gen_mask
PURPOSE
gen_mask generates a mask from an image according to one of several rule options. A mask designates which pixels of an image are to be considered valid and used in AlignTK image processing tools. Other pixels are ignored and their intensity values have no effect on the output.
TYPICAL USAGE
gen_mask -input image.tif -output mask.pbm -threshold 1
COMMAND LINE OPTIONS
-input image[.tif | .pgm | .jpg]
specifies the input image.
-output mask.pbm
specifies the filename that the output mask will be written to.
-threshold pixel_value
applies a fixed threshold to the entire image to produce the output mask. Image pixels whose value is less than the threshold will have their corresponding mask bit cleared, indicating that the pixel will be ignored. Image pixels whose value is greater than or equal to the threshold will have their corresponding mask bit set, indicating a valid pixel.
-boundary-fill
gen_imaps
PURPOSE
gen_imaps generates intensity maps for a set of images that are partially overlapping. One can then give these intensity maps to apply_map in order to produce a composite image where intensity variations in the original images do not produce obvious stitching artifacts.
TYPICAL USAGE
gen_imaps -image_list images.lst \
-images images/ \
-masks masks/ \
-map_list maps.lst \
-maps maps/ \
-output imaps/
COMMAND LINE OPTIONS
-image_list images.lst
specifies the file that lists the images in the set. This file contains just a list of image names, one per line. Empty lines or lines beginning with '#' are ignored.
-images directory/
specifies the directory where the images are stored.
-level output_imap_level
specifies the level of resolution of the output maps. This is expressed as the base-2 logarithm of the pixel distance between map points (in both x and y diimensions). For example, a value of 6 would indicate that the maps should have a grid point every 64 pixels in x and y. The default value is 6.
-map_list maps.lst
specifies the maps that define the overlaps within the image set. The map list file contains the names (one per line) of all maps relating one image to another. The format of each line in this file is:
image_name0 image_name1 map_name [ weight ]
These components are as follows:
image_name0 - the first image that the map refers to
image_name1 - the second image that the map refers to (the reference image)
map_name - the name given to the map. This will be used to construct the filename in the maps directory. Often the map_name is formed as some combination of the image names that it refers to.
weight - an optional weight factor to multiply all the inter-image spring constants by for springs generated from this map. This can be used to make some maps more important than others. If omitted, this defaults to 1.
-maps directory/
specifies the directory where the maps are stored.
-masks directory/
specifies the directory where the image masks are stored. It is necessary to have the gen_imaps program use image masks if not all the pixels of the images contain valid image content. See the section on image masks in the CONCEPTS page of AlignTK for more details.
-output directory/
specifies the directory where output intensity maps will be saved.
find_rst
PURPOSE
find_rst takes pairs of images and attempts to find the rotation, scale, and translation that best maps the first image (a.k.a. the source image) into the second image (a.k.a. the target image). If the images differ appreciably in content or have signifcant distortions, this program may not be able to find the correct map, so a downstream inspector step is recommended in those situations.
TYPICAL USAGE
find_rst -pairs pairs.lst -input images/ -output initial_maps/
COMMAND LINE OPTIONS
-candidates max_number_of_candidates
specifies the maximum number of (rotation,scale,translation) candidates that should be printed out; however, only the best candidate will be used for the output map file. Default value for this is 1.
-distortion float_distortion_factor
specifies a guess at how distorted the images are, as a percentage of the overall image width or height; this measures how far a pixel must be displaced to match up with its corresponding pixel in the other image when the images are rigidly aligned. The default for this is 1%.
-feature min_feature_size_in_pixels[-max_feature_size_in_pixels]
specifies the range of features sizes that should be considered most important when doing the registration. The default for this is a minimum of 1 pixel to a maximum of halt the smallest dimension (width or height) of either image.
-frac_res float_fr1[,float_fr2[,float_fr3]]
specifies resolution parameters for doing the fractional Fourier transform. These should be in the range of 0 to 1.0. Generally, it is OK to go with the defaults of 1.0, 0.6, and 0.2. For more details, see the paper by Pan et al.
-images directory/
specifies the directory where the input images are stored
-margin margin_in_pixels
specifies the margin to be applied to the images to reduce edge-effects. By default this is 20% of the smallest dimension of the input image. If the images have very little overlap, it may be necessary to reduce this value so that the periphery has greater weight.
-mask directory/
specifies the directory where the input image masks are stored
-max_res maximum_FFT_resolution_in_pixels
specifies the resolution at which the FFTs will be performed. This will default to the smallest power of 2 that will accommodate both of the images. However, this can be exceedingly time-consuming and memory-intensive for large images, so a reasonable value for this option might be 2048.
-output directory/
specifies the directory into which the output maps will be written.
-output_images
if this option is given, then the intermediate images used in doing the registration will be saved in the output directory. This is useful for debugging purposes.
-pairs pairs.lst
specifies the list of image pairs that are to be registered. This list consists of lines of the following form:
image0_name [ image0_minX image0_maxX image0_minY image0_maxY ] image1_name [ image1_minX image1_maxX image1_minY image1_maxY ] pair_name
If the optional minX, maxX, minY, and maxY components are specified, they designate a subregion of the image to register. This is useful for instance when one is matching up the right side of one image with the left side of an overlapping image. To use the entire image, the minX, maxX, minY, and maxY compoments may be omitted, or they may all be set to -1. The pair_name is the name given to the image pair. Often, it will just consist of the individual image names concatenated together, perhaps with an intervening underscore or hyphen. Occasionally,as when many images are being registered to a single reference image, it would make sense for the pair name to just be the same as the image0_name.
-partial
if this option is given, ignore any image pairs that are missing, and do not report an error.
-reference directory/
specifies the directory where the reference images are stored; this may be the same as the -input directory
-reference_mask directory/
specifies the directory where the reference image masks are stored
-rotation min_theta_in_degrees[-max_theta_in_degrees]
specifies the range of rotations that the program will consider when registering the images; if the range of rotation can a priori be known to lie in some range, then this can be helpful in preventing spurious matches from being investigated that lie outside that range, thus speeding the program up. The rotation should be in the range 0 to 360 degrees, and represents how much the input image has to be turned clockwise [check this] to make it match up with the reference image. The default for this is 0-360 degrees.
-rs_candidates max_number_of_rotation_and_scale_candidates
specifies the maximum number of (rotation,scale) candidates that will be retained for further consideration; because the strongest (rotation, scale) candidate may not necessrily turn out to be the best candidate once translation is taken in account, it is beneficial (though slower) to consider how translation would affect more (rotation, scale) candidates. The default value for this is 4.
-scale min_scale_factor[-max_scale_factor]
specifies the range of scale adjustment that the program will consider when registering the images; if the scale range can a priori be known to lie in some range, then this can be helpful in preventing spurious matches from being investigated that lie outside that range, thus speeding the program up. The scale should be in the 0.1 to 10.0, and represents how much the input image has to be scaled up to make it match the reference image. The default for this is 0.5 to 2.0.
-summary summary.out
If this option is given, then write out a summary of each pair's registration scores to the specified file.
-trans_feature min_trans_feature_size_in_pixels[-max_trans_feature_size_in_pixels]
specifies the range of feature sizes to use when computing the image translation; by default, all features sizes will be used.
-tx min_x_translation_percentage[-max_x_translation_percentage]
specifies the range of possible x translations that the program will consider when reigstering the images; if the translation can a priori be known to lie in some range, then this can be helpful in preventing spurious matches from being investigated that lie outside that range, thus speeding the program up. This translation should be expressed a signed percentage of the image width; a negative percentage indicates a translation to the left; a positive percentage a translation to the right.
-ty min_y_translation_percentage[-max_y_translation_percentage]
specifies the range of possible y translations that the program will consider when reigstering the images; if the translation can a priori be known to lie in some range, then this can be helpful in preventing spurious matches from being investigated that lie outside that range, thus speeding the program up. This translation should be expressed a signed percentage of the image height; a negative percentage indicates a translation upward; a positive percentage a translation downward.
-update
If this option is specified, then only perform the registration for image pairs where the output map is non-existent or older than the input files (images, masks, etc.).
extrapolate_map
PURPOSE
extrapolate_map takes a map which has some map elements unspecified (confidence value = 0), and produces a map where all map elements are specified. It does this by extrapolating the known map elements to areas where the map elements are unknown.
TYPICAL USAGE
extrapolate_map -map in.map -width 800 -height 600 -extra_c 0.1 -output out.map
COMMAND LINE OPTIONS
-extra_c float_confidence_of_extrapolated_regions
Specifies the confidence value to be assigned to all map elements that are extrapolated.
-height height_in_pixels
specifies the map's source image height in pixels; this is required because a map file does not contain information about the total size of the source or target images -- it only contains information about how to map specific pixel areas.
-map input.map
-output output.map
-power integer_exponent
-width width_in_pixels
specifies the map's source image width in pixels; this is required because a map file does not contain information about the total size of the source or target images -- it only contains information about how to map specific pixel areas.
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Research Highlights
The Bahar (TR&D1) and Sorkin (DBP3) labs published an article in the Journal of Biological Chemistry, selected as one of JBC's "Editors' Picks. Our results demonstrate a direct coupling between conformational dynamics of DAT, functional activity of the transporter and its oligomerization leading to endocytosis. The high specificity of such coupling for DAT makes the TM4-9 hub a new target for pharmacological modulation of DAT activity and subcellular localization. (Read more)
Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation
Comparison with RNA-seq expression data reveals a strong overlap between highly expressed genes and those distinguished by high mobilities in the present study, in support of the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation. (Read more)
Nanoscale co-organization and coactivation of AMPAR, NMDAR, and mGluR at excitatory synapses
Work by TR&D2 Investigators and collaborators provide insights into the nanometer scale organization of postsynaptic glutamate receptors using a combination of dual-color superresolution imaging, electrophysiology, and computational modeling. (Read more)
Parallel Tempering with Lasso for model reduction in systems biology
TR&D3 Investigators and collaborators develop PTLasso, a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization, to automatically extract minimal subsets of detailed models that are sufficient to explain experimental data. On both synthetic and real biological data, PTLasso is an effective method to isolate distinct parts of a larger signaling model that are sufficient for specific data. (Read more)
Image-derived models of cell organization changes during differentiation and drug treatments
Our work on modeling PC12 cells undergoing differentiation into neuron-like morphologies (under C&SP11, completed) has been published in Molecular Biology of the Cell. We have also made the large dataset of 3D images collected in that study available through Dryad. (Read more)
Monoamine transporters: structure, intrinsic dynamics and allosteric regulation
T&RD1 investigators Mary Cheng and Ivet Bahar published an invited review article in Nature Structural & Molecular Biology, addressing recent progress in the elucidation of the structural dynamics of MATs and their conformational landscape and transitions, as well as allosteric regulation mechanisms. (Read more)
Trimerization of dopamine transporter triggered by AIM-100 binding
The Bahar (TR&D1) and Sorkin (DBP3) labs explored the trimerization of dopamine transporter (DAT) triggered by a furopyrimidine, AIM-100, using a combination of computational and biochemical methods, and single-molecule live-cell imaging assays. (Read more)
Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency
TR&D2 investigators and collaborators describe organizing role of neuroligin-1 to align post-synaptic AMPA Receptors with pre-synaptic release sites into trans-synaptic “nano-columns” to enhance signaling.(Read more)
Inferring the Assembly Network of Influenza Virus
In an article in PLoS Computational Biology, MMBioS TR&D4 members Xiongto Ruan and Bob Murphy collaborated with Seema Lakdawala to address this question of the assembly network of the Influenza virus.(Read more)
Our findings highlight an important mechanism by which proteins genetically implicated in Parkinson’s disease (PD; PINK1) and frontotemporal dementia (FTD; VCP) interact to support the health and maintenance of neuronal arbors.(Read more)
Improved methods for modeling cell shape
In a recent paper in Bioinformatics, Xiongtao Ruan and Bob Murphy of TR&D4 addressed the question of how best to model cell and nuclear shape.(Read more)
New tool to predict pathogenicity of missense variants based on structural dynamics: RHAPSODY
We demonstrated that the analysis of a protein’s intrinsic dynamics can be successfully used to improve the prediction of the effect of point mutations on a protein functionality. This method employs ANM/GNM tools (Read more)
New method for investigating chromatin structural dynamics.
By adapting the Gaussian Network Model (GNM) protein-modeling framework, we were able to model chromatin dynamics using Hi-C data, which led to the identification of novel cross-correlated distal domains (CCDDs) that were found to also be associated with increased gene co-expression. (Read more)
Structural elements coupling anion conductance and substrate transport identified
We identified an intermediate anion channeling state (iChS) during the global transition from the outward facing (OF) to inward facing state (IFS). Our prediction was tested and validated by experimental study conducted in the Amara lab (NIMH). Critical residues and interactions were analyzed by SCAM, electrophysiology and substrate uptake experiments (Read more)
Integrating MMBioS technologies for multiscale discovery
TR&D teams driven by individual DBPs are naturally joining forces, integrating their tools to respond to the needs of the DBP, and creating integrative frameworks for combining structural and kinetic data and computing technologies at multiple scales. (Read more)
Large scale visualization of rule-based models.
Signaling in living cells is mediated through a complex network of chemical interactions. Current predictive models of signal pathways have hundreds of reaction rules that specify chemical interactions, and a comprehensive model of a stem cell or cancer cell would be expected to have many more. Visualizations of rules and their interactions are needed to navigate, organize, communicate and analyze large signaling models. (Read more)
Integration of MCellR into MCell/CellBlender
Using spatial biochemical models of SynGAP/PSD95, MMBioS investigators were able to merge the MCellR code-base with the MCell code-base and validate its utility and correctness of this sophisticated technology now easily accessible through the MCell/CellBlender GUI. (Read more)
Causal relationships of spatial distributions of T cell signaling proteins
The idea is to identify a relationship in which a change in the concentration of one protein in one cell region consistently is associated with a change in the concentration of another protein in the same or a different region. We used the data from our Science Signaling paper reported last year to construct a model for T cells undergoing stimulation by both the T cell receptor and the costimulatory receptor. (Read more...)
BioNetGen modeling helps reveal immune system response decision
To attack or to let be is an important decision that our immune systems must make to protect our bodies from foreign invaders or protect bodily tissues from an immune attack. Using modeling and experiments, we have painted a sharper picture of how T cells make these critical decisions. (Read more)
Tools for determining the spatial relationships between different cell components
An important task for understanding how cells are organized is determining which components have spatial patterns that are related to each other.Read more
Pipeline for creation of spatiotemporal maps
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Multi-scale Hybrid Methodology
The hybrid methodology, coMD, that we have recently developed [1] has been recently extended to construct the energy landscape near the functional states of LeuT (Fig 1) [2]. This is the first energy landscape constructed for this NSS family member. Read more
Insights into the cooperative dynamics of AMPAR
Comparative analysis of AMPAR and NMDAR dynamics reveals striking similarities, opening the way to designing new modulators of allosteric interactions. Read more
Improved Sampling of Cell-Scale Models using the WE Strategy
The WE strategy for orchestrating a large set of parallel simulations has now been extended to spatially resolved cell-scale systems. The WESTPA implementation of WE has been used to control MCell simulations, including models built using a BioNetGen-CellOrganizer pipeline for situating complex biochemistry within spatially realistic cell models. Read more
Anatomy and Function of an Excitatory Network in the Visual Cortex
MMBioS researcher Greg Hood’s collaboration with Wei-Chung Allen Lee of Harvard University and R. Clay Reid of the Allen Institute for Brain Science concerning the reconstruction of an excitatory nerve-cell network in the mouse brain cortex at a subcellular level using the AlignTK software has been published in Nature. Read more
Molecular Mechanism of Dopamine Transport by hDAT
Dopamine transporters (DATs) control neurotransmitter dopamine (DA) homeostasis by reuptake of excess DA, assisted by sodium and chloride ions. The recent resolution of DAT structure (dDAT) from Drosophila permits us for the first time to directly view the sequence of events involved in DA reuptake in human DAT (hDAT). Read more
Synaptic Facilitation Revealed
An investigation of several mechanisms of short-term facilitation at the frog neuromuscular junction concludes that the presence of a second class of calcium sensor proteins distinct from synaptotagmin can explain known properties of facilitation. Read more
Sparse Graphical Models of Protein:Protein Interactions
DgSpi is a new method for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity and extend earlier classification-oriented approaches to predict ΔG of binding. Read more
Advancing Parallel Bio-simulations
A new non-Markovian analysis can eliminate bias in estimates of long-timescale behavior, such as the mean first-passage time for the dissociation of methane molecules in explicit solvent. Read more