Discovering common stem-loop motifs in unaligned RNA sequences.

By Jan Gorodkin, Shawn L. Stricklin, Gary D. Stormo. 2001

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In Molecular Biology

Citations in the corpus

Assessment of three-dimensional RNA structure prediction in CASP15 (2023, positive): “…used to find lDDT, a metric that measures structural similarity (unlike for proteins, our implementation of lDDT for RNA did not penalize for stereochemical violations); and (5) PHENIX, which reports a clashscore metric for all non-hydrogen bonded atom pairs that overlap worse than 0.4 Å.…”
CMfinder - a covariance model based RNA motif finding algorithm (2006, methodology): “For decent sensitivity and specificity, the latter metric approximates Matthews Correlation (Gorodkin et al., 2001).”
Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction (2004, methodology): “Both mfold and RNAstructure was run with MAX = 750 P = 20 W = 0 and efn2 rearrangement as recommended by [7].”
New metrics for comparing and assessing discrepancies between RNA 3D structures and models. (2009, methodology): “The Matthews correlation coefficient (MCC) is estimated by: MCC ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PPV 3 STY p ; where PPV specificityð Þ ¼ TPj j TPj j+ FPj j ; and STY sensitivityð Þ ¼ TPj j TPj j+ FNj j ; (Gorodkin et al. 2001).”
A comprehensive comparison of comparative RNA structure prediction approaches (2004, methodology): “Foldalign [27] can be interpreted as “a mixture of local alignment and maximum number of base-pairs algorithm” [28].”
PicXAA-R: Efficient structural alignment of multiple RNA sequences using a greedy approach (2011, methodology): “Hence, several studies have proposed various approximations to the Sankoff algorithm [5-19].”