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Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing

BMC BIOINFORMATICS 2011年第1期12卷 1-13页

作者： Prosperi, Mattia C. F. Prosperi, Luciano Bruselles, Alessandro Abbate, Isabella Rozera, Gabriella Vincenti, Donatella Solmone, Maria Carmela Capobianchi, Maria Rosaria Ulivi, Giovanni Univ Cattolica Sacro Cuore Clin Infect Dis I-00168 Rome Italy Univ Florida Coll Med Emerging Pathogens Inst Dept Pathol Immunol & Lab Med Gainesville FL USA Natl Inst Infect Dis L Spallanzani Dept Virol Rome Italy Univ Roma TRE Fac Comp Sci Engn Dept Comp Sci & Automat Rome Italy

Background: Next-generation sequencing (NGS) offers a unique opportunity for high-throughput genomics and has potential to replace Sanger sequencing in many fields, including de-novo sequencing, re-sequencing, meta-genomics, and characterisation of infectious pathogens, such as viral quasispecies. Although methodologies and software for whole genome assembly and genome variation analysis have been developed and refined for NGS data, reconstructing a viral quasispecies using NGS data remains a challenge. This application would be useful for analysing intra-host evolutionary pathways in relation to immune responses and antiretroviral therapy exposures. Here we introduce a set of formulae for the combinatorial analysis of a quasispecies, given a NGS re-sequencing experiment and an algorithm for quasispecies reconstruction. We require that sequenced fragments are aligned against a reference genome, and that the reference genome is partitioned into a set of sliding windows (amplicons). The reconstruction algorithm is based on combinations of multinomial distributions and is designed to minimise the reconstruction of false variants, called in-silico recombinants. Results: The reconstruction algorithm was applied to error-free simulated data and reconstructed a high percentage of true variants, even at a low genetic diversity, where the chance to obtain in-silico recombinants is high. Results on empirical NGS data from patients infected with hepatitis B virus, confirmed its ability to characterise different viral variants from distinct patients. Conclusions: The combinatorial analysis provided a description of the difficulty to reconstruct a quasispecies, given a determined amplicon partition and a measure of population diversity. The reconstruction algorithm showed good performance both considering simulated data and real data, even in presence of sequencing errors.

关键词： Reference Genome reconstruction algorithm Multinomial Distribution Genome Length Combinatorial Analysis

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Compressive Sensing Signal reconstruction by Weighted Median Regression Estimates

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第6期59卷 2585-2601页

作者： Paredes, Jose L. Arce, Gonzalo R. Univ Los Andes Dept Elect Engn Merida 5101 Venezuela Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

In this paper, we propose a simple and robust algorithm for compressive sensing (CS) signal reconstruction based on the weighted median (WM) operator. The proposed approach addresses the reconstruction problem by solving a l(0)-regularized least absolute deviation (l(0)-LAD) regression problem with a tunable regularization parameter, being suitable for applications where the underlying contamination follows a statistical model with heavierthan- Gaussian tails. The solution to this regularized LAD regression problem is efficiently computed, under a coordinate descent framework, by an iterative algorithm that comprises two stages. In the first stage, an estimation of the sparse signal is found by recasting the reconstruction problem as a parameter location estimation for each entry in the sparse vector leading to the minimization of a sum of weighted absolute deviations. The solution to this one-dimensional minimization problem turns out to be the WM operator acting on a shifted-and-scaled version of the measurement samples with weights taken from the entries in the measurement matrix. The resultant estimated value is then passed to a second stage that identifies whether the corresponding entry is relevant or not. This stage is achieved by a hard threshold operator with adaptable thresholding parameter that is suitably tuned as the algorithm progresses. This two-stage operation, WM operator followed by a hard threshold operator, adds the desired robustness to the estimation of the sparse signal and, at the same time, ensures the sparsity of the solution. Extensive simulations demonstrate the reconstruction capability of the proposed approach under different noise models. We compare the performance of the proposed approach to those yielded by state-of-the-art CS reconstruction algorithms showing that our approach achieves a better performance for different noise distributions. In particular, as the distribution tails become heavier the performance gain achieved by the

关键词： Basis selection compressive sensing inverse problem model selection reconstruction algorithm robust regression sparse model weighted median

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BLIND MULTIBAND SPECTRUM SIGNALS reconstruction algorithmS COMPARISON 19th

BLIND MULTIBAND SPECTRUM SIGNALS RECONSTRUCTION ALGORITHMS C...

