Please enable JavaScript.
Coggle requires JavaScript to display documents.
LU1: Introduction to Bioinformatics (Systems biology (Requires genomic,…
LU1: Introduction to Bioinformatics
Bioinformatics
Aspects
Using ICT on "big data"
create
large data sets
analyze
large data sets
Databases
Algorithms & Statistics
Analysis & Interpretation
Biology + Information technology
Importance
Next phase of biotechnology
Career opportunity
Big Data Revolution
Trends: single structure; sequences, sequence-structure relationships; genomics; integrative analysis
High throughput data and text mining
Deep analysis
to infer and predict functions and pathways
Total
dry lab
concept
Advanced methods and tools
for extracting and analyzing big data
Requires
huge storage capacity
and
high performance machine
Structural Bioinformatics
Aspects
Protein-protein binding prediction
Molecular docking simulation
Model quality assessment
Inferring functional interaction
Structural alignment and model prediction
Structure-function relationships
constructing 2-D (
RNA molecules
) and 3-D structures (
Proteins
)
Structural neighbor analysis
- derivation of function
"
Sequence > Structure > Function
" paradigm
The Genome Revolution
Human Genome Project (HGP) and other genome projects have produced a
huge storehouse of data
that will influence all biological and medical research
Genome revolution = (Biology)
Informatics + Genetics
Systems biology
Requires
genomic, transcriptomics, metabolomics, & proteomic data
Involves
knowledge of metabolic
and
cell signalling networks
Whole cell/system approaches to
molecular biology
Insights into the
properties
of cells, tissues and organisms functioning as a system
Logical modelling
of biological systems
Focus on
complex interactions
within biological systems
Functional Big Data
Transcriptomes, proteomes, metabolomics
Data from analysis of
differential gene expression, expression-based classifiers, regulatory networks, pathway analysis, expression-based annotation of genes and gene sets, database meta-analysis
Biological expression data from high-throughput analysis:
microarrays, protein arrays, mass spectrometry
Big Data
- driven circle of systems immunology
Biological observation
Design omics experiment
Omics experiment
Big data analysis
Computational modelling
Data-driven hypotheses
Hypothesis prioritization
Experimental validation
Computational tools in bioinformatics
Personal computers (PCs)
common and specialized software for equipment and analysis
Internet & Supercomputers
biological services for data management and organization
supercomputer centres that host public, project-specific, and commercial databases (
storage capacity; simulation of complex systems; machine learning
)