Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Panga J Reddy, Zhi Sun, Helisa H Wippel, David H Baxter, Kristian Swearingen, David D Shteynberg, Mukul K Midha, Melissa J Caimano, Klemen Strle, Yongwook Choi, Agnes P Chan, Nicholas J Schork, Andrea S Varela-Stokes, Robert L Moritz
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引用次数: 0

Abstract

Lyme disease is caused by an infection with the spirochete Borrelia burgdorferi, and is the most common vector-borne disease in North America. B. burgdorferi isolates harbor extensive genomic and proteomic variability and further comparison of isolates is key to understanding the infectivity of the spirochetes and biological impacts of identified sequence variants. Here, we applied both transcriptome analysis and mass spectrometry-based proteomics to assemble peptide datasets of B. burgdorferi laboratory isolates B31, MM1, and the infective isolate B31-5A4, to provide a publicly available Borrelia PeptideAtlas. Included are total proteome, secretome, and membrane proteome identifications of the individual isolates. Proteomic data collected from 35 different experiment datasets, totaling 386 mass spectrometry runs, have identified 81,967 distinct peptides, which map to 1,113 proteins. The Borrelia PeptideAtlas covers 86% of the total B31 proteome of 1,291 protein sequences. The Borrelia PeptideAtlas is an extensible comprehensive peptide repository with proteomic information from B. burgdorferi isolates useful for Lyme disease research.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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