**9.1 What is Bioinformatics?
Bioinformatics can be described in simple terms as the use of
information technology (IT) for the analysis of biological data
sets; it links the areas of bioscience and computer science,
although it can be difficult to define the limits of the subject.
This is fairly typical of emerging interdisciplinary subjects, as
they by definition have no long-established history and generally
push the boundaries of research in the topic. When two rapidly
developing subjects such as bioscience and computer science are
involved, there can be excitement and confusion in equal measure at
times!
*Although it can be difficult to define what bioinformatics is,
it is relatively easy to define what bioinformatics is not -- it is
not simply using computers to look at sequence data. The IT
requirements are of course central to and critical for the
development of the discipline, but in fact bioinformatics has very
quickly become established as a major branch of modern bioscience,
with a range of sophisticated tools for analysing genes and
proteins in vivo, in vitro, and in silico. The field of
bioinformatics has broadened considerably over the past few years,
with novel interactive and predictive applications emerging to
supplement the original function of data storage and analysis. I
have attempted to summarise the key elements of bioinformatics in
Fig. 9.1.
Fig. 9.1 The core of bioinformatics is the database. The two key
supporting branches are data input (here shown to the left of the
vertical line) and data management (shown to the right of the
vertical line). Data management relies on hardware and software
developments, with data input requiring the generation of data
using experimental techniques such as DNA sequencing.
*9.2 The role of the computer
Computers are ideal for the task of analysing complex data sets,
such as sequence data for nucleic acids and proteins. This often
requires a series of computing operations, each of which may be
relatively simple in isolation. However, the computation may need
to be repeated thousands or millions of times; therefore, it is
important that this is achieved quickly and accurately. Even a
short sequence is tedious to analyse without the help of a
computer, and there is always the possibility of error due to
misreading of the sequence or to a loss in concentration. Computers
dont get tired, they generally dont make mistakes if programmed
correctly, and they can store large amounts of information in
digital form. The emergence of genome sequencing as a realistic
proposition required the concomitant development of IT systems to
deal with the output of large-scale sequencing projects and, thus,
bioinformatics became fully established as a discipline in its own
right. New terms such as data warehouse (a store of information)
and data mining (interrogation of databases of various types) have
been coined to describe aspects of bioinformatics, with text mining
(interrogation of bibliographic databases) providing an important
supporting role.
Over the past 10--15 years or so, the staggering developments in
desktop and server-based computer hardware have meant that
bioinformatics is not the preserve of those with access to major
computing facilities. Even a relatively low-cost desktop machine is
powerful enough to access the various databases that hold the
information, and the requirements of a host server are no longer
prohibitive in terms of purchase and maintenance costs. Thus, there
is essentially no constraint on accessing and using the information
from a hardware point of view. The software has, as might be
expected, been developed in tandem with the computing power
available and the requirements imposed by the nature of the data
sets that are generated. Much of the software that is needed for
the manipulation and interrogation of information is now made
available free of charge by the various host organisations who run
the bioinformatics network, although some packages are available
from various companies on a commercial basis.
*9.1 What is Bioinformatics?
Over the past 10--15 years or so, the staggering developments in
desktop and server-based computer hardware have meant that
bioinformatics is not the preserve of those with access to major
computing facilities. Even a relatively low-cost desktop machine is
powerful enough to access the various databases that hold the
information, and the requirements of a host server are no longer
prohibitive in terms of purchase and maintenance costs. Thus, there
is essentially no constraint on accessing and using the information
from a hardware point of view. The software has, as might be
expected, been developed in tandem with the computing power
available and the requirements imposed by the nature of the data
sets that are generated. Much of the software that is needed for
the manipulation and interrogation of information is now made
available free of charge by the various host organisations who run
the bioinformatics network, although some packages are available
from various companies on a commercial basis.
*9.3 Biological data sets
It could be argued that bioinformatics really began to take off
with the advent of rapid DNA sequencing methods in the late 1970s
(see Section 3.7). Up until this point, the rate of acquisition of
biological sequence data was limited; thus, there was relatively
little pressure to develop common storage and analysis methods to
cope with the information. As sequencing techniques became
established and used more extensively, the rate of data generation
obviously increased and, thus, the need for co-ordinated database
management became greater. This need can be illustrated by looking
at a simple example using some DNA sequence data (Fig. 9.2). This
figure shows a short sequence of 50 bases, listed in various
formats. Even with this length of sequence, it is clear that there
are several requirements if sense isto be made of the information.
