ALBERT

Description: Author(s): X. Q. Liu, X. B. Li, L. Zhang, Y. Q. Cheng, Z. G. Yan, M. Xu, X. D. Han, S. B. Zhang, Z. Zhang, and E. Ma [Phys. Rev. Lett. 108, 239602] Published Tue Jun 05, 2012

Description: Author(s): X. Q. Liu, X. B. Li, L. Zhang, Y. Q. Cheng, Z. G. Yan, M. Xu, X. D. Han, S. B. Zhang, Z. Zhang, and E. Ma Using electron microscopy and diffraction techniques, as well as first-principles calculations, we demonstrate that as much as 35% of the total Ge atoms in the cubic phase of Ge_{2} Sb_{2} Te_{5} locate in tetrahedral environments. The Ge-vacancy interactions play a crucial stabilizing role, leading... [Phys. Rev. Lett. 106, 025501] Published Thu Jan 13, 2011

Description: : In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ~60% when the causal variant is of low frequency (MAF 〈 0.01). Availability and implementation : UNC combo and the associated R files, including documentation, examples, are available at http://www.unc.edu/~yunmli/UNCcombo/ Contact: yunli@med.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Author(s): D. P. Kumah, M. Dogan, J. H. Ngai, D. Qiu, Z. Zhang, D. Su, E. D. Specht, S. Ismail-Beigi, C. H. Ahn, and F. J. Walker The strong interaction at an interface between a substrate and thin film leads to epitaxy and provides a means of inducing structural changes in the epitaxial film. These induced material phases often exhibit technologically relevant electronic, magnetic, and functional properties. The 2 × 1 surface o… [Phys. Rev. Lett. 116, 106101] Published Mon Mar 07, 2016

Description: Author(s): P. L. Cai, J. Hu, L. P. He, J. Pan, X. C. Hong, Z. Zhang, J. Zhang, J. Wei, Z. Q. Mao, and S. Y. Li The quantum oscillations of the magnetoresistance under ambient and high pressure have been studied for WTe 2 single crystals, in which extremely large magnetoresistance was discovered recently. By analyzing the Shubnikov–de Haas oscillations, four Fermi surfaces are identified, and two of them are f… [Phys. Rev. Lett. 115, 057202] Published Tue Jul 28, 2015

