Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/001990

An Improved Search Algorithm to Find G-Quadruplexes in Genome Sequences

Anna Varizhuk·Dmitry Ischenko·Igor Smirnov·Olga Tatarinova·Vyacheslav Severov·Roman Novikov·Vladimir Tsvetkov·Vladimir Naumov·Dmitry Kaluzhny·Galina Pozmogova
View original source ↗Export metadata ↓

Abstract

A growing body of data suggests that the secondary structures adopted by G-rich polynucleotides may be more diverse than previously thought and that the definition of G-quadruplex-forming sequences should be broadened. We studied solution structures of a series of naturally occurring and model single-stranded DNA fragments defying the G3+NL1G3+NL2G3+NL3G3+ formula, which is used in most of the current GQ-search algorithms. The results confirm the GQ-forming potential of such sequences and suggest the existence of new types of GQs. We developed an improved (broadened) GQ-search algorithm (http://niifhm.ru/nauchnye-issledovanija/otdel-molekuljarnoj-biologii-i-genetiki/laboratorija-iskusstvennogo-antitelogeneza/497-2/) that accounts for the recently reported new types of GQs.

Explore related subjects

BioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anna Varizhuk, Dmitry Ischenko, Igor Smirnov, Olga Tatarinova, Vyacheslav Severov, Roman Novikov, Vladimir Tsvetkov, Vladimir Naumov, Dmitry Kaluzhny, Galina Pozmogova. 2014-01-23. An Improved Search Algorithm to Find G-Quadruplexes in Genome Sequences. https://doi.org/10.1101/001990

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

iCARE: An R Package to Build and Apply Absolute Risk Models

This report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individuals risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Bioinformatics↗
bioRxiv2016

deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

SummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.\n\nAvailabilitythe C++ source code of deSPI is available at https://github.com/hitbc/deSPI\n\nContactydwang@hit.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2016

De novo assembly of viral quasispecies using overlap graphs

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Bioinformatics↗