These are:the need for accurate generation and capture of
information,a clear and logical presentation of the sequence in
visual format,annotation of the sequence to enable orientation and
identification of features, andconsistent use of annotation by
different users/providers.As sequence databases now deal with
billions of bases rather than hundreds, it is clearly a major task
to organise, collate, and annotate the information -- the sequence
itself is actually one of the simplest components in terms of
organisational complexity.
* Fig. 9.2 (a) A short DNA sequence is shown in double-stranded
format. (b) Three different ways of writing the sequence are shown:
i, ii, and iii. By convention only one strand is listed. In (b)i
uppercase type is used with numbering above every 10 bases. In
(b)ii spaces are used to separate groups of 10 bases, and in (b)iii
lowercase type is used with separation. Lowercase type avoids any
confusion between G and C, as g and c are more easily
distinguished. (c) An annotated version of the sequence, with the
three reading frames identified as RF1, RF2, and RF3. The numbering
shows bases from the start of the chromosome. The region shown here
is therefore some 24 million base pairs from the end of the
chromosome.
This figure shows a short sequence of 50 bases, listed in
various formats. Even with this length of sequence, it is clear
that there are several requirements if sense is to be made of the
information. These are:the need for accurate generation and capture
of information,a clear and logical presentation of the sequence in
visual format,annotation of the sequence to enable orientation and
identification of features, andconsistent use of annotation by
different users/providers.
*This figure shows a short sequence of 50 bases, listed in
various formats. Even with this length of sequence, it is clear
that there are several requirements if sense is to be made of the
information. These are:the need for accurate generation and capture
of information,a clear and logical presentation of the sequence in
visual format,annotation of the sequence to enable orientation and
identification of features, andconsistent use of annotation by
different users/providers.
*As sequence databases now deal with billions of bases rather
than hundreds, it is clearly a major task to organise, collate, and
annotate the information -- the sequence itself is actually one of
the simplest components in terms of organisational complexity.There
are obviously many different ways to collate and store sequence
information and, thus, many different databases exist. It can be
useful to think of databases as falling into two main categories.
Repositories for experimentally derived sequence information are
often known as primary databases. The main primary databases today
are nucleic acid sequence databases, although it is interesting to
note that protein sequences were collated in the early 1960s and
thus protein databases actually pre-date large-scale nucleic acid
sequence databases! Secondary databases are variants that have been
derived from primary databases in some way. They may represent
collections of sequence data arranged by organism or phylogenetic
group, or may be based on genome projects. Alternatively, derived
protein sequences, gene expression data, or data restricted to a
particular group of nucleic acids (e.g. ribosomal RNA sequences)
can be used as the basis for establishing a secondary database.
*9.3.1 Generation and organisation of informationData generation
is a central part of any scientific procedure, and forms the core
of bioinformatics. As we have seen, DNA sequencing was essentially
the first procedure to begin to generate data in sufficiently large
amounts to require serious co-ordination and organisation of
biological databases. Significant milestones occurred with the
sequencing of the genomes of the bacteriophages phi-X174 (5 386
base pairs, 1977) and lambda (48 502 base pairs, 1982). During this
period those involved in sequence determination realised that there
was a need for a co-ordinated approach to database management if
full benefit was to be obtained from the sequences that were being
determined. We can illustrate the development of sequence databases
by looking briefly at the two major types, those dealing with
nucleic acid and protein sequence data.
*9.3.2 Nucleic acid databasesThe first nucleic acid database to
be established was set up by the European Molecular Biology
Laboratory (EMBL) in 1980. Soon afterwards GenBank was established
in the USA, and in 1986 the DNA Data Bank of Japan (DDBJ) began
collating information. These three database providers now make up
an international collaboration that is the major source of primary
annotated nucleic acid sequence data, with the International
Nucleotide Sequence Database Collaboration (INSDC) playing a key
role in overseeing the collaboration. The EMBL database is known as
the EMBL Nucleotide Sequence Database (or simply EMBL-Bank) and is
hosted and maintained by the European Bioinformatics Institute
(EBI). In the USA the National Center for Biotechnology Information
(NCBI) runs GenBank, and the DDBJ is hosted by the National
Institute of Genetics in Japan. More information about these
databases can be found in the websites listed in Table 9.2.
