Omics Data Analysis
Omics data analysis is a multidisciplinary field that involves the study and interpretation of various biological data types such as genomics, transcriptomics, proteomics, metabolomics, and others. These approaches provide comprehensive insights into the molecular components of biological systems and their functions.
4omics offers services for key omics data analysis areas:
Genomics: Genomic data analysis involves tasks such as variant calling, genome assembly. Technologies: WGS, WES, target sequencing with short (Illumina) or long (Nanopore) reads, arrays.
Transcriptomics: Transcriptomics focuses on the analysis of RNA molecules, particularly messenger RNA (mRNA). Technologies: RNAseq, single cell RNAseq, microarrays.
Epigenomics: Epigenomics include DNA methylation, histone modification, transcription factors, open chromatin. Technologies: 450K and 850K Illumina BeadChip Array, ChIPseq for histone markers, ChIPseq for transcription factors, ATACseq, single cell ATACseq.
Metagenomics: Metagenomics is a branch of genomics that focuses on the study of genetic material recovered directly from complex environmental samples, often containing a mixture of microorganisms. Technologies: 16S rRNA Sequencing, Shotgun Metagenomic Sequencing,
Omics data analysis typically follows a series of steps:
Data Acquisition: Generate or obtain raw data through techniques like DNA sequencing, microarray experiments, or mass spectrometry.
Preprocessing: Clean and preprocess the data to remove noise, correct for technical biases, and normalize data if needed.
Data Analysis: Perform various analyses, such as differential expression analysis, pathway analysis, clustering, and data visualization.
Integration: Integrate data from different omics layers to gain a holistic understanding of biological processes.
Interpretation: Interpret the results in the context of the biological question under investigation. This may involve identifying biomarkers, understanding regulatory networks, or discovering new biological insights.
Validation: Experimentally validate key findings from the analysis using wet lab experiments.
Visualization: Communicate the results through scientific publications, reports, or presentations.