成品弹性抽样基于蒙特卡罗的稳定网络的模式识别

Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling.

Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.

Contact: dorothee.childs@embl.de

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i214?rss=1

[详细]

通过建立高度可靠的负样本提高复合蛋白质相互作用预测

Motivation: Computational prediction of compound–protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods.

Results: This article aims at building up a set of highly credible negative samples of CPIs via an in silico screening method. As most existing computational models assume that similar compounds are likely to interact with similar target proteins and achieve remarkable performance, it is rational to identify potential negative samples based on the converse negative proposition that the proteins dissimilar to every known/predicted target of a compound are not much likely to be targeted by the compound and vice versa. We integrated various resources, including chemical structures, chemical expression profiles and side effects of compounds, amino acid sequences, protein–protein interaction network and functional annotations of proteins, into a systematic screening framework. We first tested the screened negative samples on six classical classifiers, and all these classifiers achieved remarkably higher performance on our negative samples than on randomly generated negative samples for both human and Caenorhabditis elegans. We then verified the negative samples on three existing prediction models, including bipartite local model, Gaussian kernel profile and Bayesian matrix factorization, and found that the performances of these models are also significantly improved on the screened negative samples. Moreover, we validated the screened negative samples on a drug bioactivity dataset. Finally, we derived two sets of new interactions by training an support vector machine classifier on the positive interactions annotated in DrugBank and our screened negative interactions. The screened negative samples and the predicted interactions provide the research community with a useful resource for identifying new drug targets and a helpful supplement to the current curated compound–protein databases.

Availability: Supplementary files are available at: http://admis.fudan.edu.cn/negative-cpi/.

Contact: sgzhou@fudan.edu.cn

Supplementary Information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i221?rss=1

[详细]

来自不同分布的数据融合基因网络推理

Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets.

Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies.

Availability and implementation: Source code is at https://github.com/marinkaz/fusenet.

Contact: blaz.zupan@fri.uni-lj.si

Supplementary information: Supplementary information is available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i230?rss=1

[详细]

遗传异质性与复杂性状相关的全基因组检测间隔

Motivation: Genetic heterogeneity, the fact that several sequence variants give rise to the same phenotype, is a phenomenon that is of the utmost interest in the analysis of complex phenotypes. Current approaches for finding regions in the genome that exhibit genetic heterogeneity suffer from at least one of two shortcomings: (i) they require the definition of an exact interval in the genome that is to be tested for genetic heterogeneity, potentially missing intervals of high relevance, or (ii) they suffer from an enormous multiple hypothesis testing problem due to the large number of potential candidate intervals being tested, which results in either many false positives or a lack of power to detect true intervals.

Results: Here, we present an approach that overcomes both problems: it allows one to automatically find all contiguous sequences of single nucleotide polymorphisms in the genome that are jointly associated with the phenotype. It also solves both the inherent computational efficiency problem and the statistical problem of multiple hypothesis testing, which are both caused by the huge number of candidate intervals. We demonstrate on Arabidopsis thaliana genome-wide association study data that our approach can discover regions that exhibit genetic heterogeneity and would be missed by single-locus mapping.

Conclusions: Our novel approach can contribute to the genome-wide discovery of intervals that are involved in the genetic heterogeneity underlying complex phenotypes.

Availability and implementation: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/sis.html.

Contact: felipe.llinares@bsse.ethz.ch

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i240?rss=1

[详细]

传呼：构建PAG和网络生物学的新pag-pag关系

In this article, we described a new database framework to perform integrative "gene-set, network, and pathway analysis" (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene–gene relationships and two types of 3 174 323 PAG–PAG regulatory relationships—co-membership based and regulatory relationship based. To help users assess each PAG’s biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG–PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.

Contact: jakechen@iupui.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i250?rss=1

[详细]

推理模型的多尺度拷贝数演变为单个肿瘤发生

Motivation: Phylogenetic algorithms have begun to see widespread use in cancer research to reconstruct processes of evolution in tumor progression. Developing reliable phylogenies for tumor data requires quantitative models of cancer evolution that include the unusual genetic mechanisms by which tumors evolve, such as chromosome abnormalities, and allow for heterogeneity between tumor types and individual patients. Previous work on inferring phylogenies of single tumors by copy number evolution assumed models of uniform rates of genomic gain and loss across different genomic sites and scales, a substantial oversimplification necessitated by a lack of algorithms and quantitative parameters for fitting to more realistic tumor evolution models.

