Dr. Ravi Prabhakar More

Clinical Diagnosis of Rare Disorders using the Genomics Analysis

The NeuralNet project aims to enhance the early detection of brain and mental health conditions, allowing timely intervention to mitigate progression or even prevent disease. This project establishes methods to analyze the genomes of children with neurological problems within the National Health Service (NHS) for improved diagnosis of genetic causes of cerebral palsy (CP) and other rare disorders. By analysing large-scale WGS datasets comprising 100 patients-parents trios, project objective is to identify and validate potential therapeutic targets and study their underlying mechanisms to accelerate the development of effective treatments. To evaluate recent analysis platforms and establish cost-effectiveness, we conducted a feasibility study on diagnosed trios from the NGC cohort (French et al., 2022), comparing resequencing from stored DNA samples and archived WGS data reanalysis. This work is in collaboration with the EGLH, NHS, Cambridge, and Illumina, England. In other project work, we are also performing periodic reanalysis of undiagnosed patients from the French et al. (2022) rare disorders cohort to improve clinical diagnosis by using archival WGS data, updated databases, phenotype data, and advanced analysis platforms.

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Identifying Rare Genetic Variants in Autistic Families and Mathematicians

This study analyzed WGS data from 112 samples (92 autistic individuals and 20 controls) across 21 multiplex autism families. Using a combination of autistic traits and clinical diagnoses, we identified 37 variants in 35 genes, with 15 previously linked to autism or severe developmental disorders, such as CHD7, ANKRD11, and SON. Further analysis of 61 exceptionally talented mathematicians revealed 18 SNP variants in 14 candidate genes. These findings were cross-checked against 774 exome samples, and a manuscript is in preparation.

 

Genetic variant analysis of Severe Mental Illnesses (SMI) and SeSAME Syndrome

This project involved WES data analysis of 32 patients with SMI and 31 healthy controls. Diagnoses included Bipolar Disorder, schizophrenia, schizoaffective disorder, and schizophrenia-like psychosis. Using a three-step filtering process, we identified genes associated with various neuropsychiatric syndromes. Additionally, WES analysis of a SeSAME syndrome family revealed a novel rare damaging missense mutation in KCNJ10 (Nadella et al., 2019). The findings were published in Psychiatry Clin Neurosci.

 

Quality Assessment of iPSC Cell Lines by developing GenomicQC Tool

I developed GenomicQC, an integrated WGS analysis pipeline, for assessing the quality of iPSC cell lines. The tool produces a comprehensive genomic profile, including SNP/INDEL QC, gene-specific coverage, and structural variant analysis. This work was published in Cell & Gene Therapy Insights Journal, with me as the first author.

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Bacterial Genomics, DNA Barcoding, Metagenomics, and Insect Transcriptomics

I have analyzed the whole transcriptome (RNA-Seq) data of agriculturally important insect pests and performed DNA barcoding using Bayesian inference and maximum likelihood methods. Additionally, I conducted metagenomic NGS data analysis from a Common Effluent Treatment Plant (CETP), revealing microbial communities involved in various metabolic pathways. This work led to two publications in Bioresource Technology Journal and one in PLOS ONE.

 

During my PhD, I developed DNA BarID and matK-QR Classifier for identifying bacterial and plant species. My findings were published in the Journal of Computational Biology and BMC BioDataMining Journals as the first author.