Shenggao ZhouSoochow UniversityR. G. WeissETH ZurichLi-Tien ChengUniversity of California, San DiegoJoachim DzubiellaUniversity of FreiburgJ. Andrew McCammonUniversity of California, San DiegoBo LiUniversity of California, San Diego
Numerical Analysis and Scientific ComputingData Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:2005.25001
Proceedings of the National Academy of Sciences of the United States of America, 116, (30), 14989–14994, 2019.7
Ligand-receptor binding and unbinding are fundamental biomolecular processes and particularly essential to drug efficacy. Environmental water fluctuations, however, impact the corresponding thermodynamics and kinetics and thereby challenge theoretical descriptions. Here, we devise a holistic, implicit-solvent, multi-method approach to predict the (un)binding kinetics for a generic ligand-pocket model. We use the variational implicit-solvent model (VISM) to calculate the solute-solvent interfacial structures and the corresponding free energies, and combine the VISM with the string method to obtain the minimum energy paths and transition states between the various metastable (“dry” and “wet”) hydration states. The resulting dry-wet transition rates are then used in a spatially-dependent multi-state continuous-time Markov chain Brownian dynamics simulations, and the related Fokker–Planck equation calculations, of the ligand stochastic motion, providing the mean first-passage times for binding and unbinding. We find the hydration transitions to significantly slow down the binding process, in semi-quantitative agreement with existing explicit-water simulations, but significantly accelerate the unbinding process. Moreover, our methods allow the characterization of non-equilibrium hydration states of pocket and ligand during the ligand movement, for which we find substantial memory and hysteresis effects for binding versus unbinding. Our study thus provides a significant step forward towards efficient, physics-based interpretation and predictions of the complex kinetics in realistic ligand-receptor systems.
Many cellular processes are governed by stochastic reaction events. These events do not necessarily occur in single steps of individual molecules, and, conversely, each birth or death of a macromolecule (e.g., protein) could involve several small reaction steps, creating a memory between individual events and thus leading to nonmarkovian reaction kinetics. Characterizing this kinetics is challenging. Here, we develop a systematic approach for a general reaction network with arbitrary intrinsic waiting-time distributions, which includes the stationary generalized chemical-master equation (sgCME), the stationary generalized Fokker–Planck equation, and the generalized linear-noise approximation. The first formulation converts a nonmarkovian issue into a markovian one by introducing effective transition rates (that explicitly decode the effect of molecular memory) for the reactions in an equivalent reaction network with the same substrates but without molecular memory. Nonmarkovian features of the reaction kinetics can be revealed by solving the sgCME. The latter 2 formulations can be used in the fast evaluation of fluctuations. These formulations can have broad applications, and, in particular, they may help us discover new biological knowledge underlying memory effects. When they are applied to generalized stochastic models of gene-expression regulation, we find that molecular memory is in effect equivalent to a feedback and can induce bimodality, fine-tune the expression noise, and induce switch.
Yujie YeDepartment of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Tennessee, United States of AmericaXin KangShanghai Center for Mathematical Sciences, Fudan University, Shanghai, ChinaJordan BaileyDepartment of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Tennessee, United States of AmericaChunhe LiShanghai Center for Mathematical Sciences, Fudan University, Shanghai, ChinaTian HongDepartment of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Tennessee, United States of America
Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.
HIV-1 is the most common and pathogenic strain of human immunodeficiency virus consisting of many subtypes. To study the difference among HIV-1 subtypes in infection, diagnosis and drug design, it is important to identify HIV-1 subtypes from clinical HIV-1 samples. In this work, we propose an effective numeric representation called Subsequence Natural Vector (SNV) to encode HIV-1 sequences. Using the representation, we introduce an improved linear discriminant analysis method to classify HIV-1 viruses correctly. SNV is based on distribution of nucleotides in HIV-1 viral sequences. It not only computes the number of nucleotides, but also describes the position and variance of nucleotides in viruses. To validate our alignment-free method, 6902 complete genomes and 11,668 pol gene sequences of HIV-1 subtypes were collected from the up-to-date Los Alamos HIV database. SNV outperforms the three popular methods, Kameris, Comet and REGA, with almost 100% Sensitivity and Specificity, also with much less time. Our subtyping algorithm especially works better for circulating recombinant forms (CRFs) consisting of a few sequences. Our approach is also powerful to separate unique recombinant forms (URFs) from other subtypes with 100% Sensitivity and Specificity. Moreover, phylogenetic trees based on SNV representation are constructed using full-length HIV-1 genomes and pol genes respectively, where viruses from the same subtype are clustered together correctly.
