Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Primary progressive aphasia: a clinical approach

This work was supported by the Alzheimer’s Society (AS-PG-16-007), the National Institute for Health Research University College London Hospitals Biomedical Research Centre and the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575). Individual authors were supported by the Leonard Wolfson Foundation (Clinical Research Fellowship to CRM), the National Institute for Health Research (NIHR Doctoral Training Fellowship to AV), the National Brain Appeal–Frontotemporal Dementia Research Fund (CNC) and the Medical Research Council (PhD Studentships to CJDH and RLB, MRC Research Training Fellowship to PDF, MRC Clinician Scientist to JDR). MNR and NCF are NIHR Senior Investigators. SJC is supported by Grants from ESRC-NIHR (ES/L001810/1), EPSRC (EP/M006093/1) and Wellcome Trust (200783). JDW was supported by a Wellcome Trust Senior Research Fellowship in Clinical Science (091673/Z/10/Z)