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  • 1.

    【2hr】Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time

    包量

    机译 计算线性时间系统发生随机映射摘要的高阶矩
    摘要：Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences.
  • 2.

    【2hr】New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

    包量

    机译 从异构大生物和遗传数据推断和可视化贝叶斯网络的新算法和软件（BNOmics）
    摘要：Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology—type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types—single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.
  • 3.

    【2hr】Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation

    包量

    机译 电刺激过程中平滑肌力产生的非参数模型
    摘要：A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
  • 4.

    【2hr】An Enumerative Combinatorics Model for Fragmentation Patterns in RNA Sequencing Provides Insights into Nonuniformity of the Expected Fragment Starting-Point and Coverage Profile

    包量

    机译 RNA测序中片段化模式的枚举组合模型提供了对预期片段起点和覆盖范围的不均匀性的见解
    摘要：RNA sequencing (RNA-seq) has emerged as the method of choice for measuring the expression of RNAs in a given cell population. In most RNA-seq technologies, sequencing the full length of RNA molecules requires fragmentation into smaller pieces. Unfortunately, the issue of nonuniform sequencing coverage across a genomic feature has been a concern in RNA-seq and is attributed to biases for certain fragments in RNA-seq library preparation and sequencing. To investigate the expected coverage obtained from fragmentation, we develop a simple fragmentation model that is independent of bias from the experimental method and is not specific to the transcript sequence. Essentially, we enumerate all configurations for maximal placement of a given fragment length, F, on transcript length, T, to represent every possible fragmentation pattern, from which we compute the expected coverage profile across a transcript. We extend this model to incorporate general empirical attributes such as read length, fragment length distribution, and number of molecules of the transcript. We further introduce the fragment starting-point, fragment coverage, and read coverage profiles. We find that the expected profiles are not uniform and that factors such as fragment length to transcript length ratio, read length to fragment length ratio, fragment length distribution, and number of molecules influence the variability of coverage across a transcript. Finally, we explore a potential application of the model where, with simulations, we show that it is possible to correctly estimate the transcript copy number for any transcript in the RNA-seq experiment.
  • 5.

    【2hr】A Graph Approach to Mining Biological Patterns in the Binding Interfaces

    包量

    机译 在绑定界面中挖掘生物模式的图方法
    摘要：Protein–RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein–RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, we present a graph-mining method for discovering biological patterns in the protein–RNA interfaces. We represented known protein–RNA interfaces using graphs and then discovered graph patterns enriched in the interfaces. Comparison of the discovered graph patterns with UniProt annotations showed that the graph patterns had a significant overlap with residue sites that had been proven crucial for the RNA binding by experimental methods. Using 200 patterns as input features, a support vector machine method was able to classify protein surface patches into RNA-binding sites and non-RNA-binding sites with 84.0% accuracy and 88.9% precision. We built a simple scoring function that calculated the total number of the graph patterns that occurred in a protein–RNA interface. That scoring function was able to discriminate near-native protein–RNA complexes from docking decoys with a performance comparable with that of a state-of-the-art complex scoring function. Our work also revealed possible patterns that might be important for binding affinity.
  • 6.

    【2hr】Improved Versions of Common Estimators of the Recombination Rate

    包量

    机译 重组率的常用估计量的改进版本
    摘要：The scaled recombination parameter ρ is one of the key parameters, turning up frequently in population genetic models. Accurate estimates of ρ are difficult to obtain, as recombination events do not always leave traces in the data. One of the most widely used approaches is composite likelihood. Here we show that popular implementations of composite likelihood estimators can often be uniformly improved by optimizing the trade-off between bias and variance. The amount of possible improvement depends on parameters such as the sequence length, the sample size, and the mutation rate, and can be considerable in some cases. It turns out that ABC, with composite likelihood as a summary statistic, also leads to improved estimates, but now in terms of the posterior risk. Finally, we demonstrate a practical application on real data from Drosophila.
  • 7.

    【2hr】Constructing Structure Ensembles of Intrinsically Disordered Proteins from Chemical Shift Data

    包量

    机译 从化学位移数据构建固有无序蛋白的结构整合体
    摘要：Modeling the structural ensemble of intrinsically disordered proteins (IDPs), which lack fixed structures, is essential in understanding their cellular functions and revealing their regulation mechanisms in signaling pathways of related diseases (e.g., cancers and neurodegenerative disorders). Though the ensemble concept has been widely believed to be the most accurate way to depict 3D structures of IDPs, few of the traditional ensemble-based approaches effectively address the degeneracy problem that occurs when multiple solutions are consistent with experimental data and is the main challenge in the IDP ensemble construction task. In this article, based on a predefined conformational library, we formalize the structure ensemble construction problem into a least squares framework, which provides the optimal solution when the data constraints outnumber unknown variables. To deal with the degeneracy problem, we further propose a regularized regression approach based on the elastic net technique with the assumption that the weights to be estimated for individual structures in the ensemble are sparse. We have validated our methods through a reference ensemble approach as well as by testing the real biological data of three proteins, including alpha-synuclein, the translocation domain of Colocin N, and the K18 domain of Tau protein.
  • 8.

    【2hr】comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Protein Design Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence

    包量

    机译 彗星（通过树搜索约束多状态能量的优化）：一种可行且有效的蛋白质设计算法，可针对序列优化结合亲和力和特异性
    摘要：Practical protein design problems require designing sequences with a combination of affinity, stability, and specificity requirements. Multistate protein design algorithms model multiple structural or binding “states” of a protein to address these requirements. comets provides a new level of versatile, efficient, and provable multistate design. It provably returns the minimum with respect to sequence of any desired linear combination of the energies of multiple protein states, subject to constraints on other linear combinations. Thus, it can target nearly any combination of affinity (to one or multiple ligands), specificity, and stability (for multiple states if needed). Empirical calculations on 52 protein design problems showed comets is far more efficient than the previous state of the art for provable multistate design (exhaustive search over sequences). comets can handle a very wide range of protein flexibility and can enumerate a gap-free list of the best constraint-satisfying sequences in order of objective function value.
  • 9.

    【2hr】Unbiased Prediction and Feature Selection in High-Dimensional Survival Regression

    包量

    机译 高维生存回归的无偏预测和特征选择
    摘要：With widespread availability of omics profiling techniques, the analysis and interpretation of high-dimensional omics data, for example, for biomarkers, is becoming an increasingly important part of clinical medicine because such datasets constitute a promising resource for predicting survival outcomes. However, early experience has shown that biomarkers often generalize poorly. Thus, it is crucial that models are not overfitted and give accurate results with new data. In addition, reliable detection of multivariate biomarkers with high predictive power (feature selection) is of particular interest in clinical settings. We present an approach that addresses both aspects in high-dimensional survival models. Within a nested cross-validation (CV), we fit a survival model, evaluate a dataset in an unbiased fashion, and select features with the best predictive power by applying a weighted combination of CV runs. We evaluate our approach using simulated toy data, as well as three breast cancer datasets, to predict the survival of breast cancer patients after treatment. In all datasets, we achieve more reliable estimation of predictive power for unseen cases and better predictive performance compared to the standard CoxLasso model. Taken together, we present a comprehensive and flexible framework for survival models, including performance estimation, final feature selection, and final model construction. The proposed algorithm is implemented in an open source R package (SurvRank) available on CRAN.
  • 10.

    【2hr】Zero-Inflated Beta Regression for Differential Abundance Analysis with Metagenomics Data

    包量

    机译 零膨胀Beta回归用于使用元基因组学数据进行差分丰度分析
    摘要：Metagenomics data have been growing rapidly due to the advances in NGS technologies. One goal of human microbial studies is to detect abundance differences across clinical conditions. Besides small sample size and high dimension, metagenomics data are usually represented as compositions (proportions) with a large number of zeros and skewed distribution. Efficient tools for handling such compositional data need to be developed.We propose a zero-inflated beta regression approach (ZIBSeq) for identifying differentially abundant features between multiple clinical conditions. The proposed method takes the sparse nature of metagenomics data into account and handle the compositional data efficiently. Compared with other available methods, the proposed approach demonstrates better performance with large AUC values for most simulation studies. When applied to a human metagenomics data, it also identifies biologically important taxa reported from previous studies. The software in R is available upon request from the first author.
  • 11.

    【2hr】Impact of Microarray Preprocessing Techniques in Unraveling Biological Pathways

    包量

    机译 微阵列预处理技术对揭示生物途径的影响
    摘要：To better understand the impact of microarray preprocessing normalization techniques on the analysis of biological pathways in the prediction of chronic fatigue (CF) following radiation therapy, this study has compared the list of predictive genes found using the Robust Multiarray Averaging (RMA) and the Affymetrix MAS5 method, with the list that is obtained working with raw data (without any preprocessing). First, we modeled the spiked-in data set where differentially expressed genes were known and spiked-in at different known concentrations, showing that the precisions established by different gene ranking methods were higher than working with raw data. The results obtained from the spiked-in experiment were extrapolated to the CF data set to run learning and blind validation. RMA and MAS5 provided different sets of discriminatory genes that have a higher predictive accuracy in the learning phase, but lower predictive accuracy during the blind validation phase, suggesting that the genetic signatures generated using both preprocessing techniques cannot be generalizable. The pathways found using the raw data set better described what is a priori known for the CF disease. Besides, RMA produced more reliable pathways than MAS5. Understanding the strengths of these two preprocessing techniques in phenotype prediction is critical for precision medicine. Particularly, this article concludes that biological pathways might be better unraveled working with raw expression data. Moreover, the interpretation of the predictive gene profiles generated by RMA and MAS5 should be done with caution. This is an important conclusion with a high translational impact that should be confirmed in other disease data sets.
  • 12.

    【2hr】PTENpred: A Designer Protein Impact Predictor for PTEN-related Disorders

    包量

    机译 PTENpred：针对PTEN相关疾病的设计师蛋白质影响预测因子
    摘要：Connecting a genotype with a phenotype can provide immediate advantages in the context of modern medicine. Especially useful would be an algorithm for predicting the impact of nonsynonymous single-nucleotide polymorphisms in the gene for PTEN, a protein that is implicated in most human cancers and connected to germline disorders that include autism. We have developed a protein impact predictor, PTENpred, that integrates data from multiple analyses using a support vector machine algorithm. PTENpred can predict phenotypes related to a human PTEN mutation with high accuracy. The output of PTENpred is designed for use by biologists, clinicians, and laymen, and features an interactive display of the three-dimensional structure of PTEN. Using knowledge about the structure of proteins, in general, and the PTEN protein, in particular, enables the prediction of consequences from damage to the human PTEN gene. This algorithm, which can be accessed online, could facilitate the implementation of effective therapeutic regimens for cancer and other diseases.
  • 13.

    【2hr】cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design

    包量

    机译 cOSPREY：大规模计算蛋白质设计的基于云的分布式算法
    摘要：Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches.
  • 14.

    【2hr】Design of Biomedical Robots for Phenotype PredictionProblems

    包量

    机译 用于表型预测的生物医学机器人的设计问题
    摘要：Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. Thefinal aim of a biomedical robot is to improve knowledge discovery and providedecision systems to optimize diagnosis, treatment, and prognosis. This analysisshows that the biomedical robots are robust against different kinds of noises andparticularly to a wrong class assignment of the samples. Assessing the uncertaintythat is inherent to any phenotype prediction problem is the right way to addressthis kind of problem.
  • 15.

    【2hr】EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein–Ligand Docking

    包量

    机译 EDGA：蛋白质配体对接的种群进化指导遗传算法
    摘要：Protein–ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein–ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein–ligand docking. We consider four search methods—a tradition genetic algorithm, LGA, SODOCK, and EDGA—and compare their performance in docking of six protein–ligand docking problems. The results show that EDGA is the most stable, reliable, and successful.
  • 16.

    【2hr】Rapidly Registering Identity-by-Descent Across Ancestral Recombination Graphs

    包量

    机译 跨宗族重组图快速注册按血统身份
    摘要：The genomes of remotely related individuals occasionally contain long segments that are identical by descent (IBD). Sharing of IBD segments has many applications in population and medical genetics, and it is thus desirable to study their properties in simulations. However, no current method provides a direct, efficient means to extract IBD segments from simulated genealogies. Here, we introduce computationally efficient approaches to extract ground-truth IBD segments from a sequence of genealogies, or equivalently, an ancestral recombination graph. Specifically, we use a two-step scheme, where we first identify putative shared segments by comparing the common ancestors of all pairs of individuals at some distance apart. This reduces the search space considerably, and we then proceed by determining the true IBD status of the candidate segments. Under some assumptions and when allowing a limited resolution of segment lengths, our run-time complexity is reduced from O(n3 log n) for the naïve algorithm to O(n log n), where n is the number of individuals in the sample.
  • 17.

    【2hr】BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

    包量

    机译 BWM *：一种新颖的，可证明的，基于集合的动态规划算法，用于计算蛋白质设计的稀疏近似
    摘要：Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O() time and enumerates each additional conformation in merely O() time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.
  • 18.

    【2hr】Immunoglobulin Classification Using the Colored Antibody Graph

    包量

    机译 使用彩色抗体图对免疫球蛋白进行分类
    摘要：The somatic recombination of V, D, and J gene segments in B-cells introduces a great deal of diversity, and divergence from reference segments. Many recent studies of antibodies focus on the population of antibody transcripts that show which V, D, and J gene segments have been favored for a particular antigen, a repertoire. To properly describe the antibody repertoire, each antibody must be labeled by its constituting V, D, and J gene segment, a task made difficult by somatic recombination and hypermutation events. While previous approaches to repertoire analysis were based on sequential alignments, we describe a new de Bruijn graph–based algorithm to perform VDJ labeling and benchmark its performance.
  • 19.

    【2hr】Deconvolution of Ensemble Chromatin Interaction Data Reveals the Latent Mixing Structures in Cell Subpopulations

    包量

    机译 集合染色质相互作用数据的反卷积揭示了细胞亚群中潜在的混合结构。
    摘要：Chromosome conformation capture (3C) experiments provide a window into the spatial packing of a genome in three dimensions within the cell. This structure has been shown to be correlated with gene regulation, cancer mutations, and other genomic functions. However, 3C provides mixed measurements on a population of typically millions of cells, each with a different genome structure due to the fluidity of the genome and differing cell states. Here, we present several algorithms to deconvolve these measured 3C matrices into estimations of the contact matrices for each subpopulation of cells and relative densities of each subpopulation. We formulate the problem as that of choosing matrices and densities that minimize the Frobenius distance between the observed 3C matrix and the weighted sum of the estimated subpopulation matrices. Results on HeLa 5C and mouse and bacteria Hi-C data demonstrate the methods' effectiveness. We also show that domain boundaries from deconvolved matrices are often more enriched or depleted for regulatory chromatin markers when compared to boundaries from convolved matrices.
  • 20.

    【2hr】An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations

    包量

    机译 一种全基因组边缘和相互作用遗传变异全基因组检测的有效非线性回归方法
    摘要：Genome-wide association studies have revealed individual genetic variants associated with phenotypic traits such as disease risk and gene expressions. However, detecting pairwise interaction effects of genetic variants on traits still remains a challenge due to a large number of combinations of variants (∼1011 SNP pairs in the human genome), and relatively small sample sizes (typically <104). Despite recent breakthroughs in detecting interaction effects, there are still several open problems, including: (1) how to quickly process a large number of SNP pairs, (2) how to distinguish between true signals and SNPs/SNP pairs merely correlated with true signals, (3) how to detect nonlinear associations between SNP pairs and traits given small sample sizes, and (4) how to control false positives. In this article, we present a unified framework, called SPHINX, which addresses the aforementioned challenges. We first propose a piecewise linear model for interaction detection, because it is simple enough to estimate model parameters given small sample sizes but complex enough to capture nonlinear interaction effects. Then, based on the piecewise linear model, we introduce randomized group lasso under stability selection, and a screening algorithm to address the statistical and computational challenges mentioned above. In our experiments, we first demonstrate that SPHINX achieves better power than existing methods for interaction detection under false positive control. We further applied SPHINX to late-onset Alzheimer's disease dataset, and report 16 SNPs and 17 SNP pairs associated with gene traits. We also present a highly scalable implementation of our screening algorithm, which can screen ∼118 billion candidates of associations on a 60-node cluster in <5.5 hours.
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