Journal: Briefings in Bioinformatics

Article Title: Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

doi: 10.1093/bib/bbac043

Figure Lengend Snippet: Tools for the study of inversions with their characteristics

Article Snippet: Birdsuite , Command-line (Bash, needs R, JAVA, Matlab, Python) , Free , Affymetrix CEL files (Genome-Wide Human SNP Array 6.0) Illumina 610 (beta version) , Birdseye - Hidden Markov Model (HMM) Canary - One-dimensional Gaussian mixture model (GMM) , Linux only PLINK conversion pipeline , 2008 , [ ] .

Techniques: Transformation Assay

Journal: Briefings in Bioinformatics

Article Title: Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

doi: 10.1093/bib/bbac043

Figure Lengend Snippet: Top-five tools for the study of CNVs with their characteristics

Article Snippet: Birdsuite , Command-line (Bash, needs R, JAVA, Matlab, Python) , Free , Affymetrix CEL files (Genome-Wide Human SNP Array 6.0) Illumina 610 (beta version) , Birdseye - Hidden Markov Model (HMM) Canary - One-dimensional Gaussian mixture model (GMM) , Linux only PLINK conversion pipeline , 2008 , [ ] .

Techniques: Genome Wide

Journal: Current biology : CB

Article Title: Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

doi: 10.1016/j.cub.2020.04.090

Figure Lengend Snippet: (A) Schematic of simultaneous mesoscale imaging and video capture of spontaneous behavior of head-fixed mice on a transparent treadmill. Two new mice, not from the original cohort, were used. Blue and red squares indicate area used to measure whisker pad motion energy (WME) and nose motion energy (NME), respectively. Colored dots indicate tracked position of the forelimbs, nose, and tail. These were used to estimate limb speed (LS). Distribution of behavioral variables are shown in three histograms along bottom. Gaussian mixture models fit to the distributions of LS, WME, and NME simultaneously. Two states were discovered: an “active” and “inactive” state (inset; purple and green respectively, see STAR Methods).

Article Snippet: To categorize behavioral state, a gaussian mixture model (GMM) was fit to the distributions of these three behavioral variables (MATLAB; fitgmdist function; 2 components).

Techniques: Imaging, Whisker Assay

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: a , Distance distributions of spin-labelled µOR under different ligand conditions. b , Distance distributions in the presence of ligand and G i . c , Distance distributions of phosphorylated µOR (µORp) in the presence of ligand and pre-activated β-arrestin-1 (βarr1). a – c , Shaded areas along the line indicate 95% confidence interval. d , Gaussian populations centred around 26 Å, 33 Å, 39 Å and 43 Å. Data represent median population ± 95% confidence interval derived from bootstrapping analysis using n = 1,000 iterations. Populations marked with asterisks have non-overlapping confidence intervals in the presence and absence of transducer.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: Derivative Assay

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: Populations from 6 Gaussian peaks of 30 DEER datasets are shown as scatter plot. Each blue dot represents one of the 30 samples. Red lines are the results of a linear fit. Numbers in each subpanel are corresponding correlation coefficients, which are labeled by a star (*) and red color if p < 0.05.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: Labeling, IF-P

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: a , Schematic of single-molecule FRET experiment. Labelled µOR was tethered to a cover slip via its Flag tag, biotinylated M1 Fab, streptavidin (SA) and biotinylated PEG. TIRFM, total internal reflection fluorescence microscopy. b , c , SmFRET distributions of µOR–Cy3/Cy5 ( b ) and µOR–Cy3/Cy7 ( c ) in the presence of different ligands. Gaussian peaks were fitted to FRET states (red and blue) and background noise (black). Green lines represent the cumulative fitted distributions. Dashed lines in blue and red represent peak centres of naloxone- and DAMGO-bound samples, respectively ( n represents the number of fluorescence traces used to calculate the corresponding histograms). Data are mean ± s.d. from three repeats.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: FLAG-tag, Fluorescence, Microscopy

Journal: Nature

Article Title: Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

doi: 10.1038/s41586-024-07295-2

Figure Lengend Snippet: a , b , µOR∆7-R182C/R273C is labeled with Cy3/Cy5. c – e , µOR∆7-R182C/R276C is labeled with Cy3/Cy5. f – h , µOR∆7-R182C/R276C is labeled with Cy3/Cy7. FRET distributions of µOR∆7-R182C/R276C labeled with Cy3/Cy5 (c) and Cy3/Cy7 (f). Error bars in c and f indicate s.d. from 3 repeats. FRET peak centers of µOR∆7-R182/R273 + Cy3/Cy5 (a, related to Fig. ), µOR∆7-R182/R276 + Cy3/Cy5 (d), and µOR∆7-R182/R276 + Cy3/Cy7 (g). The numbers on each bar are the peak centers extracted from the Gaussian fitting. Error bars indicate standard errors of the fitting. FRET values of each frame of µOR samples in Fig. (b), Extended Data Fig. 9c (e), and Extended Data Fig. 9f (h) are plotted as box-and-whisker plots. IQR, inter qaurtile range. The number of traces of each condition is indicated in the corresponding histograms. FRET efficiencies between 0.6 and 1.2 (b and e) and between 0 and 1.2 (h) were used for one-way ANOVA Tukey’s test. ***, p < 0.001. n.s., not significant.

Article Snippet: DEER data were processed via Gaussian mixture models (GMM) implemented in Matlab (v.2019b) using the DEERlab toolbox (v.0.9.2) .

Techniques: Labeling, Whisker Assay

Journal: Nmr in Biomedicine

Article Title: Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

doi: 10.1002/nbm.70050

Figure Lengend Snippet: Scatterplot of R against f IC for all tumor voxels ( n = 11,519) and subjects (A; left side). The black contours show the 2D Gaussian mixture model (GMM) fit with each voxel data point color‐coded based on the probability of belonging to each component (blue, green, and red). Contours of the three individual GMM components are shown as smaller plots (right side). R and f IC maps of tumor ROIs were used to generate color‐coded posterior probability maps of each GMM component (B; Subject 6 shown as example).

Article Snippet: A 2D Gaussian mixture model (GMM) was then fitted to the f IC and R values of all tumor voxels using an algorithm based on the MATLAB function fitgmdist with 20 random initializations to avoid local optima and a regularization value of 3.5 × 10 −3 to avoid overfitting ( mathworks.com/matlabcentral/fileexchange/71496‐identification‐of‐subregions‐in‐parameter‐maps‐by‐gmm ) [ ].

Techniques:

Journal: Nmr in Biomedicine

Article Title: Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

doi: 10.1002/nbm.70050

Figure Lengend Snippet: Model fit output of the Gaussian mixture model (GMM) of three clusters fitted to R and f IC for all tumor voxels. The table shows cluster ID as defined by histological analysis (Figures 4 and 6 ), mean values of R and f IC ( μ ) for each cluster, and the cluster fraction indicating the percentage of tumor voxel data that is associated with each Gaussian component.

Article Snippet: A 2D Gaussian mixture model (GMM) was then fitted to the f IC and R values of all tumor voxels using an algorithm based on the MATLAB function fitgmdist with 20 random initializations to avoid local optima and a regularization value of 3.5 × 10 −3 to avoid overfitting ( mathworks.com/matlabcentral/fileexchange/71496‐identification‐of‐subregions‐in‐parameter‐maps‐by‐gmm ) [ ].

Techniques:

Journal: Nmr in Biomedicine

Article Title: Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

doi: 10.1002/nbm.70050

Figure Lengend Snippet: Gaussian mixture model (GMM) probability maps from the VERDICT cluster analysis of R and f IC (left columns) and classification maps from the histology analysis (right columns). The colors in the histology classification maps represent different tissue types: necrotic (red), fibrotic (blue), and viable cancer cells (green). Black pixels indicate areas where no stain was present. The colors in the VERDICT cluster maps represent the probability of each voxel belonging to the GMM clusters, with colors chosen for each cluster to best match with the histology maps.

Article Snippet: A 2D Gaussian mixture model (GMM) was then fitted to the f IC and R values of all tumor voxels using an algorithm based on the MATLAB function fitgmdist with 20 random initializations to avoid local optima and a regularization value of 3.5 × 10 −3 to avoid overfitting ( mathworks.com/matlabcentral/fileexchange/71496‐identification‐of‐subregions‐in‐parameter‐maps‐by‐gmm ) [ ].

Techniques: Staining

Journal: PLoS ONE

Article Title: How Lovebirds Maneuver Rapidly Using Super-Fast Head Saccades and Image Feature Stabilization

doi: 10.1371/journal.pone.0129287

Figure Lengend Snippet: (A) The downstroke / upstroke phase ratio vs . instantaneous flap frequency distribution for individual wingbeats of five birds. A phase ratio of 1 indicates up- and downstrokes of equal duration, values <1 indicate longer upstrokes, values >1 longer downstrokes. Normalized bimodal Gaussian fits are shown for flap frequency (top) and for downstroke / upstroke time ratios (right). The bird-specific bimodal distribution parameters for the flapping frequency are: 2dg: μ 1 = 9.78, σ 1 = 1 . 61 , μ 2 = 17.26, σ 2 = 1 . 01 ; 2lg: μ 1 = 10.26, σ 1 = 1 . 83 , μ 2 = 18.14, σ 2 = 0 . 91 ; 2y: μ 1 = 9.39, σ 1 = 1 . 1 , μ 2 = 17.19, σ 2 = 0 . 86 ; 1y: μ 1 = 8.97, σ 1 = 0 . 6 , μ 2 = 15.76, σ 2 = 0 . 96 ; 3g: μ 1 = 9.49, σ 1 = 2 . 3 , μ 2 = 16.72, σ 2 = 1 ; For downstroke / upstroke periods the obtained bimodal distribution parameters are: 2dg: μ 1 = 0.5, σ 1 = 0.07, μ 2 = 1.26, σ 2 = 0.27; 2lg: μ 1 = 0.56, σ 1 = 0.13, μ 2 = 1.43, σ 2 = 0.17; 2y: μ 1 = 0.48, σ 1 = 0.07, μ 2 = 1.26, σ 2 = 0.27; 1y: μ 1 = 0.62, σ 1 = 0.09, μ 2 = 1.49, σ 2 = 0.17; 3g: μ 1 = 0.48, σ 1 = 0.12, μ 2 = 1.3, σ 2 = 0.17. The horizontal gray line separates the bimodal distributions at a downstroke / upstroke ratio of 0.94 (average midpoint between bimodal distribution peaks among birds). The vertical gray line separates the bimodal distribution at a flap frequency of 13.3 Hz (average among birds); n = 697 wing beats, N = 5 birds. Due to the 2000 fps sample frequency, and the fact that wingbeat, downstroke, and upstroke time are all integer values measured in number of frames, the data appear in a raster and can overlap precisely among wings beats, flights and birds. (B) The normalized saccade distributions illustrate when a saccade was started and ended during the downstroke vs . the upstroke phase. Shown is the average across birds (solid lines) and the standard deviation (shaded area). Binning: 0:10:100; n = 72 saccades, N = 5 birds.

Article Snippet: Wingbeat and downstroke / upstroke periods were analyzed for bimodal distributions using the Gaussian mixture models algorithm (GMM) of the MATLAB statistics toolbox.

Techniques: Standard Deviation