Shruti sinha

I am Senior Bioinformatician with PhD in Computational Biology. I have 15 years of experience both within industry and academic settings. I have been involved in managing and developing methods and pipelines pertinent to research. I hold a patent in designing clustering algorithm and several peer reviewed publications in international journals. My expertise spans next generation sequence analysis in Transcriptomics, Genomics, Epigenomics and integrative multi-omics studies. 

I am motivated by challenging projects and their applications to the real world. A learner at heart, I believe in simple solutions and innovations without re-inventing the wheel.

I have 15 years of experience in Bioinformatics field with more than 5 years as postdoctoral researcher. I have actively planned, managed, and successfully completed several projects. My major experience is in analyses of next generation sequencing studies particularly, in genomics, epigenomics and transcriptomics. I have developed method for identifying enriched transposable repeats, denovo transcript assembly for repeat containing transcripts (Nature Genetics; Marasca et. al. 2022), integrative strategy for multi-omics (Genome Research; Cortesi et. al 2019), detecting copy number variations from targeted re-sequencing data (Nature Communication; Iannelli et. al. 2014) and designed a novel method for gene expression clustering to identify candidate genes (International patent). In my recent role as Senior Consultant, I had been actively managing and automating various pipelines for easy use of non-bioinformatics scientists using python and shell scripting. I have mentored members and students and managed teams to deliver projects on time. I am proficient in R, Perl, Bash scripting and Python and actively used them in linux and AWS environment.

I have completed my doctoral studies in Computational Biology under the guidance of Dr. Francesca Ciccarelli (King’s college, London). The study aims to map acquired genomic alterations such as copy number variations (CNVs), structural rearrangements and mutations in Hepatocellular Carcinoma as a direct contribution of inflammation. My contribution in the project was to detect CNVs and structural rearrangements for which I developed “GeneCNV” that detect CNVs from next generation sequencing datasets.