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GeM-Pro: a tool for genome functional mining and microbial profiling.
Torres Manno, Mariano A; Pizarro, María D; Prunello, Marcos; Magni, Christian; Daurelio, Lucas D; Espariz, Martín.
Affiliation
  • Torres Manno MA; Laboratorio de Biotecnología e Inocuidad de los Alimentos, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Municipalidad de Granadero Baigorria, Sede Suipacha 590, Rosario, Santa Fe, Argentina.
  • Pizarro MD; Laboratorio de Genética y Fisiología de Bacterias Lácticas, Instituto de Biología Molecular y Celular de Rosario (IBR - CONICET), sede FCByF - UNR, Rosario, Santa Fe, Argentina.
  • Prunello M; Laboratorio de Investigaciones en Fisiología y Biología Molecular Vegetal (LIFiBVe), Cátedra de Fisiología Vegetal, Facultad de Ciencias Agrarias, Universidad Nacional del Litoral, Esperanza, Santa Fe, Argentina.
  • Magni C; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
  • Daurelio LD; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
  • Espariz M; Área Estadística y Procesamiento de Datos, Departamento de Matemática y Estadística, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Rosario, Santa Fe, Argentina.
Appl Microbiol Biotechnol ; 103(7): 3123-3134, 2019 Apr.
Article in En | MEDLINE | ID: mdl-30729287
Gem-Pro is a new tool for gene mining and functional profiling of bacteria. It initially identifies homologous genes using BLAST and then applies three filtering steps to select orthologous gene pairs. The first one uses BLAST score values to identify trivial paralogs. The second filter uses the shared identity percentages of found trivial paralogs as internal witnesses of non-orthology to set orthology cutoff values. The third filtering step uses conditional probabilities of orthology and non-orthology to define new cutoffs and generate supportive information of orthology assignations. Additionally, a subsidiary tool, called q-GeM, was also developed to mine traits of interest using logistic regression (LR) or linear discriminant analysis (LDA) classifiers. q-GeM is more efficient in the use of computing resources than Gem-Pro but needs an initial classified set of homologous genes in order to train LR and LDA classifiers. Hence, q-GeM could be used to analyze new set of strains with available genome sequences, without the need to rerun a complete Gem-Pro analysis. Finally, Gem-Pro and q-GeM perform a synteny analysis to evaluate the integrity and genomic arrangement of specific pathways of interest to infer their presence. The tools were applied to more than 2 million homologous pairs encoded by Bacillus strains generating statistical supported predictions of trait contents. The different patterns of encoded traits of interest were successfully used to perform a descriptive bacterial profiling.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Phylogeny / Bacteria / Software / DNA Fingerprinting / Genomics Type of study: Prognostic_studies Language: En Journal: Appl Microbiol Biotechnol Year: 2019 Document type: Article Affiliation country: Argentina Country of publication: Germany

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Phylogeny / Bacteria / Software / DNA Fingerprinting / Genomics Type of study: Prognostic_studies Language: En Journal: Appl Microbiol Biotechnol Year: 2019 Document type: Article Affiliation country: Argentina Country of publication: Germany