Investigating single nucleotide polymorphism (SNP) density in the human genome and its implications for molecular evolution

Anaphalis longifolia merupakan anggota dari family Asteraceae yang tersebar di dataran tinggi Eropa, Amerika, hingga Asia. Penelitian tentang tanaman ini masih terbatas pada studi habitat, sedangkan penelitian terkait identifikasi molekuler masih belum dilakukan. Penelitian ini bertujuan untuk menganalisis DNA barcode dari A. longifolia menggunakan sekuen matK gene. Sampel yang diperoleh dari Sumatera Utara kemudian di Isolasi DNA, di amplifikasi menggunakan primer spesifik, lalu disequencing. Hasil sequencing dianalisis menggunakan program Molecular Evolution Genetics Analysis (MEGA) Version X. Hasil penelitian menunjukkan bahwa sekuen matK gen berhasil diamplifikasi pada panjang 800-850 kb. Hasil analisis pohon filogenetik menunjukkan bahwa sekuen matK gene dapat mengelompokkan A. longifolia. Pada sekuen matK gene A. longifolia, AT content lebih tinggi dibandingkan dengan GC conten. Jarak genetik yang diperoleh berkisar 0-0.0014. Hasil analisis alignment sekuen matK gene menunjukkan terdapat 1521 karakter yang dapat diamati, 1403 karakter conserved site, 118 karakter variable site, 9 karakter parsimony informative site, dan 7 karakter single nucleotide polymorphism (SNP) site. Sekuen matK gene dapat digunakan sebagai DNA barcoding untuk mengidentifikasi A. longifolia. Hasil penelitian ini diharapkan dapat memberikan informasi penting dalam konservasi A. longifolia.

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Faculty Opinions recommendation of Linkage disequilibrium and inference of ancestral recombination in 538 single-nucleotide polymorphism clusters across the human genome.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1014555.194657 ◽

2003 ◽

Author(s):

Josep M. Comeron

Keyword(s):

Single Nucleotide Polymorphism ◽

Linkage Disequilibrium ◽

Human Genome ◽

Nucleotide Polymorphism ◽

Single Nucleotide

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Faculty Opinions recommendation of Bases adjacent to mononucleotide repeats show an increased single nucleotide polymorphism frequency in the human genome.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.9459956.10101054 ◽

2011 ◽

Author(s):

Nicola Mulder

Keyword(s):

Single Nucleotide Polymorphism ◽

Human Genome ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Single Nucleotide Polymorphism Frequency

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Flanking monomer repeats determine decreased context complexity of single nucleotide polymorphism sites in the human genome

Russian Journal of Genetics Applied Research ◽

10.1134/s2079059716070121 ◽

2016 ◽

Vol 6 (8) ◽

pp. 809-815

Author(s):

N. S. Safronova ◽

M. P. Ponomarenko ◽

I. I. Abnizova ◽

G. V. Orlova ◽

I. V. Chadaeva ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

Human Genome ◽

Nucleotide Polymorphism ◽

Single Nucleotide

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A comparison on predicting functional impact of genomic variants

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab122 ◽

2022 ◽

Vol 4 (1) ◽

Author(s):

Dong Wang ◽

Jie Li ◽

Yadong Wang ◽

Edwin Wang

Keyword(s):

Single Nucleotide Polymorphism ◽

Human Genome ◽

Computational Methods ◽

Data Sets ◽

Nucleotide Polymorphism ◽

Excellent Performance ◽

Single Nucleotide ◽

Functional Impact ◽

Genomic Variants ◽

Predictive Methods

ABSTRACT Single-nucleotide polymorphism (SNPs) may cause the diverse functional impact on RNA or protein changing genotype and phenotype, which may lead to common or complex diseases like cancers. Accurate prediction of the functional impact of SNPs is crucial to discover the ‘influential’ (deleterious, pathogenic, disease-causing, and predisposing) variants from massive background polymorphisms in the human genome. Increasing computational methods have been developed to predict the functional impact of variants. However, predictive performances of these computational methods on massive genomic variants are still unclear. In this regard, we systematically evaluated 14 important computational methods including specific methods for one type of variant and general methods for multiple types of variants from several aspects; none of these methods achieved excellent (AUC ≥ 0.9) performance in both data sets. CADD and REVEL achieved excellent performance on multiple types of variants and missense variants, respectively. This comparison aims to assist researchers and clinicians to select appropriate methods or develop better predictive methods.

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