ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes

Abstract

Background: The recent advancements in high-throughput sequencing have resulted in the availability of annotated genomes, as well as of multi-omics data for many living organisms. This has increased the need for graphic tools that allow the concurrent visualization of genomes and feature-associated multi-omics data on single publication-ready plots.

Results: We present chromoMap, an R package, developed for the construction of interactive visualizations of chromosomes/chromosomal regions, mapping of any chromosomal feature with known coordinates (i.e., protein coding genes, transposable elements, non-coding RNAs, microsatellites, etc.), and chromosomal regional characteristics (i.e. genomic feature density, gene expression, DNA methylation, chromatin modifications, etc.) of organisms with a genome assembly. ChromoMap can also integrate multi-omics data (genomics, transcriptomics and epigenomics) in relation to their occurrence across chromosomes. ChromoMap takes tab-delimited files (BED like) or alternatively R objects to specify the genomic co-ordinates of the chromosomes and elements to annotate. Rendered chromosomes are composed of continuous windows of a given range, which, on hover, display detailed information about the elements annotated within that range. By adjusting parameters of a single function, users can generate a variety of plots that can either be saved as static image or as HTML documents.

Conclusions: ChromoMap's flexibility allows for concurrent visualization of genomic data in each strand of a given chromosome, or of more than one homologous chromosome; allowing the comparison of multi-omic data between genotypes (e.g. species, varieties, etc.) or between homologous chromosomes of phased diploid/polyploid genomes. chromoMap is an extensive tool that can be potentially used in various bioinformatics analysis pipelines for genomic visualization of multi-omics data.

Keywords: Genome visualization; Multi-omics data visualization; R package.

Fig. 1

Example of chromoMap plot constructed using various features of chromoMap including polyploidy (used as multi-track), feature-associated data visualization (scatter and bar plots), chromosome heatmaps, data filters (color-coded scatter and bars). Differential gene expression in a cohort of patients positive for COVID19 and healthy individuals (NCBI Gene Expression Omnibus id: GSE162835) [12]. Each track contains information for chromosome 1 (For a complete interactive plot containing all 23 chromosomes see Additional file 1). Each track from top to bottom describes: (1) number of differentially expressed genes (DEGs) (FDR < 0.05) (bars over the chromosome depictions) per genomic window (green boxes within the chromosome). Windows containing ≥ 5 DEGs are shown in yellow. (2) DEGs (FDR < 0.05) between healthy individuals and patients positive for COVID19 visualized as a scatterplot above the chromosome depiction (genes with logFC ≥ 2 or logFC ≤ −2 are highlighted in orange). Dots above the grey dashed line represent upregulated genes in COVID19 positive patients. Heatmap within chromosome depictions indicates the average LogFC value per window. (3–4) Normalized expression of differentially expressed genes (scatterplot) and of each genomic window containing DEG (green scale heatmap) in (3) patients with severe/critical outcomes and (4) asymptomatic/mild outcome patients. (5) logFC of DEGs between healthy individuals and patients positive for COVID19 visualized as scatter plot color-coded based on the metabolic pathway each DEG belongs to

Fig. 2

Example of chromoMap plot generated using the segment-annotation feature. 28 Splice variants of the human ABCD4 (ATP binding cassette subfamily D member 4) gene visualized using the segment-annotation feature. Exons are highlighted in red and introns in green. Feature coordinates were extracted from Ensembl BioMart [13]

Fig. 3

Example of chromoMap plot constructed using group annotations. Genomic location of 5 ortholog genes in the rice and maize genomes. Gene coordinates were extracted from Ensembl BioMart [13]

Fig. 4

Feature-associated data visualization using chromoMap. A Visualization of genomic and epigenomic information for chromosomes 4 and 5 in barley (Hordeum vulgare). Vertical bars show the number of protein coding genes per genomic window (windows with ≥ 50 genes shown in yellow), and differential methylation (-Log10 (FDR)) of ms-GBS markers between plants grown under control conditions and under salt stress (75 mM NaCl) (Green: Hypermethylated markers under salt stress. Red: Hypomethylated markers under salt stress) European Nucleotide Archive (ENA) (Study Accession Number: PRJEB27251) [14]. B gene expression differences between healthy and breast cancer patients (NCBI Gene Expression Omnibus id: GSE59055) [15]. Solid black chromosomes depict difference in gene expression (logFC) between the healthy and cancer diagnose cohorts, while empty chromosomes depict the mean normalized expression for individuals within the control (Green) diseased (Red) cohorts

Fig. 5

Example plot generated using chromLinks: Normalized expression of the top 50 highly expressed orthologous genes in mouse (Mus musculus) and rat (Rattus norvegicus) during pluripotent cell determination (NCBI Gene Expression Omnibus id: GSE42081) [16]. Orthologous gene pairs are connected with colored links. Each orthologous pair is indicated by a different link color. For an interactive version of this plot see Additional file 2