S Celniker

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

The paper presents two large-scale proteomics datasets from mice exposed to varying X-ray doses and time points, enabling biomarker discovery and machine learning applications for radiation exposure assessment.

Is Protein Quantification and Physical Normalization Always Necessary in Proteomics?

This study investigates whether protein quantification and physical normalization are necessary in proteomics by comparing normalized and unnormalized sample inputs, showing that computational normalization can reduce variability in some cases.

Binding profiles for 954 Drosophila and C. elegans transcription factors revealtissue specific regulatory relationships

The paper presents a catalog of transcription factor binding sites in Drosophila and C. elegans, revealing tissue-specific regulatory relationships using machine learning.

Two neuronal peptides encoded from a single transcript regulate mitochondrial complex III in Drosophila

Molecular and functional characterization of the Drosophila melanogaster conserved smORFome

The paper characterizes conserved smORFs in Drosophila melanogaster, showing diverse expression patterns and functional roles, including mitochondrial localization and embryonic translation.

Labeling proteins within Drosophila embryos by combining FRET reporters, position-specific genomic integration, and GAL4-reponsive expression

The paper introduces a method to label proteins in Drosophila embryos using FRET reporters, genomic integration, and GAL4-responsive expression to study protein interactions in situ.

Role and impact of the gut microbiota in a Drosophila model for parkinsonism

This study investigates the role of parkin in maintaining gut microbiota in Drosophila parkinsonism models, finding that parkin loss increases bacterial load and that germ-free conditions improve paraquat resistance.

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

The paper identifies a class of enhancers with high prediction accuracy by focusing on regulatory heterogeneity in Drosophila, achieving 86.7% precision and 98% recall.

Refining interaction search through signed iterative Random Forests

This paper introduces signed iRF (s-iRF), a method to refine interaction search in biological systems, identifying stable and predictive feature interactions for enhancer activity and spatial gene expression.

An important class of intron retention events in human erythroblasts is regulated by cryptic exons proposed to function as splicing decoys

The paper identifies cryptic exons in human erythroblasts that act as splicing decoys, promoting intron retention through interactions with splice sites.

De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing

Base-by-base tests of raw nanopore current, native versus amplified DNA, detect 4mC, 5mC and 6mA without training data, recovering seven methylases' motifs (AUC 0.62–0.97).