Ting-Li ChenInstitute of Statistical Science, Academia SinicaDai-Ni HsiehInstitute of Statistical Science, Academia SinicaHung HungInstitute of Epidemiology and Preventive Medicine I-Ping TuInstitute of Statistical Science, Academia SinicaPei-Shien WuDept. of Biostatistics, Duke UniversityYi-Ming WuInstitute of Chemistry, Academia SinicaWei-Hau ChangInstitute of Chemistry, Academia SinicaSu-Yun HuangInstitute of Statistical Science, Academia Sinica
Statistics Theory and MethodsData Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:2004.33002
The Annals of Applied Statistics , 8, (1), 259-285, 2014
Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful
tool for obtaining three-dimensional (3D) structures of biological macromolecules
in native states. A minimum cryo-EM image data set for deriving a
meaningful reconstruction is comprised of thousands of randomly orientated
projections of identical particles photographed with a small number of electrons.
The computation of 3D structure from 2D projections requires clustering,
which aims to enhance the signal to noise ratio in each view by grouping
similarly oriented images. Nevertheless, the prevailing clustering techniques
are often compromised by three characteristics of cryo-EM data: high noise
content, high dimensionality and large number of clusters. Moreover, since
clustering requires registering images of similar orientation into the same
pixel coordinates by 2D alignment, it is desired that the clustering algorithm
can label misaligned images as outliers. Herein, we introduce a clustering algorithm
γ-SUP to model the data with a q-Gaussian mixture and adopt the
minimum γ-divergence for estimation, and then use a self-updating procedure
to obtain the numerical solution. We apply γ-SUP to the cryo-EM images
of two benchmark macromolecules, RNA polymerase II and ribosome.
In the former case, simulated images were chosen to decouple clustering from
alignment to demonstrate γ-SUP is more robust to misalignment outliers than
the existing clustering methods used in the cryo-EM community. In the latter
case, the clustering of real cryo-EM data by our γ-SUP method eliminates
noise in many views to reveal true structure features of ribosome at the projection
Classification of DNA sequences is an important issue in the bioinformatics study, yet most existing methods for phylogenetic analysis including Multiple Sequence Alignment (MSA) are time-consuming and computationally expensive. The alignment-free methods are popular nowadays, whereas the manual intervention in those methods usually decreases the accuracy. Also, the interactions among nucleotides are neglected in most methods. Here we propose a new Accumulated Natural Vector (ANV) method which represents each DNA sequence by a point in R18. By calculating the Accumulated Indicator Functions of nucleotides, we can further find an Accumulated Natural Vector for each sequence. This new Accumulated Natural Vector not only can capture the distribution of each nucleotide, but also provide the covariance among nucleotides. Thus global comparison of DNA sequences or genomes can be done easily in R18. The tests of ANV of datasets of different sizes and types have proved the accuracy and time-efficiency of the new proposed ANV method.
Genome comparison is a vital research area of bioinformatics. For large-scale genome comparisons, the Multiple Sequence Alignment (MSA) methods have been impractical to use due to its algorithmic complexity. In this study, we propose a novel alignment-free method based on the one-to-one correspondence between a DNA sequence and its complete central moment vector of the cumulative Fourier power and phase spectra. In addition, the covariance between the four nucleotides in the power and phase spectra is included. We use the cumulative Fourier power and phase spectra to define a 28-dimensional vector for each DNA sequence. Euclidean distances between the vectors can measure the dissimilarity between DNA sequences. We perform testing with datasets of different sizes and types including simulated DNA sequences, exon-intron and complete genomes. The results show that our method is more accurate and efficient for performing hierarchical clustering than other alignment-free methods and MSA methods.
