Managing genomic and proteomic knowledge

Lutz, Michael W.; Warren, Patrick V.; Gill, Rob; Searls, David B.

doi:10.1016/j.ddtec.2005.08.001

Cited by 4 publications

(2 citation statements)

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“…These approaches have different pros and cons which are discussed elsewhere [120]. Integration of phenotype data, however, is a notoriously difficult and time-consuming endeavour, especially for cross-platform or cross-species data [57,[121][122][123], some attempts will be discussed in this section. For illustration purposes, we show available phenotype information for cdk-7 of Caenorhabditis elegans in 'WormBase' [82], 'PhenoBank' [86], and 'RNAi Database' [39], and the corresponding yeast and mouse orthologue in Fig.…”

Section: Step 1: Integrationmentioning

confidence: 99%

Phenotype Data: A Neglected Resource in Biomedical Research?

Groth¹,

Weiß²

2006

CBIO

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To a great extent, our phenotype is determined by our genetic material. Many genotypic modifications may ultimately become manifest in more or less pronounced changes in phenotype. Despite the importance of how specific genetic alterations contribute to the development of diseases, surprisingly little effort has been made towards exploiting systematically the current knowledge of genotype-phenotype relationships. In the past, genes were characterized with the help of so-called "forward genetics" studies in model organisms, relating a given phenotype to a genetic modification. Analogous studies in higher organisms were hampered by the lack of suitable high-throughput genetic methods. This situation has now changed with the advent of new screening methods, especially RNA interference (RNAi) which allows to specifically silence gene by gene and to observe the phenotypic outcome. This ongoing large-scale characterization of genes in mammalian in-vitro model systems will increase phenotypic information exponentially in the very near future. But will our knowledge grow equally fast? As in other scientific areas, data integration is a key problem. It is thus still a major bioinformatics challenge to interpret the results of large-scale functional screens, even more so if sets of heterogeneous data are to be combined. It is now time to develop strategies to structure and use these data in order to transform the wealth of information into knowledge and, eventually, into novel therapeutic approaches. In light of these developments, we thoroughly surveyed the available phenotype resources and reviewed different approaches to analyzing their content. We discuss hurdles yet to be overcome, i.e. the lack of data integration, the missing adequate phenotype ontologies and the shortage of appropriate analytical tools. This review aims to assist researchers keen to understand and make effective use of these highly valuable data.

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Section: Step 1: Integrationmentioning

confidence: 99%

Phenotype Data: A Neglected Resource in Biomedical Research?

Groth¹,

Weiß²

2006

CBIO

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“…Data mining methods applied in drug discovery process generally include artificial neural networks, Bayesian probability approaches, genetic algorithms, decision trees, nearest neighbor methods, rule induction, new data visualization and virtual screening techniques. Database mining clearly has increased the number of putative targets [6,7]. Two applied data mining methods involve database compilation and data visualization employed to class, cluster, associate and model raw data generated from genomics and postgenomics studies for achieving novel anticancer drug like compounds [8].…”

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confidence: 99%