Abstract
Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.
Highlights
Large amounts of data have been generated from highthroughput profiling platforms
Based on the numbers of reads supporting the reference and altered alleles for each variant between tumour and normal samples, log2ratios (LRR) and Ballele frequencies (BAFs) values are calculated for each tumour–normal pair with the depth information normalized by dividing the depth of each variant by the median depth across all variants
Samples RNA sequencing (RNA-Seq) data from The Cancer Genome Atlas (TCGA) prostate adenocarcinoma (PRAD) project were downloaded and subjected to quality control (QC) and alignments steps. These pre-processed data were uploaded to O-miner, and genes/gene ontology (GO) terms differentially altered between prostate cancer (PCa) and normal samples were identified (Figure 7)
Summary
Large amounts of data have been generated from highthroughput profiling platforms. Public repositories, such as the Gene Expression Omnibus (GEO) [1], ArrayExpress [2], Sequence Read Archive (SRA) [3] and the European Genome-phenome Archive (EGA) [4], contain thousands of profiles from transcriptomic, genomic and methylation platforms across many experimental conditions and sample types. Genomics O-miner offers two analytical workflows, CBS and ASCAT [21], for the analysis of copy number data generated on Affymetrix array platforms (Table 1, Figures 3 and 4). Both workflows can conduct a complete genomic analysis from raw data files. Based on the numbers of reads supporting the reference and altered alleles for each variant between tumour and normal samples, log2ratios (LRR) and BAF values are calculated for each tumour–normal pair with the depth information normalized by dividing the depth of each variant by the median depth across all variants These files are used as input to the ASCAT algorithm to estimate copy number calls, annotate the regions of CNA as well as generating frequency and aberration plots. Case study 1: Meta-analysis of BC transcriptomics data to investigate the relationship between triple-negative BC and basality
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