Description: Motivation : Transposon insertion sequencing (Tn-seq) is an emerging technology that combines transposon mutagenesis with next-generation sequencing technologies for the identification of genes related to bacterial survival. The resulting data from Tn-seq experiments consist of sequence reads mapped to millions of potential transposon insertion sites and a large portion of insertion sites have zero mapped reads. Novel statistical method for Tn-seq data analysis is needed to infer functions of genes on bacterial growth. Results : In this article, we propose a zero-inflated Poisson model for analyzing the Tn-seq data that are high-dimensional and with an excess of zeros. Maximum likelihood estimates of model parameters are obtained using an expectation–maximization (EM) algorithm, and pseudogenes are utilized to construct appropriate statistical tests for the transposon insertion tolerance of normal genes of interest. We propose a multiple testing procedure that categorizes genes into each of the three states, hypo-tolerant, tolerant and hyper-tolerant, while controlling the false discovery rate. We evaluate the proposed method with simulation studies and apply the proposed method to a real Tn-seq data from an experiment that studied the bacterial pathogen, Campylobacter jejuni . Availability and implementation : We provide R code for implementing our proposed method at http://github.com/ffliu/TnSeq . A user’s guide with example data analysis is also available there. Contact : pliu@iastate.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: How chromatin folds in three-dimensional (3D) space is closely related to transcription regulation. As powerful tools to study such 3D chromatin conformation, the recently developed Hi-C technologies enable a genome-wide measurement of pair-wise chromatin interaction. However, methods for the detection of biologically meaningful chromatin interactions, i.e. peak calling, from Hi-C data, are still under development. In our previous work, we have developed a novel hidden Markov random field (HMRF) based Bayesian method, which through explicitly modeling the non-negligible spatial dependency among adjacent pairs of loci manifesting in high resolution Hi-C data, achieves substantially improved robustness and enhanced statistical power in peak calling. Superior to peak callers that ignore spatial dependency both methodologically and in performance, our previous Bayesian framework suffers from heavy computational costs due to intensive computation incurred by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure. Results: In this work, we have developed FastHiC, a novel approach based on simulated field approximation, which approximates the joint distribution of the hidden peak status by a set of independent random variables, leading to more tractable computation. Performance comparisons in real data analysis showed that FastHiC not only speeds up our original Bayesian method by more than five times, bus also achieves higher peak calling accuracy. Availability and Implementation: FastHiC is freely accessible at: http://www.unc.edu/~yunmli/FastHiC/ Contacts : yunli@med.unc.edu or ming.hu@nyumc.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. Results: In this study, we propose a novel Area Under Curve (AUC) optimization method for multi-biomarker panel identification named Nearest Centroid Classifier for AUC optimization (NCC-AUC). Our method is motived by the connection between AUC score for classification accuracy evaluation and Harrell’s concordance index in survival analysis. This connection allows us to convert the survival time regression problem to a binary classification problem. Then an optimization model is formulated to directly maximize AUC and meanwhile minimize the number of selected features to construct a predictor in the nearest centroid classifier framework. NCC-AUC shows its great performance by validating both in genomic data of breast cancer and clinical data of stage IB Non-Small-Cell Lung Cancer (NSCLC). For the genomic data, NCC-AUC outperforms Support Vector Machine (SVM) and Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) in classification accuracy. It tends to select a multi-biomarker panel with low average redundancy and enriched biological meanings. Also NCC-AUC is more significant in separation of low and high risk cohorts than widely used Cox model (Cox proportional-hazards regression model) and L 1 -Cox model (L 1 penalized in Cox model). These performance gains of NCC-AUC are quite robust across 5 subtypes of breast cancer. Further in an independent clinical data, NCC-AUC outperforms SVM and SVM-RFE in predictive accuracy and is consistently better than Cox model and L 1 -Cox model in grouping patients into high and low risk categories. Conclusion: In summary, NCC-AUC provides a rigorous optimization framework to systematically reveal multi-biomarker panel from genomic and clinical data. It can serve as a useful tool to identify prognostic biomarkers for survival analysis. Availability and implementation: NCC-AUC is available at http://doc.aporc.org/wiki/NCC-AUC . Contact: ywang@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, detecting non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfillment of this task has many important implications including identifying long-range interactions that assist interpreting a sizable fraction of the results from genome-wide association studies. The task – distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions – poses great challenges both statistically and computationally. Model-based methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration. Results: In this paper, we propose a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabilities in the two-dimensional space based on the contact frequency matrix. By borrowing information from neighboring loci pairs, our method demonstrates superior reproducibility and statistical power in both simulation studies and real data analysis. Availability and implementation: The Source codes can be downloaded at: http://www.unc.edu/~yunmli/HMRFBayesHiC . Contact: ming.hu@nyumc.org or yunli@med.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Accurate detection of differentially expressed genes between tumor and normal samples is a primary approach of cancer-related biomarker identification. Due to the infiltration of tumor surrounding normal cells, the expression data derived from tumor samples would always be contaminated with normal cells. Ignoring such cellular contamination would deflate the power of detecting DE genes and further confound the biological interpretation of the analysis results. For the time being, there does not exists any differential expression analysis approach for RNA-seq data in literature that can properly account for the contamination of tumor samples. Results: Without appealing to any extra information, we develop a new method ‘contamDE’ based on a novel statistical model that associates RNA-seq expression levels with cell types. It is demonstrated through simulation studies that contamDE could be much more powerful than the existing methods that ignore the contamination. In the application to two cancer studies, contamDE uniquely found several potential therapy and prognostic biomarkers of prostate cancer and non-small cell lung cancer. Availability and implementation: An R package contamDE is freely available at http://homepage.fudan.edu.cn/zhangh/softwares/ . Contact: zhanghfd@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.