Noncoder : a web interface for exon array-based detection of long non-coding RNAs

Due to recent technical developments, a high number of long non-coding RNAs (lncRNAs) have been discovered in mammals. Although it has been shown that lncRNAs are regulated differently among tissues and disease statuses, functions of these transcripts are still unknown in most cases. GeneChip Exon 1.0 ST Arrays (exon arrays) from Affymetrix, Inc. have been used widely to profile genome-wide expression changes and alternative splicing of protein-coding genes. Here, we demonstrate that re-annotation of exon array probes can be used to profile expressions of tens of thousands of lncRNAs. With this annotation, a detailed inspection of lncRNAs and their isoforms is possible. To allow for a general usage to the research community, we developed a user-friendly web interface called 'noncoder'. By uploading CEL files from exon arrays and with a few mouse clicks and parameter settings, exon array data will be normalized and analysed to identify differentially expressed lncRNAs. Noncoder provides the detailed annotation information of lncRNAs and is equipped with unique features to allow for an efficient search for interesting lncRNAs to be studied further. The web interface is available at http://noncoder.mpi-bn.mpg.de

BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction

A novel discrete mathematical approach is proposed as an additional tool for molecular systematics which does not require prior statistical assumptions concerning the evolutionary process. The method is based on algorithms generating mathematical representations directly from DNA/RNA or protein sequences, followed by the output of numerical (scalar or vector) and visual characteristics (graphs). The binary encoded sequence information is transformed into a compact analytical form, called the Iterative Canonical Form (or ICF) of Boolean functions, which can then be used as a generalized molecular descriptor. The method provides raw vector data for calculating different distance matrices, which in turn can be analyzed by neighbor-joining or UPGMA to derive a phylogenetic tree, or by principal coordinates analysis to get an ordination scattergram. The new method and the associated software for inferring phylogenetic trees are called the Boolean analysis or BOOL-AN

Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks

How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

A Framework for XML-based Integration of Data, Visualization and Analysis in a Biomedical Domain

Biomedical data are becoming increasingly complex and heterogeneous in nature. The data are stored in distributed information systems, using a variety of data models, and are processed by increasingly more complex tools that analyze and visualize them. We present in this paper our framework for integrating biomedical research data and tools into a unique Web front end. Our framework is applied to the University of Washington’s Human Brain Project. Specifically, we present solutions to four integration tasks: definition of complex mappings from relational sources to XML, distributed XQuery processing, generation of heterogeneous output formats, and the integration of heterogeneous data visualization and analysis tools