论文标题
线性化：RNA比对的线性时间共识结构预测
LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments
论文作者
论文摘要
预测一组对齐RNA同源物的共识结构是在RNA基因组中找到保守结构的方便方法，该方法具有许多应用，包括病毒诊断和治疗剂。然而，由于序列长度的立方缩放，对于长序列而言，最常用的该任务的工具RNAAlifold在长序列中非常缓慢，在400 SARS-COV-2和与SARS相关的基因组（约30,000nt）上花费了一天的时间。我们提出了线性化的替代方案，这是一种更快的替代方案，它基于我们的工作线性折叠的序列长度和序列数量，在线性时间内折叠单个RNA。我们的工作是比RNAAlifold快的数量级（以上400个基因组为0.7小时，或〜36 $ \ times $速度），与已知结构的数据库相比，我们的精度更高。更有趣的是，线性化的对SARS-COV-2的预测与实验确定的结构非常相关，大大超过了Rnaalifold。最后，线性化含量支持两种能量模型（维也纳和BL*）和四种模式：最小自由能（MFE），最大预期准确性（MEA），阈值和随机抽样，每种采样都花了数百个SARS-COV变体一个小时。我们的资源位于：https：//github.com/linearfold/linearalifold（code）和http://linearfold.org/linear-alifold（server）。
Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (~30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 hours on the above 400 genomes, or ~36$\times$ speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).
