CBIO314: Algorithmic Bioinformatics & Sequence Analysis

• Algorithmic Bioinformatics & Sequence analysis is covered in CBIO314 in Spring 2025
• The course is led by Marwa Amer, PhD, an adjunct professor at NU and a lecturer at MUST

Grade Distribution

• Theoretical makes up 40%
• Practical assessment accounts for 20%
• Quizzes, projects & participation, and the mid-term exam each contribute 10%
• Attendance and assignments make up the final 10%

Covered Topics

• Fundamentals of Bioinformatics Algorithms
• Algorithmic Principles in Molecular Biology
• Sequence analysis algorithms
• Algorithms for sequence alignment
• Genomic data assembly algorithms
• Error correction algorithms in sequencing
• Phylogentic analysis algorithms
• Machine learning algorithms in bioinformatics
• Capstone project

Relevant Textbooks

• "Algorithms on Strings, Trees, and Sequences" by Dan Gusfield
• "Bioinformatics Algorithms: An Active Learning Approach" (2nd Edition, Vol. I) by Phillip Compeau & Pavel Pevzner

Genomics

• Genomics integrates computer science, biology, and statistics
• The complexity of genomics includes mathematics, biophysics, algorithms, ethical/legal implications, and more

Bioinformatics

• Bioinformatics involves collecting, archiving, organizing, and analyzing biological data
• The scope of bioinformatics includes genomics, proteomics, drug discovery, personalized medicine, and evolutionary biology
• The primary goal is to extract meaningful information from biological data

Data Challenge

• North America held the largest bioinformatics market share in 2023
• The Global Bioinformatics Market exhibits a 13.2% CAGR from 2024-2030
• The Global Bioinformatics Market size was USD 11.12 billion in 2023
• The Global Bioinformatics Market is projected to be USD 24.98 billion by 2030

Algorithms

• Algorithms are precise, step-by-step computational procedures used to solve problems
• Algorithms are required to process and analyze the vast amounts of data generated by biological and medical research

Algorithm Types

• Sequence Analysis: Used to align DNA, RNA, or protein sequences to identify regions of similarity that may indicate functional, structural, or evolutionary relationships
• Genome Assembly: Algorithms assemble short DNA sequencing reads into longer sequences to represent complete genomes or genomic regions
• Machine Learning: Used to make predictions or decisions from data, useful for pattern recognition, gene expression analysis, and predicting disease susceptibility

Algorithmic Applications

• Disease Gene Identification: Use algorithms to analyze genetic data from patients to identify mutations associated with diseases
• Protein Structure Prediction: Predict the three-dimensional structure of proteins from amino acid sequences
• Drug Discovery and Design: Model molecular interactions to identify potential drug candidates and optimize their properties.

Impact of Algorithms on Bioinformatics

• Algorithms have shifted bioinformatics from mere data collection to a frontier of data-driven discoveries, unveiling the intricate tapestry of life itself.
• The human genome project cost approximately $2.7 billion
• In 2014 it cost around $1,000
• As of 2024, sequencing can cost as low as $600 in some cases

Bioinformatics Market

• The global bioinformatics market overview indicates a market size of $12.312 billion (USD) in 2021
• The global bioinformatics market is expected to grow at a 16.2% CAGR from 2022-2030.
• North America held 38% of the market share by region in 2021
• Increasing funding from public and private sectors and increasing R&D spending on pharmaceuticals and biotechs are driving growth
• Higher operational costs and a lack of skilled bioinformatics specialists are restraints
• Algorithms in bioinformatics has revolutionized bioinformatics
• The revolution brought about by algorithms in bioinformatics is transformative
• Algorithms have empowered scientists to make significant discoveries pushing the boundaries of biology and medicine
• The future of bioinformatics, powered by algorithms, promises greater breakthroughs and a new era of scientific discovery

Breakthroughs Enabled

• Understanding Genetic Diseases
• Complex Biological Systems
• Advancements in Drug Development
• Algorithms are used to analyze vast datasets of genetic information to find correlations between specific genetic variations and disease phenotypes
• Linkage analysis, helped narrow down the location of the BRCA gene mutations is conducted through algorithms
• Algorithms design tests to detect markers in an individual's genetic material
• Algorithms have enabled non-invasive prenatal testing (NIPT) by analyzing DNA fragments in the mother's blood
• Algorithms enables treatments tailored to target the genetic factors directly for an individuals disease
• Algorithms minimize side effects
• Algorithms that analyze genetic markers inform oncologists about the most effective chemotherapy drugs for the genetic profile of their tumor.
• Algorithms have significantly expedited the journey from target identification to optimizing drug candidates, reducing time and costs in drug development.

Advancements in Drug Development

• Target Identification and Validation
• The first step in drug discovery involves identifying biological targets implicated in diseases
• Machine learning algorithms sift through datasets to identify patterns and correlations between proteins and disease phenotypes, highlighting targets for intervention
• High-Throughput Screening (HTS)
• After identifying a target, lead compounds can the target's activity
• HTS involves testing thousands to millions of compounds for their activity against the target
• Algorithms analyze data to identify hits, predict efficacy/toxicity, and streamline selection
• Drug Optimization and ADMET Prediction
• After identifying lead compounds, optimizing their pharmacological properties while minimizing toxicity is next
• Algorithms, especially machine learning and QSAR models, predict how chemical structure modifications affect a drug's ADMET profile
• Computational approaches allow for rapid and cost-effective drug optimization before clinical trials

Complex Biological Systems

• Study emphasizes the interactions and networks within biological entities, from cellular processes to physiology
• Systems biology is an interdisciplinary field, that leverages algorithms to model and decipher interactions that define life
• Computational models simulate biological processes, integrating data from genomics, proteomics, and other omics fields
• Algorithms enable the simulation of complex interactions among genes, proteins, metabolites, and environmental factors
• Another crucial application of algorithms for systems biology is the analysis of biological networks eg gene regulatory networks, protein-protein interaction networks, and metabolic networks
• Algorithms designed help to identify key critical components, and their analysis, combined with techniques like graph theory, uncover insights into how biological systems maintain stability and respond to perturbations