Multi-Omics Data Analysis
Bioinformatics for Functional Genomics
CHRIM BCL has 3 main pillars:
Analysis of empirical –omics data on a cost-recovery basis
Development and provision of training modules to researchers, clinicians and other trainees
Development of novel and improvement of current computational methodsfor the analysis and integration of –omics data.
The service module involves discussions with PI’s regarding their data analysis needs after an intake form has been received and reviewed (please refer to the intake form here). Where possible, bioinformatics services will be sought at the inception of the project to enable detailed discussions regarding the design of experiments in view of the biological question and their context. Under this arrangement, the bioinformatics facility will be responsible for processing all the raw data as well as all the downstream analyses including producing reports.
The definition for genomics as stated by EMBL-EBI is that it is the study of whole genomes of organisms. Whole genome sequencing is the process of determining the DNA sequence of organisms simultaneously.
Several texts exist on the definitions of whole genome sequencing (WGS), whole exome (WES) and their applications as well as technological developments in this field.
We provide services for alignment of raw DNAseq data to whole genomes and follow standard pipelines such as those outlined in the GATK best practices
The objective of epigenomics studies is to characterize the heritable repressions in gene expression that do not involve changes in the underlying genomic DNA sequences of an organism. This represents a classic case of phenotype - genotype mismatch. In general, epigenetic changes are a regular and natural occurrence and affect several phenotypic characteristics such as aging, lifestyle and health among others. Three of the most common epigenetic modifications include DNA methylation, histone modification and non-coding RNA associated changes to gene expression levels .
DNA methylation is a chemical modification of the DNA structure where a methyl group is added to carbon-5 of cytosine. This is pivotal to several phenotypic outcomes such as gene expression, embryonic development, cellular proliferation and chromosome stability. Detection of methylated Cytosines within a genome sequence can be accomplished by using one of three experimental approaches: (i) enzyme digestion, (ii) affinity enrichment and, (iii) bifulfite conversion. This is then followed by next-generation sequencing. An excellent review article by Yong et al.  summarizes these techniques.
Our facility is equipped to analyze these and other data types for Epigenomic analyses.
Transcriptomics refers to the study of the complete set of RNA transcripts that are differentially expressed by the genome under specific environmental and/or patho-physiological circumstances, or due to an inherent genomic or epigenomic blueprint. Differential expression is usually measured using high throughput methods such as gene expression microarrays or shotgun next-generation whole transcriptome sequencing (RNA-seq).
Comparison of transcriptomes allows genes that are differentially expressed in cell types or in response to pathogenesis or therapy to be identified. There are excellent reviews and tutorials on gene expression microarrays analysis  and RNA-seq for transcriptomics .
At BCL, we have developed the expertise to analyse all transcriptomics data begining from the raw data (image data for microarrays, alignment files such as BCL, FASTQ or BAM files from RNA-seq platforms) to functional analyses of genes identified to be differentially expressed. We have experience with both single cell RNA-seq and Bulk RNA-seq data analysis.
Proteomics is the study of the full protein complement of a cell and includes its identification, quantification, and localization. The most widespread bioanalytical tool for large-scale proteomics measurements is mass spectrometry which has been reviewed expertly by Yates et al. . Like transcriptomics, proteomics studies can be performed on bulk samples, single cells using techniques such as mass cytometry (cytof) , and even on sub-cellular components using spatial proteomics approaches .
As with the previous omics approaches, bioanalytical techniques for proteomics yield massive and complex amounts of data. We employ popular workflows such as Perseus  for identification and quantification of proteomics data and develop in-house tools for multivariate analysis depending on the experimental study design. We also employ a standard workflow  for single cell proteomics analysis of cytof data in addition to custom scripts in R and MATLAB (MathWorks).
Similar to proteomics, metabolomics is the study of the full metabolite complement of a cell. Bioanalytical methods for measuring the metabolome include, but are not limited to, mass spectrometry (with or without a seperation front-end) and magnetic resonance spectroscopy. We provide the full suite of services for analysis of these data.