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

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.

[1]  S. Taheri,et al.  The banana (Musa acuminata) MYB gene family and MaMYB14, MaMYB63 and MaMYB110 expression in response to salinity-stress in cv. Berangan , 2020 .

[2]  Yang Yang,et al.  Identification of potential blood biomarkers for Parkinson’s disease by gene expression and DNA methylation data integration analysis , 2019, Clinical Epigenetics.

[3]  M. Okoniewski,et al.  Cross-Species Genome Wide Expression Analysis during Pluripotent Cell Determination in Mouse and Rat Preimplantation Embryos , 2012, PloS one.

[4]  P. Bayer,et al.  Resistance Gene Analogs in the Brassicaceae: Identification, Characterization, Distribution, and Evolution. , 2020, Plant physiology.

[5]  M. Gerdol,et al.  Single individual structural variant detection uncovers widespread hemizygosity in molluscs , 2020, bioRxiv.

[6]  B. Berger,et al.  Salt Stress Induces Non-CG Methylation in Coding Regions of Barley Seedlings (Hordeum vulgare) , 2018, Epigenomes.

[7]  B. Gaut,et al.  How Single Molecule Real-Time Sequencing and Haplotype Phasing Have Enabled Reference-Grade Diploid Genome Assembly of Wine Grapes , 2017, Front. Plant Sci..

[8]  Suzanna E Lewis,et al.  JBrowse: a dynamic web platform for genome visualization and analysis , 2016, Genome Biology.

[9]  Ann McCartney,et al.  An exploration of assembly strategies and quality metrics on the accuracy of the Knightia excelsa (rewarewa) genome , 2020, bioRxiv.

[10]  P. Langridge,et al.  Development of an Australian Bread Wheat Nested Association Mapping Population, A New Genetic Diversity Resource for Breeding under Dry and Hot Climates , 2021, International journal of molecular sciences.

[11]  K. Folta,et al.  Genomic structure and transcript analysis of the Rapid Alkalinization Factor (RALF) gene family during host-pathogen crosstalk in Fragaria vesca and Fragaria x ananassa strawberry , 2020, PloS one.

[12]  Astrid Gall,et al.  Ensembl 2020 , 2019, Nucleic Acids Res..

[13]  Elliot A. Hershberg,et al.  3D mapping and accelerated super-resolution imaging of the human genome using in situ sequencing. , 2020, Nature Methods.

[14]  Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes , 2020, Computational and Structural Biotechnology Journal.

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  Publisher's Note , 2018, Anaesthesia.