TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs

Abstract

This paper presents TurboFold II, an extension of the TurboFold algorithm for predicting secondary structures for multiple RNA homologs. TurboFold II augments the structure prediction capabilities of TurboFold by additionally providing multiple sequence alignments. Probabilities for alignment of nucleotide positions between all pairs of input sequences are iteratively estimated in TurboFold II by incorporating information from both the sequence identity and secondary structures. A multiple sequence alignment is obtained from these probabilities by using a probabilistic consistency transformation and a hierarchically computed guide tree. To assess TurboFold II, its sequence alignment and structure predictions were compared with leading tools, including methods that focus on alignment alone and methods that provide both alignment and structure prediction. TurboFold II has comparable alignment accuracy with MAFFT and higher accuracy than other tools. TurboFold II also has comparable structure prediction accuracy as the original TurboFold algorithm, which is one of the most accurate methods. TurboFold II is part of the RNAstructure software package, which is freely available for download at http://rna.urmc.rochester.edu under a GPL license.

PhyloFold: Precise and Swift Prediction of RNA Secondary Structures to Incorporate Phylogeny among Homologs

“…The PhyloFold program performed the best trade-off of the above metrices (Figure 3) among the PhyloFold, CentroidFold (Hamada et al, 2009b), CentroidHomFold (Hamada et al, 2009c), and TurboFold-smp (Tan et al, 2017) programs (Table 2) on test set "unaligned" (Table 3) while demanding comparable running time (Table 4). Ten percent of ncRNA families whose reference seed structural alignments have at most 200 columns and whose number of homologs is less than 11 are sampled from the Rfam database (Kalvari et al, 2018).…”

“…(Do et al, 2005) The transformation has been required because the computational complexities involved in computing posterior probabilities among all the homologs is NP-complete as with multiple precise alignment. (Do et al, 2005;Hamada et al, 2009c;Tan et al, 2017)…”

“…(Klein and Eddy, 2003) To more correctly predict secondary structures, simultaneous consideration of sequence alignment and secondary structure, or structural alignment (Sankoff, 1985;Hofacker et al, 2004;Havgaard et al, 2007), has been taken into account. (Hamada et al, 2009c(Hamada et al, , 2011Tan et al, 2017) However, the consideration has been proven intractable (Sankoff, 1985) and thus has been conducted roughly to decompose structural alignments. (Hamada et al, 2009c,a;Sato et al, 2012;Tan et al, 2017) To more accurately accomplish this goal, the PhyloFold method ( Figure 1) was developed in this study.…”

“…(Hamada et al, 2009c(Hamada et al, , 2011Tan et al, 2017) However, the consideration has been proven intractable (Sankoff, 1985) and thus has been conducted roughly to decompose structural alignments. (Hamada et al, 2009c,a;Sato et al, 2012;Tan et al, 2017) To more accurately accomplish this goal, the PhyloFold method ( Figure 1) was developed in this study. The method predicts secondary structures of homologs considering likely pairwise structural alignments between the homologs by a sparsification technique.…”

CRISPR/Cas9-based mutagenesis frequently provokes on-target mRNA misregulation

“…Conversely, hairpin structures 3' to AUG start codons can facilitate translation initiation, even at non-canonical start sites [18][19][20] . We used TurboFold II 21 , an algorithm that predicts RNA secondary structures conserved in a set of homologous RNA sequences and aligns the sequences, to model the possible RNA structures of several mutant clones that generated the Met51 initiated protein compared to those that did not (see Fig. 3G, Supp.…”