arXiv subjects

Braslav Rabar

Publications and source records attributed to Braslav Rabar.

3 recordsLinked to original sources

arXiv2022

A Clique-Based Method for Improving Motif Scanning Accuracy

We present a new approach for improving motif scanning accuracy, based on analysis of in-between similarity. Given a set of motifs obtained from a scanning process, we construct an associated weighted graph. We also compute the expected weight of an edge in such a graph. It turns out that restricting results to the maximal clique in the graph, computed with respect to the expected weight, greatly increases precision, hence improves accuracy of the scan. We tested the method on an ungapped motif-characterized protein family from five plant proteomes. The method was applied to three iterative motif scanners - PSI-BLAST, JackHMMer and IGLOSS - with very good results

q-bio.QM↗

arXiv2019

The transport speed and optimal work in pulsating Frenkel-Kontorova models

We consider a generalized one-dimensional chain in a periodic potential (the Frenkel-Kontorova model), with dissipative, pulsating (or ratchet) dynamics as a model of transport when the average force on the system is zero. We find lower bounds on the transport speed under mild assumptions on the asymmetry and steepness of the site potential. Physically relevant applications include explicit estimates of the pulse frequencies and mean spacings for which the transport is non-zero, and more specifically the pulse frequencies which maximize work. The bounds explicitly depend on the pulse period and subtle number-theoretical properties of the mean spacing. The main tool is the study of time evolution of spatially invariant measures in the space of measures equipped with the $L^1$-Wasserstein metric.

math.DS↗

arXiv2018

IGLOSS: iterative gapless local similarity search

Searching for local sequence patterns is one of the basic tasks in bioinformatics. Sequence patterns might have structural, functional or some other relevance, and numerous methods have been developed to detect and analyze them. These methods often depend on the wealth of information already collected. The explosion in the number of newly available sequences calls for novel methods to explore local sequence similarity. We have developed a high sensitivity web-based iterative local similarity scanner, that finds sequence patterns similar to a submitted query.

q-bio.QM↗