Browsing by Author "Tambi, Richa"
Now showing items 1-4 of 4
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Analyzing single cell transcriptome data from severe COVID-19 patients
Nassir, Nasna; Tambi, Richa; Bankapur, Asma; Karuvantevida, Noushad; Zehra, Binte; Begum, Ghausia; Hameid, Reem Abdel; Ahmed, Awab; Shabestari, Seyed Ali Safizadeh; Hachim, Mahmood Yaseen; Alsheikh-Ali, Alawi; Berdiev, Bakhrom; Al Heialy, Saba; Uddin, Mohammed (2022)SUMMARY: We describe the protocol for identifying COVID-19 severity specific cell types and their regulatory marker genes using single-cell transcriptomics data. We construct COVID-19 comorbid disease-associated gene list ... -
Large-scale all-atom molecular dynamics alanine-scanning of IAPP octapeptides provides insights into the molecular determinants of amyloidogenicity
Tambi, Richa (2019-02-21)Abstract: In order to investigate the early phase of the amyloid formation by the short amyloidogenic octapeptide sequence (‘NFGAILSS’) derived from IAPP, we carried out a 100ns all-atom molecular dynamics (MD) simulations ... -
Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome
Begum, Ghausia; Albanna, Ammar; Bankapur, Asma; Berdiev, Bakhrom; Karuvantevida, Noushad; Alhashmi, Deena; Alsheikh-Ali, Alawi; Uddin, Mohammed; Nassir, Nasna; Tambi, Richa (2021)Abstract: 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 ... -
Single-cell transcriptome identifies FCGR3B upregulated subtype of alveolar macrophages in patients with critical COVID-19
Nassir, Nasna; Tambi, Richa; Bankapur, Asma; Al Heialy, Saba; Karuvantevida, Noushad; Zehra, Binte; Begum, Ghausia; Hameid, Reem Abdel; Ahmed, Awab; Shabestari, Seyed Ali Safizadeh; Kandasamy, Richard K; Loney, Tom; Tayoun, Ahmad Abou; Nowotny, Norbert; Hachim, Mahmood Yaseen; Berdiev, Bakhrom; Alsheikh-Ali, Alawi; Uddin, Mohammed (2021)Summary: Understanding host cell heterogeneity is critical for unraveling disease mechanism. Utilizing large-scale single-cell transcriptomics, we analyzed multiple tissue specimens from patients with life-threatening ...