Pub Date : 2012-09-07DOI: 10.2174/1875036201206010043
V. Martyanov, R. H. Gross
The transcription factor complexes Mlu1-box binding factor (MBF) and Swi4/6 cell cycle box binding factor (SBF) regulate the cell cycle in Saccharomyces cerevisiae. They activate hundreds of genes and are responsible for nor- mal cell cycle progression from G1 to S phase. We investigated the conservation of MBF and SBF binding sites during fungal evolution. Orthologs of S. cerevisiae targets of these transcription factors were identified in 37 fungal species and their upstream regions were analyzed for putative transcription factor binding sites. Both groups displayed enrichment in specific putative regulatory DNA sequences in their upstream regions and showed different preferred upstream motif loca- tions, variable patterns of evolutionary conservation of the motifs and enrichment in unique biological functions for the regulated genes. The results indicate that despite high sequence similarity of upstream DNA motifs putatively associated with G1-S transcriptional regulation by MBF and SBF transcription factors, there are important upstream sequence feature differences that may help differentiate the two seemingly similar regulatory modes. The incorporation of upstream motif sequence comparison, positional distribution and evolutionary variability of the motif can complement functional infor- mation about roles of the respective gene products and help elucidate transcriptional regulatory pathways and functions.
{"title":"Transcriptional Regulation in the G1-S Cell Cycle Stage in Fungi: Insights through Computational Analysis","authors":"V. Martyanov, R. H. Gross","doi":"10.2174/1875036201206010043","DOIUrl":"https://doi.org/10.2174/1875036201206010043","url":null,"abstract":"The transcription factor complexes Mlu1-box binding factor (MBF) and Swi4/6 cell cycle box binding factor (SBF) regulate the cell cycle in Saccharomyces cerevisiae. They activate hundreds of genes and are responsible for nor- mal cell cycle progression from G1 to S phase. We investigated the conservation of MBF and SBF binding sites during fungal evolution. Orthologs of S. cerevisiae targets of these transcription factors were identified in 37 fungal species and their upstream regions were analyzed for putative transcription factor binding sites. Both groups displayed enrichment in specific putative regulatory DNA sequences in their upstream regions and showed different preferred upstream motif loca- tions, variable patterns of evolutionary conservation of the motifs and enrichment in unique biological functions for the regulated genes. The results indicate that despite high sequence similarity of upstream DNA motifs putatively associated with G1-S transcriptional regulation by MBF and SBF transcription factors, there are important upstream sequence feature differences that may help differentiate the two seemingly similar regulatory modes. The incorporation of upstream motif sequence comparison, positional distribution and evolutionary variability of the motif can complement functional infor- mation about roles of the respective gene products and help elucidate transcriptional regulatory pathways and functions.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"6 1","pages":"43-54"},"PeriodicalIF":0.0,"publicationDate":"2012-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-08-31DOI: 10.2174/1875036201206010037
Dajie Luo, Prithish Banerjee, E. Harner, J. Mobley, Dongquan Chen
Background: Pre-processing, including normalization of raw microarray data is crucial to microarray-related data analysis. It takes time and effort to build newly-developed algorithms into commercial software or locally developed systems. While most new algorithms emerge in the form of sharable R packages, it can be difficult for many biologists to apply them as soon as they are available. Currently, we rely on statisticians and experienced programmers to develop and implement code to access those R packages. Therefore, we need a robust procedure to quickly implement pre-processing methods as they appear. The newly emerging cloud computing concept has directed us toward a new way for providing an easily accessible service to the biologists without requiring them to have any programming knowledge in R. Results: Based on our earlier Java-based software tool JavaStat, we developed an internet based application prototype to upload data and carry out pre-processing applications that include normalization, statistical analyses and plots. More im- portantly, R packages, e. g., for newly-developed normalization methods, and GC-robust multichip algorithm (RMA) for exon arrays, can be easily incorporated into the system with limited inputs from a biologist or a programmer. The data are stored in the cloud and the R code runs on server. Conclusion: The newly emerged cloud computing concept provides us a new way to provide an easily accessible and up- to-date service to biologists, as evidenced by our JavaStat system to incorporate new pre-processing package as they ap- pear. Users can access the application with a newly incorporated module through the Web. We expect this and other simi- lar systems greatly decrease turn-around time, improve accessibility of newly developed R model for pre-processing algo- rithms.
