Massively parallel identification of zipcodes in primary cortical neurons

Cells adopt highly polarized shapes and form distinct subcellular compartments largely due to the localization of many mRNAs to specific areas, where they are translated into proteins with local functions. This mRNA localization is mediated by specific cis-regulatory elements in mRNAs, commonly called "zipcodes." Their recognition by RNA-binding proteins (RBPs) leads to the integration of the mRNAs into macromolecular complexes and their localization. While there are hundreds of localized mRNAs, only a few zipcodes have been characterized. Here, we describe a novel neuronal zipcode identification protocol (N-zip) that can identify zipcodes across hundreds of 3'UTRs. This approach combines a method of separating the principal subcellular compartments of neurons – cell bodies and neurites - with a massively parallel reporter assay. Our analysis identifies the let-7 binding site and (AU)n motif as de novo zipcodes in mouse primary cortical neurons and suggests a strategy for detecting many more.

seGMM: a new tool to infer sex from massively parallel sequencing data

Inspecting concordance between self-reported sex and genotype-inferred sex from genomic data is a significant quality control measure in clinical genetic testing. Numerous tools have been developed to infer sex for genotyping array, whole-exome sequencing, and whole-genome sequencing data. However, improvements in sex inference from targeted gene sequencing panels are warranted. Here, we propose a new tool, seGMM, which applies unsupervised clustering (Gaussian Mixture Model) to determine the gender of a sample from the called genotype data integrated aligned reads. seGMM consistently demonstrated >99% sex inference accuracy in publicly available (1000 Genomes) and our in-house panel dataset, which achieved obviously better sex classification than existing popular tools. Compared to including features only in the X chromosome, our results show that adding additional features from Y chromosomes (e.g. reads mapped to the Y chromosome) can increase sex classification accuracy. Notably, for WES and WGS data, seGMM also has an extremely high degree of accuracy. Finally, we proved the ability of seGMM to infer sex in single patient or trio samples by combining with reference data and pinpointing potential sex chromosome abnormality samples. In general, seGMM provides a reproducible framework to infer sex from massively parallel sequencing data and has great promise in clinical genetics.