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The discovery of N 6 -methyldeoxyadenine (6mA) across eukaryotes led to a search for additional epigenetic mechanisms. However, some studies have highlighted confounding factors that challenge the prevalence of 6mA in eukaryotes. We developed a metagenomic method to quantitatively deconvolve 6mA events from a genomic DNA sample into species of interest, genomic regions, and sources of contamination. Applying this method, we observed high-resolution 6mA deposition in two protozoa. We found that commensal or soil bacteria explained the vast majority of 6mA in insect and plant samples. We found no evidence of high abundance of 6mA in Drosophila , Arabidopsis , or humans. Plasmids used for genetic manipulation, even those from Dam methyltransferase mutant Escherichia coli , could carry abundant 6mA, confounding the evaluation of candidate 6mA methyltransferases and demethylases. On the basis of this work, we advocate for a reassessment of 6mA in eukaryotes.

A new algorithm is described for label-free quantitation of relative protein abundances across multiple complex proteomic samples. Q-MEND is based on the denoising and peak picking algorithm, MEND, previously developed in our laboratory. Q-MEND takes advantage of the high resolution and mass accuracy of the hybrid LTQFT MS mass spectrometer (or other high resolution mass spectrometers, such as a Q-TOF MS). The strategy, termed "cross-assignment", is introduced to increase substantially the number of quantitated proteins. In this approach, all MS/MS identifications for the set of analyzed samples are combined into a master ID list, and then each LC/ MS run is searched for the features that can be assigned to a specific identification from that master list. The reliability of quantitation is enhanced by quantitating separately all peptide charge states, along with a scoring procedure to filter out less reliable peptide abundance measurements. The effectiveness of Q-MEND is illustrated in the relative quantitative analysis of E.coli samples spiked with known amounts of non-E.coli protein digests. A mean quantitation accuracy of 7% and mean precision of 15% is demonstrated. Q-MEND can perform relative quantitation of a set of LC/MS datasets without manual intervention and can generate files compatible with the Guidelines for Proteomic Data Publication.

Untargeted metabolomics based on liquid chromatography−mass spectrometry is affected by nonlinear batch effects, which cover up biological effects, result in nonreproducibility, and are difficult to be calibrate. In this study, we propose a novel deep learning model, called Normalization Autoencoder (NormAE), which is based on nonlinear autoencoders (AEs) and adversarial learning. An additional classifier and ranker are trained to provide adversarial regularization during the training of the AE model, latent representations are extracted by the encoder, and then the decoder reconstructs the data without batch effects. The NormAE method was tested on two real metabolomics data sets. After calibration by NormAE, the quality control samples (QCs) for both data sets gathered most closely in a PCA score plot (average distances decreased from 56.550 and 52.476 to 7.383 and 14.075, respectively) and obtained the highest average correlation coefficients (from 0.873 and 0.907 to 0.997 for both). Additionally, NormAE significantly improved biomarker discovery (median number of differential peaks increased from 322 and 466 to 1140 and 1622, respectively). NormAE was compared with four commonly used batch effect removal methods. The results demonstrated that using NormAE produces the best calibration results.