Lior Pachter's Zika Paper

Lynn Yi, Harlod Pimentel and Lior Pachter published a new RNAseq paper that our readers will definitely find interesting. In this paper, the authors showcase the new RNAseq technologies Pachterlab has been developing over the last few years. We covered those components (e.g Kallisto, Sleuth) in earlier posts, but here you can see a biological application to get new insights from already published data.

Another interesting aspect of the paper is their effort to try new ‘publication tools’ and add some dynamic nature to the publication. The authors refer the readers to use their Shiny R app to play with the data and view charts. This is much better that sticking massive number of images in a supplementary file.


Background A recent study of the gene expression patterns of Zika virus (ZIKV) infected human neural progenitor cells (hNPCs) revealed transcriptional dysregulation and identified cell-cycle-related pathways that are affected by infection. However deeper exploration of the information present in the RNA-Seq data can be used to further elucidate the manner in which Zika infection of hNPCs affects the transcriptome, refining pathway predictions and revealing isoform-specific dynamics. Methodology/Principal Findings We analyzed data published by Tang et al. using state-of-the-art tools for transcriptome analysis. By accounting for the experimental design and estimation of technical and inferential variance we were able to pinpoint Zika infection affected pathways that highlight Zika’s neural tropism. The examination of differential genes reveals cases of isoform divergence. Conclusions/Significance Transcriptome analysis of Zika infected hNPCs has the potential to identify the molecular signatures of Zika infected neural cells. These signatures may be useful for diagnostics and for the resolution of infection pathways that can be used to harvest specific targets for further study.

Nobody got infected by Zika by performing computational analysis of RNAseq data or by joining our new membership site. They are completely safe.

Written by M. //