twilight; a Bioconductor package for estimating the local false discovery rate

SUMMARY: twilight is a Bioconductor compatible package for analysing the statistical significance of differentially expressed genes. It is based on the concept of the local false discovery rate (FDR), a generalization of the frequently used global FDR. twilight implements the heuristic search algorithm for estimating the local FDR introduced in our earlier work. In addition to the raw significance measures, it produces diagnostic plots, which provide insight into the extent of differential expression across genes. AVAILABILITY: http://www.bioconductor.org CONTACT: stefanie.scheid@molgen.mpg.de SUPPLEMENTARY INFORMATION: Please visit our software webpage on http://compdiag.molgen.mpg.de/software.     

MACAT--microarray chromosome analysis tool

SUMMARY: By linking differential gene expression to the chromosomal localization of genes, one can investigate microarray data for characteristic patterns of expression phenomena involving sizeable parts of specific chromosomes. We have implemented a statistical approach for identifying significantly differentially expressed chromosome regions. We demonstrate the applicability of the approach on a publicly available data set on acute lymphocytic leukemia. AVAILABILITY: The R-package MACAT can be obtained from http://www.compdiag.molgen.mpg.de/software/macat.shtml SUPPLEMENTARY INFORMATION: http://www.compdiag.molgen.mpg.de/software/macat.shtml.     

A stochastic downhill search algorithm for estimating the local false discovery rate

Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a statistical concept for quantifying uncertainty in multiple testing. We introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p-values of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.     

ROCR: visualizing classifier performance in R

SUMMARY: ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. AVAILABILITY: http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent. CONTACT: tobias.sing@mpi-sb.mpg.de.     

EpiExplorer: live exploration and global analysis of large epigenomic datasets

Epigenome mapping consortia are generating resources of tremendous value for studying epigenetic regulation. To maximize their utility and impact, new tools are needed that facilitate interactive analysis of epigenome datasets. Here we describe EpiExplorer, a web tool for exploring genome and epigenome data on a genomic scale. We demonstrate EpiExplorer's utility by describing a hypothesis-generating analysis of DNA hydroxymethylation in relation to public reference maps of the human epigenome. All EpiExplorer analyses are performed dynamically within seconds, using an efficient and versatile text indexing scheme that we introduce to bioinformatics. EpiExplorer is available at http://epiexplorer.mpi-inf.mpg.de.     

Annotation-based distance measures for patient subgroup discovery in clinical microarray studies

MOTIVATION: Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been suggested, but little attention has been given to the distance measure between patients. Even with the Euclidean metric, including and excluding genes from the analysis leads to different distances between the same objects, and consequently different clustering results. RESULTS: We describe a new clustering algorithm, in which gene selection is used to derive biologically meaningful clusterings of samples by combining expression profiles and functional annotation data. According to gene annotations, candidate gene sets with specific functional characterizations are generated. Each set defines a different distance measure between patients, leading to different clusterings. These clusterings are filtered using a resampling-based significance measure. Significant clusterings are reported together with the underlying gene sets and their functional definition. CONCLUSIONS: Our method reports clusterings defined by biologically focused sets of genes. In annotation-driven clusterings, we have recovered clinically relevant patient subgroups through biologically plausible sets of genes as well as new subgroupings. We conjecture that our method has the potential to reveal so far unknown, clinically relevant classes of patients in an unsupervised manner. AVAILABILITY: We provide the R package adSplit as part of Bioconductor release 1.9 and on http://compdiag.molgen.mpg.de/software.     

The protein information resource (PIR)

The Protein Information Resource (PIR) produces the largest, most comprehensive, annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Sequence Database (JIPID). The expanded PIR WWW site allows sequence similarity and text searching of the Protein Sequence Database and auxiliary databases. Several new web-based search engines combine searches of sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. New capabilities for searching the PIR sequence databases include annotation-sorted search, domain search, combined global and domain search, and interactive text searches. The PIR-International databases and search tools are accessible on the PIR WWW site at http://pir.georgetown.edu and at the MIPS WWW site at http://www. mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.     

PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs

The regulation of intragenic miRNAs by their own intronic promoters is one of the open problems of miRNA biogenesis. Here, we describe PROmiRNA, a new approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features. We validate our results with existing annotation, PolII occupancy data and read coverage from RNA-seq data. Compared to previous methods PROmiRNA increases the detection rate of intronic promoters by 30%, allowing us to perform a large-scale analysis of their genomic features, as well as elucidate their contribution to tissue-specific regulation. PROmiRNA can be downloaded from http://promirna.molgen.mpg.de.     

Gene-Ontology-based clustering of gene expression data

The expected correlation between genetic co-regulation and affiliation to a common biological process is not necessarily the case when numerical cluster algorithms are applied to gene expression data. GO-Cluster uses the tree structure of the Gene Ontology database as a framework for numerical clustering, and thus allowing a simple visualization of gene expression data at various levels of the ontology tree. AVAILABILITY: The 32-bit Windows application is freely available at http://www.mpibpc.mpg.de/go-cluster/     

Mtreemix: a software package for learning and using mixture models of mutagenetic trees

SUMMARY: Mixture models of mutagenetic trees constitute a class of probabilistic models for describing evolutionary processes that are characterized by the accumulation of permanent genetic changes. They have been applied to model the accumulation of chromosomal gains and losses in tumor development and the development of drug resistance-associated mutations in the HIV genome.Mtreemix is a software package for estimating mutagenetic trees mixture models from observed cross-sectional data and for using these models for predictions. We provide programs for model fitting, model selection, simulation, likelihood computation and waiting time estimation. AVAILABILITY: Mtreemix, including source code, documentation, sample data files and precompiled Solaris and Linux binaries, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de/     

Predictive medicine by cytomics: potential and challenges

Predictive medicine by cytomics represents a new concept which provides disease course predictions for individual patients. The predictive information is derived from the molecular cell phenotypes as they are determined by patient's genotype and exposure to external or internal influences. The predictions are dynamic because they are therapy dependent. They may provide a therapeutic lead time for preventive therapy or for the diminution of disease associated irreversible tissue damage. Multiparametric data from cytometry, multiple clinical chemistry assays, chip or bead arrays serve as input for an algorithmic data sieving procedure (http://www.biochem.mpg.de/valet/classif1.html). Data sieving enriches the discriminatory parameters in form of standardized data masks for predictive or diagnostic disease classification in the individual patient (http://www.biochem.mpg.de/valet/cellclas.html). Besides predictive and diagnostic utility, the data patterns can be used in a bottom-up approach for the development of scientific hypotheses on disease inducing mechanisms in complex inflammatory, infectious, allergic, malignant or degenerative diseases.     

HOMGL-comparing genelists across species and with different accession numbers

Homology Gene List (HOMGL) is a web-based tool for comparing gene lists with different accession numbers and identifiers and between different organisms. UniGene, LocusLink, HomoloGene and Ensembl databases are utilized to map between these lists and to retrieve upstream or transcribed sequences for genes in these lists. We illustrate the use of HOMGL with respect to microarray studies and promoter analysis. AVAILABILITY: http://homgl.biologie.hu-berlin.de/     

The guide RNA database (3.0).

The RNA editing process within the mitochondria of kinetoplastid organisms is controlled by small, trans -acting RNA molecules referred to as guide RNAs. The guide RNA database is a compilation of published guide RNA sequences, currently containing 254 entries from 11 different organisms. Additional information includes RNA secondary and tertiary structure models, information on the gene localisation, literature citations and other relevant facts. The database can be accessed through the World Wide Web (WWW) at http://www.biochem.mpg.de/ goeringe/     

The Database of Ribosomal Cross-links: an update.

The Database of Ribosomal Cross-links (DRC) was created in 1997. Here we describe new data incorporated into this database and several new features of the DRC. The DRC is freely available via World Wide Web at http://visitweb.com/database/ or http://www. mpimg-berlin-dahlem.mpg.de/ approximately ag_ribo/ag_brimacombe/drc/     

Xdigitise: visualization of hybridization experiments

Xdigitise is a software system for visualization of hybridization experiments giving the user facilities to analyze the corresponding images manually or automatically. Images of the high-density DNA arrays are displayed as well as the results of an external image analysis bundled with Xdigitise, e.g. the spot locations are marked and the duplicate correlations are shown by a color scale. AVAILABILITY: Xdigitise can be downloaded from http://www.molgen.mpg.de/~xdigitise.