Tanmay Tanna,

"It is not because things are difficult that we do not dare; it is because we do not dare that things are difficult" Seneca

PhD Student

E-Mail
tanmay.tanna@get-your-addresses-elsewhere.bsse.ethz.ch

My research focuses on creating computational tools for synthetic biology and transcriptomics. I am also interested in integrating different types of omics data for health and diagnostic applications.

I am a joint doctoral student at Prof. Gunnar Rätsch’s group at the D-INFK and Prof. Randall Platt’s group at the D-BSSE.
I completed my MSc in Biology with a major in Molecular Health Sciences at the ETH on an Inlaks foundation fellowship. During my MSc, I focused on computational biology, testing algorithms for the analysis of time-series transcriptomic data or working on the prediction of protein structural changes by applying machine learning to NMR spectra. As part of my master’s thesis at Prof. Platt’s group, I was involved in developing the statistical and computational framework for analyzing data generated by a novel transcriptomic technology, Record-seq.
Prior to joining ETH, I completed a Bachelor of Technology in Biotechnology at the National Institute of Technology, Warangal, India. During my bachelor’s, I spent some time at the University of Tokyo working with ChIP-seq data analysis, and undertook my thesis as an Erasmus exchange fellow at the Warsaw University of Life Sciences.

 

Abstract It is difficult to elucidate the transcriptional history of a cell using current experimental approaches, as they are destructive in nature and therefore describe only a moment in time. To overcome these limitations, we recently established Record-seq, a technology that enables transcriptional recording by CRISPR spacer acquisition from RNA. The recorded transcriptomes are recovered by SENECA, a method that selectively amplifies expanded CRISPR arrays, followed by deep sequencing. The resulting CRISPR spacers are aligned to the host genome, thereby enabling transcript quantification and associated analyses. Here, we describe the experimental procedures of the Record-seq workflow as well as subsequent data analysis. Beginning with the experimental design, Record-seq data can be obtained and analyzed within 1–2 weeks.

Authors Tanmay Tanna, Florian Schmidt, Mariia Y. Cherepkova, Michal Okoniewski, Randall J. Platt

Submitted Nature Protocols

Link DOI