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European Signal Processing Conference

作者： Hao Shen Thomas Arildsen Deepaknath Tandur Torben Larsen Aalborg University Dept. of Electronic Systems Agilent Technologies

This paper investigates sparse sampling techniques applied to down-sampling and interference detection for multiband radio frequency (RF) signals. To reconstruct a signal from sparse samples is a compressive sensing problem. This paper compares three different reconstruction algorithms: 1) L_1 minimization;2) greedy pursuit;and 3) Multiple Signal Classification (MUSIC). We compare the performance of these algorithms and investigate the robustness to noise effects. Characteristics and limitations of each algorithm are discussed.

关键词： reconstruction algorithm Noise robustness downsampling Multiple signal classification Blind interference detection algorithms multiband signals Compressed sensing

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DPCM FOR QUANTIZED BLOCK-BASED COMPRESSED SENSING OF IMAGES

DPCM FOR QUANTIZED BLOCK-BASED COMPRESSED SENSING OF IMAGES

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European Signal Processing Conference

作者： Sungkwang Mun James E. Fowler Department of Electrical and Computer Engineering Geosystems Research Institute Mississippi State University

ISBN: (纸本)9781467310680

Differential pulse-code modulation is coupled with uniform scalar quantization to provide block-based quantized compressed sensing of images. Experimental results demonstrate significant improvement in rate-distortion performance as compared scalar quantization used alone in several block-based compressed-sensing reconstruction algorithms. Additionally, rate-distortion performance superior to that of alternative quantized-compressed-sensing techniques relying on optimized quantization or reconstruction is observed.

关键词： Compressed sensing Quantization DPCM differential pulse code modulation images Rate distortion reconstruction algorithm COMPRESSED modulation coding Sensing Techniques uniform scalar quantization scalar quantization Compressed sensing

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Reducing abdominal CT radiation dose with the adaptive statistical iterative reconstruction technique in children: a feasibility study

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PEDIATRIC RADIOLOGY 2011年第9期41卷 1174-1182页

作者： Vorona, Gregory A. Ceschin, Rafael C. Clayton, Barbara L. Sutcavage, Tom Tadros, Sameh S. Panigrahy, Ashok Allegheny Gen Hosp Dept Radiol Pittsburgh PA 15212 USA Childrens Hosp Pittsburgh UPMC Dept Radiol Pittsburgh PA USA

The use of the adaptive statistical iterative reconstruction (ASIR) algorithm has been shown to reduce radiation doses in adults undergoing abdominal CT studies while preserving image quality. To our knowledge, no studies have been done to validate the use of ASIR in children. To retrospectively evaluate differences in radiation dose and image quality in pediatric CT abdominal studies utilizing 40% ASIR compared with filtered-back projection (FBP). Eleven patients (mean age 8.5 years, range 2-17 years) had separate 40% ASIR and FBP enhanced abdominal CT studies on different days between July 2009 and October 2010. The ASIR studies utilized a 38% mA reduction in addition to our pediatric protocol mAs. Study volume CT dose indexes (CTDIvol) and dose-length products (DLP) were recorded. A consistent representative image was obtained from each study. The images were independently evaluated by two radiologists in a blinded manner for diagnostic utility, image sharpness and image noise. The average CTDIvol and DLP for the 40% ASIR studies were 4.25 mGy and 185.04 mGy-cm, compared with 6.75 mGy and 275.79 mGy-cm for the FBP studies, representing 37% and 33% reductions in both, respectively. The radiologists' assessments of subjective image quality did not demonstrate any significant differences between the ASIR and FBP images. In our experience, the use of 40% ASIR with a 38% decrease in mA lowers the radiation dose for children undergoing enhanced abdominal examinations by an average of 33%, while maintaining diagnostically acceptable images.