*One of the most striking aspects of nucleic acid databases is
the increase in size since the first sequences were entered
manually in the early 1980s. The early growth of the data deposited
in the global nucleic acid sequence database is shown in Fig. 9.3.
An impressive rate of growth is shown, with the total entries
reaching some 250 Mb (megabases) by the mid 1990s. At this point
DNA sequencing moved from relatively small-scale projects to much
larger genome projects and, thus, the rate of data acquisition
increased even more. Recent growth in sequence data (i.e. over the
past 10 years or so) is shown in Figs. 9.4 and 9.5. Fig. 9.4 shows
data as recorded by GenBank. In addition to the sequence data, the
figure also shows how the increase in use of these data has
developed. This is presented in the form of data-base searches per
day, and (as might be expected) this measure of the use of the
database closely mirrors the growth in the database itself.
The final figure showing nucleic acid database growth is Fig.
9.5, in which data are shown as recorded by EMBL. This shows the
total sequence entries as released in different versions of the
database, which is constructed by sequence deposition to one of the
three main database hosts (GenBank, EMBL-Bank, and DDBJ). Daily
exchange of data between these three organisations means that a
continually updated version of the entire database is available at
all times.The database passed the 100 Gb (gigabase) milestone in
September 2005 with release 84 -- a staggering achievement
involving widespread international collaboration, developed over a
25-year period. By October 2006, the database held over 148 billion
nucleotides in some 81 million entries from around 273000 organisms
-- you might like to check the current statistics at the time you
are reading this text!
*One of the most striking aspects of nucleic acid databases is
the increase in size since the first sequences were entered
manually in the early 1980s. The early growth of the data deposited
in the global nucleic acid sequence database is shown in Fig. 9.3.
An impressive rate of growth is shown, with the total entries
reaching some 250 Mb (megabases) by the mid 1990s. At this point
DNA sequencing moved from relatively small-scale projects to much
larger genome projects and, thus, the rate of data acquisition
increased even more. Recent growth in sequence data (i.e. over the
past 10 years or so) is shown in Figs. 9.4 and 9.5. Fig. 9.4 shows
data as recorded by GenBank. In addition to the sequence data, the
figure also shows how the increase in use of these data has
developed. This is presented in the form of data-base searches per
day, and (as might be expected) this measure of the use of the
database closely mirrors the growth in the database itself.
The final figure showing nucleic acid database growth is Fig.
9.5, in which data are shown as recorded by EMBL. This shows the
total sequence entries as released in different versions of the
database, which is constructed by sequence deposition to one of the
three main database hosts (GenBank, EMBL-Bank, and DDBJ). Daily
exchange of data between these three organisations means that a
continually updated version of the entire database is available at
all times.The database passed the 100 Gb (gigabase) milestone in
September 2005 with release 84 -- a staggering achievement
involving widespread international collaboration, developed over a
25-year period. By October 2006, the database held over 148 billion
nucleotides in some 81 million entries from around 273000 organisms
-- you might like to check the current statistics at the time you
are reading this text!
*One of the most striking aspects of nucleic acid databases is
the increase in size since the first sequences were entered
manually in the early 1980s. The early growth of the data deposited
in the global nucleic acid sequence database is shown in Fig. 9.3.
An impressive rate of growth is shown, with the total entries
reaching some 250 Mb (megabases) by the mid 1990s. At this point
DNA sequencing moved from relatively small-scale projects to much
larger genome projects and, thus, the rate of data acquisition
increased even more. Recent growth in sequence data (i.e. over the
past 10 years or so) is shown in Figs. 9.4 and 9.5. Fig. 9.4 shows
data as recorded by GenBank. In addition to the sequence data, the
figure also shows how the increase in use of these data has
developed. This is presented in the form of data-base searches per
day, and (as might be expected) this measure of the use of the
database closely mirrors the growth in the database itself.
The final figure showing nucleic acid database growth is Fig.
