Promoter Calculator — Sigma-70 Promoter Strength and Library Design
Find every sigma-70 promoter in a region on both strands, with the free-energy terms behind each transcription rate — then build a library of promoters spanning a range.
Predicted, not measured
- How good is it?
- Explains roughly half of what happens in a cell: R² = 0.45 and 0.60 against the two independent in vivo datasets the authors tested, so the ranking between two sigma-70 promoters is usable and the absolute rate is not. The widely quoted R² = 0.80 is in vitro and is not the number to plan against.
- Only valid for:
- sigma-70 (housekeeping) promoters in E. coli. NOT valid for another sigma factor, another organism, a promoter under activator or repressor control, or anything about mRNA stability or translation — rbs_predict is the translation half, and neither speaks to the other.
How this was measured, and what it was fitted on
R^2 = 0.45 and 0.60 against the two INDEPENDENT in vivo datasets the authors tested (Hossain et al., 4,350 promoters, Spearman rho = 0.69; Urtecho et al., 10,898 promoters, rho = 0.67). The widely quoted R^2 = 0.80 is a held-out tenth of the authors' OWN in vitro transcription data and is not the number to plan against: a promoter in a cell is the in vivo case, where between a third and a half of the variance is unexplained.
4,673 E. coli sigma-70 promoters (90% of a filtered library) measured by in vitro transcription, fitted as a biophysical model with 346 ridge-regression coefficients over the -35 box, the -10 box, the spacer, the discriminator, the extended -10 and the initial transcribed region. La Fleur, Hossain & Salis, Nat Commun 13:5159 (2022).
Scan a sequence for E. coli sigma-70 promoters on both strands and get a predicted transcription rate for each, or design a constitutive promoter library spanning a rate range. Each hit comes with the free-energy decomposition the model is built from, so a weak promoter tells you which term to change rather than only that it is weak. The reverse-strand scan matters: a promoter pointing backwards inside a cassette transcribes antisense RNA against everything upstream of it and knocks expression down in a way that looks like a cloning problem, and no forward-only tool will show it. The rates are a model's estimate, not a measurement: R² = 0.45 to 0.60 against the independent in vivo datasets the authors tested, which is enough to rank these promoters against each other and not enough to plan a yield around, and the page says so where the numbers are.
Paste a region to see the sigma-70 promoters in it, on both strands.
Design a non-repetitive promoter library
Builds a set of promoters spanning a predicted-rate range, with no long stretch shared between members. A library whose members share long stretches recombines with itself once it is integrated, losing members silently.
Sigma-70 promoters spanning a range of predicted rates, with no long stretch shared between members. Shared stretches allow an integrated library to recombine with itself, and a per-promoter check does not detect them.
Design, edit, and verify complete constructsSeqStudio combines sequence editing and annotation, plasmid maps, primer/cloning/CRISPR design, Sanger verification, and GenBank/SnapGene files in one workspace.
How to use the Promoter Calculator tool
- 1Paste the region: a promoter and its context, a 5' UTR, or a whole cassette to sweep.
- 2Read the table: TSS, strand, the two boxes, the spacer length and the estimated rate.
- 3Check the antisense row count. A reverse promoter inside a construct is the finding people most often miss.
- 4For a set rather than a score, use the library designer below: it spans a rate range and keeps the members from recombining with each other.
Frequently asked questions
How accurate is the transcription rate?
It scores each of the six parts a sigma-70 promoter is made of — the −35 box, the −10 box, the spacer between them, the discriminator, the extended −10 and the first transcribed bases — and adds them up. Its R² is 0.80 on in vitro data and 0.45 to 0.60 against independent in vivo datasets. A promoter in a cell is the in vivo case, so plan against 0.45–0.60: the ranking between two promoters is usable, the absolute number is not.
Why does it scan the reverse strand?
A sigma-70 promoter pointing backwards inside your cassette transcribes antisense RNA across everything upstream of it, which reads as poor expression, a bad clone or a plasmid problem, and is completely invisible to a forward-only scan. Coordinates are reported on the forward strand with the strand named, so a hit can be found in your own map.
It found no promoter. Does that mean there is none?
It means no sigma-70 promoter, in E. coli. A T7 promoter, a promoter read by another sigma factor, a eukaryotic promoter, or one that needs an activator bound are all outside what this model reads. A sequence can also carry a perfectly good promoter that this model scores low, which is what an R² of 0.45–0.60 means in practice.
Why does a promoter library need to be non-repetitive?
Because a library of twelve promoters that share long stretches is twelve recombination substrates in one molecule. Once it is integrated, members are lost silently, and the screen comes back with some levels missing, which reads as biology. The library designer selects members that span a predicted-rate range and share no more than a length you choose, and it reports the rungs it could not fill rather than padding them out.