scholarly journals RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking

2020 ◽  
Vol 36 (20) ◽  
pp. 5068-5075 ◽  
Author(s):  
Yue Wu ◽  
Michael T Judge ◽  
Jonathan Arnold ◽  
Suchendra M Bhandarkar ◽  
Arthur S Edison
Keyword(s):  
Time Series ◽  
Nmr Spectra ◽  
Chemical Shifts ◽  
Supplementary Information ◽  
Multiple Time ◽  
Local Curvature ◽  
Peak Overlap ◽  
3D Surface ◽  
Multiple Regions ◽  
Multiple Time Points

Abstract Motivation Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. Results We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. Availability and implementation Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Henriette Miko ◽  
Yunjiang Qiu ◽  
Bjoern Gaertner ◽  
Maike Sander ◽  
Uwe Ohler
Keyword(s):  
Time Series ◽  
Histone Modifications ◽  
Gene Activation ◽  
Expectation Maximization Algorithm ◽  
Chromatin State ◽  
Open Chromatin ◽  
Multiple Time ◽  
Enhancer Activity ◽  
Chromatin States ◽  
Multiple Time Points

Abstract Background Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points (“chromatin state trajectories”) have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs. Results We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex. Conclusions TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.

Random order autoregressive time series model with structural break

2020 ◽  
Vol 15 (3) ◽  
pp. 225-237
Author(s):  
Saurabh Kumar ◽  
Jitendra Kumar ◽  
Vikas Kumar Sharma ◽  
Varun Agiwal
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Random Order ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

scholarly journals Developmental Normalization of Phenomics Data Generated by High Throughput Plant Phenotyping Systems

2020 ◽  
Author(s):  
Diego Lozano-Claros ◽  
Xiangxiang Meng ◽  
Eddie Custovic ◽  
Guang Deng ◽  
Oliver Berkowitz ◽  
...  
Keyword(s):  
Time Series ◽  
High Throughput ◽  
Leaf Size ◽  
Plant Phenotyping ◽  
Early Developmental Stage ◽  
Multiple Time ◽  
Sowing Time ◽  
Statistical Dispersion ◽  
Multiple Time Points ◽  
Development Trajectories

AbstractBackgroundSowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number.ResultsThe DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.

scholarly journals NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting

2021 ◽  
Author(s):  
Min Shi ◽  
Shamim Mollah
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Short Term Memory ◽  
Distinct Advantage ◽  
Multiple Time ◽  
Omics Data ◽  
Data Imputation ◽  
Time Points ◽  
Forecasting Methods ◽  
Multiple Time Points

Abstract: High-throughput studies of biological systems are rapidly generating a wealth of 'omics'-scale data. Many of these studies are time-series collecting proteomics and genomics data capturing dynamic observations. While time-series omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values resulting in data shortage. Data missing and shortage are especially problematic for downstream applications such as omics data integration and computational analyses that need complete and sufficient data representations. Data imputation and forecasting methods have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. As a result, these techniques lack the ability to capture the time-ordered nature of data and cannot handle omics data containing missing values at multiple time points. Result: We propose a network-based method for time-series omics data imputation and forecasting (NeTOIF) that handle omics data containing missing values at multiple time points. NeTOIF takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform time-series imputation and forecasting using a long short-term memory network. Evaluating NeTOIF with a proteomic and a genomic dataset demonstrated a distinct advantage of NeTOIF over existing data imputation and forecasting methods. The average mean square error of NeTOIF improved 11.3% for imputation and 6.4% for forcasting compared to the baseline methods.

scholarly journals Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data

2018 ◽  
Author(s):  
Tal Zinger ◽  
Pleuni S. Pennings ◽  
Adi Stern
Keyword(s):  
Time Series ◽  
Population Size ◽  
Deep Sequencing ◽  
Time Series Data ◽  
Simulated Data ◽  
Ease Of Use ◽  
Series Data ◽  
Multiple Time ◽  
Sequencing Data ◽  
Multiple Time Points

1AbstractWith the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here we present FITS (Flexible Inference from Time-Series) – a computational framework that allows inference of either the fitness of a mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments, or for rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on noisy simulated data, and highlight its ability to infer meaningful information even in those circumstances. In particular FITS is able to categorize a mutation as Advantageous, Neutral or Deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate extremely high accuracy in inference. We highlight the ease of use of FITS for step-wise or iterative inference of mutation rates, population size, and fitness values for each mutation sequenced, when deep sequencing data is available at multiple time-points.AvailabilityFITS is written in C++ and is available both with a highly user friendly graphical user interface but also as a command line program that allows parallel high throughput analyses. Source code, binaries (Windows and Mac) and complementary scripts, are available from GitHub at https://github.com/SternLabTAU/[email protected]

Some Considerations Regarding the 1H and 13C Spectra of 4-(1-azulenyl)-2,6-bis(2-heteroaryl-vinyl)- pyrylium and Pyridinium and their Corresponding Pyridines

2018 ◽  
Vol 69 (1) ◽  
pp. 64-69
Author(s):  
Liviu Birzan ◽  
Mihaela Cristea ◽  
Constantin C. Draghici ◽  
Alexandru C. Razus
Keyword(s):  
Double Bond ◽  
13C Nmr ◽  
Nmr Spectra ◽  
Chemical Shifts ◽  
Pyridinium Salts ◽  
13C Nmr Spectra ◽  
1H And 13C Nmr ◽  
Pyrylium Salts

The 1H and 13C NMR spectra of several 2,6-diheteroarylvinyl heterocycles containing 4-azulenyl moiety were recorded and their proton and carbon chemical shifts were compared with those of the compounds without double bond between the heterocycles. The influence of the nature of central and side heterocycles, molecule polarization and anisotropic effects were revealed. The highest chemical shifts were recorded for the pyrylium salts and the lowest at pyridines, but in the case of the pyridinium salts, the protons chemical shifts at the central heterocycle are more shielded due to a peculiar anisotropy of the attached vinyl groups.

17O and 13C NMR spectra of some geminal diacetates

1988 ◽  
Vol 53 (3) ◽  
pp. 588-592 ◽  
Author(s):  
Antonín Lyčka ◽  
Josef Jirman ◽  
Jaroslav Holeček
Keyword(s):  
13C Nmr ◽  
Nmr Spectra ◽  
Chemical Shifts ◽  
The Other ◽  
13C Nmr Spectra ◽  
Other Hand ◽  
Carboxylic Esters ◽  
C Nmr ◽  
Oxygen Atoms

The 17O and 13C NMR spectra of eight geminal diacetates RCH(O(CO)CH3)2 derived from simple aldehydes have been measured. In contrast to the dicarboxylates R1R2E(O(CO)R3)2, where E = Si, Ge, or Sn, whose 17O NMR spectra only contain a single signal, and, on the other hand, in accordance with organic carboxylic esters, the 17O NMR spectra of the compound group studied always exhibit two well-resolved signals with the chemical shifts δ(17O) in the regions of 183-219 ppm and 369-381 ppm for the oxygen atoms in the groups C-O and C=O, respectively.

Carbon-13 and nitrogen-14 NMR spectra of 1-(substituted phenyl)pyridinium salts

1980 ◽  
Vol 45 (10) ◽  
pp. 2766-2771 ◽  
Author(s):  
Antonín Lyčka
Keyword(s):  
Heavy Water ◽  
Nmr Spectra ◽  
Chemical Shifts ◽  
Coupling Constants ◽  
Pyridinium Salts ◽  
Nmr Signals ◽  
C Nmr

The 13C and 14N NMR spectra of 1M solutions of 1-(substituted phenyl)pyridinium salts (4-CH3, 4-OCH3, H, 4-Cl, 4-Br, 4-I, 3-NO2, 4-NO2, 2,4-(NO2)2 (the 13C NMR only)) have been measured in heavy water at 30 °C. The 13C and 14N chemical shifts, the 1J(CH) coupling constants, some 3J(CH) coupling constants, and values of half-widths Δ 1/2 of the 14N NMR signals are given. The 13C chemical shifts of C(4) correlate with the σ0 constants (δC(4) = (1.79 ± 0.097) σ0 + (147.67 ± 0.041)), whereas no correlation of the nitrogen chemical shifts with the σ constants has been found. The half-widths Δ 1/2 correlate with the σ0 constants (Δ 1/2 = (76.2 ± 4.9) σ0 + (106.4 ± 2.2)) except for 1-phenylpyridinium chloride.

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