Sequencing and analysis,from a single amplicon to a whole genome.
WGS, exome and panels, bulk and single-cell RNA-seq, methylation, microbiome and Sanger validation on Illumina and Nanopore. Analysis is part of every project.
DNA
Genomics
Variants, structure and sequence at the depth your question needs.
Whole-genome sequencing (WGS)
Short-read Illumina at 30× or higher, and long-read Oxford Nanopore for structural variants, repeats and phasing.
Background and references
Short-read WGS at 30× captures single-nucleotide variants and small indels, while long reads add structural variants, repeat regions and haplotype phasing (Logsdon et al., 2020). We combine the two when the question needs both.
- Logsdon GA, Vollger MR, Eichler EE (2020). Long-read human genome sequencing and its applications. Nat Rev Genet 21:597-614. PubMed · DOI · Free full text
Whole-exome sequencing (WES)
Hybrid-capture exome with SNV, indel and CNV calling and clinical-style annotation.
Background and references
Exome sequencing targets the coding 1 to 2 percent of the genome where most known disease variants lie, and variants are classified following the ACMG and AMP standards (Richards et al., 2015). We report variants in those categories with the supporting evidence.
- Richards S et al. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17:405-24. PubMed · DOI · Free full text
Targeted panels and amplicon sequencing
Custom amplicon or capture panels for genes of interest, with primer design and multiplexing optimisation.
Background and references
Target enrichment by hybrid capture or amplicon PCR trades breadth for depth, and the right choice depends on the number of targets and samples (Mamanova et al., 2010). We design and validate the panel, then sequence it at the depth your variant frequency requires.
Sanger sequencing and clone verification
Variant confirmation, plasmid and insert verification, and primer walking.
Background and references
Sanger sequencing confirms NGS variants and verifies clones; a systematic evaluation found that confirmation adds most value for variants with lower NGS quality scores (Beck et al., 2016). We offer it as a stand-alone service or as a validation step.
- Beck TF et al. (2016). Systematic Evaluation of Sanger Validation of Next-Generation Sequencing Variants. Clin Chem 62:647-54. PubMed · DOI · Free full text
De novo assembly and genome finishing
Hybrid short- and long-read assembly, polishing and annotation for microbial, plant and animal genomes.
Background and references
Hybrid assembly with Unicycler (Wick et al., 2017) or long-read assembly with Canu (Koren et al., 2017) produces complete microbial genomes, and the same tools scale to larger genomes with polishing. We deliver assembled, annotated contigs with quality metrics.
- Wick RR et al. (2017). Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13:e1005595. PubMed · DOI · Free full text
- Koren S et al. (2017). Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27:722-736. PubMed · DOI · Free full text
Variant calling and annotation
SNVs, indels, CNVs and structural variants with population-frequency and functional annotation.
Background and references
From FASTQ to an annotated, prioritised variant list: alignment, calling with DeepVariant or GATK (Poplin et al., 2018), VEP annotation and ACMG classification principles (Richards et al., 2015).
- Poplin R et al. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 36:983-987. PubMed · DOI
- Richards S et al. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17:405-24. PubMed · DOI · Free full text
RNA
Transcriptomics
Expression, isoforms and small RNAs, with the statistics done properly.
Bulk RNA-seq
Poly-A or total RNA libraries; alignment, quantification, differential expression, splicing and fusion detection.
Background and references
Design, QC, alignment, quantification and differential expression each have established best practices (Conesa et al., 2016), and DESeq2 (Love et al., 2014) remains the standard for count-based testing. We follow them and deliver the code.
- Conesa A et al. (2016). A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. PubMed · DOI · Free full text
- Love MI, Huber W, Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. PubMed · DOI · Free full text
Single-cell RNA-seq
Cell-level profiling with clustering, marker discovery, annotation and trajectory analysis.
Background and references
Quality control, normalisation, integration, clustering and annotation follow community best practices (Luecken and Theis, 2019; Heumos et al., 2023). We deliver annotated cell populations with marker genes and the parameters used at each step.
