Projects can be funded through several different options. Please choose which option best suites your project.
Price per sample is noted below for standard services. This is preliminary pricing, for all projects we highly recommend you discuss with Core staff to ensure your project falls within standard guidelines. Pricing with an asterisks indicates projects with high degrees of customization, and ALL projects with this note require a pre-project consultation. Any custom work will be proactively discussed and negotiated on prior to project inception. Click on each service item for additional details, or review our services page.
As an alternative to fee-for-service workflows, a common way to cover bioinformatics services in a more collaborative fashion is through salary percent. MaGIC actively accepts collaborative efforts, and due to the iterative nature of bioinformatic analyses this option is frequently selected. Generally all percent efforts will be done in 5% increments, and will be based upon confirmed and active funding.
Each 5% increment shall allot 24 hours dedicated staff time per quarter for bioinformatics analysis without billing. Any additional time shall be billed following the standard rates and shall be discussed proactively between staff members and respective investigators
Another alternative method to cover bioinformatics services is through a Department or Center Subsidy. In this case, the larger entity will cover MaGIC staff members with a flat fee per year. Subsidies like these are meant to primarily cover preliminary data for grants and non-funded research, but can be applied to funded research as well.
Most subsidy agreements can be negotiated for a larger portion of a staff member's time. It is recommended to be either a 20% increment (one day/week per 20%) or a 50% increment.
WGS Variant analysis is generally used to identify genomic variants such as single-nucleotide polymorphisms (SNPs), inserts and deletions (indels), and comparing to a reference genome. This service requires a reference genome, or at least one sample that can be de novo assembled as a reference genome for comparisons. Alignment is performed using BWA, followed by alignment filtering and variant calling to output a variant call file (VCF), generally following GATK best practices. For larger genomes, Dragen may be used in place of BWA upon discussion.
Input QC, Aligned BAM file, alignment QC, VCF file
De novo assembly is the process of generating a new reference genome for your organism. A small genome is considered your average prokayotic genome and small eukaryotes (IE yeast/nematodes). In general, it is recommended to use long reads such as those generated by Oxford Nanopore or PacBio for the most complete genomes, with Illumina data for error correction. For short read only assemblies, a De Bruijn graph assembler such as SPAdes will be utilized, whereas for long read assemblers either Canu or a hybrid approach will be utilized. If possible, annotation can be performed with Prokka or PGAP
Input QC, assembly QC, FASTA output genome, annotation file (if applicable)
De novo assembly is the process of generating a new reference genome for your organism. A large genome is considered anything with multiple chromosomes and physically larger genomes such as human, animals, and plants. In general, it is highly recommended to use long reads such as those generated by Oxford Nanopore or PacBio for the most complete genomes, with Illumina data for error correction. For more complex genomes with high repeat regions, long reads are required. Large genome de novo assembly is generally an iterative process, layering multiple technologies together to work towards a more complete genome. Initial assemblies can be provided more quickly and cost effectively, but pushing towards a truly complete genome can be a multi-year project that continually evolves.
Input QC, assembly QC, FASTA output genome, annotation file (if applicable)
RNA sequencing is one of the most highly versatile genomics tools, as it can be used to evaluate comparatively the myriad of conditions that can be applied to organisms. Do ensure your project has sufficient replicates to perform downstream statistical comparisons, generally a minimum of 3 samples per group. A reference genome is required for this service. Trimmed reads are aligned to the reference genome using STAR, followed by transcript abundance calculation and hit count extraction. By default this is via StringTie/featureCounts, but can be done using Salmon upon request. Differential gene expression is calculated using DESeq2, followed by over-enrichment analysis if a sufficient number of significant genes are identified.
Input QC, aligned BAM file (on request), samples PCA plot, pairwise differential gene expression tables, per comparison volcano plot, significant genes heatmap, gene ontology enrichment analysis (if applicable)
Single cell RNA-sequencing takes the versatility of bulk RNA-sequencing in comparing conditions, and allows the isolation of unique cell types within a bulk population. This is predominantly used in immunology, neurology, and oncology settings but can be used beyond that. Single cell isolation is a critical step in this workflow, and we highly recommend discussing with your genomic sequencing provider on the optimal way to generate high quality single cell suspensions to feed into a workflow such as 10X Genomics. Raw reads are processed using the 10X cellranger software, followed by secondary analysis using Seurat to perform cell clustering. Cell annotations can be an interative process, and generally requires domain-specific expertise for more nuanced separations. Cellranger only analysis can be provided at discounted rates.
Loupe cell browser files, .h5 matrices, html QC file, BAM alignment files (on pre-request only), cell QC, UMAP clustering, per-cluster marker identification, differential expression analysis per cluster (if applicable)
Single cell RNA-sequencing takes the versatility of bulk RNA-sequencing in comparing conditions, and allows the isolation of unique cell types within a bulk population. This is predominantly used in immunology, neurology, and oncology settings but can be used beyond that. Single cell isolation is a critical step in this workflow, and we highly recommend discussing with your genomic sequencing provider on the optimal way to generate high quality single cell suspensions to feed into a workflow such as 10X Genomics. As the technology has evolved, additional flavors can be added to traditional single-cell RNA analysis. These predominantly include VDJ capture (TCR/BCR), or using protocols such as CITE-Seq to capture surface cell protein information simultaneously with RNA expression. Raw reads are processed using the 10X cellranger software, followed by secondary analysis using Seurat to perform cell clustering and modality overlay. Cell annotations can be an interative process, and generally requires domain-specific expertise for more nuanced separations. Cellranger only analysis can be provided at discounted rates.
