From cell lines to cancer patients: personalized drug synergy prediction.

Motivation: Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations.

Bioactive peptides: an alternative therapeutic approach for cancer management.

Cancer is still considered a lethal disease worldwide and the patients' quality of life is affected by major side effects of the treatments including post-surgery complications, chemo-, and radiation therapy. Recently, new therapeutic approaches were considered globally for increasing conventional cancer therapy efficacy and decreasing the adverse effects. Bioactive peptides obtained from plant and animal sources have drawn increased attention because of their potential as complementary therapy.

FlyVISTA, an Integrated Machine Learning Platform for Deep Phenotyping of Sleep in .

Animal behavior depends on internal state. While subtle movements can signify significant changes in internal state, computational methods for analyzing these "microbehaviors" are lacking. Here, we present FlyVISTA, a machine-learning platform to characterize microbehaviors in freely-moving flies, which we use to perform deep phenotyping of sleep.

MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing.

Motivation: With the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously.

Tuning Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding.

Researchers need a rich trove of genomic datasets that they can leverage to gain a better understanding of the genetic basis of the human genome and identify associations between phenol-types and specific parts of DNA. However, sharing genomic datasets that include sensitive genetic or medical information of individuals can lead to serious privacy-related consequences if data lands in the wrong hands. Restricting access to genomic datasets is one solution, but this greatly reduces their usefulness for research purposes.

Bump-and-hole engineering of human polypeptide N-acetylgalactosamine transferases to dissect their protein substrates and glycosylation sites in cells.

Despite the known disease relevance of glycans, the biological function and substrate specificities of individual glycosyltransferases are often ill-defined. Here, we describe a protocol to develop chemical, bioorthogonal reporters for the activity of the GalNAc-T family of glycosyltransferases using a tactic termed bump-and-hole engineering. This allows identification of the protein substrates and glycosylation sites of single GalNAc-Ts.

Discovering misannotated lncRNAs using deep learning training dynamics.

Motivation: Recent experimental evidence has shown that some long non-coding RNAs (lncRNAs) contain small open reading frames (sORFs) that are translated into functional micropeptides, suggesting that these lncRNAs are misannotated as non-coding. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (Ribo-Seq) and mass-spectrometry experiments, which are cell-type dependent and expensive.

Results: Here, we propose a computational method to identify possible misannotated lncRNAs from sequence information alone.

Cell-specific bioorthogonal tagging of glycoproteins.

Altered glycoprotein expression is an undisputed corollary of cancer development. Understanding these alterations is paramount but hampered by limitations underlying cellular model systems. For instance, the intricate interactions between tumour and host cannot be adequately recapitulated in monoculture of tumour-derived cell lines.

Combined use of vacuum impregnation and encapsulation technologies for phenolic enrichment of strawberries.

Plant-based phenolic extracts have gained significant attention in the food industry due to their antimicrobial and health-promoting effects. However, their usage is limited because of poor water solubility and instability during processing. Therefore, encapsulation of phenolics with a suitable carrier system is essential for overcoming these problems and increasing their application in food products.

PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations.

Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and the loss of function in proteins. The use of multiple sequence alignment alone-without considering the evolutionary relationships among sequences-results in the redundant counting of evolutionarily related alteration events, as if they were independent. Here, we propose a new method, PHACT, that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins.

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features.

Uncovering complementary sets of variants for predicting quantitative phenotypes.

Motivation: Genome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples.

The lumbar erector spinae plane block: a cadaveric study.

Background: The aim of this cadaveric study was to investigate the erector spinae plane block (ESPB) in lumbar region and to elucidate the possible mechanisms of action of these injections in lumbar radicular pain by means of detecting expected dye dispersion to the neural structures.

Methods: Ultrasound-guided lumbar ESPB was performed in three formaldehyde-embalmed human cadavers. For this purpose, a 10 mL of methylene blue was injected into the fascial space between the L4 transverse process and the erector spinae muscles.

NoRCE: non-coding RNA sets cis enrichment tool.

Background: While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together.

PRER: A patient representation with pairwise relative expression of proteins on biological networks.

Changes in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins' expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER (Pairwise Relative Expressions with Random walks).

Benefits of Chemical Sugar Modifications Introduced by Click Chemistry for Glycoproteomic Analyses.

Mucin-type O-glycosylation is among the most complex post-translational modifications. Despite mediating many physiological processes, O-glycosylation remains understudied compared to other modifications, simply because the right analytical tools are lacking. In particular, analysis of intact O-glycopeptides by mass spectrometry is challenging for several reasons; O-glycosylation lacks a consensus motif, glycopeptides have low charge density which impairs ETD fragmentation, and the glycan structures modifying the peptides are unpredictable.

Optimization of Metabolic Oligosaccharide Engineering with AcGalNAlk and AcGlcNAlk by an Engineered Pyrophosphorylase.

Metabolic oligosaccharide engineering (MOE) has fundamentally contributed to our understanding of protein glycosylation. Efficient MOE reagents are activated into nucleotide-sugars by cellular biosynthetic machineries, introduced into glycoproteins and traceable by bioorthogonal chemistry. Despite their widespread use, the metabolic fate of many MOE reagents is only beginning to be mapped.

Polarised maintenance of cytoophidia in Drosophila follicle epithelia.

The metabolic enzyme CTP synthase (CTPS) can form filamentous structures named cytoophidia in numerous types of cells, including follicle cells. However, the regulation of cytoophidium assembly remains elusive. The apicobasal polarity, a defining characteristic of Drosophila follicle epithelium, is established and regulated by a variety of membrane domains.

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests.

Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity among miRNAs is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics.

Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection.

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Metabolic precision labeling enables selective probing of O-linked -acetylgalactosamine glycosylation.

Protein glycosylation events that happen early in the secretory pathway are often dysregulated during tumorigenesis. These events can be probed, in principle, by monosaccharides with bioorthogonal tags that would ideally be specific for distinct glycan subtypes. However, metabolic interconversion into other monosaccharides drastically reduces such specificity in the living cell.