{"title":"A Cloud Computing System to Quickly Implement New Microarray Data Pre-processing Methods","authors":"Dajie Luo, Prithish Banerjee, E. Harner, J. Mobley, Dongquan Chen","doi":"10.2174/1875036201206010037","DOIUrl":"https://doi.org/10.2174/1875036201206010037","url":null,"abstract":"Background: Pre-processing, including normalization of raw microarray data is crucial to microarray-related data analysis. It takes time and effort to build newly-developed algorithms into commercial software or locally developed systems. While most new algorithms emerge in the form of sharable R packages, it can be difficult for many biologists to apply them as soon as they are available. Currently, we rely on statisticians and experienced programmers to develop and implement code to access those R packages. Therefore, we need a robust procedure to quickly implement pre-processing methods as they appear. The newly emerging cloud computing concept has directed us toward a new way for providing an easily accessible service to the biologists without requiring them to have any programming knowledge in R. Results: Based on our earlier Java-based software tool JavaStat, we developed an internet based application prototype to upload data and carry out pre-processing applications that include normalization, statistical analyses and plots. More im- portantly, R packages, e. g., for newly-developed normalization methods, and GC-robust multichip algorithm (RMA) for exon arrays, can be easily incorporated into the system with limited inputs from a biologist or a programmer. The data are stored in the cloud and the R code runs on server. Conclusion: The newly emerged cloud computing concept provides us a new way to provide an easily accessible and up- to-date service to biologists, as evidenced by our JavaStat system to incorporate new pre-processing package as they ap- pear. Users can access the application with a newly incorporated module through the Web. We expect this and other simi- lar systems greatly decrease turn-around time, improve accessibility of newly developed R model for pre-processing algo- rithms.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"6 1","pages":"37-42"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-18DOI: 10.2174/1875036201206010028
Jianfeng Zhu, Songgang Li, Wei-Mou Zheng
We extend the self-organizing approach for annotation of a bacterial genome to analyzing the raw sequencing data of the human gut metagenome without sequence assembling. The original approach divides the genomic sequence of a bacterium into non-overlapping segments of equal length and assigns to each segment one of seven 'phases', among which one is for the noncoding regions, three for the direct coding regions to indicate the three possible codon positions of the segment starting site, and three for the reverse coding regions. The noncoding phase and the six coding phases are described by two frequency tables of the 64 triplet types or 'codon usages'. A set of codon usages can be used to update the phase assignment and vice versa. After an initialization of phase assignment or codon usage tables, an iteration leads to a convergent phase assignment to give an annotation of the genome. In the extension of the approach to a metagenome, we consider a mixture model of a number of categories of genomes. The Illumina Genome Analyzer sequencing data of the total DNA from faecal samples are then examined to understand the diversity of the human gut microbiome.
{"title":"Self-organizing Approach for the Human Gut Meta-genome","authors":"Jianfeng Zhu, Songgang Li, Wei-Mou Zheng","doi":"10.2174/1875036201206010028","DOIUrl":"https://doi.org/10.2174/1875036201206010028","url":null,"abstract":"We extend the self-organizing approach for annotation of a bacterial genome to analyzing the raw sequencing data of the human gut metagenome without sequence assembling. The original approach divides the genomic sequence of a bacterium into non-overlapping segments of equal length and assigns to each segment one of seven 'phases', among which one is for the noncoding regions, three for the direct coding regions to indicate the three possible codon positions of the segment starting site, and three for the reverse coding regions. The noncoding phase and the six coding phases are described by two frequency tables of the 64 triplet types or 'codon usages'. A set of codon usages can be used to update the phase assignment and vice versa. After an initialization of phase assignment or codon usage tables, an iteration leads to a convergent phase assignment to give an annotation of the genome. In the extension of the approach to a metagenome, we consider a mixture model of a number of categories of genomes. The Illumina Genome Analyzer sequencing data of the total DNA from faecal samples are then examined to understand the diversity of the human gut microbiome.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"6 1","pages":"28-36"},"PeriodicalIF":0.0,"publicationDate":"2012-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-05-09DOI: 10.2174/1875036201206010020
Nicole C. Arrigo, P. Paci, L. Paola, D. Santoni, M. Ruvo, A. Giuliani, F. Castiglione
A fully quantitative shape index relying upon the asymmetry of mass distribution of protein molecules along the three space dimensions is proposed. Multidimensional statistical analysis, based on principal component extraction and subsequent linear discriminant analysis, showed the presence of three major 'attractor forms' roughly correspondent to rod-like, discoidal and spherical shapes. This classification of protein shapes was in turn demonstrated to be strictly connected with topological features of proteins, as emerging from complex network invariants of their contact maps.
