Fibromine is a multi-omics database and mining tool for target discovery in pulmonary fibrosis

Abstract

Idiopathic pulmonary fibrosis is a lethal lung fibroproliferative disease with limited therapeutic options. Differential expression profiling of affected sites has been instrumental for involved pathogenetic mechanisms dissection and therapeutic targets discovery. However, there have been limited efforts to comparatively analyse/mine the numerous related publicly available datasets, to fully exploit their potential on the validation/creation of novel research hypotheses. In this context and towards that goal, we present Fibromine, an integrated database and exploration environment comprising of consistently re-analysed, manually curated transcriptomic and proteomic pulmonary fibrosis datasets covering a wide range of experimental designs in both patients and animal models. Fibromine can be accessed via an R Shiny application (http://www.fibromine.com/Fibromine) which offers dynamic data exploration and real-time integration functionalities. Moreover, we introduce a novel benchmarking system based on transcriptomic datasets underlying characteristics, resulting to dataset accreditation aiming to aid the user on dataset selection. Cell specificity of gene expression can be visualised and/or explored in several scRNA-seq datasets, in an effort to link legacy data with this cutting-edge methodology and paving the way to their integration. Several use case examples are presented, that, importantly, can be reproduced on-the-fly by a non-specialist user, the primary target and potential user of this endeavour.

Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE

“…SeqCVIBE, apart from an RNA-Seq analytics tool, comprises also a database of pre-analyzed data, ready for exploration, querying, and additional on-the-fly analyses. While domain or disease-specific gene expression and multi-omics data and knowledge bases are appearing, making use of the wealth of public data [ 31 , 32 , 33 , 34 ], this knowledge usually is presented in tab-delimited and sometimes network format, with limited support for additional visualization tools, such as gene networks, expression heatmaps, scatterplots, and genome browsing support. SeqCVIBE can be used to create deeper knowledge instances for such use cases and domain-specific databases through the support of more extensive analytics, while at the same time offering the means to visualize RNA-Seq signals in graph and non-tabular formats.…”

A guide to multi-omics data collection and integration for translational medicine

“… Name Type Omics content Link Species Answer ALS [7] Repository whole-genome sequencing, RNA transcriptomics, ATAC-sequencing, proteomics. Also: deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition https://dataportal.answerals.org/ Human Fibromine [8] Database transcriptomics and proteomics http://www.fibromine.com/Fibromine/ Human/ Mouse DevOmics [9] Database normalized gene expression, DNA methylation, histone modifications (H3K4me3, H3K9me3, H3K27me3, H3K27ac), chromatin accessibility and 3D chromatin architecture profiles of human and mouse early embryos spanning six developmental stages http://devomics.cn Human/ Mouse The Cancer Genome Atlas (TCGA) [10] Repository genomics, epigenomics, transcriptomics, proteomics https://portal.gdc.cancer.gov/ Human jMorp [11] Database/ Repository genomics, methylomics, transcriptomics, and metabolomics https://jmorp.megabank.tohoku.ac.jp/ Human DriverDBv3 [12] Database somatic mutation, RNA expression, miRNA expression, methylation, copy number variation and clinical data in addition to annotation bases http://driverdb.tms.cmu.edu.tw/ Human The Personal Genome Project-UK [83] Repository DNA methylation (bisulfite sequencing), whole genome sequencing, transcriptomics https://www.personalgenomes.org.uk/ Human STATegra [13] Repository RNA-seq, microRNA-seq, ChIP-seq, RRBS, Dnase-seq, ATAC-seq, scRNA-seq, scATAC-seq, proteomics, metabolomics https://opendata.lifebit.ai/table/stategra Mouse ColPortal [14] Repository Mehylation 450 k arrays, 16 S sequencing, expression arrays, microRNA arrays https://colportal.imib.es/ Human The Omics Discovery Index [15] Knowledge dis...…”

Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone

“…Many examples of multi-omics studies are available in literature, especially applied to biomarker discovery, such as Jiang et al for pancreatic cancer [ 130 ], Zheng et al for non-small cell lung cancer [ 131 ] and Buttacavoli et al for colon cancer [ 132 ]. Other studies extended from drug discovery, such as Sha et al for diabetic nephropathy [ 133 ] and Meng et al for chordoma [ 134 ], to the understanding of pathogenesis, as performed by Wang et al in clear cell renal cell carcinoma [ 135 ] and to the establishment of accessible databases of clinical and/or multi-omics data, as Answer ALS [ 136 ] or Fibromine [ 137 ]. Recently, this multi-omic approach has also been applied to the developing single-cell technology, as it could allow to capture multiple omic layers from the same cell.…”