Reading an ngs sequencing price honestly: what a per-sample figure includes and excludes, how next generation sequencing companies quote ngs rna seq against whole genome sequencing cost and the cost of whole genome sequencing, what ngs cost and the cost of next generation sequencing mean once library preparation for next generation sequencing is counted, why ngs sample preparation dominates a small project, and the questions that make two quotations comparable

Making two quotations comparable

1. Separate library preparation from sequencing. These are different operations with different costs and they scale differently. For a small project the library preparation frequently dominates; for a large one the sequencing does. A quotation that bundles them without stating the split cannot be compared with one that separates them.
2. Fix the unit: per sample, per lane or per gigabase. A per-lane price is only comparable to a per-sample one once you know how many samples share the lane and at what depth. Convert every quotation to your own unit, which is cost per sample at your required depth, before comparing anything.
3. State the depth and the read configuration in the request. Depth, read length and paired or single end change the price substantially. Asking three providers for a price without specifying these produces three quotations for three different experiments.
4. Ask what happens when a library fails. Quality control failure rates are real and the policy differs: some providers remake at their cost, some charge, some sequence anyway and report. This clause changes the expected cost of a project more than most line items.
5. Settle data delivery, retention and analysis. Which files, in what format, delivered how, retained for how long, and what any bioinformatics costs. Raw data storage and a basic analysis are often assumed to be included and often are not.

Academic core against commercial provider

A core facility is often cheaper and works to its own schedule and priorities; a commercial provider commits to a turnaround and charges for it. For work on a deadline the difference in price frequently buys the deadline.

Ask both about queue length honestly. A lower price with a ten week queue is not cheaper for a project that has to report in eight.

Sample quality is the cost nobody quotes

Poor input material causes failed libraries, repeats and delay, all of which cost more than the difference between two providers. Measure integrity and quantity properly before shipping and reject your own bad samples rather than paying to discover them.

Agree the acceptance criteria in advance, in writing, with the measurement method named. Disputes about sample quality are unpleasant and entirely preventable.

What to keep when the project ends

The raw reads, the quality reports, the library preparation details including index sequences, and the exact analysis version. Providers delete raw data on a schedule, and re-sequencing is far more expensive than storage.

Deposit in a public archive where the funder requires it, and do it while the metadata is still fresh. Reconstructing sample metadata a year later is the part everybody underestimates.

Common questions

Why do two ngs sequencing price quotations differ so much?

Usually because they cover different things: one may include library preparation, quality control and analysis, the other may be sequencing alone. Convert both to cost per sample at your required depth with everything you need included, and the gap normally shrinks.

Is the sequencing or the library preparation the bigger cost?

For small projects, usually the library preparation, because it is per sample and labour intensive. For large projects at depth, the sequencing dominates. Which side you are on changes which provider is cheaper.

Should I ask for a price per gigabase?

It is a useful normaliser between platforms, and it is not what you pay. Translate it into cost per sample at the depth your experiment needs, including library preparation, and compare on that.

What is usually left out of a quotation?

Failed library remakes, data storage beyond a short window, anything past primary analysis, and shipping. Each is small individually and together they routinely move a project's real cost by a noticeable margin.

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