Hybrid journal (It can contain Open Access articles) ISSN (Print) 1467-5463 - ISSN (Online) 1477-4054 Published by Oxford University Press[370 journals]
Authors:Zhao N; Liu Y, Wei Y, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Cell lines are widely used as <span style="font-style:italic;">in vitro</span> models of tumorigenesis. However, an increasing number of researchers have found that cell lines differ from their sourced tumour samples after long-term cell culture. The application of unsuitable cell lines in experiments will affect the experimental accuracy and the treatment of patients. Therefore, it is imperative to identify optimal cell lines for each cancer type. Here, we review the methods used to evaluate cell lines since 2005. Furthermore, gene expression, copy number and mutation profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia are used to calculate similarity between tumours and cell lines. Then, the ideal cell lines to use for experiments for eight types of cancers are found by combining the results with Gene Ontology functional similarity. After verification, the optimal cell lines have the same genomic characteristics as their homologous tumour samples. The contaminated cell lines identified in previous research are also determined to be unsuitable <span style="font-style:italic;">in vitro</span> cancer models here. Moreover, our study suggests that some of the commonly used cell lines are not suitable cancer models. In summary, we provide a reference for ideal cell lines to use in <span style="font-style:italic;">in vitro</span> experiments and contribute to improving the accuracy of future cancer research. Furthermore, this research provides a foundation for identifying more effective treatment strategies.</span> PubDate: 2016-10-02
Authors:Loucoubar C; Grant AV, Bureau J, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.</span> PubDate: 2016-05-13
Authors:Thomas R; Thomas S, Holloway AK, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an important tool for studying gene regulatory proteins, such as transcription factors and histones. Peak calling is one of the first steps in the analysis of these data. Peak calling consists of two sub-problems: identifying candidate peaks and testing candidate peaks for statistical significance. We surveyed 30 methods and identified 12 features of the two sub-problems that distinguish methods from each other. We picked six methods GEM, MACS2, MUSIC, BCP, Threshold-based method (TM) and ZINBA] that span this feature space and used a combination of 300 simulated ChIP-seq data sets, 3 real data sets and mathematical analyses to identify features of methods that allow some to perform better than the others. We prove that methods that explicitly combine the signals from ChIP and input samples are less powerful than methods that do not. Methods that use windows of different sizes are more powerful than the ones that do not. For statistical testing of candidate peaks, methods that use a Poisson test to rank their candidate peaks are more powerful than those that use a Binomial test. BCP and MACS2 have the best operating characteristics on simulated transcription factor binding data. GEM has the highest fraction of the top 500 peaks containing the binding motif of the immunoprecipitated factor, with 50% of its peaks within 10 base pairs of a motif. BCP and MUSIC perform best on histone data. These findings provide guidance and rationale for selecting the best peak caller for a given application.</span> PubDate: 2016-05-11
Authors:Galeota E; Pelizzola M. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Public repositories of large-scale biological data currently contain hundreds of thousands of experiments, including high-throughput sequencing and microarray data. The potential of using these resources to assemble data sets combining samples previously not associated is vastly unexplored. This requires the ability to associate samples with clear annotations and to relate experiments matched with different annotation terms. In this study, we illustrate the semantic annotation of Gene Expression Omnibus samples metadata using concepts from biomedical ontologies, focusing on the association of thousands of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) samples with a given target, tissue and disease state. Next, we demonstrate the feasibility of quantitatively measuring the semantic similarity between different samples, with the aim of combining experiments associated with the same or similar semantic annotations, thus allowing the generation of large data sets without the need of additional experiments. We compared tools based on Unified Medical Language System with tools that use topic-specific ontologies, showing that the second approach outperforms the first both in the annotation process and in the computation of semantic similarity measures. Finally, we demonstrated the potential of this approach by identifying semantically homogeneous groups of ChIP-seq samples targeting the Myc transcription factor, and expanding this data set with semantically coherent epigenetic samples. The semantic information of these data sets proved to be coherent with the ChIP-seq signal and with the current knowledge about this transcription factor.</span> PubDate: 2016-05-03