关键词： Adaptive statistical iterative reconstruction Image quality Radiation dose reconstruction algorithm CT

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reconstruction for limited-projection fluorescence molecular tomography based on projected restarted conjugate gradient normal residual

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OPTICS LETTERS 2011年第23期36卷 4515-4517页

作者： Cao, Xu Zhang, Bin Liu, Fei Wang, Xin Bai, Jing Tsinghua Univ Dept Biomed Engn Sch Med Beijing 10084 Peoples R China

Limited-projection fluorescence molecular tomography (FMT) can greatly reduce the acquisition time, which is suitable for resolving fast biology processes in vivo but suffers from severe ill-posedness because of the reconstruction using only limited projections. To overcome the severe ill-posedness, we report a reconstruction method based on the projected restarted conjugate gradient normal residual. The reconstruction results of two phantom experiments demonstrate that the proposed method is feasible for limited-projection FMT. (C) 2011 Optical Society of America

关键词： reconstruction algorithm Distress Fluorescence acquisition time Tomography conjugate gradient RESIDUAL flexible manufacturing

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Experimental validation of free-energy-landscape reconstruction from non-equilibrium single-molecule force spectroscopy measurements

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NATURE PHYSICS 2011年第8期7卷 631-634页

作者： Gupta, Amar Nath Vincent, Abhilash Neupane, Krishna Yu, Hao Wang, Feng Woodside, Michael T. Univ Alberta Dept Phys Edmonton AB T6G 2G7 Canada Natl Inst Nanotechnol Edmonton AB T6G 2M9 Canada

Free-energy-landscape formalisms provide the fundamental conceptual framework for physical descriptions of how proteins and nucleic acids fold into specific three-dimensional structures(1,2). Although folding landscapes are difficult to measure experimentally, recent theoretical work by Hummer and Szabo(3) has shown that landscape profiles can be reconstructed from non-equilibrium single-molecule force spectroscopy measurements using an extension of the Jarzynski equality(4). This method has been applied to simulations(5,6) and experiments(7,8) but never validated experimentally. We tested it using force-extension measurements on DNA hairpins with distinct, sequence-dependent folding landscapes. Quantitative agreement was found between the landscape profiles obtained from the non-equilibrium reconstruction and those from equilibrium probability distributions(9). We also tested the method on a riboswitch aptamer with three partially folded intermediate states, successfully reconstructing the landscape but finding some states difficult to resolve owing to low occupancy or overlap of the potential wells. These measurements validate the landscape-reconstruction method and provide a new test of non-equilibrium work relations.

关键词： Landscape drawings Quantum confinement FINDINGS Riboswitch FOLDING SINGLE MOLECULE FORCE SPECTROSCOPY hair clasp landscapes reconstruction algorithm

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Monte Carlo evaluation of the Filtered Back Projection method for image reconstruction in proton computed tomography

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2011年第1期658卷 78-83页

作者： Cirrone, G. A. P. Bucciolini, M. Bruzzi, M. Candiano, G. Civinini, C. Cuttone, G. Guarino, P. Lo Presti, D. Mazzaglia, S. E. Pallotta, S. Randazzo, N. Sipala, V. Stancampiano, C. Talamonti, C. Natl Inst Nucl Phys INFN INFN LNS Lab Nazl Sud I-95100 Catania Italy Lab Tecnol Oncol HSR I-90015 Palermo Italy Univ Palermo Dept Nucl Engn Palermo Italy Natl Inst Nucl Phys INFN Sect Catania I-95123 Catania Italy Univ Florence Dept Fisiopatol Clin I-50134 Florence Italy Univ Catania Dept Phys I-95123 Catania Italy Univ Florence Energet Dept I-50139 Florence Italy Natl Inst Nucl Phys INFN Sect Florence I-50019 Florence Italy

