TY  - JOUR
AB  - MOTIVATION: The availability of bulk-omic data is steadily increasing, necessitating collaborative efforts between experimental and computational researchers. While software tools with graphical user interfaces (GUIs) enable rapid and interactive data assessment, they are limited to pre-implemented methods, often requiring transitions to custom code for further adjustments. However, the most available tools lack GUI-independent reproducibility such as direct integration with R, resulting in very limited support for transition. RESULTS: We introduce the customizable Omics Analysis and reporting tool-cOmicsArt. cOmicsArt aims to enhance collaboration through integration of GUI-based analysis with R. The GUI allows researchers to perform user-friendly exploratory and statistical analyses with interactive visualizations and automatic documentation. Downloadable R scripts and results ensure reproducibility and seamless integration with R, supporting both novice and experienced programmers by enabling easy customizations and serving as a foundation for more advanced analyses. This versatility also allows for usage in educational settings guiding students from GUI-based analysis to R Code. AVAILABILITY AND IMPLEMENTATION: cOmicsArt is freely available at https://shiny.iaas.uni-bonn.de/cOmicsArt/. User documentation is available at https://icb-dcm.github.io/cOmicsArt/. Source code is available at https://github.com/ICB-DCM/cOmicsArt. A docker available from https://hub.docker.com/r/pauljonasjost/comicsart/tags. A snapshot upon publication available from https://zenodo.org/records/14907620. A screen recording of cOmicsArt is available at: https://www.youtube.com/watch?v=pTGjtIYQOakp.
AU  - Seep, L.*
AU  - Jost, P.J.*
AU  - Lisowski, C.*
AU  - Huang, H.*
AU  - Grein, S.*
AU  - Hermannsdottir, H.*
AU  - Kuellmer, K.*
AU  - Fromme, T.*
AU  - Klingenspor, M.*
AU  - Mass, E.*
AU  - Kurts, C.*
AU  - Hasenauer, J.
C1  - 74600
C2  - 57549
CY  - Great Clarendon St, Oxford Ox2 6dp, England
TI  - cOmicsArt-a customizable omics analysis and reporting tool.
JO  - Bioinfo. Adv.
VL  - 5
IS  - 1
PB  - Oxford Univ Press
PY  - 2025
SN  - 2635-0041
ER  - 

TY  - JOUR
AB  - SUMMARY: Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions. AVAILABILITY AND IMPLEMENTATION: The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
AU  - Monroy Kuhn, J.M.
AU  - Miok, V.
AU  - Lutter, D.
C1  - 67355
C2  - 53544
TI  - Correlation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks.
JO  - Bioinfo. Adv.
VL  - 2
IS  - 1
PY  - 2022
SN  - 2635-0041
ER  -