Hamming Distance as a Concept in DNA Molecular Recognition

Mohammadi-Kambs, Mina; Hölz, Kathrin; Somoza, Mark M.; Ott, Albrecht

doi:10.1021/acsomega.7b00053

Cited by 13 publications

(14 citation statements)

References 50 publications

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“…We tracked the evolution of TBL1−TBL5 throughout the SELEX process (Figure 4a, Figure 4b, and Figure S7) by submitting intermediate SELEX rounds to high-throughput sequencing. We analyzed the enrichment of a given aptamer sequence by calculating the Hamming distances 35 between the aforementioned aptamer sequence and all members of a given library (Figure S7). The resulting distribution of distance scores are plotted in a histogram.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from in Vitro Evolution

et al. 2019

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Expanding the chemical diversity of aptamers remains an important thrust in the field in order to increase their functional potential. Previously, our group developed LOOPER, which enables the incorporation of up to 16 unique modifications throughout a ssDNA sequence, and applied it to the in vitro evolution of thrombin binders. As LOOPER-derived highly modified nucleic acids polymers are governed by two interrelated evolutionary variables, namely, functional modifications and sequence, the evolution of this polymer contrasts with that of canonical DNA. Herein we provide in-depth analysis of the evolution, including structure−activity relationships, mapping of evolutionary pressures on the library, and analysis of plausible evolutionary pathways that resulted in the first LOOPER-derived aptamer, TBL1. A detailed picture of how TBL1 interacts with thrombin and how it may mimic known peptide binders of thrombin is also proposed. Structural modeling and folding studies afford insights into how the aptamer displays critical modifications and also how modifications enhance the structural stability of the aptamer. A discussion of benefits and potential limitations of LOOPER during in vitro evolution is provided, which will serve to guide future evolutions of this highly modified class of aptamers.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from in Vitro Evolution

et al. 2019

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“…The Hamming distance is ubiquitous and used in domains ranging from genetics [7,20] to cryptography [6,26]. In bioinformatics (BI), it is used for DNA sequence comparison [20,25].…”

Section: Introductionmentioning

confidence: 99%

“…The Hamming distance is ubiquitous and used in domains ranging from genetics [7,20] to cryptography [6,26]. In bioinformatics (BI), it is used for DNA sequence comparison [20,25]. It is also omnipresent in software engineering (SE), appearing in areas of static and dynamic analysis, such as test selection and generation [17,34,22,12], code coverage inspection [12,11], log or trace analysis [9], and cybersecurity [9,30,13,29].…”

Section: Introductionmentioning

confidence: 99%

String Comparison on a Quantum Computer Using Hamming Distance

Khan¹,

Miranskyy²

2021

Preprint

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The Hamming distance is ubiquitous in computing. Its computation gets expensive when one needs to compare a string against many strings. Quantum computers (QCs) may speed up the comparison.In this paper, we extend an existing algorithm for computing the Hamming distance. The extension can compare strings with symbols drawn from an arbitrary-long alphabet (which the original algorithm could not). We implement our extended algorithm using the QisKit framework to be executed by a programmer without the knowledge of a QC (the code is publicly available). We then provide four pedagogical examples: two from the field of bioinformatics and two from the field of software engineering. We finish by discussing resource requirements and the time horizon of the QCs becoming practical for string comparison.

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“…• Biometric identifiers such as signatures for human irises, DNA sequences etc. are compared leveraging edit distance and Hamming distance [6,26,38]. A privacy-preserving version comparing two such sets of identifiers sequences requires retrieving pairs within a certain distance.…”

Section: Introductionmentioning

confidence: 99%

Distance-Aware Private Set Intersection

Chakraborti¹,

Fanti²,

Reiter³

2021

Preprint

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Private set intersection (PSI) allows two mutually-untrusting parties to compute an intersection of their respective sets, without revealing any information to each other about items that are not in the intersection. This work introduces a variant of the problem called distance-aware PSI (DA-PSI) for computing intersection of items in defined metric spaces. The intersection contains pairs of items that are within a specified distance threshold of each other. This paper puts forward constructions for two such metric spaces: (i) Minkowski distance of order 1 over the set of integers (i.e., for integers a and b, their distance is |a − b|); and (ii) Hamming distance over the set {0, 1} . The communication volume scales logarithmically in the distance threshold, and linearly in the set size for the Minkowski DA-PSI protocol. On the other hand, the communication volume scales quadratically in the distance threshold for the Hamming DA-PSI protocol and is independent of the number of dimensions. Applying the Minkowski DA-PSI protocol to compare IP addresses contacting honeypots for private collaborative blacklisting both improves findings relative to PSI, doubling the number of IPs in the intersection, and reduces communication and computation by 10× relative to a naïve baseline. Applying the Hamming DA-PSI protocol to the task of Iris recognition reduces communication volume by 4× over a generic 2PC baseline.

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Hamming Distance as a Concept in DNA Molecular Recognition

Cited by 13 publications

References 50 publications

Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from in Vitro Evolution

Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from in Vitro Evolution

String Comparison on a Quantum Computer Using Hamming Distance

Distance-Aware Private Set Intersection

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