@article {17478513,
	title = {DBAli tools: mining the protein structure space},
	journal = {Nucleic Acids Res},
	volume = {35},
	number = {Web Server issue},
	year = {2007},
	note = {Marti-Renom, Marc A Pieper, Ursula Madhusudhan, M S Rossi, Andrea Eswar, Narayanan Davis, Fred P Al-Shahrour, Fatima Dopazo, Joaquin Sali, Andrej GM 62529/GM/NIGMS NIH HHS/United States GM074929/GM/NIGMS NIH HHS/United States GM54762/GM/NIGMS NIH HHS/United States GM71790/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2007 Jul;35(Web Server issue):W393-7. Epub 2007 May 3.},
	pages = {W393-7},
	abstract = {The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.},
	keywords = {*Algorithms Amino Acid Sequence Computational Biology/*methods Data Interpretation, Amino Acid *Software Structure-Activity Relationship, Protein Internet Molecular Sequence Data Protein Conformation Proteins/*chemistry/classification/*metabolism Pseudomonas aeruginosa/*metabolism Sequence Alignment/*methods Sequence Analysis, Protein/*methods Sequence Homology, Statistical *Databases},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17478513},
	author = {M. A. Marti-Renom and Pieper, U. and Madhusudhan, M. S. and Rossi, A. and Eswar, N. and Davis, F. P. and Fatima Al-Shahrour and Dopazo, J. and Sali, A.}
}
@article {15840702,
	title = {Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information},
	journal = {Bioinformatics},
	volume = {21},
	number = {13},
	year = {2005},
	note = {<p>Al-Shahrour, Fatima Diaz-Uriarte, Ramon Dopazo, Joaquin Evaluation Studies Research Support, Non-U.S. Gov{\textquoteright}t England Bioinformatics (Oxford, England) Bioinformatics. 2005 Jul 1;21(13):2988-93. Epub 2005 Apr 19.</p>},
	pages = {2988-93},
	abstract = {<p>MOTIVATION: The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS: We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.</p>},
	keywords = {babelomics, Biological Neoplasm Proteins/genetics/*metabolism Phenotype Software Structure-Activity Relationship Systems Integration Tumor Markers, Biological/genetics/*metabolism, Breast Neoplasms/genetics/*metabolism Computer Simulation *Database Management Systems *Databases, Protein Documentation/methods Gene Expression Profiling/*methods Humans *Models},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15840702},
	author = {Fatima Al-Shahrour and Diaz-Uriarte, R. and Dopazo, J.}
}
