ReviewCold Spring Harbor perspectives in biology2019
Linking RNA Sequence, Structure, and Function on Massively Parallel High-Throughput Sequencers.
Review in Cold Spring Harbor perspectives in biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Decoding RNA-protein interactions using high-throughput methods.RNA biology · 2026Review
- Systematic analysis of cotranscriptional RNA folding using transcription elongation complex display.Nature communications · 2025Article
- Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening.Nature biomedical engineering · 2024Article
- High-throughput biochemistry in RNA sequence space: predicting structure and function.Nature reviews. Genetics · 2023Review
- High-Throughput Selection and Characterisation of Aptamers on Optical Next-Generation Sequencers.International journal of molecular sciences · 2021Review
- High-throughput dissection of the thermodynamic and conformational properties of a ubiquitous class of RNA tertiary contact motifs.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- High throughput approaches to study RNA-protein interactions in vitro.Methods (San Diego, Calif.) · 2020Review
- Application of magnetic nanoparticles in nucleic acid detection.Journal of nanobiotechnology · 2020Review
- Approaches for measuring the dynamics of RNA-protein interactions.Wiley interdisciplinary reviews. RNA · 2020Review
- Automated Design of Diverse Stand-Alone Riboswitches.ACS synthetic biology · 2019Article
- Zipcode Binding Protein 1 (ZBP1; IGF2BP1): A Model for Sequence-Specific RNA Regulation.Cold Spring Harbor symposia on quantitative biology · 2019Article
- The Story of RNA Folding, as Told in Epochs.Cold Spring Harbor perspectives in biology · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput sequencing methods have revolutionized our ability to catalog the diversity of RNAs and RNA-protein interactions that can exist in our cells. However, the relationship between RNA sequence, structure, and function is enormously complex, demonstrating the need for methods that can provide quantitative thermodynamic and kinetic measurements of macromolecular interaction with RNA, at a scale commensurate with the sequence diversity of RNA. Here, we discuss a class of methods that extend the core functionality of DNA sequencers to enable high-throughput measurements of RNA folding and RNA-protein interactions. Topics discussed include a description of the method and multiple applications to RNA-binding proteins, riboswitch design and engineering, and RNA tertiary structure energetics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.