{"title":"Characterizing Protein Shape by a Volume Distribution Asymmetry Index","authors":"Nicole C. Arrigo, P. Paci, L. Paola, D. Santoni, M. Ruvo, A. Giuliani, F. Castiglione","doi":"10.2174/1875036201206010020","DOIUrl":"https://doi.org/10.2174/1875036201206010020","url":null,"abstract":"A fully quantitative shape index relying upon the asymmetry of mass distribution of protein molecules along the three space dimensions is proposed. Multidimensional statistical analysis, based on principal component extraction and subsequent linear discriminant analysis, showed the presence of three major 'attractor forms' roughly correspondent to rod-like, discoidal and spherical shapes. This classification of protein shapes was in turn demonstrated to be strictly connected with topological features of proteins, as emerging from complex network invariants of their contact maps.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"6 1","pages":"20-27"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-02-21DOI: 10.2174/1875036201206010009
H. Wako, S. Endo
The database ProMode-Oligomer (http://promode.socs.waseda.ac.jp/promode_oligomer) was constructed by collecting normal-mode-analysis (NMA) results for oligomeric proteins including protein-protein complexes. As in the ProMode database developed earlier for monomers and individual subunits of oligomers (Bioinformatics vol. 20, pp. 2035-2043, 2004), NMA was performed for a full-atom system using dihedral angles as independent variables, and we re- leased the results (fluctuations of atoms, fluctuations of dihedral angles, correlations between atomic fluctuations, etc.). The vibrating oligomer is visualized by animation in an interactive molecular viewer for each of the 20 lowest-frequency normal modes. In addition, displacement vectors of constituent atoms for each normal mode were decomposed into two characteristic motions in individual subunits, i.e., internal and external (deformation and rigid-body movements of the in- dividual subunits, respectively), and then the mutual movements of the subunits and the movement of atoms around the interface regions were investigated. These results released in ProMode-Oligomer are useful for characterizing oligomeric proteins from a dynamic point of view. The analyses are illustrated with immunoglobulin light- and heavy-chain variable domains bound to lysozyme and to a 12-residue peptide.
通过收集低聚蛋白(包括蛋白-蛋白复合物)的正常模式分析(NMA)结果，构建数据库ProMode-Oligomer (http://promode.socs.waseda.ac.jp/promode_oligomer)。正如之前为单体和低聚物的单个亚基开发的ProMode数据库一样(生物信息学vol. 20, pp. 2035-2043, 2004)， NMA是对使用二面角作为自变量的全原子系统进行的，我们公布了结果(原子的波动、二面角的波动、原子波动之间的相关性等)。在交互式分子观察器中，对20种最低频率正态模式中的每一种，通过动画显示振动低聚物。此外，将每个法向模的组成原子的位移向量分解为单个亚基的内部和外部两个特征运动(分别为单个亚基的变形和刚体运动)，然后研究亚基之间的相互运动和原子在界面区域周围的运动。这些结果在ProMode-Oligomer上发表，对从动态的角度表征寡聚蛋白是有用的。免疫球蛋白轻链和重链可变结构域与溶菌酶和12个残基肽结合。
{"title":"ProMode-Oligomer: Database of Normal Mode Analysis in Dihedral Angle Space for a Full-Atom System of Oligomeric Proteins","authors":"H. Wako, S. Endo","doi":"10.2174/1875036201206010009","DOIUrl":"https://doi.org/10.2174/1875036201206010009","url":null,"abstract":"The database ProMode-Oligomer (http://promode.socs.waseda.ac.jp/promode_oligomer) was constructed by collecting normal-mode-analysis (NMA) results for oligomeric proteins including protein-protein complexes. As in the ProMode database developed earlier for monomers and individual subunits of oligomers (Bioinformatics vol. 20, pp. 2035-2043, 2004), NMA was performed for a full-atom system using dihedral angles as independent variables, and we re- leased the results (fluctuations of atoms, fluctuations of dihedral angles, correlations between atomic fluctuations, etc.). The vibrating oligomer is visualized by animation in an interactive molecular viewer for each of the 20 lowest-frequency normal modes. In addition, displacement vectors of constituent atoms for each normal mode were decomposed into two characteristic motions in individual subunits, i.e., internal and external (deformation and rigid-body movements of the in- dividual subunits, respectively), and then the mutual movements of the subunits and the movement of atoms around the interface regions were investigated. These results released in ProMode-Oligomer are useful for characterizing oligomeric proteins from a dynamic point of view. The analyses are illustrated with immunoglobulin light- and heavy-chain variable domains bound to lysozyme and to a 12-residue peptide.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"6 1","pages":"9-19"},"PeriodicalIF":0.0,"publicationDate":"2012-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-01-24DOI: 10.2174/1875036201206010001
H. Zeng, Ke-Song Liu, Wei Zheng
The Miyazawa-Jernigan (MJ) contact potential for globular proteins is a widely used knowledge-based potential. It is well known that MJ's contact energies mainly come from one-body terms. Directly in the framework of the MJ energy for a protein, we derive the one-body term based on a probabilistic model, and compare the term with several hydrophobicity scales of amino acids. This derivation is based on a set of native structures, and the only information of structures manipulated in the analysis is the contact numbers of each residue. Contact numbers strongly correlate with layers of a protein when it is viewed as an ellipsoid. Using an entropic clustering approach, we obtain two coarse-grained states by maximizing the mutual information between coordination numbers and residue types, and find their differences in the two-body correction. A contact definition using sidechain centers roughly estimated from C atoms results in no significant changes.
