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

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.

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

BMC Genomics ◽

10.1186/s12864-021-07373-z ◽

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.

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Random order autoregressive time series model with structural break

Model Assisted Statistics and Applications ◽

10.3233/mas-200490 ◽

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.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa631 ◽

2020 ◽

Vol 36 (20) ◽

pp. 5068-5075 ◽

Cited By ~ 1

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.

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

10.1101/2021.06.05.447209 ◽

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.

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

10.1101/437483 ◽

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]

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Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2017.02.001 ◽

2017 ◽

Vol 135 ◽

pp. 222-232 ◽

Cited By ~ 14

Author(s):

Nan An ◽

Stephen M. Welch ◽

R.J. Cody Markelz ◽

Robert L. Baker ◽

Christine M. Palmer ◽

...

Keyword(s):

Time Series ◽

High Throughput ◽

Leaf Morphology ◽

Plant Phenotyping ◽

3D Photogrammetry ◽

2D And 3D

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The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽

10.3390/s20164550 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4550

Author(s):

Huajian Liu ◽

Brooke Bruning ◽

Trevor Garnett ◽

Bettina Berger

Keyword(s):

Hyperspectral Imaging ◽

High Throughput ◽

Management Practices ◽

Leaf Tissue ◽

Plant Phenotyping ◽

Proximal Sensing ◽

N Content ◽

Hyperspectral Sensors ◽

Wheat Leaves ◽

Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

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Physical activity levels among ovarian cancer survivors: a prospective longitudinal cohort study

International Journal of Gynecological Cancer ◽

10.1136/ijgc-2020-002107 ◽

2021 ◽

pp. ijgc-2020-002107

Author(s):

Tamara Jones ◽

Carolina Sandler ◽

Dimitrios Vagenas ◽

Monika Janda ◽

Andreas Obermair ◽

...

Keyword(s):

Physical Activity ◽

Ovarian Cancer ◽

Stage Iv ◽

Multiple Time ◽

Activity Levels ◽

Prospective Longitudinal Study ◽

Physical Activity Advice ◽

Physical Activity Levels ◽

Multiple Time Points ◽

Prospective Longitudinal

ObjectivePhysical activity following cancer diagnosis is associated with improved outcomes, including potential survival benefits, yet physical activity levels among common cancer types tend to decrease following diagnosis and remain low. Physical activity levels following diagnosis of less common cancers, such as ovarian cancer, are less known. The objectives of this study were to describe physical activity levels and to explore characteristics associated with physical activity levels in women with ovarian cancer from pre-diagnosis to 2 years post-diagnosis.MethodsAs part of a prospective longitudinal study, physical activity levels of women with ovarian cancer were assessed at multiple time points between pre-diagnosis and 2 years post-diagnosis. Physical activity levels and change in physical activity were described using metabolic equivalent task hours and minutes per week, and categorically (sedentary, insufficiently, or sufficiently active). Generalized Estimating Equations were used to explore whether participant characteristics were related to physical activity levels.ResultsA total of 110 women with ovarian cancer with a median age of 62 years (range 33–88) at diagnosis were included. 53–57% of the women were sufficiently active post-diagnosis, although average physical activity levels for the cohort were below recommended levels throughout the 2-year follow-up period (120–142.5min/week). A decrease or no change in post-diagnosis physical activity was reported by 44–60% of women compared with pre-diagnosis physical activity levels. Women diagnosed with stage IV disease, those earning a lower income, those receiving chemotherapy, and those currently smoking or working were more likely to report lower physical activity levels and had increased odds of being insufficiently active or sedentary.ConclusionsInterventions providing patients with appropriate physical activity advice and support for behavior change could potentially improve physical activity levels and health outcomes.

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Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽

10.1186/s13007-021-00747-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuo Zhou ◽

Xiujuan Chai ◽

Zixuan Yang ◽

Hongwu Wang ◽

Chenxue Yang ◽

...

Keyword(s):

Computer Vision ◽

Image Analysis ◽

Deep Learning ◽

High Throughput ◽

Batch Processing ◽

Plant Phenotyping ◽

Plant Science ◽

Analysis Software ◽

Image Analysis Software ◽

Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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The molecular make up of smoldering myeloma highlights the evolutionary pathways leading to multiple myeloma

Nature Communications ◽

10.1038/s41467-020-20524-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Eileen M. Boyle ◽

Shayu Deshpande ◽

Ruslana Tytarenko ◽

Cody Ashby ◽

Yan Wang ◽

...

Keyword(s):

Tumour Suppressor Genes ◽

Multiple Time ◽

Clonal Structure ◽

Time To Progression ◽

Novel Mutations ◽

Evolutionary Patterns ◽

Risk Of Progression ◽

Multiple Time Points ◽

Smoldering Myeloma ◽

Copy Number Changes

AbstractSmoldering myeloma (SMM) is associated with a high-risk of progression to myeloma (MM). We report the results of a study of 82 patients with both targeted sequencing that included a capture of the immunoglobulin and MYC regions. By comparing these results to newly diagnosed myeloma (MM) we show fewer NRAS and FAM46C mutations together with fewer adverse translocations, del(1p), del(14q), del(16q), and del(17p) in SMM consistent with their role as drivers of the transition to MM. KRAS mutations are associated with a shorter time to progression (HR 3.5 (1.5–8.1), p = 0.001). In an analysis of change in clonal structure over time we studied 53 samples from nine patients at multiple time points. Branching evolutionary patterns, novel mutations, biallelic hits in crucial tumour suppressor genes, and segmental copy number changes are key mechanisms underlying the transition to MM, which can precede progression and be used to guide early intervention strategies.

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