Machine Learning in Mass Spectrometric Analysis of DIA Data.

Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) based methods are currently the top choice for high-throughput, quantitative measurements of the proteome. While traditional proteomics LC-MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data-independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert-knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data-driven DL pipelines.

Improved enzyme annotation with EC-specific cutoffs using DETECT v2.

Summary: We present DETECT v2-an enzyme annotation tool which considers the effect of sequence diversity when assigning enzymatic function [as an Enzyme Commission (EC) number] to a protein sequence. In addition to capturing more enzyme classes than the previous version, we now provide EC-specific cutoffs that greatly increase precision and recall of assignments and show its performance in the context of pathways. Availability and implementation: https://github.com/ParkinsonLab/DETECT-v2. Supplementary information: Supplementary data are available at Bioinformatics online.