BIOINFORMATICS WORKSHOP SERIES 2025/26
Welcome to the Bioinformatics Workshop Series 2025/26, organized by the Department of Biomedical Sciences, College of Health Sciences, QU-Health, Qatar University.
This series aims to provide comprehensive training in various bioinformatics themes and packages, utilizing the Qatar University Microsoft Azure High Performance Computing (HPC) bioinformatics environment. The workshop series offers students the ability to pick and choose specific modules that they want to learn. There are 29 modules from 8 themes/packages to choose from.
Upon successful completion of a specific theme, students will receive a certificate of attendance. They will also be eligible to sit for a test, and if they pass, they will be issued a certificate of achievement.
Please bookmark this page to stay up-to-date with the latest workshop being offered!
Themes
- Germline Mutation Analysis +
- Quality assessment of NGS reads
- Sequence alignment for Next Generation Sequencing
- Germline variant calling
- Functional annotation of genetic variants
- Variant prioritization and ranking
- Somatic Mutation Analysis +
- Quality assessment of NGS reads
- Sequence alignment for Next Generation Sequencing
- Somatic variant calling
- Functional annotation of genetic variants
- Variant prioritization and ranking
Description: This module covers the techniques and tools used to assess the quality of Next Generation Sequencing (NGS) reads. We will be learning about raw reads related to genomic, transcriptomics, microbiome and metagenome short-reads and long-reads.
Learning Outcome: Students will learn how to evaluate the quality of NGS data, identify potential issues, and apply quality control measures.
Description: This module focuses on aligning NGS reads to a reference genome. We will learn how to align both short and long-reads for the purpose of genomics, transcriptomics, metagenomics and microbiome.
Learning Outcome: Students will gain proficiency in using alignment tools and interpreting alignment results.
Description: This module teaches the process of identifying germline variants from NGS data.
Learning Outcome: Students will learn how to call and annotate germline variants, and understand their significance.
Description: This module covers the annotation of genetic variants to predict their functional impact.
Learning Outcome: Students will be able to annotate variants and interpret their potential effects on gene function.
Description: This module focuses on prioritizing and ranking genetic variants based on their potential impact.
Learning Outcome: Students will learn methods to prioritize variants for further study or clinical relevance.
Description: This module covers the techniques and tools used to assess the quality of Next Generation Sequencing (NGS) reads. We will be learning about raw reads related to genomic, transcriptomics, microbiome and metagenome short-reads and long-reads.
Learning Outcome: Students will learn how to evaluate the quality of NGS data, identify potential issues, and apply quality control measures.
Description: This module focuses on aligning NGS reads to a reference genome. We will learn how to align both short and long-reads for the purpose of genomics, transcriptomics, metagenomics and microbiome.
Learning Outcome: Students will gain proficiency in using alignment tools and interpreting alignment results.
Description: This module teaches the identification of somatic variants from NGS data.
Learning Outcome: Students will learn how to call and annotate somatic variants, and understand their role in diseases like cancer.
Description: This module covers the annotation of genetic variants to predict their functional impact.
Learning Outcome: Students will be able to annotate variants and interpret their potential effects on gene function.
Description: This module focuses on prioritizing and ranking genetic variants based on their potential impact.
Learning Outcome: Students will learn methods to prioritize variants for further study or clinical relevance.
- Genome Assembly +
- Quality assessment of NGS reads
- Sequence alignment for Next Generation Sequencing
- Genome assembly and assessment
- Genome annotation
- Comparative genomics
- Microbiome/Metagenome Analysis +
- Quality assessment of NGS reads
- Sequence alignment for Next Generation Sequencing
- Diversity analysis
- Phylogenetic analysis
- Differential abundance analysis
Description: This module covers the techniques and tools used to assess the quality of Next Generation Sequencing (NGS) reads. We will be learning about raw reads related to genomic, transcriptomics, microbiome and metagenome short-reads and long-reads.
Learning Outcome: Students will learn how to evaluate the quality of NGS data, identify potential issues, and apply quality control measures.
