Nalls, Mike A.

Abstract Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein–protein interaction analysis, and major histocompatibility complex (MHC)–peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

Background and purposeAlthough the genetic contribution to stroke risk is well known, it remains unclear if young‐onset stroke has a stronger genetic contribution than old‐onset stroke. This study aims to compare the heritability of ischaemic stroke risk between young and old, using common genetic variants from whole‐genome array data in population‐based samples.MethodsThis analysis included 4050 ischaemic stroke cases and 5765 controls from six study populations of European ancestry; 47% of cases were young‐onset stroke (age < 55 years). To quantify the heritability for stroke risk in these unrelated individuals, the pairwise genetic relatedness was estimated between individuals based on their whole‐genome array data using a mixed linear model. Heritability was estimated separately for young‐onset stroke and old‐onset stroke (age ≥ 55 years).ResultsHeritabilities for young‐onset stroke and old‐onset stroke were estimated at 42% (±8%, P < 0.001) and 34% (±10%, P < 0.001), respectively.ConclusionsOur data suggest that the genetic contribution to the risk of stroke may be higher in young‐onset ischaemic stroke, although the difference was not statistically significant.