BACA: bubble chArt to compare annotations

Background

DAVID is the most popular tool for interpreting large lists of gene/proteins classically produced in high-throughput experiments. However, the use of DAVID website becomes difficult when analyzing multiple gene lists, for it does not provide an adequate visualization tool to show/compare multiple enrichment results in a concise and informative manner.

Result

We implemented a new R-based graphical tool, BACA (Bubble chArt to Compare Annotations), which uses the DAVID web service for cross-comparing enrichment analysis results derived from multiple large gene lists. BACA is implemented in R and is freely available at the CRAN repository (http://cran.r-project.org/web/packages/BACA/).

Conclusion

The package BACA allows R users to combine multiple annotation charts into one output graph by passing DAVID website.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0477-4) contains supplementary material, which is available to authorized users.     

Helveticoside is a biologically active component of the seed extract of Descurainia sophia and induces reciprocal gene regulation in A549 human lung cancer cells

Background

Although the pharmacological activities of the seed extract of Descurainia sophia have been proven to be useful against cough, asthma, and edema, the biologically active components, particularly at the molecular level, remain elusive. Therefore, we aimed to identify the active component of an ethanol extract of D. sophia seeds (EEDS) by applying a systematic genomic approach.

Results

After treatment with EEDS, the dose-dependently expressed genes in A549 cells were used to query the Connectivity map to determine which small molecules could closely mimic EEDS in terms of whole gene expression. Gene ontology and pathway analyses were also performed to identify the functional involvement of the drug responsive genes. In addition, interaction network and enrichment map assays were implemented to measure the functional network structure of the drug-responsive genes. A Connectivity map analysis of differentially expressed genes resulted in the discovery of helveticoside as a candidate drug that induces a similar gene expression pattern to EEDS. We identified the presence of helveticoside in EEDS and determined that helveticoside was responsible for the dose-dependent gene expression induced by EEDS. Gene ontology and pathway analyses revealed that the metabolism and signaling processes in A549 cells were reciprocally regulated by helveticoside and inter-connected as functional modules. Additionally, in an ontological network analysis, diverse cancer type-related genes were found to be associated with the biological functions regulated by helveticoside.

Conclusions

Using bioinformatic analyses, we confirmed that helveticoside is a biologically active component of EEDS that induces reciprocal regulation of metabolism and signaling processes. Our approach may provide novel insights to the herbal research field for identifying biologically active components from extracts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1918-1) contains supplementary material, which is available to authorized users.     

Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity

Background

Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent–child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis.

Results

We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington’s disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.

Conclusion

Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner.     

Turning text into research networks: information retrieval and computational ontologies in the creation of scientific databases

Background

Web-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive.

Method

We focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology.

Results

We present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances.

Conclusion

We have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.     

Kiwi: a tool for integration and visualization of network topology and gene-set analysis

Background

The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.

Results

Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.

Conclusions

Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0408-9) contains supplementary material, which is available to authorized users.     

Methodology for the inference of gene function from phenotype data

Background

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.

Results

We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.

Conclusions

We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0405-z) contains supplementary material, which is available to authorized users.     

SFMetrics: an analysis tool for scanning force microscopy images of biomolecules

Background

Scanning force microscopy (SFM) allows direct, rapid and high-resolution visualization of single molecular complexes; irregular shapes and differences in sizes are immediately revealed by the scanning tip in three-dimensional images. However, high-throughput analysis of SFM data is limited by the lack of versatile software tools accessible to SFM users. Most existing SFM software tools are aimed at broad general use: from material-surface analysis to visualization of biomolecules.

Results

We present SFMetrics as a metrology toolbox for SFM, specifically aimed at biomolecules like DNA and proteins, which features (a) semi-automatic high-throughput analysis of individual molecules; (b) ease of use working within MATLAB environment or as a stand-alone application; (c) compatibility with MultiMode (Bruker), NanoWizard (JPK instruments), Asylum (Asylum research), ASCII, and TIFF files, that can be adjusted with minor modifications to other formats.

Conclusion

Assembled in a single user interface, SFMetrics serves as a semi-automatic analysis tool capable of measuring several geometrical properties (length, volume and angles) from DNA and protein complexes, but is also applicable to other samples with irregular shapes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0457-8) contains supplementary material, which is available to authorized users.     

Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource

Background

Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results

The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion

Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of “guilt-by-association” models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0524-1) contains supplementary material, which is available to authorized users.     

OTO: Ontology Term Organizer

Background

The need to create controlled vocabularies such as ontologies for knowledge organization and access has been widely recognized in various domains. Despite the indispensable need of thorough domain knowledge in ontology construction, most software tools for ontology construction are designed for knowledge engineers and not for domain experts to use. The differences in the opinions of different domain experts and in the terminology usages in source literature are rarely addressed by existing software.

Methods

OTO software was developed based on the Agile principles. Through iterations of software release and user feedback, new features are added and existing features modified to make the tool more intuitive and efficient to use for small and large data sets. The software is open source and built in Java.

Results

Ontology Term Organizer (OTO; http://biosemantics.arizona.edu/OTO/) is a user-friendly, web-based, consensus-promoting, open source application for organizing domain terms by dragging and dropping terms to appropriate locations. The application is designed for users with specific domain knowledge such as biology but not in-depth ontology construction skills. Specifically OTO can be used to establish is_a, part_of, synonym, and order relationships among terms in any domain that reflects the terminology usage in source literature and based on multiple experts’ opinions. The organized terms may be fed into formal ontologies to boost their coverage. All datasets organized on OTO are publicly available.

Conclusion

OTO has been used to organize the terms extracted from thirty volumes of Flora of North America and Flora of China combined, in addition to some smaller datasets of different taxon groups. User feedback indicates that the tool is efficient and user friendly. Being open source software, the application can be modified to fit varied term organization needs for different domains.     

Towards accurate characterization of clonal heterogeneity based on structural variation

Background

Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis.

Results

In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural variants integratively from various forms of alignment signals. We develop a tool, BreakDown, to estimate the allele fractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications, and balanced inversions and translocations.

Conclusions

Evaluation based on both simulated and real data indicates that our method systematically enables structural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically instable tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-299) contains supplementary material, which is available to authorized users.     

Deletions of chromosomal regulatory boundaries are associated with congenital disease

Background

Recent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory backbone of the genome and are separated by regulatory boundary elements or barriers. Copy-number variations can potentially alter the topological domain architecture by deleting or duplicating the barriers and thereby allowing enhancers from neighboring domains to ectopically activate genes causing misexpression and disease, a mutational mechanism that has recently been termed enhancer adoption.

Results

We use the Human Phenotype Ontology database to relate the phenotypes of 922 deletion cases recorded in the DECIPHER database to monogenic diseases associated with genes in or adjacent to the deletions. We identify combinations of tissue-specific enhancers and genes adjacent to the deletion and associated with phenotypes in the corresponding tissue, whereby the phenotype matched that observed in the deletion. We compare this computationally with a gene-dosage pathomechanism that attempts to explain the deletion phenotype based on haploinsufficiency of genes located within the deletions. Up to 11.8% of the deletions could be best explained by enhancer adoption or a combination of enhancer adoption and gene-dosage effects.

Conclusions

Our results suggest that enhancer adoption caused by deletions of regulatory boundaries may contribute to a substantial minority of copy-number variation phenotypes and should thus be taken into account in their medical interpretation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0423-1) contains supplementary material, which is available to authorized users.