PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics

PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron)

Ridership prediction and anomaly detection in transportation hubs: an application to New York City

Ridership modeling is a growing field critical for Intelligent Transportation. Accurate traffic prediction and early surge detection are vital components in designing public transit dispatching systems. However, modeling Spatio-temporal traffic at a small geographic scale and fine time granularity is challenging due to the sparseness, low signal-to-noise ratio, and the large dimensionality of the mobility network data. We propose a framework for edge-level traffic prediction to tackle these challenges, which addresses the curse of dimensionality through a pipeline of appropriate network aggregation, nonlinear modeling, and final edge-level disaggregation. Subsequently, we show that the low-dimensional aggregated space model residuals are more suited for anomaly detection than raw ridership data. Our framework is evaluated using the for-hire vehicle and taxi ridership dataset from the two airports in New York City, experimenting with different network aggregation techniques and modeling paradigms. The results reinstate the superiority of the proposed pipeline in ridership prediction and anomaly detection compared with single-model methods, and help build up scenario design for transportation simulation and planning