AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning

The Argonne X-ray Emission Analysis Package (AXEAP) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into an XES spectrum in real time using both calculations and unsupervised machine learning. AXEAP is capable of making this transformation at a rate similar to data collection, allowing real-time comparisons during data collection, reducing the amount of data stored from gigabyte-sized image files to kilobyte-sized text files. With a user-friendly interface, AXEAP includes data processing for non-resonant and resonant XES images from multiple edges and elements. AXEAP is written in MATLAB and can run on common operating systems, including Linux, Windows, and MacOS.

Automated ExEm-spFRET Microscope

Excitation–emission-spectral unmixing-based fluorescence resonance energy transfer (ExEm-spFRET) microscopy exhibits excellent robustness in living cells. We here develop an automatic ExEm-spFRET microscope with 3.04 s of time resolution for a quantitative FRET imaging. The user-friendly interface software has been designed to operate in two modes: administrator and user. Automatic background recognition, subtraction, and cell segmentation were integrated into the software, which enables FRET calibration or measurement in a one-click operation manner. In administrator mode, both correction factors and spectral fingerprints are only calibrated periodically for a stable system. In user mode, quantitative ExEm-spFRET imaging is directly implemented for FRET samples. We implemented quantitative ExEm-spFRET imaging for living cells expressing different tandem constructs (C80Y, C40Y, C10Y, and C4Y, respectively) and obtained consistent results for at least 3 months, demonstrating the stability of our microscope. Next, we investigated Bcl-xL-Bad interaction by using ExEm-spFRET imaging and FRET two-hybrid assay and found that the Bcl-xL-Bad complexes exist mainly in Bad-Bcl-xL trimers in healthy cells and Bad-Bcl-xL2 trimers in apoptotic cells. We also performed time-lapse FRET imaging on our system for living cells expressing Yellow Cameleon 3.6 (YC3.6) to monitor ionomycin-induced rapid extracellular Ca2+ influx with a time interval of 5 s for total 250 s.

HiCeekR: A Novel Shiny App for Hi-C Data Analysis

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.