Although the number of sequenced prokaryotic genomes is growing rapidly, experimentally verified annotation of prokaryotic genome remains patchy and challenging. To facilitate genome annotation efforts for prokaryotes, we developed an open source software called GAPP for genome annotation and global profiling of post-translational modifications (PTMs) in prokaryotes. With a single command, it provides a standard workflow to validate and refine predicted genetic models and discover diverse PTM events. We demonstrated the utility of GAPP using proteomic data from Helicobacter pylori, one of the major human pathogens that is responsible for many gastric diseases. Our results confirmed 84.9% of the existing predicted H. pylori proteins, identified 20 novel protein coding genes, and corrected four existing gene models with regard to translation initiation sites. In particular, GAPP revealed a large repertoire of PTMs using the same proteomic data and provided a rich resource that can be used to examine the functions of reversible modifications in this human pathogen. This software is a powerful tool for genome annotation and global discovery of PTMs and is applicable to any sequenced prokaryotic organism; we expect that it will become an integral part of ongoing genome annotation efforts for prokaryotes. GAPP is freely available at https://sourceforge.net/projects/gappproteogenomic/.
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http://dx.doi.org/10.1074/mcp.M116.060046 | DOI Listing |
BMC Genomics
January 2025
Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 610225, China.
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Department of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, People's Republic of China.
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UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA. Electronic address:
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January 2025
Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. Electronic address:
Understanding the evolution of human traits is a fundamental yet challenging question. In a recent Cell Genomics article, Kun et al. integrate large-scale genomic and phenotypic data, including deep-learning-derived imaging phenotypes, with temporal annotations to estimate the timing of evolutionary changes that led to differences in traits between modern humans and primates or hominin ancestors.
View Article and Find Full Text PDFGenome Biol Evol
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Department of Agricultural Biology, 1177 Campus Delivery, Colorado State University, Fort Collins, CO, 80523, USA.
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