Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Tissue-based map of the human proteome

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

New therapeutic targets for noncognitive reductions in energy intake, absorption, or storage are crucial given the worldwide epidemic of obesity. The gut microbial community (microbiota) is essential for processing dietary polysaccharides. We found that conventionalization of adult germ-free (GF) C57BL/6 mice with a normal microbiota harvested from the distal intestine (cecum) of conventionally raised animals produces a 60% increase in body fat content and insulin resistance within 14 days despite reduced food intake. Studies of GF and conventionalized mice revealed that the microbiota promotes absorption of monosaccharides from the gut lumen, with resulting induction of de novo hepatic lipogenesis. Fasting-induced adipocyte factor (Fiaf), a member of the angiopoietin-like family of proteins, is selectively suppressed in the intestinal epithelium of normal mice by conventionalization. Analysis of GF and conventionalized, normal and Fiaf knockout mice established that Fiaf is a circulating lipoprotein lipase inhibitor and that its suppression is essential for the microbiota-induced deposition of triglycerides in adipocytes. Studies of Rag1-/- animals indicate that these host responses do not require mature lymphocytes. Our findings suggest that the gut microbiota is an important environmental factor that affects energy harvest from the diet and energy storage in the host. Data deposition: The sequences reported in this paper have been deposited in the GenBank database (accession nos. AY 667702--AY 668946).

https://www.pnas.org/content/pnas/101/44/15718.full.pdf

The gut microbiota as an environmental factor that regulates fat storage

Motivation: Transcription factor binding sites often differ significantly in their primary sequence and can hardly be aligned. Often one set of sites can contain several subsets of sequences that follow not just one but several different patterns. There is a need for sensitive methods to reveal multiple patterns in unaligned sets of sequences.

Results: We developed a novel method for analysis of unaligned sets of sequences based on kernel estimation. The method is able to reveal 'multiple local patterns'---a set of weight matrices. Every weight matrix characterizes a pattern that can be found in a significant subset of sequences under analysis. The method developed has been compared with several other methods of pattern discovery such as Gibbs sampling, MEME, CONSENSUS, MULTIPROFILER and PROJECTION. The kernel method showed the best performance in terms of how close the revealed weight matrices are to the original ones. We applied the kernel method to analyze three samples of promoters (cell-cycle, T-cells and muscle-specific). We compared the multiple patterns revealed with the TRANSFAC library of weight matrices and found a strong similarity to several weight matrices for transcription factors known to be involved in the mentioned specific gene regulation.

Availability: The program is available for on-line use at: http://www.biobase.de/cgi-bin/biobase/cbs2/bin/template.cgi?template=cbscall.html

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Recognition of multiple patterns in unaligned sets of sequences: comparison of kernel clustering method with other methods

ClusterScan: A Tool for Automatic Annotation of Genomic Regulatory Sequences by Searching for Composite Clusters.

1 Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan E-mail: taka@bi.is.s.u-tokyo.ac.jp 2 Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8562, Japan E-mail: tt@k.u-tokyo.ac.jp 3 Dept. of Bioinformatics, Medical School, University of Gottingen, Goldschmidtstr. 1, D-37077 Gottingen, Germany E-mail: e.wingender@med.uni-goettingen.de 4 BIOBASE GmbH, Halchtersche Str. 33, D-38304 Wolfenbuttel, Germany E-mail: ewi@biobase.de

The Second Workshop on Ontology and Genome

Das DNA in RNA transkribiert wird, ist eine alte Erkenntnis und als solche Teil des „Zentralen Dogmas” der molekularen Genetik. Wie dieser Prozes jedoch auf die einzelnen Gene hingelenkt wird, ist bei hoheren Zellen (Eukaryonten) immer noch nicht vollig verstanden. Die RNA-Polymerasen bedurfen offensichtlich zur Gen-Erkennung einer ganzen Reihe helfender Faktoren (Transkriptionsfaktoren). Diese bilden am Gen zusammen mit dem Enzym einen Transkriptionskomplex. Der Aufbau dieser Komplexe beginnt klar zu werden, und am grosten ist das Wissen uber die Steuerung der RNA-Polymerase III, die fur die Synthese bestimmter kleiner RNA-Molekule zustandig ist. Wie sehr die Erkenntnisse, die an diesem System gewonnen wurden, Modellcharakter haben, erweist sich besonders deutlich in einer Reihe neuester Arbeiten. Ein besonders gut untersuchtes Protein (TF III A), das ein positiver Regulator fur die Expression ribosomaler 5S-RNA ist, hat Struktureigenschaften, die bisher bei DNA-bindenden Proteinen unbekannt waren. Es zeigt sich immer mehr, das die „Architektur” von TF III A nicht die einer exotischen Kuriositat ist, sondern wahrscheinlich als Beispiel fur einen generellen Bauplan gesehen werden mus.

Transkription in Eukaryonten – die Rolle von Transkriptionskomplexen und ihren Komponenten

Transcription factor binding sites often contain several subtypes of sequences that follow not just one but several different patterns. We developed a novel sensitive method based on kernel estimations that is able to reveal subtypes of TF binding sites. The developed method produces patterns in form of positional weight matrices for the individual subtypes and has been tested on simulated data and compared with several other methods of pattern discovery (Gibbs sampling, MEME, CONSENSUS, MULTIPROFILER and PROJECTION). The kernel method showed the best performance in terms of how close the revealed weight matrices are to the original ones. We applied the Kernel method to several TFs including nuclear receptors and ligand-activated transcription factors AhR. The revealed patterns were applied to analyze gene expression data. In promoters of differentially expressed genes we found specific combinations of different types of TF binding patterns that correlate with the level of up or down regulation.

Edgar Wingender

Papers

Recognition of multiple patterns in unaligned sets of sequences: comparison of kernel clustering method with other methods

ClusterScan: A Tool for Automatic Annotation of Genomic Regulatory Sequences by Searching for Composite Clusters.

The Second Workshop on Ontology and Genome

Transkription in Eukaryonten – die Rolle von Transkriptionskomplexen und ihren Komponenten

Application of kernel method to reveal subtypes of TF binding motifs