The design and implementation of a system that integrates pathway data from KEGG and genome sequence data from NCBI

1
The design and implementation of a system that
integrates pathway data from KEGG and genome
sequence data from NCBI

• Xiang (Sean) Zhou
• Advisor Prof. Sun Kim
• Bioinformatics Capstone Project
• Indiana University

2
Outline

• Background
• Methods
• Sample results
• Online demonstration
• Future direction

3
Why do we want to study metabolic pathway?

• One of the challenges in life science is to
  uncover the fundamental design principle that
  provides the common underlying structure and
  function in all cells and microorganisms 2 .
• Metabolic pathway network serves as the tool to
  achieve the goal.

4
Metabolic Pathway

• Definition of a metabolic pathway
• A series of enzyme-catalyzed chemical reactions
  within a cell, which results in the removal of a
  molecule from the environment to be used/stored
  by the cell, or the initiation of another
  metabolic pathway1.
• A pathway is a linked set of biochemical
  reactionslinked in the sense that the product of
  one reaction is a reactant of, or an enzyme that
  catalyzes, a subsequent reaction4.

5
Why is it so difficult to study metabolism in
multiple genomes?

• The metabolism in one organism is too large to be
  grasped by a single mind. (i.e. E. coli has a
  metabolism involving over 850 substances and 1500
  reactions.)
• Genome projects keep generating a large amount of
  sequence data.

6
A sample metabolic pathway3
7
Pathway Database(DB)

• A pathway DB is a bioinformatics DB that
  describes biochemical pathways and their
  component reactions, enzymes, and substrates4.

8
Current Pathway DBs

• KEGG (Kyoto Encyclopedia of Genes and Genomes)
• The most comprehensive metabolic pathway DB.
• EcoCyc
• Encyclopedia of Escherichia coli K12 Genes and
  Metabolism.
• CGAP (Cancer Genome Anatomy Project)
• Pathways on the CGAP web site are obtained
  directly from BioCarta and KEGG.
• WIT
• It has changed to a commercial DB.

9
Disadvantages of current DBs

• They are static.
• All data are pre-computed and stored in the DBs.
• Users flexibility of choosing their genome and
  pathway of interest is limited.
• They can only study one genome at a time.
• User cannot compare the pathways in different
  genomes at the same time.

10
Motivation

• Create a system
• User can select genomes and pathways of their
  interest and perform sequence analysis freely.
• Enables multi-genome pathways comparison

11
Data Sources

• KEGG
• NCBI GenBank
• PLATCOM genome comparison data

12
The Challenge

• In KEGG and NCBI GenBank
• The genome names and genes names are slightly
  different.
• The ids used in two DBs are totally different.
• Some of the protein id (pid) in KEGG are
  out-dated.
• Thus, integration of the two DBs is not trivial.

13
The unique features of our system

• Easy to maintain
• Only need to download the latest datasets from
  KEGG and NCBI GenBank.
• Flexibility
• Sequence analysis is based on the combination of
  the genomes and pathways of users choice.
• Everything is computed on the fly.
• Integration of KEGG and NCBI GenBank DBs in terms
  of sequence analysis.

14
Methods

• FASTA
• ClustalW
• HMMer
• A series of modules

15
Infrastructure
A query protein sequence
A pathway
A reference genome
Interested genomes
Protein information
Pathway Information
Search for missing genes
16
PLATCOM-Metabolic Pathway Division
17
Sample Result (1)
18
Sample Result (2)
19
Sample Result (3)
20
Online Demonstration

• PlatCom
• A Platform for Computational Comparative Genomics

21
Future Direction

• Use conserved domain to perform HMM search
• Enable sequence alignment and pattern search
• Connect to other DBs
• Protein-Protein Interaction DBs
• PDB
• Improve the performance by using dynamic cache.

22
Reference

1. H. JEONG, H., TOMBOR, B., ALBERT, R., OLTVAI, Z.
   N., and BARABÁSI, A.-L., (2000), The large-scale
   organization of metabolic networks, Nature,
   407651-654
2. http//www.free-definition.com/
3. http//www.genome.ad.jp/kegg/pathway.html
4. Karp, PD, (2001), Pathway Databases A Case Study
   in Computational Symbolic Theories, Science,
   2932040-2044

23
Acknowledge

• Professor Sun Kim
• Kwangmin Choi
• Arvind Gopu