dbSNP - Cancer Science

What is dbSNP?

The dbSNP (Database of Single Nucleotide Polymorphisms) is a comprehensive public-domain archive that provides information on SNPs (single nucleotide polymorphisms), small insertions, and deletions. It is maintained by the NCBI and serves as a critical resource for researchers studying genetic variations.

How is dbSNP Relevant to Cancer Research?

Cancer is often driven by genetic mutations, including SNPs. By cataloging these variations, dbSNP provides valuable data that can be used to identify cancer-associated mutations. Researchers can use this information to study the genetic basis of cancer and to develop targeted therapeutic interventions.

How Do Researchers Use dbSNP in Cancer Studies?

Researchers use dbSNP to identify and annotate SNPs in cancer genomes. By comparing the SNPs present in cancer tissues with those in normal tissues, they can pinpoint potential oncogenic mutations. This helps in understanding the molecular mechanisms of cancer and in discovering new biomarkers for diagnosis and prognosis.

What Are the Benefits of Using dbSNP in Cancer Research?

The primary benefit is the vast amount of genetic variation data available. This allows for a more comprehensive analysis of genetic mutations associated with cancer. Additionally, dbSNP is frequently updated, ensuring that researchers have access to the latest information. The database also provides tools for genetic data analysis, which can expedite the research process.

Are There Any Limitations to Using dbSNP in Cancer Research?

While dbSNP is a valuable resource, it is not without limitations. One major limitation is that it primarily includes data from populations of European descent, which can bias the findings. Additionally, not all SNPs cataloged in dbSNP are functionally annotated, making it challenging to determine their impact on cancer. Lastly, the sheer volume of data can be overwhelming, requiring sophisticated bioinformatics tools for effective analysis.

How Can dbSNP Data Be Integrated with Other Databases?

Integrating dbSNP data with other databases such as COSMIC (Catalogue of Somatic Mutations in Cancer) and TCGA (The Cancer Genome Atlas) can provide a more holistic view of cancer genetics. By cross-referencing SNPs with somatic mutations and other genomic data, researchers can gain deeper insights into the genetic landscape of cancer.

What Future Advances Can We Expect from dbSNP in Cancer Research?

Future advances may include the integration of more diverse population data to reduce bias and improve the generalizability of findings. Enhanced functional annotation of SNPs will also be crucial for understanding their roles in cancer. Advances in machine learning and artificial intelligence will likely improve the analysis and interpretation of the vast datasets available in dbSNP, accelerating the pace of cancer research.

Relevant Publications

A comprehensive whole genome database of ethnic minority populations.

Issue Release: 2024

Rs12039395 Variant Influences the Expression of hsa-miR-181a-5p and PTEN Toward Colorectal Cancer Risk.

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In Silico Characterization of Pathogenic Homeodomain Missense Mutations in the PITX2 Gene.

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Performance and characterization of 94 identity-informative SNPs in Northern Han Chinese using ForenSeq ™ DNA signature prep kit.

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Computational Analysis of Non-synonymous SNPs in ATM Kinase: Structural Insights, Functional Implications, and Inhibitor Discovery.

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Implications of IFNγ SNP rs2069705 in Primary Sjögren\'s Syndrome: Transcriptional Activation and B Cell Infiltration.

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VCAT: an integrated variant function annotation tools.

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Integrative Bioinformatics Analysis: Unraveling Variant Signatures and Single-Nucleotide Polymorphism Markers Associated with 5-FU-Based Chemotherapy Resistance in Colorectal Cancer Patients.

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Meningoencephalitis in a novel mutation in MNGIE (mitochondrial neurogastrointestinal encephalomyopathy) ending a familial diagnostic odyssey: A case series report.

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Identification of the SIRT1 gene\'s most harmful non-synonymous SNPs and their effects on functional and structural features-an analysis.

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The polymorphism analysis for CD36 among platelet donors.

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The Human OCTN Sub-Family: Gene and Protein Structure, Expression, and Regulation.

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Rare coinheritance of hemoglobin vancleave with severe beta-thalassemia mutation in a patient with secondary erythrocytosis.

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Impact of TYMS gene polymorphism on the outcome of methotrexate treatment in a sample of Iraqi rheumatoid arthritis patients - identification of novel single nucleotide polymorphism: Cross-sectional study.

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Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.

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The landscape of genetic variations in non-syndromic primary ovarian insufficiency in the MENA region: a systematic review.

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In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene.

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Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses.

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Whole-exome sequencing identifies cancer-associated variants of the endo-lysosomal ion transport channels in the Saudi population.

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