Evidence map›Paper›PMID 33600481›Full record

ArticlePloS one2021

Development of a program for in silico optimized selection of oligonucleotide-based molecular barcodes.

In Seok Yang, Sang Won Bae, BeumJin Park, Sangwoo Kim

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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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

1 citing paper in PubMed.

  1. Article
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

4 authors.

In Seok YangDepartment of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea.ORCID 0000-0001-5224-2587
Sang Won BaeDepartment of Computer Science, Kyonggi University, Suwon, Korea.
BeumJin ParkDepartment of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea.
Sangwoo KimDepartment of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Short DNA oligonucleotides (~4 mer) have been used to index samples from different sources, such as in multiplex sequencing. Presently, longer oligonucleotides (8-12 mer) are being used as molecular barcodes with which to distinguish among raw DNA molecules in many high-tech sequence analyses, including low-frequent mutation detection, quantitative transcriptome analysis, and single-cell sequencing. Despite some advantages of using molecular barcodes with random sequences, such an approach, however, makes it impossible to know the exact sequences used in an experiment and can lead to inaccurate interpretation due to misclustering of barcodes arising from the occurrence of unexpected mutations in the barcodes. The present study introduces a tool developed for selecting an optimal barcode subset during molecular barcoding. The program considers five barcode factors: GC content, homopolymers, simple sequence repeats with repeated units of dinucleotides, Hamming distance, and complementarity between barcodes. To evaluate a selected barcode set, penalty scores for the factors are defined based on their distributions observed in random barcodes. The algorithm employed in the program comprises two steps: i) random generation of an initial set and ii) optimal barcode selection via iterative replacement. Users can execute the program by inputting barcode length and the number of barcodes to be generated. Furthermore, the program accepts a user's own values for other parameters, including penalty scores, for advanced use, allowing it to be applied in various conditions. In many test runs to obtain 100000 barcodes with lengths of 12 nucleotides, the program showed fast performance, efficient enough to generate optimal barcode sequences with merely the use of a desktop PC. We also showed that VFOS has comparable performance, flexibility in program running, consideration of simple sequence repeats, and fast computation time in comparison with other two tools (DNABarcodes and FreeBarcodes). Owing to the versatility and fast performance of the program, we expect that many researchers will opt to apply it for selecting optimal barcode sets during their experiments, including next-generation sequencing.

Indexed as

Computer SimulationDNADNA Barcoding, TaxonomicGene Expression ProfilingModels, StatisticalMutationOligonucleotidesSingle-Cell AnalysisDNAOligonucleotides

Identifiers

PMID33600481
PMCPMC7891705

What OpenQuestion holds

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Registered trials

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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.