MOTIVATION: Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. RESULTS: Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82-0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. SUMMARY: Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. AVAILABILITY AND IMPLEMENTATION: Datasets are available as part of the Supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Identification of conserved syntenic blocks across microbial genomes is important for several problems in comparative genomics such as gene annotation, study of genome organization and evolution and prediction of gene interactions. Current tools for syntenic block discovery do not scale up to the large quantity of prokaryotic genomes available today. RESULTS: We present a novel methodology for the discovery, ranking and taxonomic distribution analysis of colinear syntenic blocks (CSBs)-groups of genes that are consistently located close to each other, in the same order, across a wide range of taxa. We present an efficient algorithm that identifies CSBs in large genomic datasets. The algorithm is implemented and incorporated in a novel tool with a graphical user interface, denoted CSBFinder, that ranks the discovered CSBs according to a probabilistic score and clusters them to families according to their gene content similarity. We apply CSBFinder to data mine 1487 prokaryotic genomes including chromosomes and plasmids. For post-processing analysis, we generate heatmaps for visualizing the distribution of CSB family members across various taxa. We exemplify the utility of CSBFinder in operon prediction, in deciphering unknown gene function and in taxonomic analysis of colinear syntenic blocks. AVAILABILITY AND IMPLEMENTATION: CSBFinder software and code are publicly available at https://github.com/dinasv/CSBFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: [email protected] SUPPLEMENTARY INFORMATION: Four tables available at the above site.

The interpretation of microarray expression results often includes extensive efforts to identify and annotate the gene representatives immobilised on the arrays. In this paper we describe the usage of our automatic GeneAnnot system, which links between Affymetrix arrays and the rich human gene annotations available in GeneCards. We explain GeneCards search options and results display; elaborate on the presentation of expression information in GeneCards, including both our whole-genome GeneNote project and external expression resources; describe the various parameters and displays used by GeneAnnot to assess the annotation quality and probeset specificity; and show how to search GeneAnnot and GeneNote websites directly.

Abstract Motivation: In the post-genomic era, functional analysis of genes requires a sophisticated interdisciplinary arsenal. Comprehensive resources are challenged to provide consistently improving, state-of-the-art tools. Results: GeneCards (Rebhan et al., 1998) has made innovative strides: (a) regular updates and enhancements incorporating new genes enriched with sequences, genomic locations, cDNA assemblies, orthologies, medical information, 3D protein structures, gene expression, and focused SNP summaries; (b) restructured software using object-oriented Perl, migration to schema-driven XML, and (c) pilot studies, introducing methods to produce cards for novel and predicted genes. Availability: Freely available for educational and research purposes by non-profit institutions at http://bioinfo.weizmann.ac.il/cards and academic mirror sites. Commercial usage requires a license. Contact: [email protected] Supplementary Information: http://bioinfo.weizmann.ac.il/cards/9pageGC2002Bioinformatics.doc; http://bioinfo.weizmann.ac.il/cards/GeneCardByResource.xsd and http://bioinfo.weizmann.ac.il/cards/GeneCardByFunction.xsd. * To whom correspondence should be addressed.