Asma Bankapur
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Asma Bankapur has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Alawi Alsheikh-Ali
Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
1 shared publication - Ammar Albanna
Al Amal Psychiatric Hospital, Emirates Health Services
1 shared publication - Bakhrom K. Berdiev
College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
1 shared publication - Barbara Kellam
The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON M5S 1A1, Canada
1 shared publication - Bhooma Thiruvahindrapuram
The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON M5G 1H3, Canada
1 shared publication - Deena Alhashmi
College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai 505055, United Arab Emirates
1 shared publication
Latest Publications
- Open Access
- 19 February 2021
- Published by MDPI AG in International Journal of Molecular Sciences
- Vol. 22 (4) , 2060
Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome
The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 × 10−57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1–100 kb size range (p-value < 9.080 × 10−67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 × 10−9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.
- 19 February 2021
- Published by MDPI AG in International Journal of Molecular Sciences
- Vol. 22 (4) , 2060
Open AccessLong-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome
The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 × 10−57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1–100 kb size range (p-value < 9.080 × 10−67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 × 10−9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.