Description: This module focuses on aligning NGS reads to a reference genome. We will learn how to align both short and long-reads for the purpose of genomics, transcriptomics, metagenomics and microbiome.
Learning Outcome: Students will gain proficiency in using alignment tools and interpreting alignment results.
Description: This module covers the assembly of genomes from NGS data and the assessment of assembly quality.
Learning Outcome: Students will learn how to assemble genomes and evaluate the quality of the assembled sequences.
Description: This module focuses on annotating assembled genomes to identify genes and other functional elements.
Learning Outcome: Students will gain skills in genome annotation and understand the significance of annotated features.
Description: This module teaches the comparison of genomes to identify similarities and differences.
Learning Outcome: Students will learn methods for comparative genomics and how to interpret comparative data.
Description: This module covers the techniques and tools used to assess the quality of Next Generation Sequencing (NGS) reads. We will be learning about raw reads related to genomic, transcriptomics, microbiome and metagenome short-reads and long-reads.
Learning Outcome: Students will learn how to evaluate the quality of NGS data, identify potential issues, and apply quality control measures.
Description: This module focuses on aligning NGS reads to a reference genome. We will learn how to align both short and long-reads for the purpose of genomics, transcriptomics, metagenomics and microbiome.
Learning Outcome: Students will gain proficiency in using alignment tools and interpreting alignment results.
Description: This module covers the analysis of microbial diversity in metagenomic samples.
Learning Outcome: Students will learn how to assess and interpret microbial diversity using bioinformatics tools.
Description:This module focuses on constructing and interpreting phylogenetic trees using NGS and non-NGS DNA and protein sequences. We will also be covering common evolutionary biology concepts such as homology.
Learning Outcome: Students will learn methods for phylogenetic analysis and how to interpret evolutionary relationships.
Description: This module focuses on identifying differentially abundant microbial taxa between samples.
Learning Outcome: Students will gain skills in differential abundance analysis and understand its applications in microbiome research.
- RNA-Seq Analysis +
- Quality assessment of NGS reads
- Sequence alignment for RNA-seq
- RNA transcripts quantification
- RNA-seq differential expression of transcripts
- RNA-seq functional annotation of genes
- Structural Bioinformatics +
- Protein sequence analysis
- Protein structure analysis
- Protein interaction analysis
- Protein structure/interactions and genetic variants
Description: This module covers the techniques and tools used to assess the quality of Next Generation Sequencing (NGS) reads. We will be learning about raw reads related to genomic, transcriptomics, microbiome and metagenome short-reads and long-reads.
Learning Outcome: Students will learn how to evaluate the quality of NGS data, identify potential issues, and apply quality control measures.
Description: This module teaches the alignment of RNA-seq reads to a reference genome or transcriptome.
Learning Outcome: Students will learn how to align RNA-seq data and interpret alignment results.
Description: This module covers the quantification of RNA transcripts from RNA-seq data.
Learning Outcome: Students will gain proficiency in quantifying gene expression levels and understanding their biological significance.
Description: This module focuses on identifying differentially expressed genes from RNA-seq data.
Learning Outcome: Students will learn methods for differential expression analysis and how to interpret the results.
Description: This module teaches the functional annotation of differentially expressed genes.
Learning Outcome: Students will learn how to annotate genes and understand their roles in biological processes.
Description: This module covers the analysis of protein sequences to predict structure and function.
Learning Outcome: Students will learn methods for protein sequence analysis and how to interpret the results.
Description: This module focuses on analyzing protein structures to understand their function.
Learning Outcome: Students will gain skills in protein structure analysis and understand the relationship between structure and function.
Description: This module teaches the analysis of protein-protein interactions.
Learning Outcome: Students will learn methods for studying protein interactions and their biological significance.
Description: This module covers the impact of genetic variants on protein structure and interactions.
Learning Outcome: Students will learn how to assess the effects of variants on protein function and interactions.
