-
Je něco špatně v tomto záznamu ?
Determining optical mapping errors by simulations
Status minimální Jazyk angličtina
Typ dokumentu časopisecké články
Grantová podpora
NU20-06-00269
MZ0
CEP - Centrální evidence projektů
NLK
Free Medical Journals
od 1996 do Před 1 rokem
PubMed Central
od 2007
Open Access Digital Library
od 1996-01-01
Medline Complete (EBSCOhost)
od 1998-01-01
Oxford Journals Open Access Collection
od 1985-01-01 do 2022-09-30
Oxford Journals Open Access Collection
od 1985-01-01
ROAD: Directory of Open Access Scholarly Resources
od 1998
- Publikační typ
- časopisecké články MeSH
Abstract Motivation Optical mapping is a complementary technology to traditional DNA sequencing technologies, such as next-generation sequencing (NGS). It provides genome-wide, high-resolution restriction maps from single, stained molecules of DNA. It can be used to detect large and small structural variants, copy number variations and complex rearrangements. Optical mapping is affected by different kinds of errors in comparison with traditional DNA sequencing technologies. It is important to understand the source of these errors and how they affect the obtained data. This article proposes a novel approach to modeling errors in the data obtained from the Bionano Genomics Inc. Saphyr system with Direct Label and Stain (DLS) chemistry. Some studies have already addressed this issue for older instruments with nicking enzymes, but we are unaware of a study that addresses this new system. Results The main result is a framework for studying errors in the data obtained from the Saphyr instrument with DLS chemistry. The framework’s main component is a simulation that computes how major sources of errors for this instrument (a false site, a missing site and resolution errors) affect the distribution of fragment lengths in optical maps. The simulation is parametrized by variables describing these errors and we are using a differential evolution algorithm to evaluate parameters that best fit the data from the instrument. Results of the experiments manifest that this approach can be used to study errors in the optical mapping data analysis. Availability and implementation Source codes supporting the presented results are available at: https://github.com/mvasinek/olgen-om-error-prediction. The data underlying this article are available on the Bionano Genomics Inc. website, at: https://bionanogenomics.com/library/datasets/. Supplementary information Supplementary data are available at Bioinformatics online.
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc25025865
- 003
- CZ-PrNML
- 005
- 20251212152554.0
- 007
- ta
- 008
- 251210s2021 ||| f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1093/bioinformatics/btab259 $2 doi
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 100 1_
- $a Vašinek, Michal $u Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava , Ostrava 708 00, Czech Republic $1 https://orcid.org/0000-0002-9930-3380
- 245 10
- $a Determining optical mapping errors by simulations
- 520 9_
- $a Abstract Motivation Optical mapping is a complementary technology to traditional DNA sequencing technologies, such as next-generation sequencing (NGS). It provides genome-wide, high-resolution restriction maps from single, stained molecules of DNA. It can be used to detect large and small structural variants, copy number variations and complex rearrangements. Optical mapping is affected by different kinds of errors in comparison with traditional DNA sequencing technologies. It is important to understand the source of these errors and how they affect the obtained data. This article proposes a novel approach to modeling errors in the data obtained from the Bionano Genomics Inc. Saphyr system with Direct Label and Stain (DLS) chemistry. Some studies have already addressed this issue for older instruments with nicking enzymes, but we are unaware of a study that addresses this new system. Results The main result is a framework for studying errors in the data obtained from the Saphyr instrument with DLS chemistry. The framework’s main component is a simulation that computes how major sources of errors for this instrument (a false site, a missing site and resolution errors) affect the distribution of fragment lengths in optical maps. The simulation is parametrized by variables describing these errors and we are using a differential evolution algorithm to evaluate parameters that best fit the data from the instrument. Results of the experiments manifest that this approach can be used to study errors in the optical mapping data analysis. Availability and implementation Source codes supporting the presented results are available at: https://github.com/mvasinek/olgen-om-error-prediction. The data underlying this article are available on the Bionano Genomics Inc. website, at: https://bionanogenomics.com/library/datasets/. Supplementary information Supplementary data are available at Bioinformatics online.
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Běhálek, Marek $u Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava , Ostrava 708 00, Czech Republic
- 700 1_
- $a Gajdoš, Petr $u Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava , Ostrava 708 00, Czech Republic
- 700 1_
- $a Fillerová, Regina $u Department of Immunology, Faculty of Medicine and Dentistry, Palacky University and University Hospital , Olomouc 779 00, Czech Republic
- 700 1_
- $a Kriegová, Eva $u Department of Immunology, Faculty of Medicine and Dentistry, Palacky University and University Hospital , Olomouc 779 00, Czech Republic $1 https://orcid.org/0000-0002-8969-4197
- 700 1_
- $a Martelli, Pier Luigi $4 edt
- 773 0_
- $w MED00008115 $t Bioinformatics $x 1367-4803 ; 1367-4811 $g Roč. 37, č. 20 (2021), s. 3391-3397
- 910 __
- $a ABA008 $b sig $c signa $y -
- 990 __
- $a 20251202 $b ABA008
- 999 __
- $a min $b bmc $g 2446367 $s 1264063
- BAS __
- $a 3 $a PreBMC
- BMC __
- $a 2021 $b 37 $c 20 $d 3391-3397 $e 20210513 $i 1367-4803 ; 1367-4811 $m Bioinformatics $x MED00008115
- GRA __
- $a NU20-06-00269 $p MZ0
- LZP __
- $a AZV-2023-Crossref-20251210