The prediction of the putative enzymatic function of uncharacterized proteins is a major problem in the field of metagenomic research, where large amounts of sequences can be rapidly determined. In this work a machine...
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The prediction of the putative enzymatic function of uncharacterized proteins is a major problem in the field of metagenomic research, where large amounts of sequences can be rapidly determined. In this work a machine-learning approach was developed, that attempts the prediction of enzymatic activity based on three protein domain databases, PFAM, CATH and SCOP, which contain functional and structural information of proteins as Hidden Markov Models. Separate and combined classifiers were trained by well-annotated data and their performance was assessed in order to compare the predictive power of different attribute sets corresponding to the three protein domain databases. All classifiers performed well, with an average accuracy of ~96% and an average AUC score of 0.84. As a conclusion, the classification procedure can be integrated to more extended metagenomic analysis workflows.
StRAnGER is a web application for the automated statistical analysis of annotated experiments, exploiting controlled biological vocabularies, like the Gene Ontology or the KEGG pathways terms. In the first version, St...
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StRAnGER is a web application for the automated statistical analysis of annotated experiments, exploiting controlled biological vocabularies, like the Gene Ontology or the KEGG pathways terms. In the first version, StRAnGER featured various gene profiling platforms for functional analysis of genomic datasets, starting from a list of significant genes derived from statistical and empirical thresholds. In the current version, various major improvements have been implemented, namely a new ranking algorithm, the expansion of background distributions with protein annotations, the addition of a mode for batch experiments and a noise-control analysis that evaluates the robustness of the prioritized terms through iterative addition of random genes. Overall, StRAnGER enables a systems level functional interpretation through the utilization of bootstrapping techniques and the detection of distribution-independent term enrichments.
metabolic rewiring or reprogramming is the alteration of metabolism in living organisms, leading to disordered states aberrant from homeostasis. As large amounts of omics data become available, complex mechanisms lead...
Genome-scale metabolic models (GEMs) are valuable in describing an organism's entire metabolism using genomic information and have made huge impact on systems biology and metabolicengineering. Here we review curr...
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作者:
Jong Hwan BaekNam Su HeoMee-Jung HanSang Yup LeeMetabolic and Biomolecular Engineering National Research Laboratory
Department of Chemical (&) Biomolecular Engineering (BK21 Program) BioPracess Engineering Research Center Center for Systems and Synthetic Biotechnology and Institute for the BioCentury Republic of Korea Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical (&) Biomolecular Engineering (BK21 Program) BioPracess Engineering Research Center Center for Systems and Synthetic Biotechnology and Institute for the BioCentury Republic of Korea Department of Bio and Brain Engineering
and Bioinformatics Research Center KAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea
作者:
Xiao-Xia XiaSang Yup LeeDepartment of Bio and Brain Engineering
and Bioinformatics Research Center KAIST Daejeon Republic of Korea Metabolic and Biomolecular Engineering National Research Laboratory
Department af Chemical and Biomolecular Engineering (BK21 Program) and BioProcess Engineering Research Center Center for Sys tems and Synthetic Biotechnology and Institute for the BioCenturyKAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea Metabolic and Biomolecular Engineering National Research Laboratory Department af Chemical and Biomolecular Engineering (BK21 Program) and BioProcess Engineering Research Center Center for Sys tems and Synthetic Biotechnology and Institute for the BioCenturyKAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea
One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system t...
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One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.
作者:
Zhi-Gang QianXiao-Xia XiaJong Hyun ChoiSang Yup LeeDepartment of Bio and Brain Engineering
and Bioinformatics Research Center KAIST Daejeon Republic of Korea Metabolic and Biomolecular Engineering National Research Labora tory
Department of Chemical and Biomolecular Engineering (BK21 Program) and BioProcess Engineering Research Center Center for Systems and Synthetic Biotechnology and Institute for the BioCenturyKAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea Metabolic and Biomolecular Engineering National Research Labora tory Department of Chemical and Biomolecular Engineering (BK21 Program) and BioProcess Engineering Research Center Center for Systems and Synthetic Biotechnology and Institute for the BioCenturyKAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea
作者:
Xiao-Xia XiaZhi-Gang QianMee-Jung HanSang Yup LeeMetabolic and Biomolecular Engineering National Research Lahoratory
Department of Chemical and Biomolecular Engineering (BK21 Program) and BioProcess Engineering Research Center Cen terror Sys tems and Synthetic Biotechnology and Institute for the BioCenturyKAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea Metabolic and Biomolecular Engineering National Research Lahoratory Department of Chemical and Biomolecular Engineering (BK21 Program) and BioProcess Engineering Research Center Cen terror Sys tems and Synthetic Biotechnology and Institute for the BioCenturyKAIST 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of KoreaDepartment of Bio and Brain Engineering and Bioinformatics Research Center KAIST Daejeon Republic of Korea
Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study aims to exploit bioinformatic tools in order to analyze microarray data of at...
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