Results: We propose a framework for inferring models of tumor progression from single-cell gene copy number data, including variable rates for different gain and loss events. We propose a new algorithm for identification of most parsimonious combinations of single gene and single chromosome events. We extend it via dynamic programming to include genome duplications. We implement an expectation maximization (EM)-like method to estimate mutation-specific and tumor-specific event rates concurrently with tree reconstruction. Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature. Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival.

Availability and implementation: Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

Contact: russells@andrew.cmu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i258?rss=1

[详细]

以制度化监督的多核学习与癌症亚型的发现应用整合不同的数据类型

Motivation: Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these decisions, are mainly based on individual data types. However, the analysis of multidimensional patient data involving the measurements of various molecular features could reveal intrinsic characteristics of the tumor. Large-scale projects accumulate this kind of data for various cancer types, but we still lack the computational methods to reliably integrate this information in a meaningful manner. Therefore, we apply and extend current multiple kernel learning for dimensionality reduction approaches. On the one hand, we add a regularization term to avoid overfitting during the optimization procedure, and on the other hand, we show that one can even use several kernels per data type and thereby alleviate the user from having to choose the best kernel functions and kernel parameters for each data type beforehand.

Results: We have identified biologically meaningful subgroups for five different cancer types. Survival analysis has revealed significant differences between the survival times of the identified subtypes, with P values comparable or even better than state-of-the-art methods. Moreover, our resulting subtypes reflect combined patterns from the different data sources, and we demonstrate that input kernel matrices with only little information have less impact on the integrated kernel matrix. Our subtypes show different responses to specific therapies, which could eventually assist in treatment decision making.

Availability and implementation: An executable is available upon request.

Contact: nora@mpi-inf.mpg.de or npfeifer@mpi-inf.mpg.de

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i268?rss=1

[详细]

自驱动的疾病的遗传学预测对药物发现

Motivation: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data.

Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e^–4) and 81.3% (P < e^–12) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery.

Availability and implementation: nlp.case.edu/public/data/DMN

Contact: rxx@case.edu

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i276?rss=1

[详细]

memcover：跨多个肿瘤类型的相互排斥和功能网络综合分析揭示了失调的途径

Motivation: The data gathered by the Pan-Cancer initiative has created an unprecedented opportunity for illuminating common features across different cancer types. However, separating tissue-specific features from across cancer signatures has proven to be challenging. One of the often-observed properties of the mutational landscape of cancer is the mutual exclusivity of cancer driving mutations. Even though studies based on individual cancer types suggested that mutually exclusive pairs often share the same functional pathway, the relationship between across cancer mutual exclusivity and functional connectivity has not been previously investigated.

Results: We introduce a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we show that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks.

Contact: przytyck@ncbi.nlm.nih.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i284?rss=1

[详细]

精密医学RNA测序表达的动态变化：n-of-1-pathways马氏距离的单学科内的途径预测乳腺癌患者存活率

Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change).

Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences).

Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).

Availability and implementation: http://www.lussierlab.net/publications/N-of-1-pathways.

Contact: yves@email.arizona.edu or piegorsch@math.arizona.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i293?rss=1

[详细]

通过改进从基因型表型预测共培训电子表型

Motivation: Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction.

Results: Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium.

Conclusions: Imputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction.

Availability and implementation: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.html

Contact: karsten.borgwardt@bsse.ethz.ch or menno.witteveen@bsse.ethz.ch

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i303?rss=1

[详细]

野性：网络分类器与乳腺癌的预后预测中的应用

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.

Results: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer.

Availability and implementation: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.

Contact: j.deridder@tudelft.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i311?rss=1

[详细]

在高通量的延时数据研究单细胞运动的一个通用的方法论框架

Motivation: Motility is a fundamental cellular attribute, which plays a major part in processes ranging from embryonic development to metastasis. Traditionally, single cell motility is often studied by live cell imaging. Yet, such studies were so far limited to low throughput. To systematically study cell motility at a large scale, we need robust methods to quantify cell trajectories in live cell imaging data.

Results: The primary contribution of this article is to present Motility study Integrated Workflow (MotIW), a generic workflow for the study of single cell motility in high-throughput time-lapse screening data. It is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner.

Availability and implementation: Python code and examples are available online (http://cbio.ensmp.fr/~aschoenauer/motiw.html)

Contact: thomas.walter@mines-paristech.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i320?rss=1

[详细]

比较基因组的重排和重复片段

Motivation: Large-scale evolutionary events such as genomic rearrange.ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational problem is to infer these events in the evolutionary history for given modern genomes, a task for which many algorithms have been proposed under various constraints. Algorithms that can handle both rearrangements and content-modifying events such as duplications and losses remain few and limited in their applicability.

