http://www.biomedcentral.com/bmcbioinformatics/ The latest research articles published by BMC Bioinformatics 2012-05-31T00:00:00Z Background: Adenosine-5' -triphosphate (ATP) is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. Results: In this paper, we propose a novel framework for predicting the proteins' functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target's knowledge and multiple sources of descriptors can be complementary. Conclusions: Prediction performances evaluated by both 5-fold and leave-one-out jackknife crossvalidation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances. http://www.biomedcentral.com/1471-2105/13/118 Ya-Nan Zhang Dong-Jun Yu Shu-Sen Li Yong-Xian Fan Yan Huang Hong-Bin Shen BMC Bioinformatics 2012, null:118 2012-05-31T00:00:00Z doi:10.1186/1471-2105-13-118 /content/figures/1471-2105-13-118-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 118 2012-05-31T00:00:00Z PDF Background: The generation of multiple sequence alignments (MSAs) is a crucial step for many bioinformatic analyses. Thus improving MSA accuracy and identifying potential errors in MSAs is important for a wide range of post-genomic research. We present a novel method called MergeAlign which constructs consensus MSAs from multiple independent MSAs and assigns a score to each column. Results: Using conventional benchmark tests we demonstrate that on average MergeAlign MSAs are more accurate than MSAs generated using any single model of sequence substitution. We show that MergeAlign column scores are related to alignment precision and hence provide an ab inito method of estimating alignment precision in the absence of curated reference MSAs. Using two novel and independent alignment performance tests that utilise a large set of orthologous gene families we demonstrate that increasing MSA performance leads to an increase in the performance of downstream phylogenetic analyses. Conclusion: Using multiple tests of alignment performance we demonstrate that this novel method has broad general application in biological research. http://www.biomedcentral.com/1471-2105/13/117 Peter Collingridge Steven Kelly BMC Bioinformatics 2012, null:117 2012-05-30T00:00:00Z doi:10.1186/1471-2105-13-117 /content/figures/1471-2105-13-117-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 117 2012-05-30T00:00:00Z PDF Background: More than one million terms from biomedical ontologies and controlled vocabularies are available through the Ontology Lookup Service (OLS). Although OLS provides ample possibility for querying and browsing terms, the visualization of parts of the ontology graphs is rather limited and inflexible. Results: We created the OLSVis web application, a visualiser for browsing all ontologies available in the OLS database. OLSVis shows customisable subgraphs of the OLS ontologies. Subgraphs are animated via a real-time force-based layout algorithm which is fully interactive: each time the user makes a change, e.g. browsing to a new term, hiding, adding, or dragging terms, the algorithm performs smooth and only essential reorganisations of the graph. This assures an optimal viewing experience, because subsequent screen layouts are not grossly altered, and users can easily navigate through the graph. URL: http://ols.wordvis.com Conclusions: The OLSVis web application provides a user-friendly tool to visualise ontologies from the OLS repository. It broadens the possibilities to investigate and select ontology subgraphs through a smooth visualisation method. http://www.biomedcentral.com/1471-2105/13/116 Steven Vercruysse Aravind Venkatesan Martin Kuiper BMC Bioinformatics 2012, null:116 2012-05-30T00:00:00Z doi:10.1186/1471-2105-13-116 /content/figures/1471-2105-13-116-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 116 2012-05-30T00:00:00Z PDF Background: Gas chromatography-mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines. Results: PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI-MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS). We show data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Conclusions: PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. We show that in real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. Automated sample processing and quantitation by PyMS provides substantial time savings compared to more traditional software systems that tightly integrate data processing with the graphical user interface. http://www.biomedcentral.com/1471-2105/13/115 Sean O'Callaghan David DeSouza Andrew Isaac Qiao Wang Luke Hodkinson Moshe Olshansky Tim Erwin Bill Appelbe Dedreia Tull Ute Roessner Antony Bacic Malcolm McConville Vladimir Likic BMC Bioinformatics 2012, null:115 2012-05-30T00:00:00Z doi:10.1186/1471-2105-13-115 /content/figures/1471-2105-13-115-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 115 2012-05-30T00:00:00Z PDF Background: A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to quality assessment, artefact detection is not widely used in standard gene expression analysis pipelines. Furthermore, although approaches have been proposed to detect diverse types of spatial noise on arrays, the correction of these artefacts is mostly left to either summarization methods or the corresponding arrays are completely discarded. Results: We show that state-of-the-art robust summarization procedures are vulnerable to artefacts on arrays and cannot appropriately correct for these. To address this problem, we present a simple approach to detect artefacts with high recall and precision, which we further improve by taking into account the spatial layout of arrays. Finally, we propose two correction methods for these artefacts that either substitute values of defective probes using probeset information or filter corrupted probes. We show that our approach can identify and correct defective probe measurements appropriately and outperforms existing tools. Conclusions: While summarization is insufficient to correct for defective probes, this problem can be addressed in a straightforward way by the methods we present for identification and correction of defective probes. As these methods output CEL files with corrected probe values that serve as input to standard normalization and summarization procedures, they can be easily integrated into existing microarray analysis pipelines as an additional pre-processing step. An R package is freely available from http://www.bio.ifi.lmu.de/artefact-correction. http://www.biomedcentral.com/1471-2105/13/114 Tobias Petri Evi Berchtold Ralf Zimmer Caroline Friedel BMC Bioinformatics 2012, null:114 2012-05-30T00:00:00Z doi:10.1186/1471-2105-13-114 /content/figures/1471-2105-13-114-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 114 2012-05-30T00:00:00Z PDF Background: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). Conclusions: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology. http://www.biomedcentral.com/1471-2105/13/113 Ye