Precision Medicine

Precision medicine is changing the way we understand, diagnose and treat major life-threatening diseases. The transformation is driven by high-throughput molecular data that is being collected from patients, animal models, and large-scale cell line experiments. In this program, we will explore how the various -omics data types generated in these studies can be analyzed and integrated to study the basic biology associated with viruses & their hosts, cancer onset & development and outcomes.

Biomedical researchers now have access to more and more data. The data can provide new insight into the nature of diseases, drug therapies, treatment and even disease prevention. For diseases like cancer or those like the current viral epidemic - COVID19, these insights are vital to help both clinicians and researchers find better cures and develop new treatments. 

Key Topics Covered:

Next-Generation Sequencing and Big data analysis

Many types of omics data require step-by-step preparation, exploration, annotation, and visualization to understand. The T-BioInfo platform was designed for big multi-omics data analysis hiding the complexities of data with a user-friendly and intuitive interface that eliminates the need for coding and advanced machine learning algorithms for data integration and mining.

Application for Infectious Diseases (Viral and Bacterial Infections)

A program that embeds data-driven concepts into biological projects, spanning the student learning journey from observer to participant in research. Project-based learning for big data bioinformatics is to go beyond the theory with real datasets, projects, and expert mentors. Work with curated datasets from publicly available repositories with easy-to-follow tutorials.

X-Omics Data in Cancer Research (Genomics, Transcriptomics and Epigenetics)

Data processing for Next Generation Sequencing, Mass-Spectroscopy, Structural and phenotypic data. Build and adapt pipelines using similar approaches to data mapping, quantification, and annotation that are used to prepare data for downstream statistical analysis, train machine learning models and annotate features.

Introduction to Programming: R and Python
Online bioinformatics coding exercises to learn and explore R and Python scripting and understand how to analyze and visualize -omics data to extract meaningful insights from large biological datasets. Learn, practice, and achieve bioinformatics greatness with concise exercises and interesting challenges right in the comfort of your browser!

Program Syllabus : Precision Medicine

SESSIONS

TOPICS

Bioinformatics in Translational Research

 

Bioinformatics in Translational Oncology Research

  • The cancer problem – complexity of cancer biology and typical challenges in diagnostics (organs, tissues, cells).
  • Molecular factors in tumor development, growth and metastasis.
  • Overview of clinical and molecular data types and features (phenotype-genotype relationship).

Associated Online Resources:

Next Generation Sequencing: Analysis of DNA Variation

 

Next generation Sequencing: Analysis of DNA Variation

  • Introduction to Next generation Sequencing: shotgun sequencing reads
  • The need for structured data and associated bioinformatics methods: processing (germline and somatic mutations)
  • Analysis (mapping, detecting variants) and interpretation (significant and insignificant mutations, etc.)

Associated Online Resources

Processing Next Generation Sequencing Data

 

Processing Next Generation Sequencing Data

  • An overview of RNA-seq
  • Read quality and pre-processing
  • Mapping on reference genome using various tactics (alignment and alignment-free quantification)
  • Quantification of genes and transcripts.
  • Summary of structured data: visualization (PCA) & biological significance (annotation and interpretation). 

Associated Online Resources:

Machine Learning for Gene Expression Data

 

 Machine Learning for Gene Expression Data

  • Data exploration using dimensionality reduction and clustering
  • Classification and discriminant analysis for labeled datasets
  • Unsupervised and Supervised Machine Learning

Associated Online Resources:

Oncology Data Resources: Raw Data and Public Databases

 

 Oncology Data Sources: Raw Data and Public Databases

  • NCBI (National Center for Biotechnology Information): SRA, GEO, RefSeq and other repositories of publicly available research data.
  • TCGA: The Cancer Genome Atlas and the multi-center project for cancer research studies as a case study for oncology data mining.
  • COSMIC: database of mutation variants and their impact on disease phenotype and treatment in cancer.

Associated Online Resources:

NGS - Viral genomes in the host transcriptome

 

NGS: Viral Genomes in the Host Transcriptome

  • Oncolytic viruses and their role in tumor development
  • Inflammation and tissue damage.
  • Using alignment, annotation, and non-mapped reads to detect viral genomes in clinical samples.
  • Alignment to databases of viral genomes.

Associated Online Resources:

Multiple sequence alignment and phylogeny

 

Multiple Sequence Alignment and Phylogeny
  • Multiple sequence alignment and phylogenetic analysis
  • Preparation of Data: Assembled genome sequences
  • Selecting appropriate genomic sequences for a full pipeline

Associated Online Resources:

Pipeline and Results

 

 Workflows and pipelines in bioinformatics

  • Logic behind various: Workflows and pipelines
  • Utilizing the right references: What to do if we have FASTA/FASTQ files?
  • Which databases to use: Detection of viral genomes by mapping on databases.
  • Interpretation of Phylogenetic Analysis: Evolutionary relationships between genomes, evolutionary time.

Associated Online Resources:

From infection to pandemic: Viral adaptation

 

From Infection to Pandemic: Viral Adaptation

  • The origin of human infection with MERS, SARS, and SARS-2 pandemics.
  • Activation of immune response and cytokine storm caused by viral replication.
  • Scarring, inflammation and cancer.

Associated Online Resources:

Host Pathogen Interaction

 

Host-Pathogen Interaction

  • Host response to viral replication and the implications for cell, organ and tissue.
  • Viral strategies for exploitation of host cellular machinery in viral protein production. 

Associated Online Resources:

 

NOTE : For interested participants in different time zones, the sessions will be recorded and made available to the registered cohort.

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