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  • Articles  (16)
  • Oxford University Press  (16)
  • National Academy of Sciences
  • The American Association of Immunologists (AAI)
  • The American Society for Microbiology (ASM)
  • Computer Science  (16)
  • 1
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    Identification of active transcription factor and miRNA regulatory pathways in Alzheimer's disease (2013)
    Oxford University Press
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    Publication Date: 2013-10-04
    Description: Motivation: Alzheimer’s disease (AD) is a severe neurodegenerative disease of the central nervous system that may be caused by perturbation of regulatory pathways rather than the dysfunction of a single gene. However, the pathology of AD has yet to be fully elucidated. Results: In this study, we systematically analyzed AD-related mRNA and miRNA expression profiles as well as curated transcription factor (TF) and miRNA regulation to identify active TF and miRNA regulatory pathways in AD. By mapping differentially expressed genes and miRNAs to the curated TF and miRNA regulatory network as active seed nodes, we obtained a potential active subnetwork in AD. Next, by using the breadth-first-search technique, potential active regulatory pathways, which are the regulatory cascade of TFs, miRNAs and their target genes, were identified. Finally, based on the known AD-related genes and miRNAs, the hypergeometric test was used to identify active pathways in AD. As a result, nine pathways were found to be significantly activated in AD. A comprehensive literature review revealed that eight out of nine genes and miRNAs in these active pathways were associated with AD. In addition, we inferred that the pathway hsa-miR-146a-〉STAT1-〉MYC, which is the source of all nine significantly active pathways, may play an important role in AD progression, which should be further validated by biological experiments. Thus, this study provides an effective approach to finding active TF and miRNA regulatory pathways in AD and can be easily applied to other complex diseases. Contact: lixia@hrbmu.edu.cn or lw2247@gmail.com . Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 2
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    Novel human lncRNA-disease association inference based on lncRNA expression profiles (2013)
    Oxford University Press
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    Publication Date: 2013-10-04
    Description: Motivation: More and more evidences have indicated that long–non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. Results : In this article, we proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs. Then, we further developed the method of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) in the semisupervised learning framework. Although known disease–lncRNA associations in the database are rare, LRLSLDA still obtained an AUC of 0.7760 in the leave-one-out cross validation, significantly improving the performance of previous methods. We also illustrated the performance of LRLSLDA is not sensitive (even robust) to the parameters selection and it can obtain a reliable performance in all the test classes. Plenty of potential disease–lncRNA associations were publicly released and some of them have been confirmed by recent results in biological experiments. It is anticipated that LRLSLDA could be an effective and important biological tool for biomedical research. Availability: The code of LRLSLDA is freely available at http://asdcd.amss.ac.cn/Software/Details/2 . Contact: xingchen@amss.ac.cn or yangy@amt.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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    Topics: Biology , Computer Science , Medicine
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  • 3
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    Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets (2013)
    Oxford University Press
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    Publication Date: 2013-07-26
    Description: Motivation: A molecular interaction network can be viewed as a network in which genes with related functions are connected. Therefore, at a systems level, connections between individual genes in a molecular interaction network can be used to infer the collective functional linkages between biologically meaningful gene sets. Results: We present the human interactome resource and the gene set linkage analysis (GSLA) tool for the functional interpretation of biologically meaningful gene sets observed in experiments. GSLA determines whether an observed gene set has significant functional linkages to established biological processes. When an observed gene set is not enriched by known biological processes, traditional enrichment-based interpretation methods cannot produce functional insights, but GSLA can still evaluate whether those genes work in concert to regulate specific biological processes, thereby suggesting the functional implications of the observed gene set. The quality of human interactome resource and the utility of GSLA are illustrated with multiple assessments. Availability: http://www.cls.zju.edu.cn/hir/ Contact: xinchen@zju.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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    Topics: Biology , Computer Science , Medicine
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  • 4
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    Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites (2013)
    Oxford University Press
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    Publication Date: 2013-06-24
    Description: Motivation: Systematic dissection of the ubiquitylation proteome is emerging as an appealing but challenging research topic because of the significant roles ubiquitylation play not only in protein degradation but also in many other cellular functions. High-throughput experimental studies using mass spectrometry have identified many ubiquitylation sites, primarily from eukaryotes. However, the vast majority of ubiquitylation sites remain undiscovered, even in well-studied systems. Because mass spectrometry–based experimental approaches for identifying ubiquitylation events are costly, time-consuming and biased toward abundant proteins and proteotypic peptides, in silico prediction of ubiquitylation sites is a potentially useful alternative strategy for whole proteome annotation. Because of various limitations, current ubiquitylation site prediction tools were not well designed to comprehensively assess proteomes. Results: We present a novel tool known as UbiProber, specifically designed for large-scale predictions of both general and species-specific ubiquitylation sites. We collected proteomics data for ubiquitylation from multiple species from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates the information from key positions and key amino acid residues. Cross-validation tests reveal that UbiProber achieves some improvement over existing tools in predicting species-specific ubiquitylation sites. Moreover, independent tests show that UbiProber improves the areas under receiver operating characteristic curves by ~15% by using the Combined model. Availability: The UbiProber server is freely available on the web at http://bioinfo.ncu.edu.cn/UbiProber.aspx . The software system of UbiProber can be downloaded at the same site. Contact: jdqiu@ncu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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    Topics: Biology , Computer Science , Medicine
