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Summary: Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user's database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g. lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g. human lung endothelial cells) and environmental (e.g. peat soil) samples., Availability and Implementation: Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes. This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio (Nkx2-2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level., Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives., Availability: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

While genome sequencing efforts reveal the basic building blocks of life, a genome sequence alone is insufficient for elucidating biological function. Genome annotation--the process of identifying genes and assigning function to each gene in a genome sequence--provides the means to elucidate biological function from sequence. Current state-of-the-art high-throughput genome annotation uses a combination of comparative (sequence similarity data) and non-comparative (ab initio gene prediction algorithms) methods to identify protein-coding genes in genome sequences. Because approaches used to validate the presence of predicted protein-coding genes are typically based on expressed RNA sequences, they cannot independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein. With the ability to directly measure peptides arising from expressed proteins, high-throughput liquid chromatography-tandem mass spectrometry-based proteomics approaches can be used to verify coding regions of a genomic sequence. Here, we highlight several ways in which high-throughput tandem mass spectrometry-based proteomics can improve the quality of genome annotations and suggest that it could be efficiently applied during the gene calling process so that the improvements are propagated through the subsequent functional annotation process.