{"title":"The Miyazawa-Jernigan Contact Energies Revisited","authors":"H. Zeng, Ke-Song Liu, Wei Zheng","doi":"10.2174/1875036201206010001","DOIUrl":"https://doi.org/10.2174/1875036201206010001","url":null,"abstract":"The Miyazawa-Jernigan (MJ) contact potential for globular proteins is a widely used knowledge-based potential. It is well known that MJ's contact energies mainly come from one-body terms. Directly in the framework of the MJ energy for a protein, we derive the one-body term based on a probabilistic model, and compare the term with several hydrophobicity scales of amino acids. This derivation is based on a set of native structures, and the only information of structures manipulated in the analysis is the contact numbers of each residue. Contact numbers strongly correlate with layers of a protein when it is viewed as an ellipsoid. Using an entropic clustering approach, we obtain two coarse-grained states by maximizing the mutual information between coordination numbers and residue types, and find their differences in the two-body correction. A contact definition using sidechain centers roughly estimated from C atoms results in no significant changes.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"6 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2012-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-09DOI: 10.2174/1875036201105010069
Y. Liu
Different data sources have been used to learn gene function. Whereas combining heterogeneous data sets to infer gene function has been widely studied, there is no empirical comparison to determine the relative effectiveness or usefulness of different types of data in terms of gene function prediction. In this paper, we report a comparative study of yeast gene function prediction using different data sources, namely microarray data, phylogenetic data, literature text data, and a combination of these three data sources. Our results showed that text data outperformed microarray data and phylo- genetic data in gene function prediction (p 0.05). The com- bined data led to decreased prediction performance relative to text data. In addition, we showed that feature selection did not improve the prediction performance of support vector machines.
{"title":"Yeast Gene Function Prediction from Different Data Sources: An Empirical Comparison","authors":"Y. Liu","doi":"10.2174/1875036201105010069","DOIUrl":"https://doi.org/10.2174/1875036201105010069","url":null,"abstract":"Different data sources have been used to learn gene function. Whereas combining heterogeneous data sets to infer gene function has been widely studied, there is no empirical comparison to determine the relative effectiveness or usefulness of different types of data in terms of gene function prediction. In this paper, we report a comparative study of yeast gene function prediction using different data sources, namely microarray data, phylogenetic data, literature text data, and a combination of these three data sources. Our results showed that text data outperformed microarray data and phylo- genetic data in gene function prediction (p 0.05). The com- bined data led to decreased prediction performance relative to text data. In addition, we showed that feature selection did not improve the prediction performance of support vector machines.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"5 1","pages":"69-76"},"PeriodicalIF":0.0,"publicationDate":"2011-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-20DOI: 10.2174/1875036201105010059
Y. Ishizuka, T. Kikuchi
The sequences of azurin and titin, sandwich proteins, are analyzed based on inter-residue average distance statistics. A kind of predicted contact map based on inter-residue average distance statistics (Average Distance Map, ADM) is used to pinpoint regions of possible compact regions for two proteins. We compare predicted compact regions with the positions of the residues with experimental high values for these proteins reported in the literature. The results reveal that the regions predicted by ADMs correspond to the positions of residues with the high value. Furthermore, we perform random sampling of 3D conformations using these protein sequences with a potential derived from inter-residue average distance statistics. It is demonstrated that the residues with highest contact frequency during the simulations quali- tatively correspond to the residues with the highest values for these proteins. Importantly, analysis with inter-residue av- erage distance statistics predicts the properties of folding processes of the sandwich proteins starting from only sequence information.
