Lu Zhao | bioRxiv Science

bioRxiv2016

Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins

Axons contain an endoplasmic reticulum (ER) network that is largely smooth and tubular, thought to be continuous with ER throughout the neuron, and distinct in form and function from rough ER; the mechanisms that form this continuous network in axons are not well understood. Mutations affecting proteins of the reticulon or REEP families, which contain intramembrane hairpin domains that can model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). