MotifFinder

Simple, fast and intuitive
ZBiotechMotiFinder

Unlock the big insight of glycan microarray data

In partnership with Van Andel Research Institute, we are developing a simple, fast, and intuitive analysis platform that allows end users quickly create meaningful insights from glycan array data using a variety of statistical, visualization, and quality control (QC) tools. The goal is to help researchers to turn the data into action fast. The development of this platform is supported by an NIH SBIR grant.

MotifFinder

MotifFinder automatically analyzes glycan array data and create a versatile set of glycan motifs which decipher structural codes representing protein-glycan interactions. It has many features that are not found in any other glycan-array analysis program. It uses a novel framework for representing lectin specificity as a family of motifs organized as primary motifs and fine specificities; each motif is defined using a new syntax that permits precise specifications of the complexity and tolerance of a pattern; the algorithm employs regression trees with simultaneous motif optimization to find the family of motifs that accurately describe binding specificity; it can jointly analyze multiple datasets to increase the completeness and accuracy of the output; and the output formats are usable as standalone results or as inputs to subsequent analyses. In current version of the software, it includes new functions such as mouse-over glycan structure view, Kd output, enabling searching of non-glycan elements (e.g., glycopeptides), and downstream bioinformatics mining.

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Array Binding Chart

It is the first view of the Glycan-Array data analysis results. The structures of the glycans can be reviewed by mouse-over. The glycans are classified by structural features (e.g., Fucosylated, Sialylated). The binding chart can be zoomed in and out, and downloadable. The bars are clickable linked to Glygen, a glycoinformatics database.

Boxplots of glycans grouped by primary binding motifs

The points on the boxplots represent individual glycans. Use mouse-over to review glycan structures. The points are clickable linked to Glygen, a glycoinformatics database. The primary binding motifs are presented by Primary Motif ID, Motif Graphic Structure, Relative Binding, and Number of Glycans.

Boxplots of glycans grouped by all binding motifs

The points on the boxplots represent individual glycans. Use mouse-over to review glycan structures. The points are clickable linked to Glygen, a glycoinformatics database. The all binding motifs are presented by Motif ID, Nearest Common Name, Motif Graphic Structure, Relative Binding, Number of Glycans, and P-Value.

Glycan intensity and motif distribution plot

The top half of the plot presents the observed glycan binding intensity of various glycans used in the array over their rank binding intensity (only the top glycans are shown). Use mouse-over to review glycan structures. The points are clickable linked to Glygen, a glycoinformatics database. The second plot indicates the position of glycans

Logistic curves fit to average motif binding and glycan binding

Curves are fit with as many parameters as possible given the data. All curves are based on the logistic curve with a fixed intercept of 0. Mouse-over to view curve info and highlight glycans/motifs on the other plot. The binding curves can be zoomed in and out, and downloadable.containing the various motifs in the top plot with a yellow tick.

Scatterplots of logistic curve parameters for glycans (points) and motifs (text).

Use mouse-over to review glycan structures on the scatterplots of logistic curve parameters for glycans (points) and motifs (text).

SignalFinder

SignalFinder is based on an entirely new microarray image analysis algorithm, enabling automation and robust accuracy over a wide range of image characteristics. SignalFinder first gathers statistical information from tiny areas across the entire image and then uses curve-fitting to analyze all areas with considerations of background signals. Therefore, ideally suited thresholds are determined to pick signals within those areas. Because it does not rely on assumptions about signal morphology or distributions, as do other algorithms, it does not fail to correctly find and quantify signal even when signal shapes or borders are unpredictable or when much or little (or zero!) signal is present. The current software version includes new functions such as automatic finding spots, manual gridding adjustments, outlier data removal, and integration with MotifFinder.

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