miTALOS v2: Analyzing Tissue Specific microRNA Function

MicroRNAs are involved in almost all biological processes and have emerged as regulators of signaling pathways. We show that miRNA target genes and pathway genes are not uniformly expressed across human tissues. To capture tissue specific effects, we developed a novel methodology for tissue specific pathway analysis of miRNAs. We incorporated the most recent and highest quality miRNA targeting data (TargetScan and StarBase), RNA-seq based gene expression data (EBI Expression Atlas) and multiple new pathway data sources to increase the biological relevance of the predicted miRNA-pathway associations. We identified new potential roles of miR-199a-3p, miR-199b-3p and the miR-200 family in hepatocellular carcinoma, involving the regulation of metastasis through MAPK and Wnt signaling. Also, an association of miR-571 and Notch signaling in liver fibrosis was proposed. To facilitate data update and future extensions of our tool, we developed a flexible database backend using the graph database neo4j. The new backend as well as the novel methodology were included in the updated miTALOS v2, a tool that provides insights into tissue specific miRNA regulation of biological pathways. miTALOS v2 is available at http://mips.helmholtz-muenchen.de/mitalos.     

HSC-Explorer: A Curated Database for Hematopoietic Stem Cells

HSC-Explorer (http://mips.helmholtz-muenchen.de/HSC/) is a publicly available, integrative database containing detailed information about the early steps of hematopoiesis. The resource aims at providing fast and easy access to relevant information, in particular to the complex network of interacting cell types and molecules, from the wealth of publications in the field through visualization interfaces. It provides structured information on more than 7000 experimentally validated interactions between molecules, bioprocesses and environmental factors. Information is manually derived by critical reading of the scientific literature from expert annotators. Hematopoiesis-relevant interactions are accompanied with context information such as model organisms and experimental methods for enabling assessment of reliability and relevance of experimental results. Usage of established vocabularies facilitates downstream bioinformatics applications and to convert the results into complex networks. Several predefined datasets (Selected topics) offer insights into stem cell behavior, the stem cell niche and signaling processes supporting hematopoietic stem cell maintenance. HSC-Explorer provides a versatile web-based resource for scientists entering the field of hematopoiesis enabling users to inspect the associated biological processes through interactive graphical presentation.     

The Ontology for Biomedical Investigations

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.     

GraphProt: modeling binding preferences of RNA-binding proteins

We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt.     

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This is a “Topic Page” article for PLOS Computational Biology.

     

SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases

Background

Semantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way.

Results

SPARQLGraph offers an intuitive drag & drop query builder, which converts the visual graph into a query and executes it on a public endpoint. The tool integrates several publicly available Semantic Web databases, including the databases of the just recently released EBI RDF platform. Furthermore, it provides several predefined template queries for answering biological questions. Users can easily create and save new query graphs, which can also be shared with other researchers.

Conclusions

This new graphical way of creating queries for biological Semantic Web databases considerably facilitates usability as it removes the requirement of knowing specific query languages and database structures. The system is freely available at http://sparqlgraph.i-med.ac.at.     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

dna: An R package for differential network analysis

Differential network analysis provides a framework for examining if there is sufficient statistical evidence to conclude that the structure of a network differs under two experimental conditions or if the structures of two networks are different. The R package dna provides tools and procedures for differential network analysis of genomic data. The focus of this package is on gene-gene networks, but the methods are easily adaptable for more general biological processes. This package includes preprocessing tools for simultaneously preparing a pair of networks for analysis, procedures for computing connectivity scores between pairs of genes based on many available statistical techniques, and tools for handling modules of genes based on these scores. Also, procedures are provided for performing permutation tests based on these scores to determine if the connectivity of a gene differs between the two networks, to determine if the connectivity of a particular set of important genes differs between the two networks, and to determine if the overall module structure differs between the two networks. Several built-in options are available for the types of scores and distances used in the testing procedures, and additionally, the procedures provide flexible methods that allow the user to define custom scores and distances.

Availability

dna is freely available at The Comprehensive R Archive Network, http://CRAN.R-project.org/package=dna     

Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG''s regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.     

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This is a “Topic Page” article for PLOS Computational Biology.

     

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.     

SeqFeatR for the Discovery of Feature-Sequence Associations

Specific selection pressures often lead to specifically mutated genomes. The open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures (“features”). For instance, SeqFeatR has been used to discover in viral protein sequences new T cell epitopes for hosts of given HLA types. SeqFeatR supports frequentist and Bayesian methods for the discovery of statistical sequence-feature associations. Moreover, it offers novel ways to visualize results of the statistical analyses and to relate them to further properties. In this article we demonstrate various functions of SeqFeatR with real data. The most frequently used set of functions is also provided by a web server. SeqFeatR is implemented as R package and freely available from the R archive CRAN (http://cran.r-project.org/web/packages/SeqFeatR/index.html). The package includes a tutorial vignette. The software is distributed under the GNU General Public License (version 3 or later). The web server URL is https://seqfeatr.zmb.uni-due.de.     

Count ratio model reveals bias affecting NGS fold changes

Various biases affect high-throughput sequencing read counts. Contrary to the general assumption, we show that bias does not always cancel out when fold changes are computed and that bias affects more than 20% of genes that are called differentially regulated in RNA-seq experiments with drastic effects on subsequent biological interpretation. Here, we propose a novel approach to estimate fold changes. Our method is based on a probabilistic model that directly incorporates count ratios instead of read counts. It provides a theoretical foundation for pseudo-counts and can be used to estimate fold change credible intervals as well as normalization factors that outperform currently used normalization methods. We show that fold change estimates are significantly improved by our method by comparing RNA-seq derived fold changes to qPCR data from the MAQC/SEQC project as a reference and analyzing random barcoded sequencing data. Our software implementation is freely available from the project website http://www.bio.ifi.lmu.de/software/lfc.