WINNER: A network biology tool for biomolecular characterization and prioritization

Abstract

Background and contributionIn network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.ResultsWe describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.ConclusionWINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

Cell-Cycle–Specific Autoencoding Improves Cluster Analysis of Cycling Cardiomyocytes

“…The AR P1 -associated increases in pCM1 cardiomyocyte abundance tended to be matched by similar, though less prominent, increases in the proportion of pCM3 cardiomyocytes, which is consistent with our observation that the 2 clusters are linked by a trajectory that begins with the G1/S transition and ends with mitosis. The pCM3 cluster was also enriched for TGFβ, which was also found in our heart regeneration Bulk-RNA sequencing data, 63 , 64 and Pi3k-AKT signaling pathway. These pathways contribute to cardiomyocyte proliferation in mice and zebrafish, 65-67 and the Pi3k-AKT signaling pathway promoted FOXM1 expression in a line of esophageal squamous cell carcinoma cells.…”

Prioritizing Complex Disease Genes from Heterogeneous Public Databases

“…We begin the iterative prioritization and expansion process by using BEERE to prioritize the disease gene list, determining genes with significant p-value (p < 0.05). BEERE is a user-friendly implementation of the WINNER software [8, 18]. Although BEERE only outputs one p-value per gene [18], it is sufficient for the prioritization methods employed within this methodology.…”

“…The copyright holder for this preprint this version posted February 10, 2023. ; https://doi.org/10.1101/2023.02.09.527562 doi: bioRxiv preprint 3 been used to curate significant genes related to a biological condition [1,11,12]. However, results may be biased toward literature studies that are "popular" and not necessarily directly indicative of biological importance and significance [1,8]. Network biology-based approaches, e.g., prioritizing genes based on gene network centrality measures, may overcome many inherent biases of pure statistical or literature-based gene prioritization techniques [13][14][15].…”

“…One single method of prioritizing genes is based on measured gene expression changes, e.g., by “fold change”, which may be arbitrary to the choice of threshold values and the fact that some genes may change transiently or subtly to produce profound biological effects downstream [7]. Another method is to use p-value or other statistical significance measures to sort genes that changed under different conditions [8, 9]. However, p-value analysis can depend on the choice of samples; therefore, the results may not be transferable to new samples due to inherent sample biases [10].…”

“…Correctly placing genes into a biological context can aid the determination of significant genes not found within the initial query list [19]. The ability to parameterize search results—possible in tools such as BEERE and WINNER [8]—is also critical, allowing users to fine-tune results to their specific needs. Finally, almost no network tools implement a continuous, iterative refinement process.…”