Database searching remains the main approach for mass spectrometry-based proteomics, where protein identification fundamentally requires prior inclusion in the reference database. For aquatic algal species lacking annotated genomes, six-frame translation of species-specific transcriptomes has emerged as a prevalent method. However, this approach results in databases that encompass all potential translation products, substantially increasing the database size and search space. Here, we introduce BERT-based Protein Annotation (BPA), a deep learning strategy that combines a pretrained BERT model for contextual patterns, Pseudo Amino Acid Composition for physicochemical properties, and InterProScan for functional domain prediction, to optimize reference proteome construction. These features are integrated by using a Random Forest classifier to generate dynamic Sequence Reliability Scores, enabling adaptive filtering thresholds tailored to diverse experimental designs. Based on the validation across three distinct test species, this study demonstrates a robust performance of BPA with sustained high classification accuracy (AUC > 0.95). In the application to Karenia mikimotoi, BPA achieved 90% proteome compression while maintaining 40% identification coverage, effectively resolving the peptide ambiguity from redundant translations. This framework provides a scalable and efficient solution for constructing and optimizing reference libraries, facilitating proteomic research in aquatic algae and other genomically understudied species. Source code and executables are available at (https://github.com/huangruihua/BPA.git).

UNLABELLED: ChIPseeker is an R package for annotating ChIP-seq data analysis. It supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 15 000 bed file information from GEO database. These datasets can be downloaded and compare with user's own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation. AVAILABILITY AND IMPLEMENTATION: ChIPseeker is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/ChIPseeker.html).

SUMMARY: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computations among DO terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported. AVAILABILITY AND IMPLEMENTATION: DOSE is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/DOSE.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected] or [email protected].