Bioinformatics & Data Analysis Services

Options include:

Externally generated libraries

Analysis of externally generated fastq files is available upon request, providing that the data is compatible with our pipelines. The data will be trimmed to internal project requirements. 

Re-alignment of previously sequenced samples

Alignment to a single reference, as a BAM file, is included for each sequenced library. Additional alignments can be generated if the reference sequence is publicly available.

Custom reference alignment

We support alignment to custom references that are publicly available, or which can be provided as a FASTA file, dependent upon the quality of the file.

Single Sample:

SNV and Indels

Generated from whole genome, exome and RNA sequencing, this pipeline detects small variants in the sequence data as compared to the reference genome which includes single nucleotide variants (SNVs) and insertions/deletions (indels). Variants are associated with gene and dbSNP information. *Genome, Exome or RNA  offered

Copy number variation

Generated from whole genome sequencing, this pipeline calls non-diploid regions in a single genome, using the overall coverage of the genome to calculate a background coverage, and identifying areas with higher or lower than expected coverage. CNV regions are provided. *Genome, Exome or RNA  offered

Paired Tumour/Normal:

Loss of heterozygosity (LOH)

This pipeline detects regions that are heterozygous in the normal and homozygous in the matched tumour. This can only be run on genome. LOH regions are provided, along with plots. *Genome Only

SNV and Indels

This pipeline detects SNVs and indels that are in one library and not in the other library from whole genome or exome sequencing 

Copy number variation

This pipeline can detect regions of copy number change between a normal and matched tumour. This can be run on genome and exomes, although results are generally nosier with exomes, and FFPE samples. Regions of copy number change are provided, along with gene annotation and plots. *Genome or Exome

Multiple timepoint:

Targeted analysis of specific variants

This pipeline looks for a user specified list of SNVs, indels or gene fusions to confirm if a variant is present in sample. This can be run on genome, exome or transcriptome data.

For cancer immunogenetics the GSC offers the following:

• HLA Typing
• Neoantigen prediction
• Tumour cell type abundance
• Clonetype analysis of T and B Cell receptor repertoire

Please contact us for more information.

ChIP peak calling

This pipeline calls peaks from ChIPseq data. QC metrics for the ChIP experiment are also provided.

Methylation analysis

This pipeline aligns bisulphite treated genome libraries and reports the methylation status at each base. Alignment and conversion QC metrics are also provided.

Copy number changes

Alignment-based SV Calling

• Can be preformed on RNAseq or genomic reads.
• This pipeline performs alignment-based SV calling on genome data. Events called include: translocations, inversions, deletion, duplications, small insertions.

Assembly-based SV calling

• Can be preformed on RNAseq or genomic reads.
• This pipeline performs de novo sequence assembly on the RNAseq reads (ABySS). The assembled contigs are used to call structural rearrangements including novel transcripts, alternative splicing, large scale rearrangements and fusions, small scale indels, ITDs and PTDs (RNAseq) (trans-ABySS).

Consensus SV calling

• This pipeline compares SV calls from multiple samples from the same patient. Examples include: identification of somatic SV calls in tumour and absent from matched normal, expressed SVs present in RNA and matched genome, or novel rare SVs present in a child but absent from both parents.

RNA gene and exon level quantification

• This pipeline calculates normalized coverage (RPKM) at gene and exon level. QC metrics include gene diversity, 5'/3' bias, and strand-specificity.

RNA transcript isoform level quantification

• This pipeline calculates normalized coverage of all known transcripts from the raw sequence data. The pipeline does not detect novel isoforms.

miRNA expression quantification

• This pipeline calculates normalized coverage of known miRNAs (RPM).

miRNA novel gene prediction

• The identification of possible novel miRNAs not found in public databases.

Differential expression and other custom analysis of RNA and miRNA expression data

Custom analysis of RNAseq data beyond expression quantification on individual libraries may include:

• Differential expression between 2 individual libraries or 2 groups of libraries to identify up- or down-regulated genes.
• Clustering of samples based on gene expression to identify possible subgroups.
• GO term annotation of significantly up- and down-regulated genes to identify dysregulated pathways

Genome Assembly

• non-reference organism
• human genome assembly

Microbial characterization and classification

• This pipeline calculates normalized (RPM) levels of known microbes including bacteria, viruses and fungi. Can be run on genome, exome or RNAseq data. For quantification of user-specified microbial species that are not on our production list, there will be an additional cost for customizing our analysis.
• In addition to quantification of standard microbial species, custom analysis can be done to investigate unclassified microbial content, or detect integration into the human genome using assembly based methods.

Complex tissue cell characterization

• This pipeline uses expression analysis to identify cell composition of a tissue. Results can be compared and plotted against external data (Eg. TCGA cancer types).