Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

Abstract

Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo (https://github.com/cellmapslab/kimono). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

Epithelial X-Box Binding Protein 1 Coordinates Tumor Protein p53-Driven DNA Damage Responses and Suppression of Intestinal Carcinogenesis

“…These data were used as an input for KiMONo (Knowledge-guIded Multi-Omics Network), a knowledge-guided multiomics network inference method, to infer a CRC specific multiomics network. 21 The resulting network served as blueprint to detect all multiomics features associated to mechanistic target of rapamycin (mTOR) pathway genes or XBP1, TP53, and DNA damage inducible transcript 4-like (DDIT4L). In a final step, we applied KiMONo to this trimmed data, detecting effects between the multiomics features including the mTOR pathway, XBP1, TP53, and DDIT4L.…”

“…- 21 we were able to trim the multidimensional features with possible impact on XBP1, DDIT4L, TP53, and the mTOR pathway to 120 genes, 52 proteins, 70 mutation sites, and 346 methylation sites. Of these 588 features, KiMONo identified 47 features statistically affecting XBP1 and 80 features associated to TP53 and DDIT4L (Figure 6).…”

Repeated toxic injuries of murine liver are tolerated through microsteatosis and mild inflammation

“…The multi-omic network inference was performed using KiMONo (Ogris et al 2021). This novel versatile tool can use any kind and any amount of omic data by leveraging prior knowledge.…”

“…Typically, one omic level of choice is used, and state-of-the-art data analysis strategies are applied. Using only a single level of measurement, e.g., transcriptomics or histopathology, limits the detection of possible alterations to one type, and thus captures changes only for a small subset of possible components interacting and steering the disease progression (Ogris et al 2021). To obtain a holistic view and reduce the single-level technical effects, studies utilizing multiple omic measurements are employed to support findings or identify targetable molecules in a specific disease context (Mardinoglu et al 2018; Mercado-Gómez et al 2020).…”

“…Approaches using multi-level analysis outperform single-level analysis, as those are limited in their information content to one level and therefore may lead to missing and misinterpreted results (Domenico et al 2015, Schmitt et al 2013). Recently, we developed an integrative approach that is able to exploit the complexity of ‘cross-omics’ relationships of multi-level data, called KiMONo (Ogris et al 2021). The method accounts for the limitations of the level-wise analysis by inferring condition-specific multi-omic networks.…”