1 Genomics in the Discovery and Monitoring
7
mass spectrometry and (4) At the metabolic level, with the detailed analysis of
low-molecular-weight cellular constituents (“metabolomics”) using a diversity of
enhanced analytical techniques, such as pyrolysis gas chromatography and infrared
spectrometry. Each has been associated with major improvements in throughput and
volume of data produced, as described in Chapter 3. In addition to the developments
in molecular biology, the genomics revolution is associated with three other technological advances that occurred in the 1990s in microtechnology, computing and
communication (Van Straalen and Roelofs 2006).
Microtechnology: the ability to examine very small molecules on the scale of a
few micrometers using new laser technology was crucial for the deployment
of the gene chip.
Computing technology: The vast amount of DNA data generated by high
throughput sequencing, and the analysis of expression matrices and protein
databases requires considerable computing power for efficient bioinformatic
analyses. The advent of high-speed computers and data-storage methods of
immense capacity underpins genomic approaches.
Communication technology: The ability to access and integrate global databases
using the Internet in real time is an essential component of both the
design and interpretation of genomic data, especially as attention focuses
increasingly on non-model organisms (Cock et al. 2005).
The essence then of the genomics approach for exploring biodiversity is to analyze an abundance of individuals and/or genes simultaneously, thereby enhancing
the opportunities for matching genetic and phenotypic diversity at the component
biological levels identified above.
Conceptually, there are three salient points to note in the application of genomics
to the analyses of biodiversity. First, although the field of genomics was developed initially based on applications to model organisms such as baker’s yeast
(Saccaromyces cerevisiae), the fruit fly (Drosophila melanogaster), a nematode
worm (Caenorhabditis elegans), mouse ear cress (Arabidopsis thaliana), and more
recently the mouse (Mus musculus), techniques and interest now allow improved
access to ecologically-well characterized species (Vera et al. 2008, Witham et al.
2008). Laboratory-based models have been useful for elucidating our understanding of basic processes governing growth and development, but generally are more
restricted when predicting responses to environmental change and role in ecosystem
processes. The recent increase in DNA sequence data, for example, of marine organisms including diatoms, sea urchins, Hydra, fish, shrimps, and brown algae (Wilson
et al. 2005, Cock et al. 2005), together with the availability of rapidly increasing
expression sequence tag (EST) libraries, has significantly enhanced the representation of marine taxa (see Chapter 3 and 7). Moreover, the targeting of so-called “key
species” for genomic approaches across the evolutionary tree (Cock et al. 2005)
as models for phylogenetically similar organisms will further broaden the range of
life styles, adaptations and habitats that can be examined. Second, there has been a
7
mass spectrometry and (4) At the metabolic level, with the detailed analysis of
low-molecular-weight cellular constituents (“metabolomics”) using a diversity of
enhanced analytical techniques, such as pyrolysis gas chromatography and infrared
spectrometry. Each has been associated with major improvements in throughput and
volume of data produced, as described in Chapter 3. In addition to the developments
in molecular biology, the genomics revolution is associated with three other technological advances that occurred in the 1990s in microtechnology, computing and
communication (Van Straalen and Roelofs 2006).
Microtechnology: the ability to examine very small molecules on the scale of a
few micrometers using new laser technology was crucial for the deployment
of the gene chip.
Computing technology: The vast amount of DNA data generated by high
throughput sequencing, and the analysis of expression matrices and protein
databases requires considerable computing power for efficient bioinformatic
analyses. The advent of high-speed computers and data-storage methods of
immense capacity underpins genomic approaches.
Communication technology: The ability to access and integrate global databases
using the Internet in real time is an essential component of both the
design and interpretation of genomic data, especially as attention focuses
increasingly on non-model organisms (Cock et al. 2005).
The essence then of the genomics approach for exploring biodiversity is to analyze an abundance of individuals and/or genes simultaneously, thereby enhancing
the opportunities for matching genetic and phenotypic diversity at the component
biological levels identified above.
Conceptually, there are three salient points to note in the application of genomics
to the analyses of biodiversity. First, although the field of genomics was developed initially based on applications to model organisms such as baker’s yeast
(Saccaromyces cerevisiae), the fruit fly (Drosophila melanogaster), a nematode
worm (Caenorhabditis elegans), mouse ear cress (Arabidopsis thaliana), and more
recently the mouse (Mus musculus), techniques and interest now allow improved
access to ecologically-well characterized species (Vera et al. 2008, Witham et al.
2008). Laboratory-based models have been useful for elucidating our understanding of basic processes governing growth and development, but generally are more
restricted when predicting responses to environmental change and role in ecosystem
processes. The recent increase in DNA sequence data, for example, of marine organisms including diatoms, sea urchins, Hydra, fish, shrimps, and brown algae (Wilson
et al. 2005, Cock et al. 2005), together with the availability of rapidly increasing
expression sequence tag (EST) libraries, has significantly enhanced the representation of marine taxa (see Chapter 3 and 7). Moreover, the targeting of so-called “key
species” for genomic approaches across the evolutionary tree (Cock et al. 2005)
as models for phylogenetically similar organisms will further broaden the range of
life styles, adaptations and habitats that can be examined. Second, there has been a