YOLO-PeakDetect: A Convolutional Neural Network for Automatic Analysis of Irregular Bands in Gel Electrophoresis

Gel electrophoresis (GE) is a critical tool in the fields of molecular biology and biopharmaceuticals. However, the current analysis software requires extensive manual adjustments and cannot accurately determinate particle-like bands of virus vector aggregates and monoclonal antibody (mAb) or protein precipitates in GE. Herein, we developed a convolutional neural network of YOLO-PeakDetect based on the You-only look-once (YOLO) network architecture. Comparative results demonstrate that on the simulated data set, the YOLO-PeakDetect network attains the average precision 50–95 (AP50_95) of 0.9717, which is remarkably superior to those obtained by the traditional algorithm and the existing CNN-based peak detection model. In the experimental data set evaluated against manually precisely annotated GE peak profiles, YOLO-PeakDetect outperforms the traditional algorithm for both single peaks and overlapping peaks. Meanwhile, in GE experiments employing three standard proteins, the proposed network elevates the average linear correlation coefficient from 0.9883 (achieved by ImageJ) to 0.9952. Particularly, the network could detect the aggregate ratio of particle-like adeno-associated virus (AAV) band in the sample well, full and empty AAV capsid from the crescent-like vector bands, and the precipitate ratio of particle-like bands of instable mAb accumulation. All the data showcased that the network model achieves significant improvements in the accuracy, noise resistance, and automation level of irregular band detection, providing a reliable and intelligent solution for particle-like bands of virus vector aggregates and instable mAb precipitates as well as regular bands of proteins in GE.