Authors:Jia P; Zhao Z. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Assessing the functional impacts of somatic mutations in cancer genomes is critical for both identifying driver mutations and developing molecular targeted therapies. Currently, it remains a fundamental challenge to distinguish the patterns through which mutations execute their biological effects and to infer biological mechanisms underlying these patterns. To this end, we systematically studied the association between somatic mutations in protein-coding regions and expression profiles, which represents an indirect measurement of impacts. We defined mutation features (mutation type, cluster and status) and built linear regression models to assess mutation associations with mRNA expression and protein expression. Our results presented a comprehensive landscape of the associations between mutation features and expression profile in multiple cancer types, including 62 genes showing mutation type associated expression changes, 21 genes showing mutation cluster associations and 51 genes showing mutation status associations. We revealed four characteristics of the patterns that mutations impact on expression. First, we showed that mutation type (truncation versus amino acid-altering mutations) was the most important determinant of expression levels. Second, we detected mutation clusters in well-studied oncogenes that were associated with gene expression. Third, we found both similarities and differences in association patterns existed within and across cancer types. Fourth, although many of the observed associations stay stable at both mRNA and protein expression levels, there are also novel associations uniquely observed at the protein level, which warrant future investigation. Taken together, our findings provided implications for cancer driver gene prioritization and insights into the functional consequences of somatic mutations.</span> PubDate: 2016-04-28
Authors:Wang J; Sun L, Jiang L, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.</span> PubDate: 2016-04-25
Authors:Yang H; Ju J, Wong Y, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Drug development is an expensive and time-consuming process; these could be reduced if the existing resources could be used to identify candidates for drug repurposing. This study sought to do this by text mining a large-scale literature repository to curate repurposed drug lists for different cancers. We devised a pattern-based relationship extraction method to extract disease–gene and gene–drug direct relationships from the literature. These direct relationships are used to infer indirect relationships using the ABC model. A gene-shared ranking method based on drug target similarity was then proposed to prioritize the indirect relationships. Our method of assessing drug target similarity correlated to existing anatomical therapeutic chemical code-based methods with a Pearson correlation coefficient of 0.9311. The indirect relationships ranking method achieved a significant mean average precision score of top 100 most common diseases. We also confirmed the suitability of candidates identified for repurposing as anticancer drugs by conducting a manual review of the literature and the clinical trials. Eventually, for visualization and enrichment of huge amount of repurposed drug information, a chord diagram was demonstrated to rapidly identify two novel indications for further biological evaluations.</span> PubDate: 2016-04-25
Authors:Smith SA; Pease JB. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Sequence similarity tools like Basic Local Alignment Search Tool (BLAST) are essential components of many functional genetic, genomic, phylogenetic and bioinformatic studies. Many modern analysis pipelines use significant sequence similarity scores (<span style="font-style:italic;">p</span>- or <span style="font-style:italic;">E</span>-values) and the ranked order of BLAST matches to test a wide range of hypotheses concerning homology, orthology, the timing of <span style="font-style:italic;">de novo</span> gene birth/death and gene family expansion/contraction. Despite significant contrary findings, many of these tests still implicitly assume that stronger or higher-ranked <span style="font-style:italic;">E</span>-value scores imply closer phylogenetic relationships between sequences. Here, we demonstrate that even though a general relationship does exist between the phylogenetic distance of two sequences and their <span style="font-style:italic;">E</span>-value, significant and misleading errors occur in both the completeness and the order of results under realistic evolutionary scenarios. These results provide additional details to past evidence showing that studies should avoid drawing direct inferences of evolutionary relatedness from measures of sequence similarity alone, and should instead, where possible, use more rigorous phylogeny-based methods.</span> PubDate: 2016-04-21
