Evidence map›Paper›PMID 32980993›Full record

ArticleGenes & genomics2020

NGS_SNPAnalyzer: a desktop software supporting genome projects by identifying and visualizing sequence variations from next-generation sequencing data.

Dong-Jun Lee, Taesoo Kwon, Chang-Kug Kim, Young-Joo Seol, Dong-Suk Park, Tae-Ho Lee, Byung-Ohg Ahn

Open access · hybridAbstract read
In one paragraph

Article in Genes & genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 49% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 1 country.

Dong-Jun LeeGenomics Division, National Institute of Agricultural Science, 370 Nongsaengmyeong-ro, Jeonju, 54874, Republic of Korea. leemoses1004@gmail.com.
Taesoo KwonInterdisciplinary Program in Bioinformatics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
Chang-Kug KimGenomics Division, National Institute of Agricultural Science, 370 Nongsaengmyeong-ro, Jeonju, 54874, Republic of Korea.
Young-Joo SeolGene Engineering Division, National Institute of Agricultural Science, 370 Nongsaengmyeong-ro, Jeonju, 54874, Republic of Korea.
Dong-Suk ParkGenomics Division, National Institute of Agricultural Science, 370 Nongsaengmyeong-ro, Jeonju, 54874, Republic of Korea.
Tae-Ho LeeGenomics Division, National Institute of Agricultural Science, 370 Nongsaengmyeong-ro, Jeonju, 54874, Republic of Korea.
Byung-Ohg AhnGenomics Division, National Institute of Agricultural Science, 370 Nongsaengmyeong-ro, Jeonju, 54874, Republic of Korea.
National Institute of Agricultural Science and Technology · KRSeoul National University · KR

Funding

Rural Development Administration PJ010112
6 · The paper itself

Abstract

backgroundSequence variations such as single nucleotide polymorphisms are markers for genetic diseases and breeding. Therefore, identifying sequence variations is one of the main objectives of several genome projects. Although most genome project consortiums provide standard operation procedures for sequence variation detection methods, there may be differences in the results because of human selection or error.

objectiveTo standardize the procedure for sequence variation detection and help researchers who are not formally trained in bioinformatics, we developed the NGS_SNPAnalyzer, a desktop software and fully automated graphical pipeline.

methodsThe NGS_SNPAnalyzer is implemented using JavaFX (version 1.8); therefore, it is not limited to any operating system (OS). The tools employed in the NGS_SNPAnalyzer were compiled on Microsoft Windows (version 7, 10) and Ubuntu Linux (version 16.04, 17.0.4).

resultsThe NGS_SNPAnalyzer not only includes the functionalities for variant calling and annotation but also provides quality control, mapping, and filtering details to support all procedures from next-generation sequencing (NGS) data to variant visualization. It can be executed using pre-set pipelines and options and customized via user-specified options. Additionally, the NGS_SNPAnalyzer provides a user-friendly graphical interface and can be installed on any OS that supports JAVA.

conclusionsAlthough there are several pipelines and visualization tools available for NGS data analysis, we developed the NGS_SNPAnalyzer to provide the user with an easy-to-use interface. The benchmark test results indicate that the NGS_SNPAnayzer achieves better performance than other open source tools.

Indexed as

SoftwareBreedingComputational BiologyGenetic Diseases, InbornHigh-Throughput Nucleotide SequencingHumansPolymorphism, Single NucleotideSequence Analysis, DNAGenomicsNext-generation sequencingPipelineVariant identificationWhole-genome sequencing

Identifiers

PMID32980993
PMCPMC7567733
OpenAlexW3089074224

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.