Dr. Neelima Chitturi has extensive experience in curating genomic variants associated with various clinical diseases to assess genetic susceptibility. She has worked extensively with databases such as ClinVar, dbSNP, OMIM, and GWAS, alongside literature mining, to ensure precise variant selection. Her focus has been on maintaining high-quality data curation to support accurate genetic testing and clinical diagnostics.
She has significant expertise in identifying genetic variants linked to inherited disorders and reporting findings through comprehensive clinical reports. Her work involves end-to-end variant analysis, from raw sequencing data processing to variant detection, using tools like FastQC, Trimmomatic, GATK, BWA, and Samtools. She also ensures robust variant annotation and quality control using VEP and ClinVar, making her analysis both clinically relevant and highly reliable.
Her work extends to the detection of Copy Number Variations (CNVs), where she applies tools like ExomeDepth for identification and ANNOTSV and ClassifyCNV for accurate annotation. She has also been involved in analyzing mitochondrial heteroplasmy, utilizing Mutect2 for variant calling and heteroplasmy quantification while ensuring stringent quality control with FastQC, Trimmomatic, BWA, and Samtools.
In microbiome research, she has been instrumental in designing and implementing end-to-end analysis pipelines for 16S rRNA and shotgun metagenomics. By integrating tools such as Mothur, Kraken, Bracken, and MetaPhlAn, she has enabled accurate taxonomic classification, abundance estimation, and functional analysis. Her work allows for personalized microbiome insights through automated reporting on microbial diversity and composition.
Her contributions to transcriptomic analysis include the development of a complete RNA-seq pipeline for rice, covering raw data processing to functional interpretation. She ensures high-quality sequencing data through FastQC and Trimmomatic, followed by gene alignment using HISAT2. She applies HTSeq for gene quantification and DESeq2 for differential gene expression analysis, supporting downstream functional enrichment studies.
Dr. Neelima is also well-versed in pharmacogenomics, where she investigates genetic variations affecting drug metabolism, efficacy, and potential adverse effects. This knowledge contributes to precision medicine and treatment optimization. Additionally, she has applied genotype imputation using BEAGLE, enhancing association studies by improving the accuracy of missing genetic data.
Her expertise extends to cancer genomics and targeted sequencing, where she has worked on designing custom gene panels and chip arrays to facilitate genetic screening. She has also been involved in primer design, targeting specific variants identified through whole exome sequencing for further experimental validation.
Beyond her technical skills, Dr. Neelima is actively involved in research, scientific writing, and editing, contributing to high-impact publications in bioinformatics and genomics. Her work not only advances computational biology but also makes complex bioinformatics concepts accessible to students, researchers, and professionals.