Authors:Revote J; Watson-Haigh NS, Quenette S, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP.</span> PubDate: 2016-04-15
Authors:Cambiaghi A; Ferrario M, Masseroli M. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different ‘omics’ data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCore<sup>TM</sup>, MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other ‘omics’ data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.</span> PubDate: 2016-04-12
Authors:Wen Y; Wei Y, Zhang S, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas’s identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.</span> PubDate: 2016-03-25
Authors:Dhanda S; Usmani S, Agrawal P, et al. Abstract: <span class="paragraphSection"><div class="boxTitle"></div>The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by <span style="font-style:italic;">in silico</span> tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel <span style="font-style:italic;">in silico</span> resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.</span> PubDate: 2016-03-25
Authors:Bauer CR; Knecht C, Fretter C, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by ‘informed non-experts’ but at the same time attuned to existing, more sophisticated analysis tools.</span> PubDate: 2016-03-25
Authors:Jalili V; Matteucci M, Morelli MJ, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser. We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at <a href="http://www.bioinformatics.deib.polimi.it/MuSERA/">http://www.bioinformatics.deib.polimi.it/MuSERA/</a>.</span> PubDate: 2016-03-24
Authors:Xue L; Rodrigues JM, Dobbs D, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Although many advanced and sophisticated <span style="font-style:italic;">ab initio</span> approaches for modeling protein–protein complexes have been proposed in past decades, template-based modeling (TBM) remains the most accurate and widely used approach, given a reliable template is available. However, there are many different ways to exploit template information in the modeling process. Here, we systematically evaluate and benchmark a TBM method that uses conserved interfacial residue pairs as docking distance restraints [referred to as alpha carbon–alpha carbon (CA-CA)-guided docking]. We compare it with two other template-based protein–protein modeling approaches, including a conserved non-pairwise interfacial residue restrained docking approach [referred to as the ambiguous interaction restraint (AIR)-guided docking] and a simple superposition-based modeling approach. Our results show that, for most cases, the CA-CA-guided docking method outperforms both superposition with refinement and the AIR-guided docking method. We emphasize the superiority of the CA-CA-guided docking on cases with medium to large conformational changes, and interactions mediated through loops, tails or disordered regions. Our results also underscore the importance of a proper refinement of superimposition models to reduce steric clashes. In summary, we provide a benchmarked TBM protocol that uses conserved pairwise interface distance as restraints in generating realistic 3D protein–protein interaction models, when reliable templates are available. The described CA-CA-guided docking protocol is based on the HADDOCK platform, which allows users to incorporate additional prior knowledge of the target system to further improve the quality of the resulting models.</span> PubDate: 2016-03-24
Authors:Leipzig J. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface. Here I survey and compare the design philosophies of several current pipeline frameworks. I provide practical recommendations based on analysis requirements and the user base.</span> PubDate: 2016-03-24
Authors:Luo Y; Wang F, Szolovits P. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest that tensor applications could serve as an effective tool to enable frequent updating of medical knowledge based on the continually growing scientific and clinical evidence. We encourage extensive experimental studies to tackle challenges including design choice of factorizations, integrating temporality and algorithm scalability.</span> PubDate: 2016-03-19
Authors:Wei W; Gao F, Du M, et al. Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Genomic islands are genomic fragments of alien origin in bacterial and archaeal genomes, usually involved in symbiosis or pathogenesis. In this work, we described Zisland Explorer, a novel tool to predict genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy. We have provided a web service as well as a graphical user interface and open-source code across multiple platforms for Zisland Explorer, which is available at <a href="http://cefg.uestc.edu.cn/Zisland_Explorer/">http://cefg.uestc.edu.cn/Zisland_Explorer/</a> or <a href="http://tubic.tju.edu.cn/Zisland_Explorer/">http://tubic.tju.edu.cn/Zisland_Explorer/</a>.</span> PubDate: 2016-03-18
Hybrid journal (It can contain Open Access articles)
[370 journals]
