Gitools: analysis and visualisation of genomic data using interactive heat-maps

Intuitive visualization of data and results is very important in genomics, especially when many conditions are to be analyzed and compared. Heat-maps have proven very useful for the representation of biological data. Here we present Gitools (http://www.gitools.org), an open-source tool to perform analyses and visualize data and results as interactive heat-maps. Gitools contains data import systems from several sources (i.e. IntOGen, Biomart, KEGG, Gene Ontology), which facilitate the integration of novel data with previous knowledge.     

Functional Genomics: all the king's horses and all the king's men can put Humpty together again

The integration of data from different kinds of high-throughput functional genomic analyses not only permits the accurate determination of the functions of novel genes, but also presents the prospect of a "systems" approach to biology in the future.     

Systems approach to explore components and interactions in the presynapse

The application of proteomic techniques to neuroscientific research provides an opportunity for a greater understanding of nervous system structure and function. As increasing amounts of neuroproteomic data become available, it is necessary to formulate methods to integrate these data in a meaningful way to obtain a more comprehensive picture of neuronal subcompartments. Furthermore, computational methods can be used to make biologically relevant predictions from large proteomic data sets. Here, we applied an integrated proteomics and systems biology approach to characterize the presynaptic (PRE) nerve terminal. For this, we carried out proteomic analyses of presynaptically enriched fractions, and generated a PRE literature‐based protein–protein interaction network. We combined these with other proteomic analyses to generate a core list of 117 PRE proteins, and used graph theory‐inspired algorithms to predict 92 additional components and a PRE complex containing 17 proteins. Some of these predictions were validated experimentally, indicating that the computational analyses can identify novel proteins and complexes in a subcellular compartment. We conclude that the combination of techniques (proteomics, data integration, and computational analyses) used in this study are useful in obtaining a comprehensive understanding of functional components, especially low‐abundance entities and/or interactions in the PRE nerve terminal.     

Inference of biogeographic history by formally integrating distinct lines of evidence: genetic,environmental niche and fossil

A primary focus of historical biogeography is to understand changes in species ranges, abundance and genetic connectivity, and changes in community composition. Traditionally, biogeographic inference has relied on distinct lines of evidence, including DNA sequences, fossils and hindcasted ecological niche models. In this review we propose that the development of integrative modeling approaches that leverage multiple distinct data types from diverse disciplines has the potential to revolutionize the field of biogeography. Although each data type contains information on a distinct aspect of species’ biogeographic histories, few studies formally integrate multiple types in analysis. For example, post hoc congruence among analyses based on different data types (e.g. fossils and genetics) is commonly assumed to indicate likely biogeographic histories. Unfortunately, analyses of different data often reach discordant conclusions. Thus, fundamental and unresolved debates continue regarding speed and timing of postglacial migration, location and size of glacial refugia, and degree of long distance dispersal. Formal statistical integration can help address these issues. More specifically, formal integration can leverage all available evidence, account for inherent biases associated with different data types, and quantify data and process uncertainty. Novel, quantitative integration of data and models across fields is now possible due to recent advances in cyberinfrastructure, spatial modeling, online and aggregated ecological databases, data processing and quantitative methods. Our purpose is to make the case for and give examples of rigorous integration of genetic, fossil and environmental/occurrence data for inferring biogeographic history. In particular, we 1) review the need for such a framework; 2) explain common data types and approaches used to infer biogeographic history (and the challenges with each); 3) review state‐of‐the‐art examples of data integration in biogeography; 4) lay out a series of novel, suggested improvements on current methods; and 5) provide an outlook on technical feasibility and future opportunities.     

Create and assess protein networks through molecular characteristics of individual proteins

MOTIVATION: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. RESULTS: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. AVAILABILITY: http://www.rostlab.org/services/pinat.     

Arabidopsis paves the way: genomic and network analyses in crops

Arabidopsis genomic and network analyses have facilitated crop research towards the understanding of many biological processes of fundamental importance for agriculture. Genes that were identified through genomic analyses in Arabidopsis have been used to manipulate crop traits such as pathogen resistance, yield, water-use efficiency, and drought tolerance, with the effects being tested in field conditions. The integration of diverse Arabidopsis genome-wide datasets in probabilistic functional networks has been demonstrated as a feasible strategy to associate novel genes with traits of interest, and novel genomic methods continue to be developed. The combination of genome-wide location studies, using ChIP-Seq, with gene expression profiling data is affording a genome-wide view of regulatory networks previously delineated through genetic and molecular analyses, leading to the identification of novel components and of new connections within these networks.     

Inference of Low and High-Grade Glioma Gene Regulatory Networks Delineates the Role of Rnd3 in Establishing Multiple Hallmarks of Cancer

Gliomas are a highly heterogeneous group of brain tumours that are refractory to treatment, highly invasive and pro-angiogenic. Glioblastoma patients have an average survival time of less than 15 months. Understanding the molecular basis of different grades of glioma, from well differentiated, low-grade tumours to high-grade tumours, is a key step in defining new therapeutic targets. Here we use a data-driven approach to learn the structure of gene regulatory networks from observational data and use the resulting models to formulate hypothesis on the molecular determinants of glioma stage. Remarkably, integration of available knowledge with functional genomics datasets representing clinical and pre-clinical studies reveals important properties within the regulatory circuits controlling low and high-grade glioma. Our analyses first show that low and high-grade gliomas are characterised by a switch in activity of two subsets of Rho GTPases. The first one is involved in maintaining normal glial cell function, while the second is linked to the establishment of multiple hallmarks of cancer. Next, the development and application of a novel data integration methodology reveals novel functions of RND3 in controlling glioma cell migration, invasion, proliferation, angiogenesis and clinical outcome.     