{"title":"Analysis of the Local Sequences of Folding Sites in β Sandwich Proteinswith Inter-Residue Average Distance Statistics","authors":"Y. Ishizuka, T. Kikuchi","doi":"10.2174/1875036201105010059","DOIUrl":"https://doi.org/10.2174/1875036201105010059","url":null,"abstract":"The sequences of azurin and titin, sandwich proteins, are analyzed based on inter-residue average distance statistics. A kind of predicted contact map based on inter-residue average distance statistics (Average Distance Map, ADM) is used to pinpoint regions of possible compact regions for two proteins. We compare predicted compact regions with the positions of the residues with experimental high values for these proteins reported in the literature. The results reveal that the regions predicted by ADMs correspond to the positions of residues with the high value. Furthermore, we perform random sampling of 3D conformations using these protein sequences with a potential derived from inter-residue average distance statistics. It is demonstrated that the residues with highest contact frequency during the simulations quali- tatively correspond to the residues with the highest values for these proteins. Importantly, analysis with inter-residue av- erage distance statistics predicts the properties of folding processes of the sandwich proteins starting from only sequence information.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"5 1","pages":"59-68"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-02-11DOI: 10.2174/1875036201105010053
M. Vijayabaskar, V. Niranjan, S. Vishveshwara
Protein structures can be represented as graphs/networks by defining the amino-acids as nodes and the noncovalent interactions as connections (edges). An analysis of such a graph provides valuable insights into the global structural properties, function, folding, and stability of proteins. Here we have created a webtool GraProStr to generate protein structure networks and analyze network parameters. Protein side-chain based, C /C backbone based or proteinligand Graphs/Networks can be generated using GraProStr. The well tested tool is now made available to the scientific community for the first time. GraProStr is available online and can be accessed from http://vishgraph.mbu.iisc.ernet.in/GraProStr/index.html using any of the internet browsers (best viewed in Mozilla Firefox version 3.6). The webtool is written using Perl CGI and available using Apache Webserver. With its customizable definitions of protein structure networks and well defined network parameters, GraProStr can be a very useful tool for both theoretical and experimental elucidation of protein structures.
{"title":"GraProStr - Graphs of Protein Structures: A Tool for Constructing the Graphs and Generating Graph Parameters for Protein Structures","authors":"M. Vijayabaskar, V. Niranjan, S. Vishveshwara","doi":"10.2174/1875036201105010053","DOIUrl":"https://doi.org/10.2174/1875036201105010053","url":null,"abstract":"Protein structures can be represented as graphs/networks by defining the amino-acids as nodes and the noncovalent interactions as connections (edges). An analysis of such a graph provides valuable insights into the global structural properties, function, folding, and stability of proteins. Here we have created a webtool GraProStr to generate protein structure networks and analyze network parameters. Protein side-chain based, C /C backbone based or proteinligand Graphs/Networks can be generated using GraProStr. The well tested tool is now made available to the scientific community for the first time. GraProStr is available online and can be accessed from http://vishgraph.mbu.iisc.ernet.in/GraProStr/index.html using any of the internet browsers (best viewed in Mozilla Firefox version 3.6). The webtool is written using Perl CGI and available using Apache Webserver. With its customizable definitions of protein structure networks and well defined network parameters, GraProStr can be a very useful tool for both theoretical and experimental elucidation of protein structures.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"5 1","pages":"53-58"},"PeriodicalIF":0.0,"publicationDate":"2011-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-02-02DOI: 10.2174/1875036201105010034
J. Nacher, T. Ochiai
Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emer- gence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to recon- struct, characterize and complete gene networks. We also discuss several methods based on simulations of a series of en- zyme-catalyzed reaction routes and Markov processes as well as combination of complex network properties with sto- chastic theory.
{"title":"Emergent Principles in Gene Expression Dynamics","authors":"J. Nacher, T. Ochiai","doi":"10.2174/1875036201105010034","DOIUrl":"https://doi.org/10.2174/1875036201105010034","url":null,"abstract":"Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emer- gence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to recon- struct, characterize and complete gene networks. We also discuss several methods based on simulations of a series of en- zyme-catalyzed reaction routes and Markov processes as well as combination of complex network properties with sto- chastic theory.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"5 1","pages":"34-41"},"PeriodicalIF":0.0,"publicationDate":"2011-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68106211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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