Significance: Some RNAs in both prokaryotes and eukaryotes were recently found to contain the NAD ⁺ cap, indicating a novel mechanism in gene regulation through noncanonical RNA capping. Copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry has been used to label NAD ⁺ -capped RNAs (NAD-RNAs) for their identification. However, copper-caused RNA fragmentation/degradation interferes with the analysis. We developed the NAD tagSeq II method for transcriptome-wide NAD-RNA analysis based on copper-free, strain-promoted azide-alkyne cycloaddition (SPAAC) click chemistry. This method was used to compare NAD-RNA and total transcriptome profiles in Escherichia coli . Our study reveals genome-wide alterations in E. coli RNA NAD ⁺ capping in different growth phases and indicates that NAD-RNAs could be the primary form of transcripts from some genes under certain environments.

Background: Neurodegenerative diseases remain a central topic in biomedical research, with Alzheimer’s disease (AD) being the most extensively studied. Recent advances in multi-omics integration, particularly proteomics-based approaches, have enabled a deeper understanding of AD-related molecular pathways and their interconnections. However, challenges such as data heterogeneity and the complexity of large-scale datasets continue to hinder comprehensive integration and model interpretation. Methods: A total of 792 publications were retrieved from PubMed, among which, 27 peer-reviewed studies from 2024 and 2025 focusing on proteomics-anchored multi-omics integration for AD were selected for detailed analysis. These papers were categorized based on their integration strategies, omics combinations, and analytical methodologies. Additionally, statistical analysis of 218 studies published in 2024–2025 was performed to identify dominant omics layers and common integration trends. Results: Proteomics emerged as the most frequently studied omics layer and was most often integrated with transcriptomics in AD multi-omics studies. The analysis also revealed recurrent machine learning methods used for feature extraction and integration, along with key biological pathways implicated in AD pathogenesis, including amyloid metabolism, synaptic function, and neuroinflammation. Conclusions: This review provides a systematic overview of recent trends in proteomics-based multi-omics integration for AD research. It highlights both the scientific advances and methodological limitations in current approaches, serving as a valuable reference for researchers seeking to refine analytical frameworks and design more interpretable, data-driven studies in neurodegenerative disease research.

Missing values (MVs) in omic datasets affect the power, accuracy, and consistency of statistical and functional analyses. In mass spectrometry (MS)-based proteomics, MVs can arise due to several reasons: peptides could be below instrumental detection limits, peptides or proteins might be absent or depleted from the sample for biological or technical reasons, or data processing could fail to detect a real signal. Several statistical and machine-learning methods have been described for imputing MVs in proteomics, such as Bayesian PCA estimation, random forest, and collaborative filtering. However, these approaches typically do not account for the underlying causes of MVs and treat all missing data uniformly, potentially introducing biases that affect the biological validity of the conclusions drawn from the imputed datasets. We found a strong negative correlation between the proportion of MVs and the average intensity for the individual protein, with more abundant proteins having fewer, but rarely zero, MVs. We divided the peptides from all proteins into nine bins based on their intensities and proportion of MV. Assuming the causes of MVs could be different in different regions, we then investigated the optimal imputation method in each bin, using normalized root mean square error (NRMSE), and found that the optimal imputation method varies across bins. A mix-imputed dataset was assembled using the optimal imputation method from each bin, and it was confirmed to exhibit low deviation from the original unimputed dataset, demonstrating mixing the optimal imputation method from each bin is a reliable strategy.

NAD tagSeq has recently been developed for the identification and characterization of NAD ⁺ -capped RNAs (NAD-RNAs). This method adopts a strategy of chemo-enzymatic reactions to label the NAD-RNAs with a synthetic RNA tag before subjecting to the Oxford Nanopore direct RNA sequencing. A computational tool designed for analyzing the sequencing data of tagged RNA will facilitate the broader application of this method. Hence, we introduce TagSeqTools as a flexible, general pipeline for the identification and quantification of tagged RNAs (i.e., NAD ⁺ -capped RNAs) using long-read transcriptome sequencing data generated by NAD tagSeq method. TagSeqTools comprises two major modules, TagSeek for differentiating tagged and untagged reads, and TagSeqQuant for the quantitative and further characterization analysis of genes and isoforms. Besides, the pipeline also integrates some advanced functions to identify antisense or splicing, and supports the data reformation for visualization. Therefore, TagSeqTools provides a convenient and comprehensive workflow for researchers to analyze the data produced by the NAD tagSeq method or other tagging-based experiments using Oxford nanopore direct RNA sequencing. The pipeline is available at https://github.com/dorothyzh/TagSeqTools , under Apache License 2.0.