| Título: | Use of an enrichment analysis strategy to detect potential new drug targets in fungal genomes. |
| Autores: |
SOUZA, R. LOBO, F. P. |
| Fecha: |
2011 2011-12-06 |
| Publicador: | In: INTERNATIONAL CONFERENCE OF THE BRAZILIAN ASSOCIATION FOR BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 7.; INTERNATIONAL CONFERENCE OF THE IBEROAMERICAN SOCIETY FOR BIOINFORMATICS, 3., 2011, Florianópolis. Proceedings... Florianópolis: Associação Brasileira de Bioinformática e Biologia Computacional, 2011. |
| Fuente: |
Ver documento |
| Tipo: | Resumo em anais de congresso (ALICE) |
| Tema: |
Software KOMODO Genoma de fungos Genome Software Fungi |
| Descripción: |
Fungi are a major class of pathogens in several animal and plant species, having a great agricultural, veterinary and medical importance. The search for anti-fungal drugs is an especially hard task due to the relative phylogenetic relatedness of fungi to their animal and vegetal hosts when compared with other classes of parasites, such as bacteria. This phylogenetic relatedness causes most of the potential druggable genes in fungi to be essential for both fungal and host genomes. This class of essential genes would tend to be observed at high frequencies in both groups. Essential genes specific of fungal species, however, would tend to be significantly more present in fungal genomes when compared with the frequency of the same gene in host genomes. In this study we used a software previously developed by our group named KOMODO (Kegg Orthology enrichMent analysis ? Online DetectiOn) that detects significantly under- or over-represented groups of homologous genes (as defined by KO groups) in one taxon when compared with the other. We used KOMODO to compare the Fungi and Eukarya taxa and identify significantly overrepresented KO groups in fungal genomes when compared with the eukaryotic background distribution of KO groups. Since each analysis using KOMODO tests thousands of hypotheses (one statistical test for each KO group surveyed), False Discovery Ratio approaches are implemented to control Type I error ratios and generate corrected q-values. Our software also displays the groups of biased homologous genes hierarchically classified according to their statistical significance and displayed into their proper biochemical roles as defined by Kegg Pathways into a dynamically generated webpage. The visual observation of the differentially represented genes adds an important source of information to help the biological comprehension of the phenomena behind the biased distributions detected. From the set of 2611 shared KO groups between Fungi and Eukarya taxa we found 88 of them to be significantly overrepresented in fungal genomes (q-value cutoff of 2011 X-MEETING 2011. |
| Idioma: | en |