In this paper the use of the Filtered Back Projection (FBP) algorithm, in order to reconstruct tomographic images using the high energy (200-250 MeV) proton beams, is investigated. The algorithm has been studied in detail with a Monte Carlo approach and image quality has been analysed and compared with the total absorbed dose. A proton Computed Tomography (pCT) apparatus, developed by our group, has been fully simulated to exploit the power of the Geant4 Monte Carlo toolkit. From the simulation of the apparatus, a set of tomographic images of a test phantom has been reconstructed using the FBP at different absorbed dose values. The images have been evaluated in terms of homogeneity, noise, contrast, spatial and density resolution. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Proton tomography Monte Carlo simulation Geant4 reconstruction algorithm

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reconstruction of Petroleum Feedstocks by Entropy Maximization. Application to FCC Gasolines

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OIL & GAS SCIENCE AND TECHNOLOGY-REVUE D IFP ENERGIES NOUVELLES 2011年第3期66卷 437-460页

作者： Hudebine, D. Verstraete, J. J. IFP Energies Nouvelles Rond Point Lechangeur Solaize F-69360 Solaize France

reconstruction of Petroleum Feedstocks by Entropy Maximization. Application to FCC Gasolines - In the petroleum industry, the oil fractions are usually complex mixtures containing several hundreds up to several millions of different chemical species. For this reason, even the most powerful analytical tools do not allow to separate and to identify all the species that are present. Hence, petroleum fractions are currently characterized either by using average macroscopic descriptors (density, elemental analyses, Nuclear Magnetic Resonance, etc.) or by using separative techniques (distillation, gas or liquid chromatography, mass spectrometry, etc.), which quantify only a limited number of families of molecules however. reconstruction methods for the petroleum cuts are numerical tools, which allow to evolve towards a molecular detail and which are all based on the following principle: defining simplified but consistent mixtures of chemical compounds from partial analytical data and from evert knowledge of the process under study. Thus, the reconstruction method by entropy maximization, which is proposed in this article, is a recent and powerful technique which allows to determine the molar fractions of a predefined set of chemical compounds by maximizing an entropic criterion and by satisfying the analytical constraints given by the modeler. This approach allows to reduce the number of degrees of freedom from several thousands (corresponding to the molar fractions of the compounds) to several tens (corresponding to the Lagrange parameters associated with the analytical constraints) and to greatly decrease the CPU time required to perform the calculations. This approach has been successfully applied to reconstruct FCC gasolines by precisely predicting the molecular composition of this type of feedstocks from a distillation and an overall PIONA analysis (Paraffins, Isoparaffins, Olefins, Naphthenes and Aromatics). The extension to other naphthas (Straight Run naphthas, Co

关键词： Federal Communications Commission (FCC) Petroleum distillates reconstruction algorithm Expert Opinion Petroleum Industry chemical compounds raw material Gasoline CPU time naphtha Chemical speciation Distillation distilling

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Gradually Atom Pruning for Sparse reconstruction and Extension to Correlated Sparsity

Gradually Atom Pruning for Sparse Reconstruction and Extensi...

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Iranian Conference on Electrical Engineering

作者： Seyed Hossein Hosseini Mahrokh G. Shayesteh Department of Electrical Engineering Urmia University

ISBN: (纸本)9781467311496

We propose a new algorithm for recovery of sparse signals from their compressively sensed samples. The proposed algorithm benefits from the strategy of gradual movement to estimate the positions of non-zero samples of sparse signal. We decompose each sample of signal into two variables, namely "value" and "detector", by a weighted exponential function. We update these new variables using gradient descent method. Like the traditional compressed sensing algorithms, the first variable is used to solve the Least Absolute Shrinkage and Selection Operator (Lasso) problem. As a new strategy, the second variable participates in the regularization term of the Lasso (l_1 norm) that gradually detects the non-zero elements. The presence of the second variable enables us to extend the corresponding vector of the first variable to matrix form. This makes possible use of the correlation matrix for a heuristic search in the case that there are correlations among the samples of signal. We compare the performance of the new algorithm with various algorithms for uncorrelated and correlated sparsity. The results indicate the efficiency of the proposed methods.

关键词： Smoothed l_0 L_1 minimization Compressed sensing reconstruction algorithm Correlated sparsity

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