9.5, in which data are shown as recorded by EMBL. This shows the
total sequence entries as released in different versions of the
database, which is constructed by sequence deposition to one of the
three main database hosts (GenBank, EMBL-Bank, and DDBJ). Daily
exchange of data between these three organisations means that a
continually updated version of the entire database is available at
all times.The database passed the 100 Gb (gigabase) milestone in
September 2005 with release 84 -- a staggering achievement
involving widespread international collaboration, developed over a
25-year period. By October 2006, the database held over 148 billion
nucleotides in some 81 million entries from around 273000 organisms
-- you might like to check the current statistics at the time you
are reading this text!*9.3.3 Protein databasesIn looking at protein
sequence databases, it is important to distinguish between database
entries that have been determined by direct methods, and protein
sequences derived from nucleic acid database information by
translation of the predicted mRNA sequence. The direct method is
similar to nucleic acid sequencing in that primary data are
generated from the source protein, and additional biochemical and
physical analysis is often possible. Thus, a reasonably complete
characterisation of the protein, perhaps with biological activity
fairly well established, is the standard to aim for. With derived
sequence data, modifications to the amino acids may not be picked
up, and there is a risk that the derived sequence may not in fact
be a functional protein in the cell. One of the key challenges for
database developers is to ensure that databases can talk to each
other, so that complex data conversion is avoided where possible.In
looking at protein sequence databases, it is important to
distinguish between database entries that have been determined by
direct methods, and protein sequences derived from nucleic acid
database information by translation of the predicted mRNA sequence.
The direct method is similar to nucleic acid sequencing in that
primary data are generated from the source protein, and additional
biochemical and physical analysis is often possible. Thus, a
reasonably complete characterisation of the protein, perhaps with
biological activity fairly well established, is the standard to aim
for. With derived sequence data, modifications to the amino acids
may not be picked up, and there is a risk that the derived sequence
may not in fact be a functional protein in the cell. One of the key
challenges for database developers is to ensure that databases can
talk to each other, so that complex data conversion is avoided
where possible.
*Direct sequencing of proteins by the classical Edman
degradation method was first established in 1950. However, the rate
of growth of primary protein databases has been somewhat less
spectacular than for nucleic acids. This is largely due to the
difficulties in determining the amino acid sequence of a protein -
amino acid sequences cant be cloned like genes, and proteins are
complex three-dimensional structures with biological activity
dependent on structural aspects beyond the primary sequence.
Although recent developments in protein sequencing techniques (e.g.
automation of the Edman procedure, use of mass spectrometry) have
improved the situation greatly, sequencing cloned DNA is always
going to provide data more quickly than sequencing amino acid
residues in a peptide fragment.
*Despite the constraints on protein sequence acquisition, the
databases currently available represent an invaluable resource for
the biological community. The key resource is the Universal Protein
Resource or UniProt, which represents the product of close
collaboration between the major protein sequence repositories in
Europe and the USA. UniProt was set up in 2002, with three major
partners involved. The Protein Information Resource (PIR), hosted
by Georgetown University in the USA, joined forces with the
SwissProt database, developed and maintained by the Swiss Institute
for Bioinformatics and the EBI. The central core of UniProt is the
UniParc database and UniProt Knowledgebase (UniProt KB), which
maintains a high level of consistency, accuracy, and annotation of
the sequences deposited.Although falling a little short of nucleic
acid databases in terms of entries, the scale of currently
available protein sequence data is still very impressive. At the
time of writing, the latest release of UniParc had some 7.7 million
entries, and UniProt KB some 3.5 million entries.Further details
about UniProt and its associated components can be found in the
websites listed in Table 9.3.
*9.3.2 Nucleic acid databasesThe first nucleic acid database to
be established was set up by the European Molecular Biology
Laboratory (EMBL) in 1980. Soon afterwards GenBank was established
in the USA, and in 1986 the DNA Data Bank of Japan (DDBJ) began
collating information. These three database providers now make up
an international collaboration that is the major source of primary
annotated nucleic acid sequence data, with the International
Nucleotide Sequence Database Collaboration (INSDC) playing a key
role in overseeing the collaboration. The EMBL database is known as
the EMBL Nucleotide Sequence Database (or simply EMBL-Bank) and is
hosted and maintained by the European Bioinformatics Institute
(EBI). In the USA the National Center for Biotechnology Information
(NCBI) runs GenBank, and the DDBJ is hosted by the National
Institute of Genetics in Japan. More information about these
databases can be found in the websites listed in Table 9.2.
**
