AGCT School of Data Analysis
A series of lectures on creation and analysis of the biological networks built on omics data. Special attention is paid to transcriptomic data and the peculiarities of its processing.
We propose a course of lectures with several practical tasks that covers the most important methods used by our team in the analysis of omics data for the fundamental study of the aging process.

In AgingNets project we study the aging of different organisms as the dynamics of a complex network system, more precisely, as the dynamics of the gene regulatory network. We consider a gene network as a set of the degrees of freedom that interact in a complex way. Firstly, this interaction is nonlinear, and secondly, it is determined by the network structure.

The object which we work with is a gene network, represented by adjacency matrix. We calculate adjacency matrices that correspond to different organism’s states on transcriptomic data. This step is extremely complicated and requires accurate gene expressions processing, choice of normalisation methods and choice of the algorithm that would generate the network. It is also highly important to understand the sequencing type that was used and the raw data processing methods.

During the network study, we obtain sets of genes that play important role in structure change and provide special properties to network dynamics. To biologically interpret such gene groups, we intersect them with known pathway databases.

In the lectures we aim to explain mentioned procedures in detail. 

The purpose of the course is:

  • Teaching bioinformatics and programmers modern methods and algorithms, including some neural network algorithms for aging studies
  • Finding collaborators to develop our open research of the gene networks change in the course of aging
  • A clear demonstration of the complicatedness of data analysis in aging and need of large biological longitudinal data
Course program
The course consists of open lectures that cover the following topics:
Lecture 1
Definition of the gene network. The concept of regulators: enhancers, silencers, transcription factors. Examples of regulation and motifs: zinc fingers, leucine clasps (briefly). Subnets of the metabolic network.
Lecture 2
RNA-seq, WGS and exons. Basics of sequencing. Genome and transcriptome annotation. The concept of gene expression, numerical definition of expression, FPKM, TPKM. Differential expression.
Lecture 3
DNA methylation and methylome. Chromatin and its role in gene expressions regulation. DNA methylation aging clocks.
Lecture 4
Mathematical fundamentals of graph analysis. Definition of a graph, graph traversal, community on a graph. Motives on graphs. Scale-free graphs. Random graphs. Topological peculiarities of gene networks.
Lecture 5
Community search. Clustering algorithms.
Lecture 6
Review of scale invariance and critical properties for biological networks.
Lecture 7
Enrichment Analysis of gene groups. GSEA, enrichR.
Lecture 8
Databases of gene functions and pathways: KEGG, GO, WikiPathway, their difference and properties.
Lecture 9
Modern methods of network construction - GENIE3, pathfinder, GRNBoost2.
Lecture 10
Machine learning techniques for gene clustering and annotation.
Lecture 11
Natural selection, mathematical models of evolution. dN/dS, equilibrium equations.
Lecture 12
Population genetics. Haplogroups, admixture models. 1000genomes Project.
Lecture 13
Carcinogenesis. Differences between cancer cells and healthy ones. Glycolysis, the mechanism of apoptosis, telomere restoration. The metabolic network of apoptosis and its possible damage.
Lecture 14
Aging and its manifestation in metabolism. Examples of the impact of various cellular aging processes on gene network.
Lecture 15
Review of biotech projects and startups that are based on biological networks analysis.
Relevant links
Links to papers and databases
R module for determination of statistical significance of the interaction between pairs of genes. Uses the in silico gene knockout method - removes genes from the sample and uses random forest algorithm for restoring the hidden expression profiles.

The package contains fast methods for calculating correlations, building networks based on these correlations, topological methods for comparing networks, and much more.
  • Artem Alexandrov
    Open Longevity
    Researcher in AgingNets Project
  • Tatiana Tatarinova
    University of la Verne
    Associate Professor, Biology & Fletcher Jones Endowed Chair In Computational Biology
  • Leonid Uroshlev
    Open Longevity
    Principal Bioinformatician
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