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  • 5
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    LncVar: a database of genetic variation associated with long non-coding genes (2016)
    Oxford University Press
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    Publication Date: 2016-12-30
    Description: Motivation: Long non-coding RNAs (lncRNAs) are essential in many molecular pathways, and are frequently associated with disease but the mechanisms of most lncRNAs have not yet been characterized. Genetic variations, including single nucleotide polymorphisms (SNPs) and structural variations, are widely distributed in the genome, including lncRNA gene regions. As the number of studies on lncRNAs grows rapidly, it is necessary to evaluate the effects of genetic variations on lncRNAs. Results: Here, we present LncVar, a database of genetic variation associated with long non-coding genes in six species. We collected lncRNAs from the NONCODE database, and evaluated their conservation. We systematically integrated transcription factor binding sites and m 6 A modification sites of lncRNAs and provided comprehensive effects of SNPs on transcription and modification of lncRNAs. We collected putatively translated open reading frames (ORFs) in lncRNAs, and identified both synonymous and non-synonymous SNPs in ORFs. We also collected expression quantitative trait loci of lncRNAs from the literature. Furthermore, we identified lncRNAs in CNV regions as prognostic biomarker candidates of cancers and predicted lncRNA gene fusion events from RNA-seq data from cell lines. The LncVar database can be used as a resource to evaluate the effects of the variations on the biological function of lncRNAs. Availability and Implementation: LncVar is available at http://bioinfo.ibp.ac.cn/LncVar . Contact: rschen@ibp.ac.cn Supplementary information: Supplementary materials are available at Bioinformatics online.
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    Topics: Biology , Computer Science , Medicine
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  • 6
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    Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer (2016)
    Oxford University Press
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    Publication Date: 2016-03-17
    Description: Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 7
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    Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications (2016)
    Oxford University Press
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    Publication Date: 2016-04-08
    Description: : We describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid germline and somatic analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50 x genomic coverage is analyzed in less than 20 min. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to base-pair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios. Availability and implementation: Manta is released under the open-source GPLv3 license. Source code, documentation and Linux binaries are available from https://github.com/Illumina/manta. Contact : csaunders@illumina.com Supplementary information: Supplementary data are available at Bioinformatics online.
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  • 8
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    Drug-target interaction prediction: databases, web servers and computational models (2016)
    Oxford University Press
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    Publication Date: 2016-07-16
    Description: Identification of drug–target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug–target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug–target associations on a large scale. In this review, databases and web servers involved in drug–target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug–target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug–target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug–target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.
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  • 9
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    Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions (2013)
    Oxford University Press
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    Publication Date: 2013-08-13
    Description: Motivation: MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA–gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power. However, such simple aggregation analysis results in identifying miRNA–gene interactions that are mostly common across datasets, whereas specific interactions may be missed by these methods. Recent releases of The Cancer Genome Atlas data provide paired expression profiling of miRNAs and genes in multiple tumors with sufficiently large sample size. To study both common and cancer-specific interactions, it is desirable to develop a method that can jointly analyze multiple cancers to study miRNA–gene interactions without combining all the data into one single dataset. Results: We developed a novel statistical method to jointly analyze expression profiles from multiple cancers to identify miRNA–gene interactions that are both common across cancers and specific to certain cancers. The benefit of this joint analysis approach is demonstrated by both simulation studies and real data analysis of The Cancer Genome Atlas datasets. Compared with simple aggregate analysis or single sample analysis, our method can effectively use the shared information among different but related cancers to improve the identification of miRNA–gene interactions. Another useful property of our method is that it can estimate similarity among cancers through their shared miRNA–gene interactions. Availability and implementation: The program, MCMG, implemented in R is available at http://bioinformatics.med.yale.edu/group/ . Contact: hongyu.zhao@yale.edu
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  • 10
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    Pathway hunting by random survival forests (2012)
    Oxford University Press
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    Publication Date: 2012-12-21
    Description: Motivation: Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered. Results: In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data. Availability: The R code for the analysis used in this article is available upon request. Contact: xi.steven.chen@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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