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Dang, T. C.

Publications and source records attributed to Dang, T. C..

3 recordsLinked to original sources

Bridging genomes and peptidomes: hybrid sequencing reveals conserved bioactive peptides in crustaceans

Endogenous peptides are critical regulators of signaling and immunity but remain difficult to characterize in organisms with incomplete genomic annotation. We developed a hybrid discovery platform that integrates transformer-based de novo sequencing (Casanovo), neuropeptide-focused database searching (EndoGenius), and empirical false discovery rate estimation via NovoBoard. This pipeline enables confident identification of endogenous peptides while expanding coverage beyond conventional database-only or de novo-only approaches. Applied to neuroendocrine tissues from Callinectes sapidus and Cancer borealis, the workflow revealed numerous high-abundance novel peptides and provided structural and genomic support for their biological relevance. Notably, we report the first histone-2A-derived antimicrobial peptide in the C. sapidus and characterize naturally occurring sequence variants. We also identified unexpected peptide homologies between crustaceans and Rattus norvegicus, enabling annotation of conserved housekeeping proteins in sparsely annotated genomes. This hybrid platform establishes a scalable, open-source strategy for advancing neuropeptidomics and endogenous peptide discovery in emerging model organisms.

bioinformatics↗

Unlocking the Neuropeptidome using a Novel Endogenous Peptidomics Framework

Endogenous peptides have garnered increasing attention over the past decade driven by the development of advanced analytical methods. However, large-scale investigations of peptides as potential disease biomarkers or drug candidates are still hindered by their challenging biochemical properties and the scarcity of specialized analytical tools. Among these, neuropeptides are particularly challenging to study due to their low in vivo concentration, rapid turnover rate, and high structural variability. Data-independent acquisition (DIA) mass spectrometry (MS) has shown great ability in profiling low-abundance ions. Nevertheless, most available DIA analytical tools are designed for proteomics studies and are not suitable for endogenous peptides, as there is no set enzymatic cleavage for these peptides. Here, we introduce the novel EndoGenius platform, paired with DIA-NN, to achieve high-confidence neuropeptide identification using an updated spectral library for DIA MS analysis. By employing orthogonal offline fractionation, ion mobility instrumentation, and an optimized database searching algorithm specifically for neuropeptides, we have constructed the largest crustacean neuropeptide spectral library to date. With this library, in combination with neural networking technology, we report a 100-fold increase in the number of neuropeptides identified in all Cancer borealis tissues analyzed. We also cross-validated these findings with transcriptomics data to enhance identification confidence. This workflow presents a novel analytical framework for DIA peptidomics analysis, offering a robust approach to studying neuropeptides and other endogenous peptides. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/659356v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1bae342org.highwire.dtl.DTLVardef@9e26d8org.highwire.dtl.DTLVardef@1084f06org.highwire.dtl.DTLVardef@7c1371_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisWe present a framework that capitalizes on robust analytical innovations and an optimized bioinformatics pipeline to provide the most comprehensive snapshot of the crustacean neuropeptidome to-date.

neuroscience↗

EndoGenius: Enabling comprehensive identification and quantitation of endogenous peptides

Structured abstractO_ST_ABSSummaryC_ST_ABSThe investigation of endogenous peptides, specifically with respect to neuropeptides, from mass spectrometry data is rife with bioinformatics bottlenecks, stemming from the low in vivo abundance of these analytes, increased susceptibility to degradation, and an immense search space of possible peptides. To address this, we present EndoGenius in its expanded form, strategically designed to optimize the searching for these endogenous peptides complemented with a pipeline designed for tasks including quantitation, spectral library building, motif extraction, and usage with data-independent acquisition workflows. Availability and ImplementationEndoGenius is released as an open-source software package under an MIT License. The EndoGenius package with a user interface can be installed from https://www.lilabs.org/resources. The source code for EndoGenius can be accessed at https://github.com/lingjunli-research/EndoGenius-v2.0. ContactLingjun.Li@wisc.edu

bioinformatics↗