Next-Generation Sequencing (NGS) and Third-Generation Sequencing (TGS) for the Diagnosis of Thalassemia.

Thalassemia is one of the most heterogeneous diseases, with more than a thousand mutation types recorded worldwide. Molecular diagnosis of thalassemia by conventional PCR-based DNA analysis is time- and resource-consuming owing to the phenotype variability, disease complexity, and molecular diagnostic test limitations. Moreover, genetic counseling must be backed-up by an extensive diagnosis of the thalassemia-causing phenotype and the possible genetic modifiers. Data coming from advanced molecular techniques such as targeted sequencing by next-generation sequencing (NGS) and third-generation sequencing (TGS) are more appropriate and valuable for DNA analysis of thalassemia. While NGS is superior at variant calling to TGS thanks to its lower error rates, the longer reads nature of the TGS permits haplotype-phasing that is superior for variant discovery on the homologous genes and CNV calling. The emergence of many cutting-edge machine learning-based bioinformatics tools has improved the accuracy of variant and CNV calling. Constant improvement of these sequencing and bioinformatics will enable precise thalassemia detections, especially for the CNV and the homologous HBA and HBG genes. In conclusion, laboratory transiting from conventional DNA analysis to NGS or TGS and following the guidelines towards a single assay will contribute to a better diagnostics approach of thalassemia.

Rare k-mer DNA: Identification of sequence motifs and prediction of CpG island and promoter.

Empirical analysis on k-mer DNA has been proven as an effective tool in finding unique patterns in DNA sequences which can lead to the discovery of potential sequence motifs. In an extensive study of empirical k-mer DNA on hundreds of organisms, the researchers found unique multi-modal k-mer spectra occur in the genomes of organisms from the tetrapod clade only which includes all mammals. The multi-modality is caused by the formation of the two lowest modes where k-mers under them are referred as the rare k-mers. The suppression of the two lowest modes (or the rare k-mers) can be attributed to the CG dinucleotide inclusions in them. Apart from that, the rare k-mers are selectively distributed in certain genomic features of CpG Island (CGI), promoter, 5' UTR, and exon. We correlated the rare k-mers with hundreds of annotated features using several bioinformatic tools, performed further intrinsic rare k-mer analyses within the correlated features, and modeled the elucidated rare k-mer clustering feature into a classifier to predict the correlated CGI and promoter features. Our correlation results show that rare k-mers are highly associated with several annotated features of CGI, promoter, 5' UTR, and open chromatin regions. Our intrinsic results show that rare k-mers have several unique topological, compositional, and clustering properties in CGI and promoter features. Finally, the performances of our RWC (rare-word clustering) method in predicting the CGI and promoter features are ranked among the top three, in eight of the CGI and promoter evaluations, among eight of the benchmarked datasets.