Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis. AVAILABILITY: Free, at http://ctwc.weizmann.ac.il.. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/     

Selecton: a server for detecting evolutionary forces at a single amino-acid site

SUMMARY: We present an algorithmic tool for the identification of biologically significant amino acids in proteins of known three dimensional structure. We estimate the degree of purifying selection and positive Darwinian selection at each site and project these estimates onto the molecular surface of the protein. Thus, patches of functional residues (undergoing either positive or purifying selection), which may be discontinuous in the linear sequence, are revealed. We test for the statistical significance of the site-specific scores in order to obtain reliable and valid estimates. AVAILABILITY: The Selecton web server is available at: http://selecton.bioinfo.tau.ac.il SUPPLEMENTARY INFORMATION: More information is available at http://selecton.bioinfo.tau.ac.il/overview.html. A set of examples is available at http://selecton.bioinfo.tau.ac.il/gallery.html.     

The human olfactory subgenome: from sequence to structure and evolution

Olfactory receptors (ORs) constitute the largest multigene family in multicellular organisms. Their evolutionary proliferation has been driven by the need to provide recognition capacity for millions of potential odorants with arbitrary chemical configurations. Human genome sequencing has provided a highly informative picture of the "olfactory subgenome", the repertoire of OR genes. We describe here an analysis of 224 human OR genes, a much larger number than hitherto systematically analyzed. These are derived by literature survey, data mining at 14 genomic clusters, and by an OR-targeted experimental sequencing strategy. The presented set contains at least 53% pseudogenes and is minimally divided into 11 gene families. One of these (no. 7) has undergone a particularly extensive expansion in primates. The analysis of this collection leads to insight into the origin of OR genes, suggesting a graded expansion through mammalian evolution. It also allows us to delineate a structural map of the respective proteins. A sequence database and analysis package is provided (http://bioinformatics.weizmann.ac.il/HORDE), which will be useful for analyzing human OR sequences genome-wide.     

TRACTS: A program to map oligopurine.oligopyrimidine and other binary DNA tracts

A program to map the locations and frequencies of DNA tracts composed of only two bases ('Binary DNA') is described. The program, TRACTS (URL http://bioportal.weizmann.ac.il/tracts/tracts.html and/or http://bip.weizmann.ac.il/miwbin/servers/tracts) is of interest because long tracts composed of only two bases are highly over-represented in most genomes. In eukaryotes, oligopurine.oligopyrimidine tracts ('R.Y tracts') are found in the highest excess. In prokaryotes, W tracts predominate (A,T 'rich'). A pre-program, ANEX, parses database annotation files of GenBank and EMBL, to produce a convenient one-line list of every gene (exon, intron) in a genome. The main unit lists and analyzes tracts of the three possible binary pairs (R.Y, K.M and S;W). As an example, the results of R.Y tract mapping of mammalian gene p53 is described.     

Coupled two-way clustering server

The CTWC server provides access to the software, CTWC1.00, that implements Coupled Two Way Clustering (Getz et al., 2000), a method designed to mine gene expression data Availability: Free, at http://ctwc.weizmann.ac.il. SUPPLEMENTARY INFORMATION: The site has a link to an example which provides figures and detailed explanations     

Automated analysis of interatomic contacts in proteins.

MOTIVATION: New software has been designed to assist the molecular biologist in understanding the structural consequences of modifying a ligand and/or protein. RESULTS: Tools are described for the analysis of ligand-protein contacts (LPC software) and contacts of structural units (CSU software) such as helices, sheets, strands and residues. Our approach is based on a detailed analysis of interatomic contacts and interface complementarity. For any ligand or structural unit, these software automatically: (i) calculate the solvent-accessible surface of every atom; (ii) determine the contacting residues and type of interaction they undergo (hydrophobic-hydrophobic, aromatic-aromatic, etc.); (iii) indicate all putative hydrogen bonds. LPC software further predicts changes in binding strength following chemical modification of the ligand. AVAILABILITY: Both LPC and CSU can be accessed through the PDB and are integrated in the 3DB Atlas page of all PDB files. For any given file, the tools can also be accessed at http://www.pdb.bnl. gov/pdb-bin/lpc?PDB_ID= and http://www.pdb.bnl. gov/pdb-bin/csu?PDB_ID= with the four-letter PDB code added at the end in each case. Finally, LPC and CSU can be accessed at: http://sgedg.weizmann.ac.il/lpc and http://sgedg.weizmann.ac.il/csu.     

F2CS: FSSP to CATH and SCOP prediction server

The F2CS server provides access to the software, F2CS2.00, which implements an automated prediction method of SCOP and CATH classifications of proteins, based on their FSSP Z-scores. AVAILABILITY: Free at http://www.weizmann.ac.il/physics/complex/compphys/f2cs/ SUPPLEMENTARY INFORMATION: The site contains links to additional figures and tables.     

meta-PPISP: a meta web server for protein-protein interaction site prediction

A number of complementary methods have been developed for predicting protein-protein interaction sites. We sought to increase prediction robustness and accuracy by combining results from different predictors, and report here a meta web server, meta-PPISP, that is built on three individual web servers: cons-PPISP (http://pipe.scs.fsu.edu/ppisp.html), Promate (http://bioportal.weizmann.ac.il/promate), and PINUP (http://sparks.informatics.iupui.edu/PINUP/). A linear regression method, using the raw scores of the three servers as input, was trained on a set of 35 nonhomologous proteins. Cross validation showed that meta-PPISP outperforms all the three individual servers. At coverages identical to those of the individual methods, the accuracy of meta-PPISP is higher by 4.8 to 18.2 percentage points. Similar improvements in accuracy are also seen on CAPRI and other targets. AVAILABILITY: meta-PPISP can be accessed at http://pipe.scs.fsu.edu/meta-ppisp.html     

Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification

MOTIVATION: Genes are often characterized dichotomously as either housekeeping or single-tissue specific. We conjectured that crucial functional information resides in genes with midrange profiles of expression. RESULTS: To obtain such novel information genome-wide, we have determined the mRNA expression levels for one of the largest hitherto analyzed set of 62 839 probesets in 12 representative normal human tissues. Indeed, when using a newly defined graded tissue specificity index tau, valued between 0 for housekeeping genes and 1 for tissue-specific genes, genes with midrange profiles having 0.15< tau<0.85 were found to constitute >50% of all expression patterns. We developed a binary classification, indicating for every gene the I(B) tissues in which it is overly expressed, and the 12-I(B) tissues in which it shows low expression. The 85 dominant midrange patterns with I(B)=2-11 were found to be bimodally distributed, and to contribute most significantly to the definition of tissue specification dendrograms. Our analyses provide a novel route to infer expression profiles for presumed ancestral nodes in the tissue dendrogram. Such definition has uncovered an unsuspected correlation, whereby de novo enhancement and diminution of gene expression go hand in hand. These findings highlight the importance of gene suppression events, with implications to the course of tissue specification in ontogeny and phylogeny. AVAILABILITY: All data and analyses are publically available at the GeneNote website, http://genecards.weizmann.ac.il/genenote/ and, GEO accession GSE803. CONTACT: doron.lancet@weizmann.ac.il SUPPLEMENTARY INFORMATION: Four tables available at the above site.     

Structural systems identification of genetic regulatory networks

MOTIVATION: Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. RESULTS: In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. AVAILABILITY: The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.     

Bioinformatic analysis of exon repetition, exon scrambling and trans-splicing in humans

MOTIVATION: Using bioinformatic approaches we aimed to characterize poorly understood abnormalities in splicing known as exon scrambling, exon repetition and trans-splicing. RESULTS: We developed a software package that allows large-scale comparison of all human expressed sequence tags (EST) sequences to the entire set of human gene sequences. Among 5,992,495 EST sequences, 401 cases of exon repetition and 416 cases of exon scrambling were found. The vast majority of identified ESTs contain fragments rather than full-length repeated or scrambled exons. Their structures suggest that the scrambled or repeated exon fragments may have arisen in the process of cDNA cloning and not from splicing abnormalities. Nevertheless, we found 11 cases of full-length exon repetition showing that this phenomenon is real yet very rare. In searching for examples of trans-splicing, we looked only at reproducible events where at least two independent ESTs represent the same putative trans-splicing event. We found 15 ESTs representing five types of putative trans-splicing. However, all 15 cases were derived from human malignant tissues and could have resulted from genomic rearrangements. Our results provide support for a very rare but physiological occurrence of exon repetition, but suggest that apparent exon scrambling and trans-splicing result, respectively, from in vitro artifact and gene-level abnormalities. AVAILABILITY: Exon-Intron Database (EID) is available at http://www.meduohio.edu/bioinfo/eid. Programs are available at http://www.meduohio.edu/bioinfo/software.html. The Laboratory website is available at http://www.meduohio.edu/medicine/fedorov Supplementary information: Supplementary file is available at http://www.meduohio.edu/bioinfo/software.html.     

Outcome signature genes in breast cancer: is there a unique set?

MOTIVATION: Predicting the metastatic potential of primary malignant tissues has direct bearing on the choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival. Nevertheless, the overlap between these gene sets is almost zero. Such small overlaps were observed also in other complex diseases, and the variables that could account for the differences had evoked a wide interest. One of the main open questions in this context is whether the disparity can be attributed only to trivial reasons such as different technologies, different patients and different types of analyses. RESULTS: To answer this question, we concentrated on a single breast cancer dataset, and analyzed it by a single method, the one which was used by van't Veer et al. to produce a set of outcome-predictive genes. We showed that, in fact, the resulting set of genes is not unique; it is strongly influenced by the subset of patients used for gene selection. Many equally predictive lists could have been produced from the same analysis. Three main properties of the data explain this sensitivity: (1) many genes are correlated with survival; (2) the differences between these correlations are small; (3) the correlations fluctuate strongly when measured over different subsets of patients. A possible biological explanation for these properties is discussed. CONTACT: eytan.domany@weizmann.ac.il SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/     

Generation and analysis of a protein-protein interface data set with similar chemical and spatial patterns of interactions

Protein-protein interfaces are regions between 2 polypeptide chains that are not covalently connected. Here, we have created a nonredundant interface data set generated from all 2-chain interfaces in the Protein Data Bank. This data set is unique, since it contains clusters of interfaces with similar shapes and spatial organization of chemical functional groups. The data set allows statistical investigation of similar interfaces, as well as the identification and analysis of the chemical forces that account for the protein-protein associations. Toward this goal, we have developed I2I-SiteEngine (Interface-to-Interface SiteEngine) [Data set available at http://bioinfo3d.cs.tau.ac.il/Interfaces; Web server: http://bioinfo3d.cs.tau.ac.il/I2I-SiteEngine]. The algorithm recognizes similarities between protein-protein binding surfaces. I2I-SiteEngine is independent of the sequence or the fold of the proteins that comprise the interfaces. In addition to geometry, the method takes into account both the backbone and the side-chain physicochemical properties of the interacting atom groups. Its high efficiency makes it suitable for large-scale database searches and classifications. Below, we briefly describe the I2I-SiteEngine method. We focus on the classification process and the obtained nonredundant protein-protein interface data set. In particular, we analyze the biological significance of the clusters and present examples which illustrate that given constellations of chemical groups in protein-protein binding sites may be preferred, and are observed in proteins with different structures and different functions. We expect that these would yield further information regarding the forces stabilizing protein-protein interactions.