Results: We study the comparison of two genomes under a model including general rearrangements (through double-cut-and-join) and segmental duplications. We formulate the comparison as an optimization problem and describe an exact algorithm to solve it by using an integer linear program. We also devise a sufficient condition and an efficient algorithm to identify optimal substructures, which can simplify the problem while preserving optimality. Using the optimal substructures with the integer linear program (ILP) formulation yields a practical and exact algorithm to solve the problem. We then apply our algorithm to assign in-paralogs and orthologs (a necessary step in handling duplications) and compare its performance with that of the state-of-the-art method MSOAR, using both simulations and real data. On simulated datasets, our method outperforms MSOAR by a significant margin, and on five well-annotated species, MSOAR achieves high accuracy, yet our method performs slightly better on each of the 10 pairwise comparisons.

Availability and implementation: http://lcbb.epfl.ch/softwares/coser.

Contact: mingfu.shao@epfl.ch or bernard.moret@epfl.ch

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i329?rss=1

[详细]

meshlabeler：通过整合不同的证据提高大型网格索引的准确性

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.

Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ‘learning to rank’. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy.

Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI.

Availability and implementation: The software is available upon request.

Contact: zhusf@fudan.edu.cn

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i339?rss=1

[详细]

知识驱动的地理位置分辨率为病毒迁移系统地理模型

Summary: Diseases caused by zoonotic viruses (viruses transmittable between humans and animals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveillance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public databases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles.

Motivation: In this article, we present a system for detection and disambiguation of locations (toponym resolution) in full-text articles to automate the retrieval of sufficient metadata. Our system has been tested on a manually annotated corpus of journal articles related to phylogeography using integrated heuristics for location disambiguation including a distance heuristic, a population heuristic and a novel heuristic utilizing knowledge obtained from GenBank metadata (i.e. a ‘metadata heuristic’).

Results: For detecting and disambiguating locations, our system performed best using the metadata heuristic (0.54 Precision, 0.89 Recall and 0.68 F-score). Precision reaches 0.88 when examining only the disambiguation of location names. Our error analysis showed that a noticeable increase in the accuracy of toponym resolution is possible by improving the geospatial location detection. By improving these fundamental automated tasks, our system can be a useful resource to phylogeographers that rely on geospatial metadata of GenBank sequences.

Contact: davy.weissenbacher@asu.edu

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i348?rss=1

[详细]

利用本体图疏注释基因功能预测

Motivation: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (<10) annotated genes, which constitute about half of the GO terms in yeast, mouse and human, pose a unique challenge in that any prediction algorithm that independently considers each label faces a paucity of information and thus is prone to capture non-generalizable patterns in the data, resulting in poor predictive performance. There exist a variety of algorithms for function prediction, but none properly address this ‘overfitting’ issue of sparsely annotated functions, or do so in a manner scalable to tens of thousands of functions in the human catalog.

Results: We propose a novel function prediction algorithm, clusDCA, which transfers information between similar functional labels to alleviate the overfitting problem for sparsely annotated functions. Our method is scalable to datasets with a large number of annotations. In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information. Furthermore, we show that our method can accurately predict genes that will be assigned a functional label that has no known annotations, based only on the ontology graph structure and genes associated with other labels, which further suggests that our method effectively utilizes the similarity between gene functions.

Availability and implementation: https://github.com/wangshenguiuc/clusDCA.

Contact: jianpeng@illinois.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i357?rss=1

[详细]

蛋白（多）定位预测：利用关系通过生成模型

Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein’s function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins.

Results: We introduce a probabilistic generative model for protein localization, and develop a system based on it—which we call MDLoc—that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems.

Availability and implementation: MDLoc is available at: http://www.eecis.udel.edu/~compbio/mdloc.

Contact: shatkay@udel.edu.

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i365?rss=1

[详细]

利用集体的专家判断评价质谱图像质量的措施

Motivation: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures.

Results: We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts’ optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff’s alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality.

Availability and implementation: The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality.

Contact: theodore.alexandrov@embl.de

• http://bioinformatics.oxfordjournals.org/cgi/content/short/31/12/i375?rss=1

[详细]

的APC / C和蛋白质泛素化机制的原子结构

A cryo-electron microscopy determination of the atomic structures of anaphase-promoting complex (APC/C)–coactivator complexes with either Emi1 or a UbcH10–ubiquitin conjugate.

