I have 10+ years of post-PhD experience in crafting best in-class solutions for complex questions using statistical and machine learning approaches on a highly collaborative team. Following are a broad overview bioinfrmatics services I provide.
Gain insights into the influence of genetic variation on human disease, ancestry, and evolution by utilizing GATK-based workflows or your preferred analysis tools for Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS) data. My variant discovery pipeline detects diverse variants, filters and annotates them, and integrates with curated resources to identify potential causative variants.
Sequence alignment of WGS and WES( Bowtie2, BWA, Stampy, MOSAIK, Novoalign etc)
Variant discovery from aligned reads (Picard, GATK, Samtools, VCFtools, Freebays, VarDict, CNVantor, ExomeCNV, BreakDancer, etc).
Variant annotation (ANNOVAR, VEP etc)
Integrating results with curated resources (OMIM, HPO, COSMIC etc).
RNA-seq is a versatile method for gene expression profiling in diverse species, enabling precise measurement of gene and transcript levels, identification of alternative transcripts, characterization of gene fusions, detection of single nucleotide variants, and discovery of novel gene isoforms. Tertiary analysis involving differential expression, enrichment, and co-expression network analysis offers a comprehensive view of affected genes and pathways. My services include:
Sequence alignment of reads (STAR, Tophat2, HISAT2)
Estimation of transcript abundance (Kallisto, RSEM)
Differential expression analysis (DESeq2, EdgeR, EBSeq )
Network analysis of gene expression (GSEA, WGCNA, Cytoscape, EricScript)
Epigenomics and gene regulation:
Epigenetic mechanisms play a vital role in normal growth and development, but their deregulation contributes to diseases like cancer and diabetes. Our services facilitate exploration, integration, filtering, annotation, statistical analysis, and visualization of results from peak callers, empowering researchers to gain insights from their epigenetic data.
Estimation of methylation level. (BSMAP, Bismark, BS-Seeker2)
Analysis of chromatin conformation capture experiments.
Analysis of Chip-seq experiments (MACS2, SICER, GEM, MEME Suite, HOMER).
Identification of transposable element insertion from whole genome data (TE-Detective).
By employing experimental approaches and computational methods, researchers are gaining insights into the shifts in microbial composition in human health, contamination of food sources, susceptibility of agricultural plants to diseases, infectious disease outbreaks, and environmental impact assessment. Our data analytics support encompasses meta-taxonomic profiling, metagenomic taxonomic classification, characterization of uncultivated microbes, and RNA profiling from diverse microbial environments. My services include:
Development of metagenomic assembly pipeline (QIIME2, MetaBAT, MaxBin, MetaSPAdes, MEGAN).
Profiling the composition of microbial communities
Analysis of metabolic pathways in the microbial community.
Taxonomic classification of Microbial community. (Kraken2, Sourmash)
Single-cell genomics methods have transformative potential in studying physiology, developmental biology, and anatomy in health and disease. Utilizing next-generation sequencing-based assays like RNA-seq, optimized for single-cell analysis, enables in-depth characterization of genetic and functional traits of individual cells. My services include:
QC, analysis, and exploration of single cell RNA-seq data (Scater, Seurat).
Single cell sequencing read alignment. (Bowtie2, STAR).
Detailed analysis of single cell expression data (Monocle, MAST, SCDE, BPSC)
Variant discovery from single cell data (HoneyBADGER, chromVAR, Monovar).
CRISPR screens, vital for high-throughput screening, rely on the CRISPR system. Bioinformatics plays a crucial role in analyzing and interpreting data from CRISPR-based studies. My CRISPR bioinformatics services include:
gRNA design and analysis.
AI/ML and DevOps service:
Machine learning algorithms for DNA/protein sequence analysis and classification.
Predictive modeling for personalized medicine and drug discovery.
Deep learning models for image analysis in microscopy and medical imaging.
Natural language processing (NLP) for text mining and literature analysis.
Reinforcement learning for optimizing experimental designs and parameter optimization.
AI-driven systems for variant calling, genome annotation, and variant interpretation.
Containerization platforms like Docker and Kubernetes for efficient software packaging and deployment.
Cloud computing platforms (e.g., AWS, Google Cloud, Azure) for scalable and on-demand computing resources.
Workflow management systems like Nextflow .