24 research outputs found
HiTRACE: High-throughput robust analysis for capillary electrophoresis
Motivation: Capillary electrophoresis (CE) of nucleic acids is a workhorse
technology underlying high-throughput genome analysis and large-scale chemical
mapping for nucleic acid structural inference. Despite the wide availability of
CE-based instruments, there remain challenges in leveraging their full power
for quantitative analysis of RNA and DNA structure, thermodynamics, and
kinetics. In particular, the slow rate and poor automation of available
analysis tools have bottlenecked a new generation of studies involving hundreds
of CE profiles per experiment.
Results: We propose a computational method called high-throughput robust
analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in
large-scale nucleic acid CE analysis, including the profile alignment that has
heretofore been a rate-limiting step in the highest throughput experiments. We
illustrate the application of HiTRACE on thirteen data sets representing 4
different RNAs, three chemical modification strategies, and up to 480 single
mutant variants; the largest data sets each include 87,360 bands. By applying a
series of robust dynamic programming algorithms, HiTRACE outperforms prior
tools in terms of alignment and fitting quality, as assessed by measures
including the correlation between quantified band intensities between replicate
data sets. Furthermore, while the smallest of these data sets required 7 to 10
hours of manual intervention using prior approaches, HiTRACE quantitation of
even the largest data sets herein was achieved in 3 to 12 minutes. The HiTRACE
method therefore resolves a critical barrier to the efficient and accurate
analysis of nucleic acid structure in experiments involving tens of thousands
of electrophoretic bands.Comment: Revised to include Supplement. Availability: HiTRACE is freely
available for download at http://hitrace.stanford.ed
Understanding the errors of SHAPE-directed RNA structure modeling
Single-nucleotide-resolution chemical mapping for structured RNA is being
rapidly advanced by new chemistries, faster readouts, and coupling to
computational algorithms. Recent tests have shown that selective 2'-hydroxyl
acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in
modeling the helices of RNA secondary structure. Here, we benchmark the method
using six molecules for which crystallographic data are available: tRNA(phe)
and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I
ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic
di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs
gave an overall false negative rate (FNR) of 17% and a false discovery rate
(FDR) of 21%, with at least one helix prediction error in five of the six
cases. Extensive variations of data processing, normalization, and modeling
parameters did not significantly mitigate modeling errors. Only one varation,
filtering out data collected with deoxyinosine triphosphate during primer
extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual
structure modeling errors are explained by the insufficient information content
of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping
analysis. Beyond these benchmark cases, bootstrapping suggests a low level of
confidence (<50%) in the majority of helices in a previously proposed
SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA
modeling is not always unambiguous, and helix-by-helix confidence estimates, as
described herein, may be critical for interpreting results from this powerful
methodology.Comment: Biochemistry, Article ASAP (Aug. 15, 2011
Deep learning models for predicting RNA degradation via dual crowdsourcing
Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales
Deep learning models for predicting RNA degradation via dual crowdsourcing
Messenger RNA-based medicines hold immense potential, as evidenced by their
rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA
molecules has been limited by their thermostability, which is fundamentally
limited by the intrinsic instability of RNA molecules to a chemical degradation
reaction called in-line hydrolysis. Predicting the degradation of an RNA
molecule is a key task in designing more stable RNA-based therapeutics. Here,
we describe a crowdsourced machine learning competition ("Stanford
OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on
6043 102-130-nucleotide diverse RNA constructs that were themselves solicited
through crowdsourcing on the RNA design platform Eterna. The entire experiment
was completed in less than 6 months, and 41% of nucleotide-level predictions
from the winning model were within experimental error of the ground truth
measurement. Furthermore, these models generalized to blindly predicting
orthogonal degradation data on much longer mRNA molecules (504-1588
nucleotides) with improved accuracy compared to previously published models.
Top teams integrated natural language processing architectures and data
augmentation techniques with predictions from previous dynamic programming
models for RNA secondary structure. These results indicate that such models are
capable of representing in-line hydrolysis with excellent accuracy, supporting
their use for designing stabilized messenger RNAs. The integration of two
crowdsourcing platforms, one for data set creation and another for machine
learning, may be fruitful for other urgent problems that demand scientific
discovery on rapid timescales
Cryo-EM and antisense targeting of the 28-kDa frameshift stimulation element from the SARS-CoV-2 RNA genome
Drug discovery campaigns against COVID-19 are beginning to target the SARS-CoV-2 RNA genome. The highly conserved frameshift stimulation element (FSE), required for balanced expression of viral proteins, is a particularly attractive SARS-CoV-2 RNA target. Here we present a 6.9 Å resolution cryo-EM structure of the FSE (88 nucleotides, ~28 kDa), validated through an RNA nanostructure tagging method. The tertiary structure presents a topologically complex fold in which the 5′ end is threaded through a ring formed inside a three-stem pseudoknot. Guided by this structure, we develop antisense oligonucleotides that impair FSE function in frameshifting assays and knock down SARS-CoV-2 virus replication in A549-ACE2 cells at 100 nM concentration