Evidence map›Paper›PMID 27044645›Full record

ArticleBMC bioinformatics2016

Mutanalyst, an online tool for assessing the mutational spectrum of epPCR libraries with poor sampling.

Matteo Paolo Ferla

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Making the cut with protease engineering.Cell chemical biology · 2022
    Review
  3. Learning Strategies in Protein Directed Evolution.Methods in molecular biology (Clifton, N.J.) · 2022
    Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Matteo Paolo FerlaFormerly Department of Biochemistry, University of Otago, Dunedin, New Zealand. matteo.ferla@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAssessing library diversity is an important control step in a directed evolution experiment. To do this, a limited amount of colonies from a test library are sequenced and tested. In the case of an error-prone PCR library, the spectrum of the identified mutations - the proportions of mutations of a specific nucleobase to another- is calculated enabling the user to make more informed predictions on library diversity and coverage. However, the calculations of the mutational spectrum are severely affected by the limited sample sizes.

resultsHere an online program, called Mutanalyst, is presented, which not only automates the calculations, but also estimates errors involved. Specifically, the errors are calculated thanks to the complementarity of DNA, which means that a mutation has a complementary mutation on the other sequence. Additionally, in the case of determining the mean number of mutations per sequence it does so by fitting to a Poisson distribution, which is more robust than calculating the average in light of the small sampling size.

conclusionAs a result of the added measures to keep into account of small sample size the user can better assess whether the library is satisfactory or whether error-prone PCR conditions should be adjusted. The program is available at www.mutanalyst.com .

Indexed as

User-Computer InterfaceAlgorithmsDNAEnzymesGene LibraryHumansInternetMutagenesisPolymerase Chain ReactionDNAEnzymesConfidenceDirected evolutionEnzyme engineeringError-prone mutagenesisMutagenesisMutational spectrumSmall samplingStandard error

Identifiers

PMID27044645
PMCPMC4820924

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.