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Bioinformatic Services

Analysis modules offered by the Bioinformatics Platform at the GSC are described below. Additional analysis is available upon request. Please contact us for more information.

Analysis

Description

Standard analysis, included with data generated at the GSC

The costs for Illumina sequencing include a binary alignment file (bam) for all sequenced libraries. Scripts are available for download to convert bam formatted files to fastq files for independent off-site alignment.

 

Analysis of externally generated data

Analysis of externally generated fastq files from Illumina sequencing is available on request providing the data is compatible with our pipelines. The data will be trimmed to internal project requirements. Chastity failed reads should be marked in the fastq file and will not be included in analysis.

Sequence re-alignment

The first alignment for each library is included in the sequencing costs. Additional alignments to different reference versions are available if the reference sequence is publicly available. The result is a bam file.

Custom reference alignment

Installation of a specified reference genome for generation of bam files. A reference must either be available for download from a public site, or can be provided as a fasta file providing it is of a high enough quality for alignment.

Single sample SNV and small indels calling

*Genome, Exome or RNA 

This pipeline detects small variants in the sequence data as compared to the reference genome which includes single nucleotide variants (SNVs) and indels. Variants are associated with gene and dbSNP information.

Paired somatic SNV and indels calling for matched libraries (Ex. tumour/normal)

*Genome or Exome

This pipeline detects SNVs and indels that are in one library and not in the other library.

Paired somatic copy number variation (CNV) for matched libraries

*Genome or Exome

This pipeline detects 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.

Single sample copy number variation (CNV)

* Genome Only

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.

Loss of heterozygosity (LOH) for matched libraries (Ex. normal/tumour)

* Genome Only

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.

Gene and exon level quantification and alignment based on QC of RNA data

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

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

Assembly-based structural variant calling for RNAseq

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. (trans-ABySS)

Assembly-based structural variant calling for genomes 

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

Consensus structural variant (SV) report from multiple samples

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.

Alignment based structural variant calling for genomes

This pipeline performs alignment based structural variant calling on genome data. Events called include: Translocations, inversions, deletion, duplications, small insertions.

Alignment based structural variant calling for RNAseq

This pipeline performs alignment based structural variant calling on RNAseq data. Events called include: Translocations, inversions, deletions, duplications, small insertions.
Targeted alignment variant identification This pipeline looks for a user specified list of SNVs, indels or gene fusions to confirm if variant is present in sample. This can be run on genome, exome or RNA-seq data.
Microbial Characterization 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.
Microbial Classification

In addition to quantification of standard microbial species, custom analysis can be done to:

Investigate unclassified microbial content. Detect integration into the human genome using assembly based methods.

Chromatin immunoprecipitation sequencing (ChIPseq) analysis This pipeline calls peaks from ChIPseq data. QC metrics for the ChIP experiment are also provided

Analysis of bisulphite genomes

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

 

Characterizing cell composition of complex tissues from their gene expression profiles

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)

Page last modified Oct 29, 2018