Bioinformatics Core Labratory

About

Bioinformatics is an inherently collaborative field between experimentalists and theoreticians.

The Bioinformatics Core Laboratory (BCL) at the Children’s Hospital Research Institute of Manitoba (CHRIM) is engaged in the analysis of large-scale multi-omics data.

The facility provides computational expertise and resources for the design and analysis of high throughput bio-analytically measured data and for comparisons and integration with metadata available from CHRIM knowledge bases. We employ both standard and novel methods for the analysis of these data in order to extract the maximum biological information from them. Our expertise covers the entire gamut of –omics data analysis including:

  • Genomics – via DNA-seq, aCGH, WGS, WES
  • Epigenomics – via ChIP-seq, DNA methylation
  • Transcriptomics – via microarrays, RNA sequencing of bulk sample or single cells
  • Proteomics – via mass spectrometry
  • Targeted and untargeted metabolomics – via magnetic resonance and/or mass spectrometry
     

We offer 3 Main Pillars

The Binformatics Core Labratory provides state-of-the-art, secure, regulatory-compliant and customized solutions to address clinical and research needs at CHRIM and stakeholders in the health sciences research cluster.

  • Service: Fee-for-service analysis of empirical –omics data 
  • Training: Development and provision of training modules to researchers, clinicians and other trainees
  • Methods: Development of novel and improvement of current methods for the analysis and integration of omics data.

Multi-Omics Data Analysis

Metabolomics /Proteomics

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. [1]. Like transcriptomics, proteomics studies can be performed on bulk samples, single cells using techniques such as mass cytometry (cytof) [2], and even on sub-cellular components using spatial proteomics approaches [3]. 

As with the previous omics approaches, bioanalytical techniques for proteomics yield massive and complex amounts of data. We employ popular workflows such as Perseus [4] 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 [5] 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.

Transcriptomics

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 [1] and RNA-seq for transcriptomics [2].

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.

Epigenomics

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 [1]

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. [2] summarizes these techniques.

Our facility is equipped to analyze these and other data types for Epigenomic analyses.

Genomics

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

Service

The fee-for-service module involves discussions with PIs regarding their data analysis needs after an intake form has been received and reviewed (please refer to the intake form here)

Please consult with us before acquiring your data! Remember this gem from Ronald Fisher himself: “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of” [1].

​​[1] R. A. Fisher (1938); The presidential address to the first Indian Statistical Congress

Where possible, seek bioinformatics services at the beginning of the project so that we can have detailed discussions regarding the design of experiments in view of the biological question and their context.

Under this arrangement, the BCL will be responsible for processing all the raw data as well as all the downstream analyses including producing reports.

Services follow this process:

Training

We offer continuing training for students and researchers to analyze their data using various bioinformatics tools. We also accept students to train within the facility to develop novel tools for the analysis of – omics data.

Methods

Our research aims to fill technological gaps in the -omics field that limit access to existing correlations and interplay between multiple levels of a biological system’s descriptors.
 

Specific objectives of our research are:

  • Determination of the landscape of genomic instabilities that result in the selective enhancement of important biological phenotypes that characterize environmental perturbation, disease or drug response
  • Development of comprehensive and integrative time-resolved networks describing the interplay between all the downstream products of gene expression (transcriptomics), translation and post-translational modification (proteomics) and metabolic signals (metabolomics)
  • Comprehensive characterization of the quality of bio-analytical measurement technologies employed in systems biochemistry
  • Application of classical, and development of novel bioinformatics tools for the analysis of large scale – omics data for biomarker discovery and validation.