Many bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existing
In this paper, we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be
These results lay the theoretical foundations for their practical use in analyzing sequences with high error rates while also providing insights for the hardness of designing ungapped LSH functions.
- NSF-PAR ID:
- 10434491
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Algorithms for Molecular Biology
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1748-7188
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Boucher, Christina ; Rahmann, Sven (Ed.)Many bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existing k-mer-based bucketing methods have been efficient in processing sequencing data with low error rate, but encounter much reduced sensitivity on data with high error rate. Locality-sensitive hashing (LSH) schemes are able to mitigate this issue through tolerating the edits in similar sequences, but state-of-the-art methods still have large gaps. Here we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be (d₁, d₂)-sensitive if any two sequences within an edit distance of d₁ are mapped into at least one shared bucket, and any two sequences with distance at least d₂ are mapped into disjoint subsets of buckets. We construct locality-sensitive bucketing (LSB) functions with a variety of values of (d₁,d₂) and analyze their efficiency with respect to the total number of buckets needed as well as the number of buckets that a specific sequence is mapped to. We also prove lower bounds of these two parameters in different settings and show that some of our constructed LSB functions are optimal. These results provide theoretical foundations for their practical use in analyzing sequences with high error rate while also providing insights for the hardness of designing ungapped LSH functions.more » « less
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Abstract We present the first unquenched lattice-QCD calculation of the form factors for the decay
at nonzero recoil. Our analysis includes 15 MILC ensembles with$$B\rightarrow D^*\ell \nu $$ flavors of asqtad sea quarks, with a strange quark mass close to its physical mass. The lattice spacings range from$$N_f=2+1$$ fm down to 0.045 fm, while the ratio between the light- and the strange-quark masses ranges from 0.05 to 0.4. The valence$$a\approx 0.15$$ b andc quarks are treated using the Wilson-clover action with the Fermilab interpretation, whereas the light sector employs asqtad staggered fermions. We extrapolate our results to the physical point in the continuum limit using rooted staggered heavy-light meson chiral perturbation theory. Then we apply a model-independent parametrization to extend the form factors to the full kinematic range. With this parametrization we perform a joint lattice-QCD/experiment fit using several experimental datasets to determine the CKM matrix element . We obtain$$|V_{cb}|$$ . The first error is theoretical, the second comes from experiment and the last one includes electromagnetic and electroweak uncertainties, with an overall$$\left| V_{cb}\right| = (38.40 \pm 0.68_{\text {th}} \pm 0.34_{\text {exp}} \pm 0.18_{\text {EM}})\times 10^{-3}$$ , which illustrates the tensions between the experimental data sets, and between theory and experiment. This result is in agreement with previous exclusive determinations, but the tension with the inclusive determination remains. Finally, we integrate the differential decay rate obtained solely from lattice data to predict$$\chi ^2\text {/dof} = 126/84$$ , which confirms the current tension between theory and experiment.$$R(D^*) = 0.265 \pm 0.013$$ -
Abstract Charge transport in biomolecules is crucial for many biological and technological applications, including biomolecular electronics devices and biosensors. RNA has become the focus of research because of its importance in biomedicine, but its charge transport properties are not well understood. Here, we use the Scanning Tunneling Microscopy-assisted molecular break junction method to measure the electrical conductance of particular 5-base and 10-base single-stranded (ss) RNA sequences capable of base stacking. These ssRNA sequences show single-molecule conductance values around
($$10^{-3}G_0$$ ), while equivalent-length ssDNAs result in featureless conductance histograms. Circular dichroism (CD) spectra and MD simulations reveal the existence of extended ssRNA conformations versus folded ssDNA conformations, consistent with their different electrical behaviors. Computational molecular modeling and Machine Learning-assisted interpretation of CD data helped us to disentangle the structural and electronic factors underlying CT, thus explaining the observed electrical behavior differences. RNA with a measurable conductance corresponds to sequences with overall extended base-stacking stabilized conformations characterized by lower HOMO energy levels delocalized over a base-stacking mediating CT pathway. In contrast, DNA and a control RNA sequence without significant base-stacking tend to form closed structures and thus are incapable of efficient CT.$$G_0= 2e^2/h$$ -
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