TargetSeeker-MS: A Bayesian Inference Approach for Drug-Target Discovery Using Protein Fractionation Coupled to Mass Spectrometry.

To understand the mechanism of action of a drug and assess its clinical usefulness and viability, it is imperative that its affinity for its putative targets is determined. When coupled to mass spectrometry (MS), energetics-based protein separation (EBPS) techniques, such as a thermal shift assay, have shown great potential to identify the targets of a drug on a proteome scale. Nevertheless, the computational analyses assessing the confidence of drug-target predictions made by these methods have remained tightly tied to the protocol under which the data were produced.

Trophic magnification rates of eighteen trace elements in freshwater food webs.

Trace elements play diverse roles in animal physiology ranging from essential micronutrients to potent toxicants. Despite animals accumulating many trace elements through their diets, relationships between trophic positions and biological concentrations of most trace elements remain poorly described. We report trophic transfer rates of Al, As, Ba, Cd, Co, Cu, Fe, Hg, Mn, Ni, Pb, Se, Sr, Ti, Tl, U, V, and Zn from 31 freshwaters located in distinct biogeographic regions.

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries.

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.

Identification of novel lipid droplet factors that regulate lipophagy and cholesterol efflux in macrophage foam cells.

Macrophage autophagy is a highly anti-atherogenic process that promotes the catabolism of cytosolic lipid droplets (LDs) to maintain cellular lipid homeostasis. Selective autophagy relies on tags such as ubiquitin and a set of selectivity factors including selective autophagy receptors (SARs) to label specific cargo for degradation. Originally described in yeast cells, "lipophagy" refers to the degradation of LDs by autophagy.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues.

Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform.

Objective: During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes.

Structural Analysis of Hippocampal Kinase Signal Transduction.

Kinases are a major clinical target for human diseases. Identifying the proteins that interact with kinases in vivo will provide information on unreported substrates and will potentially lead to more specific methods for therapeutic kinase regulation. Here, endogenous immunoprecipitations of evolutionally distinct kinases (i.

Temporal and Longitudinal Mercury Trends in Burbot (Lota lota) in the Russian Arctic.

Current understanding of mercury (Hg) dynamics in the Arctic is hampered by a lack of data in the Russian Arctic region, which comprises about half of the entire Arctic watershed. This study quantified temporal and longitudinal trends in total mercury (THg) concentrations in burbot (Lota lota) in eight rivers of the Russian Arctic between 1980 and 2001, encompassing an expanse of 118 degrees of longitude. Burbot THg concentrations declined by an average of 2.

Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline.

QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC.