Almac Diagnostic Services Bioinformatics Team offer a high quality service for analysis of genomics data across a range of platforms (including Next Generation Sequencing (NGS), microarray and qPCR) and biomaterials. We have robust quality control (QC) pipelines developed from our extensive experience analysing data from both in-house development projects and commercial clinical projects, giving assurance on data quality. We have standard in-house pipelines for data processing and interpretation, but can also offer bespoke pipeline development for client’s specific analysis requirements.
The team has an extensive range of capabilities using the platforms and internal & commercial software solutions listed below. Utilising a cloud-based platform for scalable, flexible analysis; and data return via a secure portal in the clients preferred file format.
The bioinformatics team has established pipelines for the analyses listed, however much of our work is tailored to specific data challenges for clients requiring bespoke solutions.
Technology analysis pipelines
NGS (Next Generation Sequencing)
- Almac established QC & pre-processing steps for Illumina TruSeq™ RNA Exome, Total RNA-Seq and mRNA Stranded Sequencing
- Includes access to the ClaraT Total mRNA Report
- Custom workflow development tailored to answer specific customer questions
- In-house software for reproducible data QC & pre-processing
Whole Exome Sequencing (WES)
- QC & Pre-Processing for WES (Whole Exome Sequencing)
- Custom pipeline / filtering options available
Medium / Small Variant Panels
- Almac analysis pipeline for Illumina TruSight™ Tumor 170 for small variant calling, fusion calling, CNV calling (validated to IUO level for 55 gene fusions and 148 SNVs and indels); including Almac variant QC and FFPE artefact filter
- Archer Analysis Unlimited pipelines applied to
- Archer® Reveal ctDNA™ 28 analysis for small variant calling
- Archer® FusionPlex for gene fusions
- Almac can also develop custom pipelines for other targeted panels
Bioinformatics for the biomarker discovery process
Almac Diagnostic Services has a well-developed gene expression biomarker discovery process incorporating in-house software that has been established over many years. Our pipelines are transferable to the majority of expression data matrices, including microarray, qPCR and data generated using RNA-Seq based Next Generation Sequencing (NGS) technology.
Our expertise in biomarker development has been demonstrated through our own research in the oncology field, where we have discovered and validated a number of Almac proprietary biomarkers from FFPE studies DDIR Signature, Angio, EMT, ProstateDx and ColDx.
We also have extensive expertise in the non-oncology field supporting client biomarker development projects. These studies typically use fresh samples rather than archival samples, meaning degradation is less of an issue; however clinical endpoints are often poorly defined. In these cases we can perform analysis to guide selection of the most appropriate endpoint for your study.
Our Bioinformatics team can perform post-hoc analyses to tailor developed models to specific patient subgroups, incorporation of other data types such as pharmacokinetic (PK Biomarkers), and building multi factorial models including predictive clinical factors in addition to off the shelf best practice solutions for differential expression and pathway / functional enrichment analyses.
Almac Diagnostic Services can offer clients the following proprietary Bioinformatics analysis solutions to facilitate their Biomarker Discovery process:
- Quality Control (QC) & Exploratory Analysis (EA)
- Subtyping Workflow
- Signature Generation Workflow
Quality Control (QC) & Exploratory Analysis (EA)
For optimal biomarker discovery, Almac’s QC and EA process ensures the best starting point for signature generation. We have defined QC processes & thresholds for microarray and RNA sequencing data. Our EA tool works with any expression data type and provides insight into all relationships within, and between, technical and clinical variables and the data structure based on principal components. The EA process aims to identify key factors driving sample groups within the dataset and reduce or remove the impact of technical effects that may hinder the biomarker discovery process.
Almac’s unsupervised analysis process is designed to find the underlying biological structure driving sample separation within a dataset, to identify stable sample and gene clusters driven by these transcripts. This process is of particular benefit when the response endpoint is poorly defined. Transcripts are identified by an iterative process of removing noise, and testing the stability of resulting clusters. Once robust sample and gene clusters are identified, the association of sample characteristics to these clusters is tested, and functional enrichment of the gene clusters is performed to characterise the biology of the subgroups.
Signature Generation Workflow (SGW)
Almac’s SGW has been developed according to MAQC best practice. All feature selection and performance metric generation is performed under a well-implemented cross-validation process. The Machine learning method depends on the nature of the endpoint of interest; dichotomous classifiers or continuous predictors can be developed to binary or continuous measures of response. Almac has custom-built functions to visualise the biological relevance of resulting signatures in order to guide final model selection, along with appropriate measures of performance.