Evidence map›Paper›PMID 30021513›Full record

ArticleBMC bioinformatics2018

De novo assembly of bacterial genomes with repetitive DNA regions by dnaasm application.

Wiktor Kuśmirek, Robert Nowak

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wiktor KuśmirekInstitute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw, 00-665, Poland. W.Kusmirek@ii.pw.edu.pl.ORCID 0000-0002-5337-6381
Robert NowakInstitute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw, 00-665, Poland.

Funding

Polish National Science Centre 2014/13/B/NZ6/00881
6 · The paper itself

Abstract

backgroundMany organisms, in particular bacteria, contain repetitive DNA fragments called tandem repeats. These structures are restored by DNA assemblers by mapping paired-end tags to unitigs, estimating the distance between them and filling the gap with the specified DNA motif, which could be repeated many times. However, some of the tandem repeats are longer than the distance between the paired-end tags.

resultsWe present a new algorithm for de novo DNA assembly, which uses the relative frequency of reads to properly restore tandem repeats. The main advantage of the presented algorithm is that long tandem repeats, which are much longer than maximum reads length and the insert size of paired-end tags can be properly restored. Moreover, repetitive DNA regions covered only by single-read sequencing data could also be restored. Other existing de novo DNA assemblers fail in such cases. The presented application is composed of several steps, including: (i) building the de Bruijn graph, (ii) correcting the de Bruijn graph, (iii) normalizing edge weights, and (iv) generating the output set of DNA sequences. We tested our approach on real data sets of bacterial organisms.

conclusionsThe software library, console application and web application were developed. Web application was developed in client-server architecture, where web-browser is used to communicate with end-user and algorithms are implemented in C++ and Python. The presented approach enables proper reconstruction of tandem repeats, which are longer than the insert size of paired-end tags. The application is freely available to all users under GNU Library or Lesser General Public License version 3.0 (LGPLv3).

Indexed as

AlgorithmsGenome, BacterialBacteriaBase SequenceComputer SimulationDatabases, GeneticDNAHigh-Throughput Nucleotide SequencingRepetitive Sequences, Nucleic AcidTandem Repeat SequencesDNADe Bruijn graphDe novo assemblingNext generation sequencingTandem repeats

Identifiers

PMID30021513
PMCPMC6052550

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.