Cytopathological evaluations combined RNA and protein analyses on defined cell regions using single frozen tissue block

The co-existence of multiple cell components in tissue samples is the main obstacle for precise molecular evaluation on defined cell types. Based on morphological examination, we developed an efficient approach for paralleled RNA and protein isolations from an identical histological region in frozen tissue section. The RNA and protein samples prepared were sufficient for RT-PCR and Western blot analyses, and the results obtained were well coincident each other as well as with the corresponding parameters revealed from immunohistochemical examinations. By this way, the sampling problem caused by cell-cross contamination can be largely avoided, committing the experimental data more specific to a denned cell type. These novel methods thus allow us to use single tissue block for a comprehensive study by integration of conventional cytological evaluations with nucleic acid and protein analyses.     

Integrating forward and reverse proteomics to unravel protein function

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.     

Multi‐modal meta‐analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory‐specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta‐analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi‐modal meta‐analysis approach also identified synthetic lethal partners of cancer drivers, including a co‐dependency of PTEN deficient endometrial cancer cells on RNA helicases.     

Data integration in the era of omics: current and future challenges

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.     

BioDataServer: a SQL-based service for the online integration of life science data

Regarding molecular biology, we see an exponential growth of data and knowledge. Among others, this fact is reflected in more than 300 molecular databases which are readily available on the Internet. The usage of these data requires integration tools capable of complex information fusion processes. This paper will present a novel concept for user specific integration of life science data. Our approach is based on a mediator architecture in conjunction with freely adjustable data schemes. The implemented prototype is called BioDataServer and can be accessed on the Internet: http://integration.genophen.de. To realize a comfortable usage of the resulted data sets of the integration process, a SQL-based query language and a XML data format were developed and implemented.     

Challenges in studying genomic structural variant formation mechanisms: the short-read dilemma and beyond

Next-generation sequencing (NGS) technologies have revolutionised the analysis of genomic structural variants (SVs), providing significant insights into SV de novo formation based on analyses of rearrangement breakpoint junctions. The short DNA reads generated by NGS, however, have also created novel obstacles by biasing the ascertainment of SVs, an aspect that we refer to as the 'short-read dilemma'. For example, recent studies have found that SVs are often complex, with SV formation generating large numbers of breakpoints in a single event (multi-breakpoint SVs) or structurally polymorphic loci having multiple allelic states (multi-allelic SVs). This complexity may be obscured in short reads, unless the data is analysed and interpreted within its wider genomic context. We discuss how novel approaches will help to overcome the short-read dilemma, and how integration of other sources of information, including the structure of chromatin, may help in the future to deepen the understanding of SV formation processes.     

Information Integration in Molecular Bioscience

Integrating information in the molecular biosciences involves more than the cross-referencing of sequences or structures. Experimental protocols, results of computational analyses, annotations and links to relevant literature form integral parts of this information, and impart meaning to sequence or structure. In this review, we examine some existing approaches to integrating information in the molecular biosciences. We consider not only technical issues concerning the integration of heterogeneous data sources and the corresponding semantic implications, but also the integration of analytical results. Within the broad range of strategies for integration of data and information, we distinguish between platforms and developments. We discuss two current platforms and six current developments, and identify what we believe to be their strengths and limitations. We identify key unsolved problems in integrating information in the molecular biosciences, and discuss possible strategies for addressing them including semantic integration using ontologies, XML as a data model, and graphical user interfaces as integrative environments.     

The supermatrix approach to systematics

Recent reviews of the construction of large phylogenies have focused on supertree methods that involve separate analyses of data sets and subsequent integration of the resulting trees. Here, we consider the alternative method of analyzing all character data simultaneously. Such 'supermatrix' analyses use information from each character directly and enable straightforward incorporation of diverse kinds of data, including characters from fossils. The approach has been extended by the development of new methods, including model-based techniques for analyzing heterogeneous data and hierarchical methods for constructing extremely large trees. Recent work also suggests that the problem of missing data in supermatrix analyses has been overstated. Although the supermatrix approach is not suited for all cases, we suggest that its inherent strengths will ensure that it will continue to have a central role in inferring large phylogenetic trees from diverse data.     

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.     

Computational aspects underlying genome to phenome analysis in plants

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high‐throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait?trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.     

Learning Word Meanings: Overnight Integration and Study Modality Effects

According to the complementary learning systems (CLS) account of word learning, novel words are rapidly acquired (learning system 1), but slowly integrated into the mental lexicon (learning system 2). This two-step learning process has been shown to apply to novel word forms. In this study, we investigated whether novel word meanings are also gradually integrated after acquisition by measuring the extent to which newly learned words were able to prime semantically related words at two different time points. In addition, we investigated whether modality at study modulates this integration process. Sixty-four adult participants studied novel words together with written or spoken definitions. These words did not prime semantically related words directly following study, but did so after a 24-hour delay. This significant increase in the magnitude of the priming effect suggests that semantic integration occurs over time. Overall, words that were studied with a written definition showed larger priming effects, suggesting greater integration for the written study modality. Although the process of integration, reflected as an increase in the priming effect over time, did not significantly differ between study modalities, words studied with a written definition showed the most prominent positive effect after a 24-hour delay. Our data suggest that semantic integration requires time, and that studying in written format benefits semantic integration more than studying in spoken format. These findings are discussed in light of the CLS theory of word learning.