Loupe cell browser files, .h5 matrices, html QC file, BAM alignment files (on pre-request only), cell QC, UMAP clustering, per-cluster marker identification, differential expression analysis per cluster (if applicable), hashtag demultiplexing (if applicable), Surface cell protein or VDJ overlay (if applicable)
The Assay for Transposase-Accessible Chromatin (ATAC) can be used to identify genetic regions that are actively open and accessible from the nucleosomes. Akin to RNA-seq, this can be used to evaluate differences across conditions, and quick often is similar in output to RNA-sequencing. The primary difference is RNA-seq only is capturing transcribed genes, whereas ATAC-seq can identify open regulatory regions that may be inhibiting certain RNA production. A reference genome is required for this service. Trimmed reads are aligned to the reference genome using BWA, followed by alignment filtering and MACS2 peak calling and differential peak comparisons using DESeq2.
Input QC, aligned BAM files, MACs peak calling, samples PCA plot, pairwise differential peak expression
Single cell ATAC-seq layers the versitility of bulk ATAC-seq in comparing nucleosome availability between conditions, and allows the isolation of unique cell types within these bulk populations. scATAC can be used in more tissue types than scRNA-seq, but requires nuclei isolation. The scNuclei isolation is a critical step in this workflow, and we highly recommend discussing with your genomic sequencing provider on the optimal way to generate high quality single nuclei suspensions for this workflow. Raw reads are processed using the 10X cellranger software, followed by secondary analysis using Seurat to perform cell clustering and modality overlay. Cell annotations can be an interative process, and generally requires domain-specific expertise for more nuanced separations. Cellranger only analysis can be provided at discounted rates.
Loupe cell browser files, .h5 matrices, html QC file, BAM alignment files (on pre-request only), cell QC, UMAP clustering, per-cluster marker identification, differential peak analysis per cluster (if applicable).
As the single cell technologies have evolved beyond laying modalities, we can now perform true multiomics that captures RNA and ATAC profiles from single nuclei simultaneously. The scNuclei isolation is a critical step in this workflow, and we highly recommend discussing with your genomic sequencing provider on the optimal way to generate high quality single nuclei suspensions for this workflow. Raw reads are processed using the 10X cellranger software, followed by secondary analysis using Seurat to perform cell clustering and modality overlay. Cell annotations can be an interative process, and generally requires domain-specific expertise for more nuanced separations. Cellranger only analysis can be provided at discounted rates.
Loupe cell browser files, .h5 matrices, html QC file, BAM alignment files (on pre-request only), cell QC, UMAP clustering, per-cluster marker identification, GEX/ATAC integration (if possible)
Activity of methylation of genomic regions can directly relate to epigenetic control of various genes. Bisulfite sequencing is a proxy method to capture methylation sites across the genome, and can be used for comparative methylation. A reference genome is required for this workflow. Trimmed reads are aligned to the reference genome using bowtie2, followed by alignment filtering and methylation calls via Bismark.
Input QC, alignment BAM files, Bismark methylation calls
Chromatin-Immunprecipitation can be used where various transcription factors or other DNA binding factors are binding to the genome. A reference genome is required for this workflow. Trimmed reads are aligned to the reference genome using BWA, followed by alignment filtering and peak calling via MACS2. Differential peak analysis can be performed using DESeq2
Input QC, alignment BAM files, MACs peak calling, differential peak comparisons (if applicable)
Cut&Run is an enhancement over ChIP seq to capture protein-DNA interactions and general epigenetic profiles. A reference genome is required for this workflow. Trimmed reads are aligned to the reference genome using bowtie2, foillowed by alignment filtering and peak calls using SEACR.
Input QC, alignment BAM files, SEACR peak calling
Bacterial populations can be found on nearly every surface and in every environment. 16s metabarcoding is a cost effective method for profiling the organisms present based on the conserved 16s gene, ITS regions for fungi, or 18s regions. This is a small snapshot of the population, and often cannot capture full speciation or function. Trimmed reads are grouped into amplicon sequence variants (ASVs) via DADA2, followed by taxonomic classification and visualization using QIIME2. Differentially abundant taxa can be calculated via ANCOM or extended with DESeq2.
Input QC, dada2 ASV classifications, qiime2: abundances, alpha rarefaction, alpha/beta diversity, barplots, ancom (if applicable per group), phylogenetic tree
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Bacterial populations can be found on nearly every surface and in every environment. 16s metabarcoding is a cost effective method for profiling the organisms present based on the conserved 16s gene, ITS regions for fungi, or 18s regions. Long reads technologies enable capturing the full length metabarcode region, which greatly enhances species level identification. This is a small snapshot of the population, and often cannot capture full speciation or function. Trimmed reads are grouped into amplicon sequence variants (ASVs) via DADA2, followed by taxonomic classification and visualization using QIIME2. Differentially abundant taxa can be calculated via ANCOM or extended with DESeq2.
Input QC, ASV classifications, alpha rarefaction, alpha/beta diversity, barplot, differential counts (if applicable)