Abstract
BackgroundIdentification of gene regulatory networks is useful in understanding gene regulation in any organism. Some regulatory network information has already been determined experimentally for model organisms, but much less has been identified for non-model organisms, and the limited amount of gene expression data available for non-model organisms makes inference of regulatory networks difficult.ResultsThis paper proposes a method to determine the regulatory links that can be mapped from a model to a non-model organism. Mapping a regulatory network involves mapping the transcription factors and target genes from one genome to another. In the proposed method, Basic Local Alignment Search Tool (BLAST) and InterProScan are used to map the transcription factors, whereas BLAST along with transcription factor binding site motifs and the GALF-P tool are used to map the target genes. Experiments are performed to map the regulatory network data of S. cerevisiae to A. thaliana and analyze the results. Since limited information is available about gene regulatory network links, gene expression data is used to analyze results. A set of rules are defined on the gene expression experiments to identify the predicted regulatory links that are well supported.ConclusionsCombining transcription factors mapped using BLAST and subfamily classification, together with target genes mapped using BLAST and binding site motifs, produced the best regulatory link predictions. More than two-thirds of these predicted regulatory links that were analyzed using gene expression data have been verified as correctly mapped regulatory links in the target genome.
Highlights
Identification of gene regulatory networks is useful in understanding gene regulation in any organism
The sequence alignment tool Basic Local Alignment Search Tool (BLAST) [17] and the functional similarity tool InterProScan [18] are used for transcription factors (TFs) mapping, which is the first step in regulatory link mapping
Even though S. cerevisiae is a much smaller genome than A. thaliana, many confirmed TFs have been identified for A. thaliana
Summary
Identification of gene regulatory networks is useful in understanding gene regulation in any organism. A regulatory relationship in a gene regulatory network consists of a transcription factor, a target gene, and the type of regulatory relationship between the regulatory elements, either positive or negative These regulatory relationships in a network can help answer current biological questions, such as the identification of genes and proteins related to various diseases, and are useful in novel drug design and development [2]. These regulatory relationships can be useful in Significant time and resources are required for the experimental determination of these gene regulatory networks. The above mentioned techniques cannot be used for most non-model organisms due to data sparseness
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