2025 Hyper Recent •CC0 1.0 Universal

This work is dedicated to the public domain. No rights reserved.

Access Preprint From Server
April 1st, 2025
Version: 1
Imperial College London
bioinformatics
biorxiv

MotifPeeker: R package for benchmarking epigenomic profiling methods using motif enrichment as a key metric

Dash, H.Open in Google Scholar•Roberts, T.Open in Google Scholar•Weinert, M.Open in Google Scholar•Skene, N.Open in Google Scholar

Summary MotifPeeker benchmarks epigenomic datasets where no \"gold standard\" reference exists, using motif enrichment as a key metric. With minimal input, users can analyse their data in a single function and receive an intuitive HTML report. Availability and Implementation MotifPeeker is available on Bioconductor at <a href=\"https://bioconductor.org/packages/devel/bioc/html/MotifPeeker.html\">https://bioconductor.org/packages/devel/bioc/html/MotifPeeker.html</a>. The complete source code is available on GitHub at <a href=\"https://github.com/neurogenomics/MotifPeeker\">https://github.com/neurogenomics/MotifPeeker</a>, with full documentation provided at <a href=\"https://neurogenomics.github.io/MotifPeeker\">https://neurogenomics.github.io/MotifPeeker</a>. Additionally, the MotifPeeker Docker image is hosted on GitHub at <a href=\"https://github.com/neurogenomics/MotifPeeker/pkgs/container/motifpeeker\">https://github.com/neurogenomics/MotifPeeker/pkgs/container/motifpeeker</a>.

Similar Papers

biorxiv
Wed Apr 02 2025
biscot: an Optimal Transport framework for multimodal bacterial single-cell data analysis
Computational optimal transport-based approaches have emerged as promising tools for the integration and interpretation of complex single-cell data. In this study, we introduce an integrative Optimal Transport (OT) framework for spatiotemporal and multimodal bacterial single-cell analysis using Gaussian Mixture Model (GMM) OT, termed biscot (bacterial integrative single-cell optimal transport). We...
Feldl, M.
•
Abbaszade, G.
•
Schattenberg, F.
•
Stuckrath, K.
...•
Mueller, C. L.
biorxiv
Wed Apr 02 2025
Serotonergic Modulation of Motoneuronal Excitability and Its Impact on Muscle Force Generation: A Computational Study
This study investigates the impact of serotonin (5-HT) on motoneuron electrical activity and muscle force generation. Using a computational model, we explore how 5-HT receptors influence motoneuron excitability and muscle function at different stimulation frequencies. Our results demonstrate that physiological 5-HT release increases motoneuron excitability, particularly at higher frequencies (40 H...
Melendez-Gallardo, J.
•
Plada-Delgado, D.
biorxiv
Wed Apr 02 2025
Systematic discovery of directional regulatory motifs associated with human insulator sites
Insulator proteins act as a barrier of enhancer-promoter interactions (EPIs). The main insulator protein in vertebrates is CTCF, a DNA-binding protein (DBP). However, other DBPs associated with insulators of EPIs are still unclear. Hence, we developed a systematic, comprehensive deep learning-based approach for identifying the DNA motifs of DBPs associated with insulators. We discovered 97 directi...
Osato, N.
•
Hamada, M.
biorxiv
Wed Apr 02 2025
Novel universal domain-centric method for protein classification
Human protein kinases constitute a large superfamily of about 500 genes, historically classified into subfamilies based on phylogenetic relationship. However, many kinases remain unclassified. Phylogeny is typically based on multiple sequence alignments, and neglects the physico-chemical properties of residues at each position of the sequence. By incorporating these properties, we can gain deeper ...
Fadaei, S.
•
Krebs, F. S.
•
Zoete, V.
biorxiv
Wed Apr 02 2025
Building A Unified Model for Drug Synergy Analysis Powered by Large Language Models
Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models...
Liu, T.
•
Chu, T.
•
Luo, X.
•
Zhao, H.
biorxiv
Wed Apr 02 2025
Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to con...
Liu, T.
•
De Brouwer, E.
•
Kuo, T.
•
Diamant, N.
...•
Heimberg, G.
biorxiv
Wed Apr 02 2025
In-Silico Stability Predictors: Investigation of Performance Towards balanced Experimental Data
Change in protein stability, quantified as the change in Gibbs free energy of folding ({Delta}{Delta}G) in kcal/mol, plays a crucial role in functional alterations of proteins, with misfolding and destabilization commonly associated with pathogenicity. The past two decades have brought the development of bioinformatics tools leveraging evolutionary knowledge, empirical force fields, and machine le...
Degn, K.
•
Utichi, M.
•
Besora, P. S.-I.
•
Tiberti, M.
•
Papaleo, E.
biorxiv
Wed Apr 02 2025
FUSE-PhyloTree: Linking functions and sequence conservation modules of a protein family through phylogenomic analysis
Summary: FUSE-PhyloTree is a phylogenomic analysis software for identifying local sequence conservation associated with the different functions of a multi-functional (e.g., paralogous or multi-domain) protein family. FUSE-PhyloTree introduces an original approach that combines advanced sequence analysis with phylogenetic methods. First, local sequence conservation modules within the family are ide...
Dennler, O.
•
Chenel, E.
•
Coste, F.
•
Blanquart, S.
...•
Theret, N.
biorxiv
Wed Apr 02 2025
Predicting microbial transcriptome using genome sequence
We present TXpredict, a transformer-based framework for predicting microbial transcriptomes using annotated genome sequences. By leveraging information learned from a large protein language model, TXpredict achieves an average Spearman correlation of 0.53 and 0.62 in predicting gene expression for new bacterial and fungal genomes. We further extend this framework to predict transcriptomes for 2,68...
FU, G.
•
YAN, Y.
•
CHEN, Y.
•
SHAO, B.
biorxiv
Wed Apr 02 2025
Predicting Rhizosphere Competence Related Catabolic Gene Clusters in plant-associated bacteria with RhizoSMASH
Plants release a substantial fraction of their photosynthesized carbon into the rhizosphere as root exudates, a mix of chemically diverse compounds that drive microbiome assembly. Deciphering how plants modulate the composition and activities of rhizosphere microbiota through root exudates is challenging, as no dedicated computational methods exist to systematically identify microbial root exudate...
Li, Y.
•
Sun, M.
•
Raaijmakers, J. M.
•
Mommer, L.
...•
Medema, M. H.