CSE5BIO Bioinformatics: Analyzing Biological Big Data Management

This assignment focuses on the management and analysis of biological big data, emphasizing its sources, types, and challenges. It covers genomics, transcriptomics, proteomics, metabolomics, and cytometry, highlighting the need for noise removal and data organization. The assignment discusses bioinformatics tools, statistical analysis methods, and data storage systems like HDFS and NoSQL databases. It also addresses the integration of biological data from various sources and concludes with the impact of big data on bioinformatics, emphasizing the importance of public databases and data management projects. The document acknowledges various sources and research papers related to biological big data and bioinformatics.

BIOLOGICAL BIG DATA
MANAGEMENT

BIOLOGICAL DATA
 Biological data is the data which comes from every living organisms.
 Biological data have a important information about a particular organism.
 Biological data can be from human, animals, plants etc.
 Biological data retrieves with help of biotechnology, bioinformatics and branches
of science.
 Biological data can be a sequence, image, pattern, models, hypothesis or any
evidence etc.

TYPES OF BIOLOGICAL DATA
Genomics
 Nucleotide sequences
 Study of gene, gene finding, mutations prediction, sequence alignment
Transcriptomics
 RNA expression, transcription factor
Proteomics
 Protein structure, interaction, identification
 Protein prediction, structure, function prediction, structure comparison, molecular dynamics, simulation, docking.
Metabolomics
 Small molecules study/ metabolite
 Network analysis, pathway analysis
Cytometry
 Cell level study, cell population
 Population clustering, cell biomarker finding
System Biology
 Study all these above
 Simulation, modeling, networking study,
target prediction for drugs

BIOLOGICAL DATA
ANALYSIS
 Now days biological data is generating on large scale and
it decreases its quality
 This data comes from different ways like metagenomic
data analysis or big data analysis
 Having noise and more dimensions
 Need to remove all noise
 Make data in understandable format
 Prepare pipelines for the analysis of biological data

 Biological data analysis starts with the development of
sequence algorithm
 It generate multiple copies side by side
 Needs its statistical analysis
 Select the specific algorithm after setting the specific
parameter
 Development and its testing needs a huge data storage
and its retrieving system
 Handling of such big data like genomics and proteomics
needs good data management ways

BIG DATA

BIG DATA MANAGEMENT
 Bioinformatics tools still depend on the files stored locally
 Statistical analysis method is also complex
 Nowadays, handling of big data is big issue
 Example: META-pipe pipeline which is from metagenomic
 Works when dataset is of small size
 Now datasets are very large and data is stored in a global
file system
 There is also a distributed data storage system such as
HDFS, GPFS and the Google file system
 Others system also there like RDBM System, NoSQL cluster
 Rational databases(MySQL) are connected to distributed
databases
 Example: Neo4j is graph data base used in bioinformatics
for protein interaction.

INTEGRATION OF
BIOLOGICAL DATA
 Everyone can store, access and analyze the biological
data which is available one the private side or web.
 Integration of all biological data into a single source make
easy to access the data
 Bioinformatics and I.T. gives solution:
- NCBI Entrez
- Multi- databases(TAMBIS)
- Data warehousing
- Distributed data system