Evidence map›Paper›PMID 22995504›Full record

ArticleBiophysical journal2012

Variational Bayes analysis of a photon-based hidden Markov model for single-molecule FRET trajectories.

Kenji Okamoto, Yasushi Sako

Abstract read
In one paragraph

Article in Biophysical journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024
    Article
  5. Review
  6. Gold Ion Beam Milled Gold Zero-Mode Waveguides.Nanomaterials (Basel, Switzerland) · 2022
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. LinkingeLife · 2018
    Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

2 authors.

Kenji OkamotoAdvanced Science Institute, RIKEN, Wako, Saitama, Japan. okamotok@riken.jp
Yasushi Sako

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-molecule fluorescence resonance energy transfer (smFRET) measurement is a powerful technique for investigating dynamics of biomolecules, for which various efforts have been made to overcome significant stochastic noise. Time stamp (TS) measurement has been employed experimentally to enrich information within the signals, while data analyses such as the hidden Markov model (HMM) have been successfully applied to recover the trajectories of molecular state transitions from time-binned photon counting signals or images. In this article, we introduce the HMM for TS-FRET signals, employing the variational Bayes (VB) inference to solve the model, and demonstrate the application of VB-HMM-TS-FRET to simulated TS-FRET data. The same analysis using VB-HMM is conducted for other models and the previously reported change point detection scheme. The performance is compared to other analysis methods or data types and we show that our VB-HMM-TS-FRET analysis can achieve the best performance and results in the highest time resolution. Finally, an smFRET experiment was conducted to observe spontaneous branch migration of Holliday-junction DNA. VB-HMM-TS-FRET was successfully applied to reconstruct the state transition trajectory with the number of states consistent with the nucleotide sequence. The results suggest that a single migration process frequently involves rearrangement of multiple basepairs.

Indexed as

Markov ChainsPhotonsBayes TheoremDNA, CruciformFluorescence Resonance Energy TransferMovementTime FactorsDNA, Cruciform

Identifiers

PMID22995504
PMCPMC3446682

What OpenQuestion holds

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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.