- Bioinformatics Databases/Resources +
- Gene expression analysis using NCBI GEO
- Sequence alignment – EBI tools and NCBI BLAST
- Phylogenetic analysis
- Proteomics databases
- Genetic variation databases
- Programming for QU-Health Students +
- Introduction to R language
- Introduction to Perl language
- Introduction to Python language
- Introduction to High Performance Computing (HPC) using the QU-Microsoft Azure platform
- Intermediate Linux programming
Description: This module teaches the use of the NCBI Gene Expression Omnibus (GEO) database for gene expression analysis.
Learning Outcome: Students will learn how to access and analyze gene expression data from GEO.
Description: This module covers the use of EBI tools and NCBI BLAST for sequence alignment.
Learning Outcome: Students will gain proficiency in using these tools for sequence analysis.
Description:This module focuses on constructing and interpreting phylogenetic trees using NGS and non-NGS DNA and protein sequences. We will also be covering common evolutionary biology concepts such as homology.
Learning Outcome: Students will learn methods for phylogenetic analysis and how to interpret evolutionary relationships.
Description: This module teaches the use of proteomics databases for protein analysis.
Learning Outcome: Students will learn how to access and analyze proteomics data.
Description: This module covers the use of databases for studying genetic variation.
Learning Outcome: Students will learn how to access and interpret data on genetic variants.
Description: This module introduces the R programming language for bioinformatics analysis.
Learning Outcome: Students will learn the basics of R programming and how to apply it to bioinformatics tasks.
Description: This module introduces the Perl programming language for bioinformatics analysis.
Learning Outcome: Students will learn the basics of Perl programming and how to apply it to bioinformatics tasks.
Description: This module introduces the Python programming language for bioinformatics analysis.
Learning Outcome: Students will learn the basics of Python programming and how to apply it to bioinformatics tasks.
Description: This module covers the basics of High Performance Computing (HPC) using the QU-Microsoft Azure platform.
Learning Outcome: Students will learn how to use HPC resources for bioinformatics analysis.
Description: This module teaches intermediate-level Linux programming skills.
Learning Outcome: Students will gain proficiency in Linux programming and how to use it for bioinformatics tasks.
Overview
Workshop details
Saturday (see the dates above), whole day from 8am to 5pm (30 mins lunch break and 10 mins break for Zuhr and Asr prayers).
Eligibility
This workshop is only open to currently active undergraduate and master students from Qatar University. Undergraduate students are highly encouraged to participate in this intensive workshop.
Prerequisite
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This is open to only undergraduate and master students. Undergraduate students are highly encouraged to apply.
Students must take the “Introduction to High Performance Computing (HPC)” module before they can take any other modules.
Students who have previously taken the CMPS101 (from Fall 2024 onwards), BIOM685, or have attended the Summer Bioinformatics Workshop do not need to take the “Introduction to High Performance Computing (HPC)”. But if they want to refresh their skills, they are welcome to do join.
Students who have taken a specific module in a specific theme do not need to take the same module when they try to complete the other themes.
Students who have 90% attendance rate for all the modules in a specific theme will be given a certificate of attendance and able to sit for a test, which if they get 80%, they will be issued with a certificate of achievement.
Registration Information
OPEN for registration
Introduction to High Performance Computing (HPC) Quality assessment of NGS reads Sequence alignment for Next Generation Sequencing Germline variant calling Variant prioritization and ranking Functional annotation of genetic variants Genetic variation databases Differential abundance analysis Diversity analysis Phylogenetic analysis
Will be open soon for registration
Introduction to Python language
Introduction to R language
Gene expression analysis using NCBI GEO
Genome annotation
Genome assembly and assessment
Intermediate Linux programming
Introduction to Perl language
Protein interaction analysis
Protein sequence analysis
Protein structure analysis
Protein structure/interactions and genetic variants
RNA transcripts quantification
RNA-seq differential expression of transcripts
RNA-seq functional annotation of genes
Sequence alignment – EBI tools and NCBI BLAST
Sequence alignment for RNA-seq
Somatic variant calling
Contact Information
For more details, please contact rozaimir@qu.edu.qa.