Grimon: graphical interface to visualize multi-omics networks

Summary Rapid advances in high‐throughput sequencing technologies have enabled more efficient acquisition of massive amount of multi‐omics data. However, interpretation of the underlying relationships across multi‐omics networks has not been fully succeeded, partly due to the lack of effective methods in visualization. To aid interpretation of the results from such multi‐omics data, we here present Grimon (Graphical interface to visualize multi‐omics networks), an R package that visualizes high‐dimensional multi‐layered data sets in three‐dimensional parallel coordinates. Grimon enables users to intuitively and interactively explore their analyzed data, helping their understanding of multiple inter‐layer connections embedded in high‐dimensional complex data. Availability and implementation Grimon is freely available at https://github.com/mkanai/grimon as an R package with example omics data sets. Supplementary information Supplementary data are available at bioinformatics online.

[1]  Pedro G. Ferreira,et al.  Transcriptome and genome sequencing uncovers functional variation in humans , 2013, Nature.

[2]  M. Kanai,et al.  Significant impact of miRNA–target gene networks on genetics of human complex traits , 2016, Scientific Reports.

[3]  M. Kanai,et al.  Construction of a population-specific HLA imputation reference panel and its application to Graves' disease risk in Japanese , 2015, Nature Genetics.

[4]  R. Andrews,et al.  Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression , 2014, Science.

[5]  P. Kharchenko,et al.  Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain , 2017, Nature Biotechnology.

[6]  Y. Saeki,et al.  Dysbiosis Contributes to Arthritis Development via Activation of Autoreactive T Cells in the Intestine , 2016, Arthritis & rheumatology.

[7]  Yang I Li,et al.  An Expanded View of Complex Traits: From Polygenic to Omnigenic , 2017, Cell.

[8]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[9]  M. Kanai,et al.  Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases , 2018, Nature Genetics.

[10]  David A. Knowles,et al.  RNA splicing is a primary link between genetic variation and disease , 2016, Science.

[11]  Stephen B. Montgomery,et al.  Transcriptome Sequencing from Diverse Human Populations Reveals Differentiated Regulatory Architecture , 2014, PLoS genetics.