Next-generation sequencing technology enables the routine detection of bacterial pathogens for clinical diagnostics and genetic research. Whole-genome sequencing has been of importance in the epidemiologic analysis of bacterial pathogens. However, few whole-genome sequencing-based genotyping pipelines are available for practical applications. Here, we present the whole-genome sequencing-based single nucleotide polymorphism(SNP) genotyping method and apply to the evolutionary analysis of methicillin-resistant Staphylococcus aureus. The SNP genotyping method calls genome variants using next-generation sequencing reads of whole genomes and calculates the pair-wise Jaccard distances of the genome variants. The method may reveal the high-resolution whole-genome SNP profiles and the structural variants of different isolates of methicillin-resistant S. aureus(MRSA) and methicillin-susceptible S. aureus(MSSA) strains. The phylogenetic analysis of whole genomes and particular regions may monitor and track the evolution and the transmission dynamic of bacterial pathogens. The computer pro-
grams of the whole genome sequencing-based SNP genotyping methods are available to the public at https://github. com/
Myxobacteria are social bacteria, that can glide in two dimensions and form counterpropagating, interacting waves. Here, we present a novel age-structured, continuous macroscopic model for the movement of myxobacteria. The derivation is based on microscopic interaction rules that can be formulated as a particle-based model and set within the Self-Organized Hydrodynamics (SOH) framework. The strength of this combined approach is that microscopic knowledge or data can be incorporated easily into the particle model, whilst the continuous model allows for easy numerical analysis of the diﬀerent eﬀects. However, we found that the derived macroscopic model lacks a diﬀusion term in the density equations, which is necessary to control the number of waves, indicating that a higher order approximation during the derivation is crucial. Upon ad hoc addition of the diﬀusion term, we found very good agreement between the age-structured model and the biology. In particular, we analyzed the inﬂuence of a refractory (insensitivity) period following a reversal of movement. Our analysis reveals that the refractory period is not necessary for wave formation, but essential to wave synchronization, indicating separate molecular mechanisms.
Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/
Huanfei Man Soochow UniversitySiyang LengFudan UniversityKazuyuki AiharaTokyo UniversityWei LinFudan UniversityLuonan Chen Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences
Data Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:1904.42006
Proceedings of the National Academy of Sciences of the United States of America, 115, (43), E9994-E10002, 2018.10
Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achieve accurate future state prediction based on short-term high-dimensional data. Specifically, from the observed data of high-dimensional variables, the RDE framework randomly generates a sufficient number of low-dimensional “nondelay embeddings” and maps each of them to a “delay embedding,” which is constructed from the data of a to be predicted target variable. Any of these mappings can perform as a low-dimensional weak predictor for future state prediction, and all of such mappings generate a distribution of predicted future states. This distribution actually patches all pieces of association information from various embeddings unbiasedly or biasedly into the whole dynamics of the target variable, which after operated by appropriate estimation strategies, creates a stronger predictor for achieving prediction in a more reliable and robust form. Through applying the RDE framework to data from both representative models and real-world systems, we reveal that a high-dimension feature is no longer an obstacle but a source of information crucial to the accurate prediction for short-term data, even under noise deterioration.
Comparing DNA and protein sequence groups plays an important role in biological evolutionary relationship research. Despite many methods available for sequence comparison, only a few can be used for group comparison. In this study, we propose a novel approach using convex hulls. We use statistical information contained within the sequences to represent each sequence as a point in high dimensional space. We find that the points belonging to one biological group are located in a different region of space than points belonging to other biological groups. To be more precise, the convex hull of the points from one group are disjoint from the convex hulls of points from other groups. This finding allows us to do phylogenetic analysis for groups in an efficient way. Five different theorems are presented for checking whether two convex hulls intersect or are disjoint. Test results for datasets related to HRV, HPV, Ebolavirus, PKC and protein phosphatase domains demonstrate that our method performs well and provides a new tool for studying group phylogeny. More significantly, the convex analysis presents a new way to search for sequences belonging to a biological group by examining points within the group’s convex hull.
Prochlorococcus marinus, one of the most abundant marine cyanobacteria in the global ocean, is classified into low-light (LL) and high-light (HL) adapted ecotypes. These two adapted ecotypes differ in their ecophysiological characteristics, especially whether adapted for growth at high-light or low-light intensities. However, some evolutionary relationships of Prochlorococcus phylogeny remain to be resolved, such as whether the strains SS120 and MIT9211 form a monophyletic group. We use the Natural Vector (NV) method to represent the sequence in order to identify the phylogeny of the Prochlorococcus. The natural vector method is alignment free without any model assumptions. This study added the covariances of amino acids in protein sequence to the natural vector method. Based on these new natural vectors, we can compute the Hausdorff distance between the two clades which represents the dissimilarity. This method enables us to systematically analyze both the dataset of ribosomal proteomes and the dataset of 16s-23s rRNA sequences in order to reconstruct the phylogeny of Prochlorococcus. Furthermore, we apply classification to inspect the relationship of SS120 and MIT9211. From the reconstructed phylogenetic trees and classification results, we may conclude that the SS120 does not cluster with MIT9211. This study demonstrates a new method for performing phylogenetic analysis. The results confirm that these two strains do not form a monophyletic clade in the phylogeny of Prochlorococcus.
Structures and functions of proteins play various essential roles in biological processes. The functions of newly discovered proteins can be predicted by comparing their structures with that of known functional proteins. Many approaches have been proposed for measuring the protein structure similarity, such as the template-modeling (TM)-score method, GRaphlet (GR)-Align method as well as the commonly used root-mean-square deviation (RMSD) measures. However, the alignment comparisons between the similarity of protein structure cost much time on large dataset, and the accuracy still have room to improve. In this study, we introduce a new three-dimensional (3D) Yau–Hausdorff distance between any two 3D objects. The (3D) Yau–Hausdorff distance can be used in particular to measure the similarity/dissimilarity of two proteins of any size and does not need aligning and super- imposing two structures. We apply structural similarity to study function similarity and perform phylogenetic analysis on several datasets. The results show that (3D) Yau–Hausdorff distance could serve as a more precise and effective method to discover biological relationships between proteins than other methods on structure comparison.
Background: In recent years, DNA barcoding has become an important tool for biologists to identify species and understand their natural biodiversity. The complexity of barcode data makes it difficult to analyze quickly and effectively. Manual classification of this data cannot keep up to the rate of increase of available data.
Results: In this study, we propose a new method for DNA barcode classification based on the distribution of nucleotides within the sequence. By adding the covariance of nucleotides to the original natural vector, this augmented 18-dimensional natural vector makes good use of the available information in the DNA sequence. The accurate classification results we obtained demonstrate that this new 18-dimensional natural vector method, together with the random forest classifier algorthm, can serve as a computationally efficient identification tool for DNA barcodes. We performed phylogenetic analysis on the genus Megacollybia to validate our method. We also studied how effective our method was in determining the genetic distance within and between species in our barcoding dataset.
Conclusions: The classification performs well on the fungi barcode dataset with high and robust accuracy. The reasonable phylogenetic trees we obtained further validate our methods. This method is alignment-free and does not depend on any model assumption, and it will become a powerful tool for classification and evolutionary analysis.
This study quantitatively validates the principle that the biological properties associated with a given genotype are determined by the distribution of amino acids. In order to visualize this central law of molecular biology, each protein was represented by a point in 250-dimensional space based on its amino acid distribution. Proteins from the same family are found to cluster together, leading to the principle that the convex hull surrounding protein points from the same family do not intersect with the convex hulls of other protein families. This principle was verified computationally for all available and reliable protein kinases and human proteins. In addition, we generated 2,328,761 figures to show that the convex hulls of different families were disjoint from each other. The classification performs well with high and robust accuracy (95.75% and 97.5%) together with reasonable phylogenetic trees validate our methods further.
Analyzing phylogenetic relationships using mathematical methods has always been of importance in bioinformatics. Quantitative research may interpret the raw biological data in a precise way. Multiple Sequence Alignment (MSA) is used frequently to analyze biological evolutions, but is very time-consuming. When the scale of data is large, alignment methods cannot finish calculation in reasonable time. Therefore, we present a new method using moments of cumulative Fourier power spectrum in clustering the DNA sequences. Each sequence is translated into a vector in Euclidean space. Distances between the vectors can reflect the relationships between sequences. The mapping between the spectra and moment vector is one-to-one, which means that no information is lost in the power spectra during the calculation. We cluster and classify several datasets including Influenza A, primates, and human rhinovirus (HRV) datasets to build up the phylogenetic trees. Results show that the new proposed cumulative Fourier power spectrum is much faster and more accurately than MSA and another alignment-free method known as k-mer. The research provides us new insights in the study of phylogeny, evolution, and efficient DNA comparison algorithms for large genomes. The computer programs of the cumulative Fourier power spectrum are available at GitHub (https://github.com/YaulabTsinghua/cumulative-Fourier-power-spectrum).
Rui DongTsinghua UniversityHui ZhengThe University of Illinois at ChicagoKun TianTsinghua UniversityShek-Chung YauThe Hong Kong University of Science and TechnologyWeiguang MaoTsinghua UniversityWenping YuNankai UniversityChangchuan YinThe University of Illinois at ChicagoChenglong YuSouth Australian Health and Medical Research InstituteRong Lucy HeChicago State UniversityJie YangThe University of Illinois at ChicagoStephen S.-T YauTsinghua University
Data Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:1903.42004
We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research.