- Luecken MD, Theis FJ (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. PubMed · DOI · Free full text
- Heumos L et al. (2023). Best practices for single-cell analysis across modalities. Nat Rev Genet 24:550-572. PubMed · DOI · Free full text
Small RNA-seq
miRNA, piRNA and other small RNAs with target prediction and integration with mRNA data.
Background and references
Small RNA-seq quantifies known miRNAs and discovers novel ones with miRDeep2 (Friedländer et al., 2012). We integrate miRNA profiles with matched mRNA data for target analysis.
- Friedländer MR et al. (2012). miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40:37-52. PubMed · DOI · Free full text
Long-read isoform sequencing
Full-length transcript sequencing on Nanopore for isoform discovery and allele-specific expression.
Background and references
Nanopore sequencing reads full-length transcripts and resolves isoforms and allele-specific expression that short reads cannot (Wang et al., 2021). We use it where isoform structure is the question.
- Wang Y et al. (2021). Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol 39:1348-1365. PubMed · DOI · Free full text
Pathway and network analysis
GO, KEGG and Reactome enrichment, GSEA and co-expression networks, delivered as figures and tables.
Background and references
Gene set enrichment analysis tests coordinated shifts across gene sets rather than single genes (Subramanian et al., 2005), and Reactome provides curated pathways (Jassal et al., 2020). Figures and tables are delivered journal-ready.
- Subramanian A et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545-50. PubMed · DOI · Free full text
- Jassal B et al. (2020). The reactome pathway knowledgebase. Nucleic Acids Res 48:D498-D503. PubMed · DOI · Free full text
Epigenetics
Epigenomics and regulation
DNA methylation and chromatin, scoped per project.
DNA methylation
Whole-genome or reduced-representation bisulfite sequencing, enzymatic methyl-seq and targeted bisulfite amplicons, with differentially methylated region analysis.
Background and references
Enzymatic methyl sequencing detects methylation at single-base resolution from picograms of DNA with less damage than bisulfite conversion (Vaisvila et al., 2021). We offer EM-seq, RRBS and targeted bisulfite amplicons with differentially methylated region analysis.
- Vaisvila R et al. (2021). Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA. Genome Res 31:1280-1289. PubMed · DOI · Free full text
Chromatin accessibility and binding
ATAC-seq, ChIP-seq and CUT&RUN with peak calling and motif analysis, on request.
Background and references
ATAC-seq maps open chromatin from as few as 500 cells (Buenrostro et al., 2013) and CUT&RUN maps DNA-binding proteins with low background (Skene and Henikoff, 2017). Both are available on request with peak calling and motif analysis.
- Buenrostro JD et al. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10:1213-8. PubMed · DOI · Free full text
- Skene PJ, Henikoff S (2017). An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife 6:e21856. PubMed · DOI · Free full text
Microbiome
Microbiome and metagenomics
Who is there and what they can do.
16S and ITS amplicon sequencing
Bacterial and fungal community profiling with taxonomic classification and diversity statistics.
Background and references
Amplicon data are processed in QIIME 2 (Bolyen et al., 2019) with exact sequence variants, taxonomic classification and diversity statistics. We report the reference database and its version with the results.
- Bolyen E et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852-857. PubMed · DOI · Free full text
Shotgun metagenomics
Species-level taxonomy, functional annotation and metagenome-assembled genomes.
Background and references
Shotgun metagenomics gives species-level taxonomy, functional genes and metagenome-assembled genomes, with the design considerations set out by Quince et al. (2017). We pair it with metaproteomics when function matters.
Metatranscriptomics
Active community function from RNA, paired with metaproteomics where useful.
Background and references
Metatranscriptomics captures which community genes are expressed, but rRNA depletion, host contamination and reference choice need care (Shakya et al., 2019). We handle these in the design and in the analysis.
- Shakya M, Lo CC, Chain PSG (2019). Advances and Challenges in Metatranscriptomic Analysis. Front Genet 10:904. PubMed · DOI · Free full text
Add-ons
Project add-ons
The small services that keep a sequencing project on schedule.
Nucleic-acid QC and library preparation
Qubit, TapeStation and RIN assessment; library preparation and QC before sequencing.
