Biology subjects

Kuchina, A.

Publications and source records attributed to Kuchina, A..

2 recordsLinked to original sources

bioRxiv2017

Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the translational efficiency of the 5 untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5 UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on translation of Kozak sequence composition, upstream open reading frames (uORFs) and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the translational efficiency of both a held-out set of the random 5 UTRs as well as native S. cerevisiae 5 UTRs. The model additionally was used to computationally evolve highly translating 5 UTRs. We confirmed experimentally that the great majority of the evolved sequences lead to higher translation rates than the starting sequences, demonstrating the predictive power of this model.

synthetic biology↗

bioRxiv2017

Scaling single cell transcriptomics through split pool barcoding

Constructing an atlas of cell types in complex organisms will require a collective effort to characterize billions of individual cells. Single cell RNA sequencing (scRNA-seq) has emerged as the main tool for characterizing cellular diversity, but current methods use custom microfluidics or microwells to compartmentalize single cells, limiting scalability and widespread adoption. Here we present Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq), a scRNA-seq method that labels the cellular origin of RNA through combinatorial indexing. SPLiT-seq is compatible with fixed cells, scales exponentially, uses only basic laboratory equipment, and costs one cent per cell. We used this approach to analyze 109,069 single cell transcriptomes from an entire postnatal day 5 mouse brain, providing the first global snapshot at this stage of development. We identified 13 main populations comprising different types of neurons, glia, immune cells, endothelia, as well as types in the blood-brain-barrier. Moreover, we resolve substructure within these clusters corresponding to cells at different stages of development. As sequencing capacity increases, SPLiT-seq will enable profiling of billions of cells in a single experiment.

genomics↗