RNA sequencing services compared per sample, per read and on what the analysis delivers

Advertised prices, verified

What sets an RNA-seq price

1. Library preparation chemistry. Poly-A selection is the standard and cheapest choice for intact eukaryotic mRNA. Ribosomal depletion costs more and is required for degraded samples, bacterial RNA and any work where non-polyadenylated transcripts matter. The chemistry is chosen by your sample, not by your budget, and it changes the per-sample price before depth is considered.
2. Depth, in reads per sample. Depth is the main dial on cost. Standard differential expression on a well-annotated genome needs far fewer reads than isoform-level work or low-expression transcript discovery, and paying for depth an experiment cannot use is the most common way an RNA-seq budget is wasted.
3. Read length and pairing. Single-end short reads are adequate for counting transcripts against a reference. Paired-end and longer reads cost more and earn it only where isoforms, fusions or novel transcripts are the point of the experiment. Match this to the question, not to the best specification offered.
4. Analysis: included, extra, or yours. Some quotes stop at demultiplexed FASTQ files, others include alignment, counts and a differential expression report. That difference is easily the largest uncontrolled variable between two per-sample prices, and it is the first thing to normalise when comparing.

Sample quality decides more than vendor choice

RNA integrity is the input that determines whether the experiment works at all. Vendors will run an integrity check on receipt and will tell you when a sample is marginal, but by then the sample has been collected and the collection cannot usually be repeated. Handling at the bench matters more to the result than the difference between two competent providers.

Ask what a vendor does when a sample fails incoming QC: whether it is dropped from the batch, re-prepped at cost, or proceeds with a note. That policy is a real cost difference on any project where some samples are precious or marginal.

Replicates before depth, almost always

For differential expression, adding biological replicates buys more statistical power than adding reads to the same libraries, and the cost per replicate at moderate depth is often lower than the cost of doubling depth. A quote that maximises reads per sample within a fixed budget is frequently the wrong shape for the experiment.

Decide the design first, then buy the depth the design needs. Vendors will happily quote either way, and the cheaper quote is not necessarily the one that answers your question.

Library preparation for stranded RNA-seq decides what the data can say

Whether the library retains strand information, whether it captures small RNAs, whether it depletes ribosomal RNA or selects for polyadenylated transcripts, and how many cycles of amplification it uses all constrain what the sequencing can answer. Those choices are made before any instrument runs.

Strand information matters because overlapping and antisense transcripts cannot otherwise be assigned, which is why strand-specific protocols became standard. Selecting on polyadenylation excludes transcripts that lack it, which is a decision about the biology rather than a technical detail.

The analysis is the deliverable

Reads are not a result. A usable deliverable includes quality metrics, alignment or quantification against a named reference and annotation version, the statistical model used, the code, and the parameters. Without the versions, the analysis cannot be repeated even by the people who ran it.

Ask for the pipeline as code with pinned versions and the intermediate files, not only a spreadsheet of differentially expressed genes. Re-analysis is the normal fate of these datasets, and it is what turns one experiment into several.

Depth follows the smallest thing you must detect

Counting abundant transcripts needs modest depth. Detecting a rare variant in a mixture needs deep coverage and molecular tags so that an error introduced during amplification can be told from a true low frequency variant. Those are different experiments at different prices.

For circulating material the fraction of interest can be a small part of the total, which makes tagging and depth both mandatory and makes the pre-analytical handling of the sample as important as the sequencing. A plasma sample handled badly cannot be rescued by depth.

NGS panels, exomes and genomes

A panel sequences a chosen set of regions deeply and cheaply and cannot report anything outside them. An exome covers coding regions at moderate depth. A genome covers everything at lower depth per base and is the only option for structural and non-coding questions.

A custom panel is worth designing where a defined set of regions is interrogated repeatedly at depth; its cost is design, validation and the fact that adding a region means revalidating. Standard panels arrive validated and constrain the question.

Methylation and single-cell work have their own constraints

Reading base modification either converts the DNA chemically, which damages it and requires more input, or enriches modified fragments, which gives regional rather than base-level information, or reads it directly on a long-read platform. Each supports different claims.

Single-cell approaches trade coverage per cell for cell number, so their output is sparse by construction and the analysis has to account for that. Interpreting a missing observation as an absence is the standard error in this kind of data.

What a price for RNA-seq services includes

Quotations vary in whether they include library preparation, sequencing, a stated depth or yield, quality control, analysis and data delivery. A price per sample with no stated depth is not a price, since depth is most of the cost.

Ask for depth or yield per sample, the failure policy if quality control fails, how long raw data are retained and in what form they arrive. Turnaround claims should be read as time from sample receipt to data delivered, which is often quite different from the run time.

Common questions

How much does RNA sequencing cost per sample?

It depends on library chemistry, depth and whether analysis is included. The vendors in this index that publish a per-sample figure are shown above, quoted verbatim with the date; treat a published per-sample price as an entry point and confirm what depth and analysis it assumes.

How many reads do I need?

Standard differential expression against a well-annotated reference needs far fewer reads than isoform-level or discovery work. Set depth from the question you are answering, and put spare budget into biological replicates before extra reads.

NGS prep for RNA: how much RNA prep does the library need?

Poly-A for intact eukaryotic mRNA, which is most experiments and is cheaper. Ribosomal depletion for degraded RNA, bacterial samples, and any work where non-polyadenylated transcripts are part of the question.

Is analysis included in the price?

Sometimes, and it is the biggest hidden difference between quotes. Ask whether the price ends at FASTQ files or includes alignment, counts and a differential expression report, and normalise for that before comparing per-sample figures.

What should a sequencing analysis deliverable include?

Quality metrics, the reference and annotation versions, the statistical model, the pipeline as code with pinned versions, and the intermediate files, not only a table of differentially expressed genes. Re-analysis is the normal fate of these datasets.

Panel, exome or genome, and what the whole genome sequencing cost decides?

A panel for a defined set of regions at depth, an exome for coding variants at moderate depth, a genome for structural and non-coding questions. A panel cannot report anything outside its regions.

Why is a whole genome sequencing price meaningless without depth?

Because depth is most of the cost and most of what determines what can be detected. Ask for depth or yield per sample, the policy if quality control fails, and how long raw data are retained.

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