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1. RNA design via structure-aware multifrontier ensemble optimization

   https://doi.org/10.1093/bioinformatics/btad252  

   Zhou, Tianshuo; Dai, Ning; Li, Sizhen; Ward, Max; Mathews, David H.; Huang, Liang (June 2023, Bioinformatics)

   Abstract MotivationRNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. ResultsWe propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. Availability and implementationOur source code and data used in this article is available at https://github.com/shanry/SAMFEO. 

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2. High throughput variant libraries and machine learning yield design rules for retron gene editors

   https://doi.org/10.1093/nar/gkae1199  

   Crawford, Kate D.; Khan, Asim G.; Lopez, Santiago C.; Goodarzi, Hani; Shipman, Seth L. (December 2024, Nucleic Acids Research)

   Abstract The bacterial retron reverse transcriptase system has served as an intracellular factory for single-stranded DNA in many biotechnological applications. In these technologies, a natural retron non-coding RNA (ncRNA) is modified to encode a template for the production of custom DNA sequences by reverse transcription. The efficiency of reverse transcription is a major limiting step for retron technologies, but we lack systematic knowledge of how to improve or maintain reverse transcription efficiency while changing the retron sequence for custom DNA production. Here, we test thousands of different modifications to the Retron-Eco1 ncRNA and measure DNA production in pooled variant library experiments, identifying regions of the ncRNA that are tolerant and intolerant to modification. We apply this new information to a specific application: the use of the retron to produce a precise genome editing donor in combination with a CRISPR-Cas9 RNA-guided nuclease (an editron). We use high-throughput libraries in Saccharomyces cerevisiae to additionally define design rules for editrons. We extend our new knowledge of retron DNA production and editron design rules to human genome editing to achieve the highest efficiency Retron-Eco1 editrons to date. 

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3. LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

   https://doi.org/10.1093/bioinformatics/btz375  

   Huang, Liang; Zhang, He; Deng, Dezhong; Zhao, Kai; Liu, Kaibo; Hendrix, David A.; Mathews, David H. (July 2019, Bioinformatics)

   Abstract MotivationPredicting the secondary structure of an ribonucleic acid (RNA) sequence is useful in many applications. Existing algorithms [based on dynamic programming] suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. ResultsWe present a novel alternative O(n3)-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in O(n) time and O(n) space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5′-to-3′) direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models. Availability and implementationOur source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100 000nt). Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. RNA language models predict mutations that improve RNA function

   https://doi.org/10.1038/s41467-024-54812-y  

   Shulgina, Yekaterina; Trinidad, Marena_I; Langeberg, Conner_J; Nisonoff, Hunter; Chithrananda, Seyone; Skopintsev, Petr; Nissley, Amos_J; Patel, Jaymin; Boger, Ron_S; Shi, Honglue; et al (December 2024, Nature Communications)

   Abstract Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to theEscherichia coliribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function. 

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5. DRAGoM: Classification and Quantification of Noncoding RNA in Metagenomic Data

   https://doi.org/10.3389/fgene.2021.669495  

   Liu, Ben; Thippabhotla, Sirisha; Zhang, Jun; Zhong, Cuncong (May 2021, Frontiers in Genetics)

   Noncoding RNAs (ncRNAs) play important regulatory and functional roles in microorganisms, such as regulation of gene expression, signaling, protein synthesis, and RNA processing. Hence, their classification and quantification are central tasks toward the understanding of the function of the microbial community. However, the majority of the current metagenomic sequencing technologies generate short reads, which may contain only a partial secondary structure that complicates ncRNA homology detection. Meanwhile, de novo assembly of the metagenomic sequencing data remains challenging for complex communities. To tackle these challenges, we developed a novel algorithm called DRAGoM (Detection of RNA using Assembly Graph from Metagenomic data). DRAGoM first constructs a hybrid graph by merging an assembly string graph and an assembly de Bruijn graph. Then, it classifies paths in the hybrid graph and their constituent readsinto differentncRNA families based on both sequence and structural homology. Our benchmark experiments show that DRAGoMcan improve the performance and robustness over traditional approaches on the classification and quantification of a wide class of ncRNA families. 

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