Bioinformatics

Quality Control and Trimming

Quality control and adapter trimming to provide you with ready-to-analyze reads in fastq format. Quality control is performed with FastQC (v0.12.1) and MultiQC (v1.27). Adapters and low-quality reads are trimmed with fastp (0.24.0). We will notify you of any anomalies in your data, or additional considerations for analysis. Provided:

Summary of sequencing results
FastQC (v0.12.1) and fastp (0.24.0) metrics summed up by MultiQC (v1.27)

Transcriptomics (Differential Expression)

Quality control is performed with FastQC (v0.12.1) and MultiQC (v1.27). Adapters and low-quality reads are trimmed with fastp (0.24.0). Reads are quantified with Salmon (v1.10.0) using a genome assembly provided by the client, or sourced from Ensembl/NCBI. Differential expression analysis is performed with DESeq2 (1.46.0).

All differential expression analysis code is made available, along with results, including:

Quality control reports
Trimmed fastq files
Salmon quant.sf files and all other generated Salmon results
A table of read counts assigned to genes in TPM format
Deseq2 results, including all contrasts and interactions from client metadata file, if provided. Otherwise, all pairwise contrasts between samples. For an additional fee, custom multifactorial analyses are available (and may be desirable for time course experimental designs).
Gene descriptions, available GO annotations, gene IDs
MA and Volcano plots
PCA plots

Metagenomics

Quality control is performed with FastQC (v0.12.1) and MultiQC (v1.27). Adapters and low-quality reads are trimmed with fastp (v0.24.0). Taxonomic assignment of reads is performed with Kraken2 (v2.1.3) followed by accurate estimation of taxa abundance using Bracken (v2.9), and visualization in Krona (v2.8.1). Results include:

Quality control reports
Trimmed fastq files
Standard Kraken2 report, including taxonomy and read assignments
Bracken table of taxa abundances
Krona hierarchical taxa abundance visualization

Genome Assembly (Bacterial)

Bacterial genome assembly is performed with Shovill (v1.1.0), which is a wrapper for SPAdes (v3.15.5).

Quality control reports
Trimmed fastq files
Assembled genome as fasta and gfa

Variant Calling

Alignment is performed with minimap2 (2.28), and variant calling is performed with the nucmer module from MUMmer4 (4.0.0rc1). MUMmer is used to find SNPs, insertions, deletions, substitutions, and structural differences.

Aligned samples in sam/bam and paf format
Standard MUMmer output files
List of SNPs, insertions, deletions, substitutions

16s/18s/23s/ITS

Quality control is performed with FastQC (v0.12.1) and MultiQC (v1.27). Adapters and low-quality reads are trimmed with fastp (v0.24.0). Taxonomic assignment of reads is performed with DADA2 (v1.34.0) using the SILVA database and summarized with phyloseq (v1.50.0).

Quality control reports
Trimmed fastq files
ASV table
Phyloseq object and taxa assignments
Ordination plots (Bray NMDS and PCoA), including interactive HTML
Species richness (ACE, Chao1, Shannon, Simpson)
Rarefaction curve

Custom Analysis

We are capable of providing custom analyses not listed here. For example, single cell sequencing, proteomics analysis, multifactorial statistical analysis, or genome/transcriptome annotation.

Fees

SERVICE	INTERNAL	ACADEMIA	COMMERCIAL
Standard Pipelines
Quality Control and Trimming	$50	$65	$80
Transcriptomics (Differential Expression)	$300	$390	$480
Metagenomics	$200	$260	$320
Genome Assembly (Bacterial)	$200	$260	$320
Variant Calling	$300	$390	$480
16s/18s/23s/ITS	$400	$520	$640
Custom Analysis
Hourly labour rate	$80	$100	$120
Technical support	$50	$65	$80

References

Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 10.1093/bioinformatics/btw354

Shifu Chen. 2023. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2: e107. https://doi.org/10.1002/imt2.107

Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods.

Andrew D. Yates et al. Ensembl Genomes 2022: an expanding genome resource for non-vertebrates. Nucleic Acids Research 2022 https://doi.org/10.1093/nar/gkab1007

Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). https://doi.org/10.1186/s13059-014-0550-8