MATERIALS AND METHODS: Fifty-three patients with supernumerary tooth were identified retrospectively from 1,275 radiographic reviews who attended the Hospital Universiti Sains Malaysia (USM) Dental Clinic. Informed consent was obtained from the patients prior to the study. Blood samples were collected from 41 patients and DNA extractions were performed out of which 10 samples were chosen randomly for PCR amplification using designated primers for RUNX2 followed by DNA sequencing analysis.
RESULTS: This study involved 28 male patients (68.3%) and 13 female patients (31.7%) with a gender ratio of 2.2:1 and mean age of 15.9 ± 6.2 years. DNA extraction yielded ~ 40 ng/μl of concentrated DNA, and each DNA sample had more than 1500 bp of DNA length. The purity ranged between 1.8 and 2.0. DNA sequencing analysis did not reveal any mutations in exons 5 and 6 of RUNX2.
CONCLUSION: This study did not reveal any mutations in exons 5 and 6 of RUNX2 in non-syndromic patients with supernumerary tooth.
CLINICAL RELEVANCE: Analysis of mutations in RUNX2 is important to enhance the understanding of tooth development in humans.
RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.