10 papers · ranked by Valyu relevance
Zuguang Gu, Daniel Hübschmann
Numerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of unique dependencies that a parent brings to a package, and we…
Gökmen Altay, Zeyneb Kurt, Nejla Altay, Nizamettin Aydin
Gene network inference algorithms (GNI) are popular in bioinformatics area. In almost all GNI algorithms, the main process is to estimate the dependency (association) scores among the genes of the dataset. We present a bioinformatics tool, DepEst (Dependency Estimators), which is a powerful and flexible R package that…
Tunç Başar Köse, Jiarong Li, Anna Ritz
A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new…
Hengyi Fu, Bojin Zhao, Peng Wang
A fundamental premise for precision oncology is a catalog of diverse actionable targets that could enable personalized treatment. Large scale Genome-wide lost-of-function screens such as cancer dependency map have systematically identified single gene vulnerabilities in numerous cell lines. However, it remains…
Li Song, Sarven Sabunciyan, Liliana Florea
Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to capture splicing variation accurately has proven difficult. Current programs find major isoforms of a gene but miss finer splicing…
Grigoriy Gogoshin, Sergio Branciamore, Andrei S. Rodin
Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for…
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee + 5 more
Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics…
Peter A. Andrews, Joan Alexander, Jude Kendall, Michael Wigler
Effective and efficient exploration of numeric data and annotations as a function of genomic position requires specialized software. We present G-Graph, an interactive genomic scatter plot viewer. G-Graph stacks or tiles multiple data series in one graph using different colors and markers. It displays gene annotation…
Andrea Guarracino, Simon Heumos, Sven Nahnsen, Pjotr Prins + 1 more
Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is…
Tom Mokveld, Jasper Linthorst, Zaid Al-Ars, Marcel Reinders
Although the characterization of within species genomic diversity continues to increase, this information is usually not incorporated in the sequencing analysis process. Existing reference genomes can easily be converted to graph-based reference genomes by extending them with known sequence variations. However, the…