Total RNA seq and choosing a sequencing service: the library is the experiment

Scoping a sequencing project

1. Choose the library fraction from the question. Selection for mature transcripts captures a defined fraction and discards the rest; depletion of ribosomal material keeps non coding and unpolyadenylated species. Degraded material usually forces depletion regardless of preference.
2. Set depth from the effect size, not from a default. Detecting a modest change in an abundant transcript needs far less depth than detecting a rare variant. Work the requirement out statistically before booking, because depth is the main cost lever.
3. Match input requirements to what you have. Every library chemistry has a minimum input and a preferred quality. Precious, degraded or low input samples need chemistries designed for them, and those have their own biases to declare.
4. Agree the deliverables in writing. Raw reads, alignments, the pipeline with versions and parameters, the reference used, per sample quality metrics and the code behind each figure. A report is a summary, not a deliverable.
5. Randomise across preparation batches. Batch, index set and lane all leave signatures. Distribute experimental groups across batches and hand the batch table to whoever analyses the data.
6. Decide in house against a service honestly. Owning a sequencer makes sense at sustained volume with a trained operator; below that, a service gives better data per unit cost and no maintenance. Synthesis capability follows the same logic.

The library is the experiment

Everything downstream is constrained by what the library captured. A question about non coding transcripts cannot be answered from a poly A selected library at any depth, and no amount of analysis recovers what was never sequenced.

Decide the library chemistry with the analyst in the room. It is the decision that most often cannot be undone.

Reproducibility is a deliverable

An analysis that cannot be rerun cannot be corrected or extended. The practical test is whether a competent person, given the handover, can regenerate a named figure without contacting the analyst.

Write that test into the contract. It separates providers who hand over work from providers who hand over pictures.

targeted rna sequencing, and when it beats whole transcriptome

Targeted RNA sequencing reads a chosen panel of transcripts or fusion junctions instead of the whole transcriptome, and it wins wherever the question is known and the sample is poor. Deeper coverage on fewer targets means lower input, tolerance of degraded and formalin-fixed material, cheaper sequencing and a smaller analysis. What it cannot do is find something that was not on the panel, which is why discovery work stays whole transcriptome. Fusion detection, expression of a defined gene set and immune repertoire work are the three places panels dominate.

An rrna depletion kit, and what it removes

Ribosomal RNA is the great majority of total RNA, so a library made without removing it spends most of its reads on four transcripts. A depletion kit hybridises probes to rRNA and removes the duplex, by bead capture or by enzymatic digestion, leaving messenger and non-coding RNA behind. That is the route for bacterial RNA, which has no poly-A tail, and for degraded or formalin-fixed material where oligo dT priming fails. The kit has to match the species, since probe sets are organism-specific, and depletion efficiency is measured on every batch rather than assumed.

Common questions

Total RNA seq or a poly A selected RNA library?

Selection where mature transcripts are the question and the material is intact; depletion where non coding species matter or the material is degraded. They are not comparable, so mixing them within a study is a confound.

How much depth do I need?

It follows from the effect size and the abundance of what you are measuring. Calculating it beforehand is the difference between an adequately powered study and an expensive underpowered one.

What should a provider hand over?

Raw and aligned data, the exact pipeline with versions, the reference and annotation, per sample quality metrics and runnable code. Anything less cannot be reproduced or extended.

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