• http://feeds.nature.com/~r/nature/rss/aop/~3/EzPLOpBdJOU/nature14471

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不寻常的生物在一组包括15%多名20域细菌

A prominent feature of the bacterial domain is a radiation of major lineages that are defined as candidate phyla because they lack isolated representatives. Bacteria from these phyla occur in diverse environments and are thought to mediate carbon and hydrogen cycles. Genomic analyses of a few representatives suggested that metabolic limitations have prevented their cultivation. Here we reconstructed 8 complete and 789 draft genomes from bacteria representing >35 phyla and documented features that consistently distinguish these organisms from other bacteria. We infer that this group, which may comprise >15% of the bacterial domain, has shared evolutionary history, and describe it as the candidate phyla radiation (CPR). All CPR genomes are small and most lack numerous biosynthetic pathways. Owing to divergent 16S ribosomal RNA (rRNA) gene sequences, 50–100% of organisms sampled from specific phyla would evade detection in typical cultivation-independent surveys. CPR organisms often have self-splicing introns and proteins encoded within their rRNA genes, a feature rarely reported in bacteria. Furthermore, they have unusual ribosome compositions. All are missing a ribosomal protein often absent in symbionts, and specific lineages are missing ribosomal proteins and biogenesis factors considered universal in bacteria. This implies different ribosome structures and biogenesis mechanisms, and underlines unusual biology across a large part of the bacterial domain.

• http://feeds.nature.com/~r/nature/rss/aop/~3/tWfCnyEP3JA/nature14486

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三维纳米结构的超快速分离DNA，蛋白质和RNA分子

Separation and analysis of biomolecules represent crucial processes for biological and biomedical engineering development; however, separation resolution and speed for biomolecules analysis still require improvements. To achieve separation and analysis of biomolecules in a short time, the use of highly-ordered nanostructures fabricated by top-down or bottom-up approaches have been proposed. Here, we reported on the use of three-dimensional (3D) nanowire structures embedded in microchannels fabricated by a bottom-up approach for ultrafast separation of small biomolecules, such as DNA, protein, and RNA molecules. The 3D nanowire structures could analyze a mixture of DNA molecules (50–1000 bp) within 50 s, a mixture of protein molecules (20–340 kDa) within 5 s, and a mixture of RNA molecules (100–1000 bases) within 25 s. And, we could observe the electrophoretic mobility difference of biomolecules as a function of molecular size in the 3D nanowire structures. Since the present methodology allows users to control the pore size of sieving materials by varying the number of cycles for nanowire growth, the 3D nanowire structures have a good potential for use as alternatives for other sieving materials.

• http://feeds.nature.com/~r/srep/rss/current/~3/bvKihNrtw4Q/srep10584

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cancerhsp：抗癌中药数据库系统药理

The numerous natural products and their bioactivity potentially afford an extraordinary resource for new drug discovery and have been employed in cancer treatment. However, the underlying pharmacological mechanisms of most natural anticancer compounds remain elusive, which has become one of the major obstacles in developing novel effective anticancer agents. Here, to address these unmet needs, we developed an anticancer herbs database of systems pharmacology (CancerHSP), which records anticancer herbs related information through manual curation. Currently, CancerHSP contains 2439 anticancer herbal medicines with 3575 anticancer ingredients. For each ingredient, the molecular structure and nine key ADME parameters are provided. Moreover, we also provide the anticancer activities of these compounds based on 492 different cancer cell lines. Further, the protein targets of the compounds are predicted by state-of-art methods or collected from literatures. CancerHSP will help reveal the molecular mechanisms of natural anticancer products and accelerate anticancer drug development, especially facilitate future investigations on drug repositioning and drug discovery. CancerHSP is freely available on the web at http://lsp.nwsuaf.edu.cn/CancerHSP.php.

• http://feeds.nature.com/~r/srep/rss/current/~3/fxyIDyIHiX0/srep11481

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trimethylguanosine合成酶TGS1自联想是高效snRNA / snoRNA的三甲基化和rRNA前体加工要求

Trimethylguanosine Synthase catalyses transfer of two methyl groups to the m7G cap of RNA polymerase II transcribed snRNAs, snoRNAs, and telomerase RNA TLC1 to form a 2,2,7-trimethylguanosine cap. While in vitro studies indicate that Tgs1 functions as a monomer and the dimethylation of m7G caps is not a processive reaction, partially methylated sn(o)RNAs are typically not detected in living cells. Here we show that both yeast and human Tgs1p possess a conserved self-association property located at the N-terminus. A disruption of Tgs1 self-association led to a strong reduction of sn(o)RNA trimethylation as well as reduced nucleolar enrichment of Tgs1. Self-association of Tgs1p and its catalytic activity were also prerequisite to bypass the requirement for its accessory factor Swm2p for efficient pre-rRNA processing and snRNA trimethylation. The ability to self-associate might enable Tgs1 to efficiently dimethylate the caps of the targeted RNAs in vivo.

• http://feeds.nature.com/~r/srep/rss/current/~3/npf03uy11bs/srep11282

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