Mayday - integrative analytics for expression data

<p>Abstract</p> <p>Background</p> <p>DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.</p> <p>Results</p> <p>We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.</p> <p>Conclusions</p> <p>We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at <url>http://microarray-analysis.org</url>.</p

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

Mayday SeaSight: Combined Analysis of Deep Sequencing and Microarray Data

Recently emerged deep sequencing technologies offer new high-throughput methods to quantify gene expression, epigenetic modifications and DNA-protein binding. From a computational point of view, the data is very different from that produced by the already established microarray technology, providing a new perspective on the samples under study and complementing microarray gene expression data. Software offering the integrated analysis of data from different technologies is of growing importance as new data emerge in systems biology studies. Mayday is an extensible platform for visual data exploration and interactive analysis and provides many methods for dissecting complex transcriptome datasets. We present Mayday SeaSight, an extension that allows to integrate data from different platforms such as deep sequencing and microarrays. It offers methods for computing expression values from mapped reads and raw microarray data, background correction and normalization and linking microarray probes to genomic coordinates. It is now possible to use Mayday's wealth of methods to analyze sequencing data and to combine data from different technologies in one analysis

GenomeRing: alignment visualization based on SuperGenome coordinates

Motivation: The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge

Histone/Protein Deacetylase 11 Targeting Promotes Foxp3+ Treg Function.

Current interest in Foxp3+ T-regulatory (Treg) cells as therapeutic targets in transplantation is largely focused on their harvesting pre-transplant, expansion and infusion post-transplantation. An alternate strategy of pharmacologic modulation of Treg function using histone/protein deacetylase inhibitors (HDACi) may allow more titratable and longer-term dosing. However, the effects of broadly acting HDACi vary, such that HDAC isoform-selective targeting is likely required. We report data from mice with constitutive or conditional deletion of HDAC11 within Foxp3+ Treg cells, and their use, along with small molecule HDAC11 inhibitors, in allograft models. Global HDAC11 deletion had no effect on health or development, and compared to WT controls, Foxp3+ Tregs lacking HDAC11 showed increased suppressive function, and increased expression of Foxp3 and TGF-β. Likewise, compared to WT recipients, conditional deletion of HDAC11 within Tregs led to long-term survival of fully MHC-mismatched cardiac allografts, and prevented development of transplant arteriosclerosis in an MHC class II-mismatched allograft model. The translational significance of HDAC11 targeting was shown by the ability of an HDAC11i to promote long-term allograft allografts in fully MHC-disparate strains. These data are powerful stimuli for the further development and testing of HDAC11-selective pharmacologic inhibitors, and may ultimately provide new therapies for transplantation and autoimmune diseases

Two Lysines in the Forkhead Domain of Foxp3 Are Key to T Regulatory Cell Function

Background: The forkhead box transcription factor, Foxp3, is master regulator of the development and function of CD4+CD25+ T regulatory (Treg) cells that limit autoimmunity and maintain immune homeostasis. The carboxyl-terminal forkhead (FKH) domain is required for the nuclear localization and DNA binding of Foxp3. We assessed how individual FKH lysines contribute to the functions of Foxp3 in Treg cells. Methodology/Principal Findings: We found that mutation of FKH lysines at position 382 (K17) and at position 393 (K18) impaired Foxp3 DNA binding and inhibited Treg suppressive function in vivo and in vitro. These lysine mutations did not affect the level of expression of Foxp3 but inhibited IL-2 promoter remodeling and had important and differing effects on Treg-associated gene expression. Conclusions/Significance: These data point to complex effects of post-translational modifications at individual lysines within the Foxp3 FKH domain that affect Treg function. Modulation of these events using small molecule inhibitors ma

OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes

Background: The identification of biomarkers for the estimation of cancer patients\u2019 survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patient

nocoRNAc: Characterization of non-coding RNAs in prokaryotes

<p>Abstract</p> <p>Background</p> <p>The interest in non-coding RNAs (ncRNAs) constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA genes across many species. However, for most organisms the non-coding transcriptome still remains unexplored to a great extent. Various experimental techniques for the identification of ncRNA transcripts are available, but as these methods are costly and time-consuming, there is a need for computational methods that allow the detection of functional RNAs in complete genomes in order to suggest elements for further experiments. Several programs for the genome-wide prediction of functional RNAs have been developed but most of them predict a genomic locus with no indication whether the element is transcribed or not.</p> <p>Results</p> <p>We present <smcaps>NOCO</smcaps>RNAc, a program for the genome-wide prediction of ncRNA transcripts in bacteria. <smcaps>NOCO</smcaps>RNAc incorporates various procedures for the detection of transcriptional features which are then integrated with functional ncRNA loci to determine the transcript coordinates. We applied RNAz and <smcaps>NOCO</smcaps>RNAc to the genome of <it>Streptomyces coelicolor </it>and detected more than 800 putative ncRNA transcripts most of them located antisense to protein-coding regions. Using a custom design microarray we profiled the expression of about 400 of these elements and found more than 300 to be transcribed, 38 of them are predicted novel ncRNA genes in intergenic regions. The expression patterns of many ncRNAs are similarly complex as those of the protein-coding genes, in particular many antisense ncRNAs show a high expression correlation with their protein-coding partner.</p> <p>Conclusions</p> <p>We have developed <smcaps>NOCO</smcaps>RNAc, a framework that facilitates the automated characterization of functional ncRNAs. <smcaps>NOCO</smcaps>RNAc increases the confidence of predicted ncRNA loci, especially if they contain transcribed ncRNAs. <smcaps>NOCO</smcaps>RNAc is not restricted to intergenic regions, but it is applicable to the prediction of ncRNA transcripts in whole microbial genomes. The software as well as a user guide and example data is available at <url>http://www.zbit.uni-tuebingen.de/pas/nocornac.htm</url>.</p

Protein sets define disease states and predict in vivo effects of drug treatment

Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousand proteins and observed significant changes in protein pathway (dys-) regulated in diet-induced obesity mice. Analysis of the expression and posttranslational modifications of proteins in various peripheral metabolic target tissues including adipose, heart and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high fat diet and medication with two anti-diabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated for example early on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates