EFFICIENT INFERENCE OF HAPLOTYPES FROM GENOTYPES ON A PEDIGREE

We study haplotype reconstruction under the Mendelian law of inheritance and the minimum recombination principle on pedigree data. We prove that the problem of finding a minimum-recombinant haplotype configuration (MRHC) is in general NP-hard. This is the first complexity result concerning the problem to our knowledge. An iterative algorithm based on blocks of consecutive resolved marker loci (called block-extension) is proposed. It is very efficient and can be used for large pedigrees with a large number of markers, especially for those data sets requiring few recombinants (or recombination events). A polynomial-time exact algorithm for haplotype reconstruction without recombinants is also presented. This algorithm first identifies all the necessary constraints based on the Mendelian law and the zero recombinant assumption, and represents them using a system of linear equations over the cyclic group Z2. By using a simple method based on Gaussian elimination, we could obtain all possible feasible haplotype configurations. A C++ implementation of the block-extension algorithm, called PedPhase, has been tested on both simulated data and real data. The results show that the program performs very well on both types of data and will be useful for large scale haplotype inference projects.

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Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽

10.1093/genetics/165.4.2269 ◽

2003 ◽

Vol 165 (4) ◽

pp. 2269-2282

Author(s):

D Mester ◽

Y Ronin ◽

D Minkov ◽

E Nevo ◽

A Korol

Keyword(s):

Discrete Optimization ◽

High Performance ◽

Large Scale ◽

Simulated Data ◽

Real Data ◽

Genetic Maps ◽

Chromosome 1 ◽

Evolutionary Strategy ◽

Group A ◽

The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

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Summarizing the solution space in tumor phylogeny inference by multiple consensus trees

Bioinformatics ◽

10.1093/bioinformatics/btz312 ◽

2019 ◽

Vol 35 (14) ◽

pp. i408-i416 ◽

Cited By ~ 12

Author(s):

Nuraini Aguse ◽

Yuanyuan Qi ◽

Mohammed El-Kebir

Keyword(s):

Solution Space ◽

Simulated Data ◽

Exact Algorithm ◽

Real Data ◽

Supplementary Information ◽

Mixed Integer ◽

Consensus Tree ◽

Large Solution ◽

Consensus Trees ◽

Topological Features

Abstract Motivation Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees. Results We introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T. Availability and implementation https://github.com/elkebir-group/MCT. Supplementary information Supplementary data are available at Bioinformatics online.

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A distance based multisample test for high-dimensional compositional data with applications to the human microbiome

BMC Bioinformatics ◽

10.1186/s12859-020-3530-x ◽

2020 ◽

Vol 21 (S9) ◽

Author(s):

Qingyang Zhang ◽

Thy Dao

Keyword(s):

Large Scale ◽

Compositional Data ◽

Statistical Significance ◽

Human Microbiome ◽

Simulated Data ◽

Real Data ◽

Nonparametric Test ◽

High Dimensional ◽

Regularity Conditions ◽

Compositional Difference

Abstract Background Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data. Results In this paper, we consider a general problem of testing for the compositional difference between K populations. Motivated by microbiome and metagenomics studies, where the data are often over-dispersed and high-dimensional, we formulate a well-posed hypothesis from a Bayesian point of view and suggest a nonparametric test based on inter-point distance to evaluate statistical significance. Unlike most existing tests for compositional data, our method does not rely on any data transformation, sparsity assumption or regularity conditions on the covariance matrix, but directly analyzes the compositions. Simulated data and two real data sets on the human microbiome are used to illustrate the promise of our method. Conclusions Our simulation studies and real data applications demonstrate that the proposed test is more sensitive to the compositional difference than the mean-based method, especially when the data are over-dispersed or zero-inflated. The proposed test is easy to implement and computationally efficient, facilitating its application to large-scale datasets.

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Analysing protein post-translational modform regions by linear programming

10.1101/456640 ◽

2018 ◽

Author(s):

Deepesh Agarwal ◽

Ryan T. Fellers ◽

Bryan P. Early ◽

Dan Lu ◽

Caroline J. DeHart ◽

...

Keyword(s):

Linear Programming ◽

Single Molecule ◽

Protein Function ◽

Dimensional Space ◽

Linear Equations ◽

Simulated Data ◽

Real Data ◽

Mitogen Activated Protein Kinase ◽

High Dimensional ◽

Mitogen Activated Protein

Post-translational modifications (PTMs) at multiple sites can collectively influence protein function but the scope of such PTM coding has been challenging to determine. The number of potential combinatorial patterns of PTMs on a single molecule increases exponentially with the number of modification sites and a population of molecules exhibits a distribution of such “modforms”. Estimating these “modform distributions” is central to understanding how PTMs influence protein function. Although mass-spectrometry (MS) has made modforms more accessible, we have previously shown that current MS technology cannot recover the modform distribution of heavily modified proteins. However, MS data yield linear equations for modform amounts, which constrain the distribution within a high-dimensional, polyhedral “modform region”. Here, we show that linear programming (LP) can efficiently determine a range within which each modform value must lie, thereby approximating the modform region. We use this method on simulated data for mitogen-activated protein kinase 1 with the 7 phosphorylations reported on UniProt, giving a modform region in a 128 dimensional space. The exact dimension of the region is determined by the number of linearly independent equations but its size and shape depend on the data. The average modform range, which is a measure of size, reduces when data from bottom-up (BU) MS, in which proteins are first digested into peptides, is combined with data from top-down (TD) MS, in which whole proteins are analysed. Furthermore, when the modform distribution is structured, as might be expected of real distributions, the modform region for BU and TD combined has a more intricate polyhedral shape and is substantially more constrained than that of a random distribution. These results give the first insights into high-dimensional modform regions and confirm that fast LP methods can be used to analyse them. We discuss the problems of using modform regions with real data, when the actual modform distribution will not be known.

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Circle Pose Estimation Based on Stereo Vision

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2408 ◽

2012 ◽

Vol 263-266 ◽

pp. 2408-2413 ◽

Cited By ~ 1

Author(s):

Wen Juan Ma ◽

Shu Sen Sun ◽

Jin Yu Song ◽

Wen Shu Li

Keyword(s):

Pose Estimation ◽

Stereo Vision ◽

Closed Form Solution ◽

Simulated Data ◽

Real Data ◽

Form Solution ◽

Perspective View ◽

Simple Method ◽

Binocular Stereo Vision ◽

Binocular Stereo

This paper presents a simple method of circle pose estimation based on binocular stereo vision. It takes the projective equation of a circle as the basis, and gives the closed form solution of the pose parameters. Since there are two possible sets of pose parameters for a circle from one calibrated perspective view, the stereo vision constraints are incorporated and the accurate pose parameters are determined. Experiments using computer simulated data and real data demonstrate the robustness and accuracy of our method.

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A Fast Lasso-Based Method for Inferring Higher-Order Interactions

10.1101/2021.12.13.471844 ◽

2021 ◽

Author(s):

Kieran Elmes ◽

Astra Heywood ◽

Zhiyi Huang ◽

Alex Gavryushkin

Keyword(s):

Large Scale ◽

Association Studies ◽

Simulated Data ◽

Real Data ◽

Resistance Testing ◽

Data Sets ◽

Gene Effects ◽

Molecular Alterations ◽

Large Scale Data ◽

Pairwise Interactions

Large-scale genotype-phenotype screens provide a wealth of data for identifying molecular alterations associated with a phenotype. Epistatic effects play an important role in such association studies. For example, siRNA perturbation screens can be used to identify combinatorial gene-silencing effects. In bacteria, epistasis has practical consequences in determining antimicrobial resistance as the genetic background of a strain plays an important role in determining resistance. Recently developed tools scale to human exome-wide screens for pairwise interactions, but none to date have included the possibility of three-way interactions. Expanding upon recent state-of-the art methods, we make a number of improvements to the performance on large-scale data, making consideration of three-way interactions possible. We demonstrate our proposed method, Pint, on both simulated and real data sets, including antibiotic resistance testing and siRNA perturbation screens. Pint outperforms known methods in simulated data, and identifies a number of biologically plausible gene effects in both the antibiotic and siRNA models. For example, we have identified a combination of known tumor suppressor genes that is predicted (using Pint) to cause a significant increase in cell proliferation.

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PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

Journal CERITA ◽

10.33050/cerita.v5i1.235 ◽

2019 ◽

Vol 5 (1) ◽

pp. 97-106

Author(s):

Rudi Budi Agung ◽

Muhammad Nur ◽

Didi Sukayadi

Keyword(s):

Soil Moisture ◽

Growth And Development ◽

Large Scale ◽

Simple Method ◽

Moisture Sensor ◽

Sensor Development ◽

Soil Moisture Sensor ◽

Application Systems ◽

Harvesting Systems ◽

Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

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Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Metabolites ◽

10.3390/metabo11040214 ◽

2021 ◽

Vol 11 (4) ◽

pp. 214

Author(s):

Aneta Sawikowska ◽

Anna Piasecka ◽

Piotr Kachlicki ◽

Paweł Krajewski

Keyword(s):

Simulated Data ◽

Principal Component ◽

Real Data ◽

Functional Principal Component Analysis ◽

Additional Advantage ◽

Time Alignment ◽

Peak Separation ◽

Biological Mixtures ◽

Overlapping Peaks ◽

Retention Time Alignment

Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

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A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10070435 ◽

2021 ◽

Vol 10 (7) ◽

pp. 435

Author(s):

Yongbo Wang ◽

Nanshan Zheng ◽

Zhengfu Bian

Keyword(s):

Closed Form ◽

Closed Form Solution ◽

Simulated Data ◽

Real Data ◽

Point Clouds ◽

Form Solution ◽

Spatial Transformation ◽

Dual Quaternions ◽

Feature Based ◽

Planar Feature

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.

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Penalized partial least squares for pleiotropy

BMC Bioinformatics ◽

10.1186/s12859-021-03968-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Camilo Broc ◽

Therese Truong ◽

Benoit Liquet

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Association Studies ◽

A Priori ◽

Simulated Data ◽

Real Data ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Multiple Traits ◽

Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

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