Background and references
RNA integrity is scored with the RIN (Schroeder et al., 2006), and library preparation is matched to input amount and application. QC data are shared before sequencing starts.
- Schroeder A et al. (2006). The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3. PubMed · DOI · Free full text
Custom primer and panel design
Primers, probes and panel content designed and validated for your targets.
Background and references
Primers and probes are designed with Primer3 (Untergasser et al., 2012) and checked for specificity and secondary structure before synthesis. Panels are validated on control samples before your samples are run.
- Untergasser A et al. (2012). Primer3--new capabilities and interfaces. Nucleic Acids Res 40:e115. PubMed · DOI · Free full text
Multiplexing optimisation and re-sequencing
Pooling strategy for coverage targets; top-up runs when a sample falls short.
Background and references
Coverage requirements differ by application, from 30× for germline WGS to thousands-fold for rare variants, and pooling must respect them (Sims et al., 2014). We calculate the pool per project and top up when a sample falls short.
Integration with proteomics
RNA-seq matched to proteomics from the same samples, including transcript-protein correlation analysis.
Background and references
mRNA and protein levels correlate only moderately, and the differences carry information about regulation (Buccitelli and Selbach, 2020). Matched RNA-seq and proteomics from the same samples let us analyse both layers together.
Choosing
Targeted or genome-wide?
| Targeted (panel, amplicon, 16S) | Genome-wide (WGS, WES, shotgun, RNA-seq) | |
|---|---|---|
| Question | What is happening at these loci or taxa? | What is happening anywhere in the genome or community? |
| Depth per sample | Very high; sensitive to low-frequency variants | Moderate; set by budget and question |
| Cost per sample | Lowest | Higher; falls with sample number |
| Discovery of the unexpected | No | Yes |
| Best for | Validation, screening, known genes, microbiome surveys | Discovery, novel variants, isoforms, functional profiling |
How a project runs
From samples to answers.
- 01
Design
Platform, read length, depth and replicate numbers agreed for the question; sample handling instructions issued.
- 02
QC and library
Nucleic-acid QC, library preparation and quantification; samples that fail QC are flagged before sequencing.
- 03
Sequence
Illumina or Oxford Nanopore at the agreed depth, with run QC.
- 04
Analyse
Alignment or assembly, variant calling or quantification, statistics, annotation and figures; pipeline and versions documented.
What to send
Sample requirements.
| Sample | Amount | Notes |
|---|---|---|
| Genomic DNA | 100 ng to 1 µg (WGS); 50 ng (panels) | A260/280 1.8 to 2.0; intact on gel |
| Total RNA | 50 to 500 ng, RIN ≥ 7 | RNase-free; frozen at −80 °C |
| Cells for scRNA-seq | Viable single-cell suspension, ≥ 80% viability | Discuss logistics before collection |
| Stool, swab, soil, water | Per kit instructions | Stabilisation buffer or frozen |
| Plasmid or PCR product | 100 ng to 1 µg | Primer provided or designed by us |
Guide values; we confirm amounts for your sample type before shipping. See the sample submission guide.
What you get
Deliverables.
- Raw FASTQ files and run QC
- Aligned BAM or assembly files; variant VCF with annotation, or count matrices
- Differential expression, enrichment and visualisation; taxonomic and functional tables for microbiome
- Methods text with software versions and parameters; pipeline documentation
- Optional joint interpretation session
Questions we are asked most.
As a guide, 100 ng of DNA for WGS and 50 ng of total RNA with RIN ≥ 7 for RNA-seq. Low-input protocols are available.
Yes: QC, alignment, quantification, differential expression or variant calling, and annotation, with the pipeline documented.
Yes, including proteomics generated elsewhere, provided raw files or quantified matrices and metadata are available.
Illumina for accuracy and cost per base; Nanopore for long reads, structural variants, isoforms and methylation without bisulfite. Many projects use both.
One laboratory, six ways in.
Most projects combine two of these. The same scientists scope, run and analyse all of them, so a proteomics study can pick up metabolomics, sequencing or a structural question without changing provider.
Need sequencing with the analysis done properly?
Tell us the organism, the sample type and the question. We reply with platform, depth, sample requirements and a quotation.