picard tools)
  • Mapping Statistics
  • Visualization of mapped read

Day 4:

Variant Calling – RNASeq Data Analysis:

  • DNA variant calling methods and tools
  • Variant Call File Format
  • Filtering and annotation of genetic variants
  • Split-read mapping
  • Tuxedo suite
  • Statistics behind DESeq2 – EdgeR
  • Quantify exons/genes/transcripts
  • Predict differential splicing
  • Differential gene expression using DESeq2 / EdgeR
  • Differential isoform expression using cuffdiff
  • Create graphics using R

Day 5:

ChIP-Seq – Metagenomics:

  • ChIP-Seq:
    • Experimental design
    • Mapping software
    • Peak calling
    • Analysis of enriched area
    • Viewing ChIP-Seq data in genome browsers
  • Metagenomics:
    • Overview of existing methods for metagenomics data analysis
    • Run QC: run assessment
    • Read QC: metrics for read quality evaluation
    • Preprocessing of raw data
    • Mapping reads to a reference database
    • Examination of the community composition (taxonomic and functional)
    • Evaluation of the community saturation and diversity



Preferred Operative System: Linux – Ubuntu. Windows users might consider a dual-boot installation or using a virtual machine. The installation of Anaconda and R is also suggested.



Relevant literature:

Giurato et al. 2013. “iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq.” BMC bioinformatics

Giurato et al. (in press). “Quantitative mapping of RNA-mediated nuclear Estrogen Receptor β interactome in human breast cancer cells.” Scientific Data (ISSN:2052-4463)


Giorgio Giurato, Ph.D.

University of Salerno, Italy – Senior Bioinformatician

Genomix4life – Co-founder

Topics: bioinformatics, molecular medicine, NGS, RNA

This event is supported by the Municipality of Modica