Deng Yihuei Jiang Yunfeng Yang Zhili He Feng Luo Jizhong Zhou BMC Bioinformatics 2012, null:113 2012-05-30T00:00:00Z doi:10.1186/1471-2105-13-113 /content/figures/1471-2105-13-113-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 113 2012-05-30T00:00:00Z PDF Background: Detailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As most compounds impact multiple targets and pathways, traditional methods for analyzing structure-function relationships are not comprehensive enough. Therefore more advanced integrative models are needed for predicting biological effects elicited by specific chemical features. As a step towards creating such computational links we developed a data-driven chemical systems biology approach to comprehensively study the relationship of 76 structural 3D-descriptors (VolSurf, chemical space) of 1159 drugs with the gene expression responses (biological space) they elicited in three cancer cell lines. The analysis covering 11350 genes was based on data from the Connectivity Map. We decomposed these biological response profiles into components, each linked to a characteristic chemical descriptor profile. Results: The integrated quantitative analysis of the chemical and biological spaces was more informative about protein-target based drug similarity than either dataset separately. We identified ten major components that link distinct VolSurf features across multiple compounds to specific biological activity types. For example, component 2 (hydrophobic properties) strongly links to DNA damage response, while component 3 (hydrogen bonding) connects to metabolic stress. Individual structural and biological features were often linked to one cell line only, such as leukemia cells (HL-60) specifically responding to cardiac glycosides. Conclusions: In summary, our approach identified specific chemical structure properties shared across multiple drugs causing distinct biological responses. The decoding of such systematic chemical-biological relationships is necessary to build better models of drug effects, including unidentified types of molecular properties with strong biological effects. http://www.biomedcentral.com/1471-2105/13/112 Suleiman Khan Ali Faisal John Mpindi Juuso Parkkinen Tuomo Kalliokoski Antti Poso Olli Kallioniemi Krister Wennerberg Samuel Kaski BMC Bioinformatics 2012, null:112 2012-05-30T00:00:00Z doi:10.1186/1471-2105-13-112 /content/figures/1471-2105-13-112-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 112 2012-05-30T00:00:00Z PDF Background: Intrinsically unstructured proteins (IUPs) lack a well-defined three-dimensional structure. Some of them may assume a locally stable structure under specific conditions, e.g. upon interaction with another molecule, while others function in a permanently unstructured state. The discovery of IUPs challenged the traditional protein structure paradigm, which stated that a specific well-defined structure defines the function of the protein. As of December 2011, approximately 60 methods for computational prediction of protein disorder from sequence have been made publicly available. They are based on different approaches, such as utilizing evolutionary information, energy functions, and various statistical and machine learning methods. Results: Given the diversity of existing intrinsic disorder prediction methods, we decided to test whether it is possible to combine them into a more accurate meta-prediction method. We developed a method based on arbitrarily chosen 13 disorder predictors, in which the final consensus was weighted by the accuracy of the methods. We have also developed a disorder predictor GSmetaDisorder3D that used no third-party disorder predictors, but alignments to known protein structures, reported by the protein fold-recognition methods, to infer the potentially structured and unstructured regions. Following the success of our disorder predictors in the CASP8 benchmark, we combined them into a meta-meta predictor called GSmetaDisorderMD, which was the top scoring method in the subsequent CASP9 benchmark. Conclusions: A series of disorder predictors described in this article is available as a MetaDisorder web server at http://iimcb.genesilico.pl/metadisorder/. Results are presented both in an easily interpretable, interactive mode and in a simple text format suitable for machine processing. http://www.biomedcentral.com/1471-2105/13/111 Lukasz Kozlowski Janusz Bujnicki BMC Bioinformatics 2012, null:111 2012-05-24T00:00:00Z doi:10.1186/1471-2105-13-111 /content/figures/1471-2105-13-111-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 111 2012-05-24T00:00:00Z PDF Background: Figures of phylogenetic trees are widely used to illustrate the result of evolutionary analyses.However, one cannot easily extract a machine-readable representation from such images.Therefore, new software emerges that helps to preserve phylogenies digitally for futureresearch. Results: TreeSnatcher Plus is a GUI-driven JAVA application that semi-automatically generates aNewick format for multifurcating, arbitrarily shaped, phylogenetic trees contained in pixelimages. It offers a range of image pre-processing methods and detects the topology of adepicted tree with adequate user assistance. The user supervises the recognition process,makes corrections to the image and to the topology and repeats steps if necessary. At the endTreeSnatcher Plus produces a Newick tree code optionally including branch lengths forrectangular and freeform trees. Conclusions: Although illustrations of phylogenies exist in a vast number of styles, TreeSnatcher Plusimposes no limitations on the images it can process with adequate user assistance. Given thata fully automated digitization of all figures of phylogenetic trees is desirable but currentlyunrealistic, TreeSnatcher Plus is the only program that reliably facilitates at least a semiautomaticconversion from such figures into a machine-readable format. http://www.biomedcentral.com/1471-2105/13/110 Thomas Laubach Arndt von Haeseler Martin Lercher BMC Bioinformatics 2012, null:110 2012-05-24T00:00:00Z doi:10.1186/1471-2105-13-110 /content/figures/1471-2105-13-110-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 110 2012-05-24T00:00:00Z PDF Background: Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules. Results: In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using "Biological Process" annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of "Biological Process". More importantly, the relationship between protein complexes and functional modules are studied.Conclusions The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinformatics/limin/DFM-CIN. http://www.biomedcentral.com/1471-2105/13/109 Min Li Xuehong Wu Jianxin Wang Yi Pan BMC Bioinformatics 2012, null:109 2012-05-23T00:00:00Z doi:10.1186/1471-2105-13-109 /content/figures/1471-2105-13-109-toc.gif BMC Bioinformatics 1471-2105 ${item.volume} 109 2012-05-23T00:00:00Z PDF