Associated Papers
Analysis of genotypic distribution and rare variants of patients with α/β-thalassemia screened in one hospital in Beijing, China.
Zhang H et al.
Human Genomics, 2025
Relevance of Next-Generation Sequencing in the Diagnosis of Thalassemia and Hemoglobinopathies: The Experience of Four Italian Diagnostic Hubs.
Selvatici R et al.
Genes, 2024
39858575 (PubMed )
Supplement: genes-16-00028-s001.zip › genes-3372072-supplementary.pdf
Spectrum of Rare and Novel Indel Mutations Responsible for β Thalassemia in Eastern India.
Sinha S et al.
Indian Journal of Clinical Biochemistry : Ijcb, 2024
Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies.
Tamana S et al.
Elife, 2022
36453528 (PubMed )
Supplement: elife-79713-supp2.xlsx › Input-Full_dataset
Reliability of the current newborn screening action value for beta thalassaemia disease detection in England: A prospective study.
Daniel Y et al.
Journal of Medical Screening, 2019
Impaired acylcarnitine profile in transfusion-dependent beta-thalassemia major patients in Bangladesh.
Kumar Sarker S et al.
Journal of Advanced Research, 2018
High resolution melting curve analysis targeting the HBB gene mutational hot-spot offers a reliable screening approach for all common as well as most of the rare beta-globin gene mutations in Bangladesh.
Islam MT et al.
Bmc Genetics, 2018
Cross-Sectional Study for the Detection of Mutations in the Beta-Globin Gene Among Patients with Hemoglobinopathies in the Bengali Population.
Panja A et al.
Genetic Testing and Molecular Biomarkers, 2017
Rapid detection of pathological mutations and deletions of the haemoglobin beta gene (HBB) by High Resolution Melting (HRM) analysis and Gene Ratio Analysis Copy Enumeration PCR (GRACE-PCR).
Turner A et al.
Bmc Medical Genetics, 2016
The molecular characterization of Beta globin gene in thalassemia patients reveals rare and a novel mutations in Pakistani population.
Yasmeen H et al.
European Journal of Medical Genetics, 2016
MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing.
Mort M et al.
Genome Biology, 2014
24451234 (PubMed )
Supplement: gb-2014-15-1-r19-S1.pdf
Disease-associated mutations that alter the RNA structural ensemble.
Halvorsen M et al.
Plos Genetics, 2010
Select a paper to view details