Yongkun LiDepartment of Mathematical Sciences, Tsinghua UniversityLily HeDepartment of Mathematical Sciences, Tsinghua UniversityRong Lucy HeDepartment of Biological Sciences, Chicago State UniversityStephen S.-T. Yau(CorrespondingDepartment of Mathematical Sciences, Tsinghua University
Data Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:1903.42003
Zika virus (ZIKV) is a mosquito-borne flavivirus. It was first isolated from Uganda in 1947 and has become an
emergent event since 2007. However, because of the inconsistency of alignment methods, the evolution of
ZIKV remains poorly understood. In this study, we first use the complete protein and an alignment-free method
to build a phylogenetic tree of 87 Zika strains in which Asian, East African, and West African lineages are
characterized. We also use the NS5 protein to construct the genetic relationship among 44 Zika strains. For the
first time, these strains are divided into two clades: African 1 and African 2. This result suggests that ZIKV
originates from Africa, then spread to Asia, Pacific islands, and throughout the Americas. We also perform the
phylogeny analysis for 53 viruses in genus Flavivirus to which ZIKV belongs using complete proteins. Our
conclusion is consistent with the classification by the hosts and transmission vectors.
Yongkun Li1Department of Mathematical Sciences, Tsinghua UniversityLily He1Department of Mathematical Sciences, Tsinghua UniversityRong Lucy He2Department of Biological Sciences, Chicago State UniversityStephen S.-T. Yau(Corresponding author)1Department of Mathematical Sciences, Tsinghua University
Data Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:1903.42002
With sharp increasing in biological sequences, the traditional sequence alignment methods become
unsuitable and infeasible. It motivates a surge of fast alignment-free techniques for sequence
analysis. Among these methods, many sorts of feature vector methods are established and applied to
reconstruction of species phylogeny. The vectors basically consist of some typical numerical features
for certain biological problems. The features may come from the primary sequences, secondary or
three dimensional structures of macromolecules. In this study, we propose a novel numerical vector
based on only primary sequences of organism to build their phylogeny. Three chemical and physical
properties of primary sequences: purine, pyrimidine and keto are also incorporated to the vector. Using
each property, we convert the nucleotide sequence into a new sequence consisting of only two kinds of
letters. Therefore, three sequences are constructed according to the three properties. For each letter of
each sequence we calculate the number of the letter, the average position of the letter and the variation
of the position of the letter appearing in the sequence. Tested on several datasets related to mammals,
viruses and bacteria, this new tool is fast in speed and accurate for inferring the phylogeny of organisms.
Lily HeDepartment of Mathematical Sciences, Tsinghua UniversityYongkun LiDepartment of Mathematical Sciences, Tsinghua UniversityRong Lucy HeDepartment of Biological Sciences, Chicago State UniversityStephen S.-T. Yau(Corresponding author)Department of Mathematical Sciences, Tsinghua University
Data Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:1903.42001
Journal of Theoretical Biology, 427, 41-52, 2017.6
Classification of protein are crucial topics in biology. The number of protein sequences stored in databases increases sharply in the past decade. Traditionally, comparison of protein sequences is usually carried out through multiple sequence alignment methods. However, these methods may be unsuitable for clustering of protein sequences when gene rearrangements occur such as in viral genomes. The computation is also very time-consuming for large datasets with long genomes. In this paper, based on three important bio- chemical properties of amino acids: the hydropathy index, polar requirement and chemical composition of the side chain, we propose a 24 dimensional feature vector describing the composition of amino acids in protein sequences. Our method not only utilizes the chemical properties of amino acids but also counts on their numbers and positions. The results on beta-globin, mammals, and three virus datasets show that this new tool is fast and accurate for classifying proteins and inferring the phylogeny of organisms.
Processing streaming data as they arrive is often necessary for high dimensional data analysis. In this paper, we analyze the convergence of a subspace online PCA iteration, as a followup of the recent work of Li, Wang, Liu, and Zhang [Math. Program., Ser. B, DOI 10.1007/s10107-017-1182-z] who considered the case for the most significant principal component only, i.e., a single vector. Under the sub-Gaussian assumption, we obtain a finite-sample error bound that closely matches the minimax information lower bound of Vu and Lei [Ann. Statist. 41:6 (2013), 2905-2947].
We propose to combine cepstrum and nonlinear time–frequency (TF) analysis
to study multiple component oscillatory signals with time-varying frequency and
amplitude and with time-varying non-sinusoidal oscillatory pattern. The concept of
cepstrum is applied to eliminate the wave-shape function influence on the TF analysis,
and we propose a new algorithm, named de-shape synchrosqueezing transform (deshape
SST). The mathematical model, adaptive non-harmonic model, is introduced
and the de-shape SST algorithm is theoretically analyzed. In addition to simulated
signals, several different physiological, musical and biological signals are analyzed to
illustrate the proposed algorithm.
Chenglong YuSouth Australian Health and Medical Research InstituteBernhard T. BauneUniversity of AdelaideJulio LicinioSouth Australian Health and Medical Research InstituteMa-Li WongSouth Australian Health and Medical Research Institute
Data Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:1703.42005
Major depressive disorder (MDD) is highly prevalent, resulting in an exceedingly high disease burden. The identification of generic risk factors could lead to advance prevention and therapeutics. Current approaches examine genotyping data to identify specific variations between cases and controls. Compared to genotyping, whole-genome sequencing (WGS) allows for the detection of private mutations. In this proof-of-concept study, we establish a conceptually novel computational approach that clusters subjects based on the entirety of their WGS. Those clusters predicted MDD diagnosis. This strategy yielded encouraging results, showing that depressed Mexican-American participants were grouped closer; in contrast ethnically-matched controls grouped away from MDD patients. This implies that within the same ancestry, the WGS data of an individual can be used to check whether this individual is within or closer to MDD subjects or to controls. We propose a novel strategy to apply WGS data to clinical medicine by facilitating diagnosis through genetic clustering. Further studies utilising our method should examine larger WGS datasets on other ethnical groups.
The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. Thus
far, the detailed classifications for all viruses are neither complete nor free from dispute. For example, the current missing
label rates in GenBank are 12.1% for family label and 30.0% for genus label. Using the proposed Natural Vector
representation, all 2,044 single-segment referenced viral genomes in GenBank can be embedded in R^12. Unlike other
approaches, this allows us to determine phylogenetic relations for all viruses at any level (e.g., Baltimore class, family,
subfamily, genus, and species) in real time. Additionally, the proposed graphical representation for virus phylogeny provides
a visualization of the distribution of viruses in R^12. Unlike the commonly used tree visualization methods which suffer from
uniqueness and existence problems, our representation always exists and is unique. This approach is successfully used to
predict and correct viral classification information, as well as to identify viral origins; e.g. a recent public health threat, the
West Nile virus, is closer to the Japanese encephalitis antigenic complex based on our visualization. Based on cross validation
results, the accuracy rates of our predictions are as high as 98.2% for Baltimore class labels, 96.6% for family
labels, 99.7% for subfamily labels and 97.2% for genus labels.
Current methods cannot tell us what the nature of the protein universe is concretely. They are based on different models of amino acid substitution and multiple sequence alignment which is an NP-hard problem and requires manual intervention. Protein structural analysis also gives a direction for mapping the protein universe. Unfortunately, now only a minuscule fraction of proteins' 3-dimensional structures are known. Furthermore, the phylogenetic tree representations are not unique for any existing tree construction methods. Here we develop a novel method to realize the nature of protein universe. We show the protein universe can be realized as a protein space in 60-dimensional Euclidean space using a distance based on a normalized distribution of amino acids. Every protein is in one-to-one correspondence with a point in protein space, where proteins with similar properties stay close together. Thus the distance between two points in protein space represents the biological distance of the corresponding two proteins. We also propose a natural graphical representation for inferring phylogenies. The representation is natural and unique based on the biological distances of proteins in protein space. This will solve the fundamental question of how proteins are distributed in the protein universe.
The free-living SAR11 clade is a globally abundant group of oceanic Alphaproteobacteria, with small genome sizes and rich genomic A+T content. However, the taxonomy of SAR11 has become controversial recently. Some researchers argue that the position of SAR11 is a sister group to Rickettsiales. Other researchers advocate that SAR11 is located within free-living lineages of Alphaproteobacteria. Here, we use the natural vector representation method to identify the evolutionary origin of the SAR11 clade. This alignment-free method does not depend on any model assumptions. With this approach, the correspondence between proteome sequences and their natural vectors is one-to-one. After fixing a set of proteins, each bacterium is represented by a set of vectors. The Hausdorff distance is then used to compute the dissimilarity distance between two bacteria. The phylogenetic tree can be reconstructed based on these distances. Using our method, we systematically analyze four data sets of alphaproteobacterial proteomes in order to reconstruct the phylogeny of Alphaproteobacteria. From this we can see that the phylogenetic position of the SAR11 group is within a group of other free-living lineages of Alphaproteobacteria.