Computational tools

Computational tools

BILCO: An Efficient Algorithm for Joint Alignment of Time Series.
BILCO (BIdirectional pushing with Linear Component Operations) is an efficient algorithm developed by Guoqiang Yu’s group to solve joint alignment problems of time series and min-cut for GTW graphs. BILCO has the same theoretical time complexity as the most popular methods, such as HIPR. However, it provides a significant empirical efficiency boost without sacrificing the accuracy of joint alignment. In thousands of datasets under various simulated scenarios and real application cases, BILCO is around 10 to 50 times faster and only costs 1/10 memory compared to the best peer methods.

Download and installation instructions: https://github.com/yu-lab-vt/BILCO.
Mi X, Wang M, Chen ABY, Lim JX, Wang Y, Ahrens M, Yu G. BILCO: An Efficient Algorithm for Joint Alignment of Time Series. NeurIPS 2022


Synbot: An open-source image analysis software for automated quantification of synapses.
Quantifying the number of synaptic contacts from light microscopy images has traditionally been a challenging and time-consuming task, with results varying between experimenters. To overcome these limitations, Cagla Eroglu’s group at Duke University has developed SynBot, a new open-source, ImageJ-based software. SynBot addresses the technical bottlenecks of traditional synapse quantification analysis by automating several stages of the process and incorporates the machine learning algorithm ilastik, which enables accurate thresholding for synaptic puncta identification. Additionally, the software’s code is easily modifiable, allowing users to adapt it to their specific needs.

Download and installation instructions: https://github.com/Eroglu-Lab/Syn_Bot
Savage JT, Ramirez J, Christopher Risher W, Wang Y, Irala D, Eroglu C. SynBot: An open-source image analysis software for automated quantification of synapses. bioRxiv [Preprint]. 2024 Apr 4:2023.06.26.546578. doi: 10.1101/2023.06.26.546578. PMID: 37425715; PMCID: PMC10327002.


AQuA: a tool to detect signaling events
AQuA (Astrocyte Quantification and Analysis) is an effective tool to detect signaling events from microscopic time-lapse imaging data of astrocytes or other cell types. The algorithm, developed by Guoqiang Yu’s group at Virginia Tech, is data-driven and based on machine learning principles. AQuA can be applied across model organisms, fluorescent indicators, experimental modalities, cell types, and imaging parameters.

Download and installation instructions: https://github.com/yu-lab-vt/AQuA
Wang Y, DelRosso NV, Vaidyanathan TV, Cahill MK, Reitman ME, Pittolo S, Mi X, Yu G, Poskanzer KE. Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology. Nat Neurosci. 2019 Nov;22(11):1936-1944. doi: 10.1038/s41593-019-0492-2. Epub 2019 Sep 30. PMID: 31570865; PMCID: PMC6858541.