RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information

Ishida, Ryoga; Adachi, Tatsuo; Yokota, Aya; Yoshihara, Hidehito; Aoki, Kazuteru; Nakamura, Yoshikazu; Hamada, Michiaki

doi:10.1093/nar/gkaa484

Cited by 43 publications

(39 citation statements)

References 36 publications

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“…The continuous motif indicated that the sequences in the data had the preference for specific subsequences. The result was partly consistent with our previous research [20] that implied…”

Section: S14 Sequence Logo Mapsupporting

confidence: 94%

“…The SPR assays were performed using a Biacore T200 instrument (GE Healthcare) as described previously with slight modifications [20]. BO uses both the search for sequences that have not been explored to a reasonable extent and the utility of utilizing sequences with known affinity to select the next sequence for evaluation.…”

Section: Surface Plasmon Resonance (Spr) Assaymentioning

confidence: 99%

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RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

Iwano

Adachi²,

Aoki³

et al. 2021

Preprint

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Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). A variety of candidates is limited by actual sequencing data from an experiment. Here, we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimension latent space dependent on motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery. Codes are available at https://github.com/hmdlab/raptgen.

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“…The continuous motif indicated that the sequences in the data had the preference for specific subsequences. The result was partly consistent with our previous research [20] that implied…”

Section: S14 Sequence Logo Mapsupporting

confidence: 94%

Section: Surface Plasmon Resonance (Spr) Assaymentioning

confidence: 99%

RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

Iwano

Adachi²,

Aoki³

et al. 2021

Preprint

Self Cite

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“…Conventional loop accessibilities on single structure succeeded in the analysis and prediction of RNA-binding proteins and the in silico RNA aptamer selection on HT-SELEX (79,80). There is a possibility that the loop accessibilities proposed in this study have a broader range of impactful applications, as with the conventional accessibilities.…”

Section: ! "mentioning

confidence: 89%

Consistent Consideration of RNA Structural Alignments Improves Prediction Accuracy of RNA Secondary Structures

Tagashira

2020

Preprint

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The probabilistic consideration of the global pairwise sequence alignment of two RNAs tied with their global single secondary structures, or global pairwise structural alignment, is known to predict more accurately global single secondary structures of unaligned homologs by discriminating between conserved local single secondary structures and those not conserved. However, conducting rigorously this consideration is computationally impractical and thus has been done to decompose global pairwise structural alignments into their independent components, i.e. global pairwise sequence alignments and single secondary structures, by conventional methods. ConsHomfold and ConsAlifold, which predict the global single and consensus secondary structures of unaligned and aligned homologs considering consistently preferable (or sparse) global pairwise structural alignments on probability respectively, were developed and implemented in this study. These methods demonstrate the best trade-off of prediction accuracy while exhibiting comparable running time compared to conventional methods. ConsHomfold and ConsAlifold optionally report novel types of loop accessibility, which are useful for the analysis of sequences and secondary structures. These accessibilities are average on sparse global pairwise structural alignment and can be computed to extend the novel inside-outside algorithm proposed in this study that computes pair alignment probabilities on this alignment.

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“…RaptRanker [ 46 ] also carries out complex data analysis. In the first stage, it passes all sequences through a rough filter, however, they must exactly match in length and in the sequence of constant regions.…”

Section: Processing Of Hts Datamentioning

confidence: 99%

“…The extracted motifs can be further clustered to achieve more precise aptamer identification. No systematic comparison of different strategies and software solutions for candidate aptamer identification is available at the moment; however, in certain cases, research articles devoted to the development of particular bioinformatics tools describe a comparison to other similar instruments [ 23 , 24 , 46 ].…”

Section: Processing Of Hts Datamentioning

confidence: 99%

Implementation of High-Throughput Sequencing (HTS) in Aptamer Selection Technology

Комарова

Barkova

Kuznetsov

2020

IJMS

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Aptamers are nucleic acid ligands that bind specifically to a target of interest. Aptamers have gained in popularity due to their high potential for different applications in analysis, diagnostics, and therapeutics. The procedure called systematic evolution of ligands by exponential enrichment (SELEX) is used for aptamer isolation from large nucleic acid combinatorial libraries. The huge number of unique sequences implemented in the in vitro evolution in the SELEX process imposes the necessity of performing extensive sequencing of the selected nucleic acid pools. High-throughput sequencing (HTS) meets this demand of SELEX. Analysis of the data obtained from sequencing of the libraries produced during and after aptamer isolation provides an informative basis for precise aptamer identification and for examining the structure and function of nucleic acid ligands. This review discusses the technical aspects and the potential of the integration of HTS with SELEX.

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RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information

Cited by 43 publications

References 36 publications

RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

Consistent Consideration of RNA Structural Alignments Improves Prediction Accuracy of RNA Secondary Structures

Implementation of High-Throughput Sequencing (HTS) in Aptamer Selection Technology

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