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Curriculum

General Requirement

  1. For Candidates with Biological Science background - MTH-101, MTH-201, CSC  201, CS-202 is required. However, some of the requirements could be waived based on your experience.
  2. For Candidates with a Computational background - BIO 101, Bio-201 is required. Experience can take care of these requirements.
  3. All candidates are required to take STA 104, BINFO 102, and BINFO 202 - Bioinformatics II.
  4. Some courses not listed could also be required for some individuals based on their experiences or track chosen.

Origin of life, prokaryotic and eukaryotic cells, viruses, structure of viruses and bacteriophages, bacteria, bacterial structure and classification; algae, fungi, cell biology, basic genetics, basic concepts about DNA, RNA and proteins with special emphasis on nature of genetic material and its organization in viruses, prokaryotes and eukaryotes, DNA replication, recombination, mutations and repair, Gene structure, transcription, regulatory elements, regulation of gene expression, etc.

Introduction to Functions: Mathematical and physical meaning of functions, graphs of various functions, Introduction to Limits: Theorems of limits and their applications to functions. Derivatives: Introduction to derivatives, Partial derivatives, and their geometrical significance Application problems (rate of change, marginal analysis) Higher derivatives, etc

Introduction to matrices, elementary row operations and vector spaces: Introduction to matrices, system of linear equations, system of non-homogeneous and homogeneous linear equation, introduction to determinants, properties of determinants of order, axiomatic definition of a determinant, etc.

Frequency distribution and probabilities, measure of central tendencies and dispersion, Elementary probability theory, Laws of Probability, Conditional Probability, Introduction to Bayes Theorem Introduction to Random Variable and Probability Distributions, Binomial Distribution, Properties of binomial distribution, Poisson distribution, Normal distribution etc.

Study of bioenergetics, introduction to metabolic pathways, metabolism of carbohydrates, Glycolysis, Citric acid cycle, Pentose pathway, electron transport chain, and oxidative phosphorylation, lipid metabolism, β-oxidation, ketone bodies formation and biosynthesis, etc

Introduction to data structures and algorithms, array-based algorithms: storage, retrieval and search, computational complexity, uses of arrays, concept of binary and linear search, Stacks and queues, priority queues, store, retrieve and search functionalities in stacks and queues, etc.

-          Introduction to Object Oriented Programming
– Python -          Introduction Functional programming language - R programming

Course Outline Basic database concepts, conceptual modelling, hierarchical, network and relational data models, relational theory and languages, databases design, database security and integrity, query languages, relational calculus, relational algebra, SQL, query processing and optimization, normalization, concurrency and recovery, front-end and back-end databases.

Databases, sequence storage, retrieval and analysis, similarity and homology, creating alignments, local and global alignment, pairwise and multiple sequence alignments, phylogenetic analysis, dot matrix plots, dynamic programming algorithm, word (k-tuple) methods, substitution matrices PAM and BLOSUM, scoring algorithms, gap penalties, online tools BLAST, BLAT and FASTA, PDB file structure

Introduction to genome, gene prediction in prokaryotes and eukaryotes, ORF, TFBS, codon usage table, EST and SNP databases, primer designing, restriction enzyme databases, RNA structure prediction, computational secondary and tertiary protein structure prediction methods, structure optimization and refinement methods, hydrogen bonding, PTMs of proteins, Chou Fasman, PHD and PSIPred, neural network, X-ray crystallography, NMR, ab initio, threading and homology modeling methods, protein fold identification using Pfam.

TRAINING TRACKS

We have four broad tracks, which are very flexible. We aim to train mentees to be proficient in either of these tracks by using real-world problems, depending on their interests. You will select a track, and we will tailor your training to your goal or unique challenges.

Functional Genomics
  1. Transcriptomics: Analysis of the expression and regulation of genes at the transcript level. This includes using Single Cell RNAseq, Bulk RNAseq, Spatial Transcriptomics Analysis, etc.
    • Bulk RNA analysis
      In this tract you will learn
      • Filtering low-count genes
      • Normalization 
      • Effect Size Estimation
      • Differential expression and its statistics 
    • Sc Analysis
      In this tract you will learn how to
      • Filtering out low-quality cells and correcting noise
      • Normalizing data and stabilising variances
      • Removing confounding sources of variation
      • Selecting informative features and reducing dimensionality
      • Creating cell clusters and mapping them to specific cell identities
      • Performing differential gene expression analysis
      • Deciphering changes in cell composition and inferring cellular perturbations
      • Investigating cellular communication pathways 
  2. Proteomics: Analysis of protein expression. Most of the things you learn in Sc/Bulk Rna-seq analysis apply here.
  3. Metabolomics: Metabolite profiling and metabolic fingerprinting
Structural Bioinformatics

In this tract, you will learn

  1. Public repositories of structural data
  2. UniProt and basic Sequence alignment tools
  3. Protein structure analysis
  4. Protein structure prediction and docking
  5. Drug discovery.
Genomic Epidemiology

Genomic epidemiology in this track you will learn how to combines genomic data with traditional epidemiological methods to understand pathogen evolution, track disease outbreaks, and inform public health strategies, enhancing the ability to control and prevent diseases.

Knowledge-Based Bioinformatics

This Track is from our research unit, and it’s tailored for individuals or groups engaging in bioinformatics projects seeking a comprehensive understanding of the entire analysis-to-interpretation workflow. It's an immersive journey designed to empower participants to confidently navigate every aspect of their projects.

Have a Question? Feel Free to Contact Us.

For all inquiries and to explore how we can collaborate on your research endeavours, please contact us at info@dataxpression.com We look forward to working with you to advance your research goals and drive meaningful discoveries.

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We are a dedicated team of experts transforming lives through the innovative application of Bioinformatics and Public Health Informatics. By harnessing the power of data science, computational biology, and health informatics, we strive to advance medical research, improve healthcare outcomes, and promote public health.

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