3 Database Warehousing in Bioinformatics

3 Database Warehousing in Bioinformatics

Judice LY Koh and Vladimir Brusic

Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613 Email: vladimir@i2r.a-star.edu.sg, judice@i2r.a-star.edu.sg,

3.1 Introduction

The applications of computer technology in biology date as early as the

1960s, progressing rapidly in the last decade and evolving into the emer-

gence of new field of bioinformatics. Bioinformatics combines the ele-

ments of biological sciences, biotechnology, computer science, and

mathematics. Recent advances in biotechnology have enabled measure-

ment of biological systems on a massive scale. Newly developed methods

and instrumentation, such as high throughput sequencing and automation

in genomics and proteomics, generate volumes of raw biological data at an

explosive rate. In parallel with the growth of data, numerous computa-

tional tools for improved data analysis and management have emerged.

These tools help extract relevant parts of the data (data reduction), estab-

lish correlations between different views of data (correlation analysis), and

convert the information to knowledge discoveries (data mining). In addi-

tion, recent research has expanded into data storage and data management

focusing on structure of the databases (data modeling), storage media (re-

lational, flat file-based, XML, and others), and quality assurance of data. 46 Judice LY Koh and Vladimir Brusic

The knowledge-based era of modern biological research seeks to combine

data management systems with sophisticated data analysis tools, thus de-

fining some of the major current activities in bioinformatics.

Molecular biology data management systems usually take the form of

publicly accessible biological databases. A database is designed to manage

a large amount of persistent, homogeneous, and structured data that is-

shared among distributed users and processes (Bressan, 2002). When a

dataset is organized in the form of a database, it must remain manageable

and usable, supporting both data growth and increase in the number of da-

tabase queries. In bioinformatics, the development of databases has been

driven by an explosive growth of data as well as increasing user access to

this data. For example, the number of entries in SWISS-Prot

(www.expasy. org), a major public protein database and in DNA Data

Bank of Japan (www.ddbj.nic.ac.jp), a major web accessible DNA data-

bank, has grown rapidly from 999 to 2003. The number of accesses to

Swiss-Prot has grown by approximately one million added connections per

year (Tables 3.1 and 3.2).

Table 3.1. Number of monthly web access example

SWISS-Prot release version Month/Year No. of entries No. of access in the

month 38.0 07/1999 80,000 2,040,437 39.0 05/2000 86,593 3,162,154 40.0 10/2001 101,602 5,642,523 41.0 02/2003 122,564 8,018,544 42.0 10/2003 135,850 9,510,337

The number of entries in SWISS-Prot and the number of monthly web accesses to SWISS-Prot from 1999 to 2003.

The growth of biological data resulted mainly from the large volume of

nucleotide sequences generated from the genome sequencing projects. The

first viral genome, bacteriophage FX-174, containing 5,386 base pairs

(bps) was sequenced in 1978 (Sanger et al., 1978). More than a decade

later, the first free-living organism, Haemophilus influenzae, containing

1.8 million base pairs, was sequenced (Fleischmann et al., 1995). The hu-

man genome of some 3.5 billion bp was published in 2001 (Lander et al.

2001), followed by the publication of mouse genome a year later (Wa-

terston et al., 2002). Today, more than 1,500 viral genomes, 110 bacterial

and archaea genomes and 20 eukaryotic genomes have been sequenced.

Because of the alternative splicing of the messenger RNA (Fields, 2001) it

is estimated that some 30,000 human genes encode as much as ten times 3 Database Warehousing in Bioinformatics 47

more proteins. Rapid accumulation of genomic sequences, followed by a

mounting pool of protein sequences, and three-dimensional (3D) structures

will continue to fuel the development of database technologies for manag-

ing these data.

Table 3.2. Number of monthly web access example DDBJ release version Month/Year No. of entries 38 07/1999 4,294,369 41 04/2000 5,962,608 47 10/2001 13,266,610 53 03/2003 23,250,813 56 12/2003 30,405,173

The number of entries in DDBJ from 1999 to 2003.

Numerous databases have been created to store and manage the nucleo-

tide sequences and related views of the same data, such as 3D biological

macromolecular structures, protein sequences, physical maps, and struc-

tural or functional domains. Among the most significant DNA databases