Pylake 1.4.0

Pylake v1.4.0 has been released with new features and improvements to existing analyses. Here’s some of the highlights:

Hidden Markov Models

Hidden Markov Models (HMMs) are often used for analyzing data that shows transitions among discrete states. Now with just a few lines of code you can fit any channel data and view the results. If you’re interested in kinetics, the model also generates data that can be used with DwelltimeModel to extract state lifetimes.

Check out the Hidden Markov Models tutorial and the HiddenMarkovModel API page for more information.

../../_images/hmm_hairpin.png

HMM analysis of a tethered DNA hairpin held at three different bead separations.

Automatic bead cropping

Added estimate_bead_edges() and crop_beads() for quickly crop the beads out of a kymograph using an estimate of the bead edges. This can help when batch processing of kymographs.

../../_images/bead_edges.png

Filter customization kymotracking

We added the option to customize the filters applied prior to peak detection to track_greedy(). To do this, we added two additional parameters:

  • filter_width allows customizing the filter applied prior to detection.

  • adjacency_filter applies a filter on the detected peaks, removing any fluorescent peaks that do not have a detected peak in an adjacent frame.

This allows using lower thresholds, while keeping false detections in check.

../../_images/tracking_comparison.png
../../_images/tracking_comparison_threshold.png

Other changes

In addition, this release contains several other bug-fixes and improvements. For a full list of all the changes, please refer to the full changelog.

Happy Pylake-ing!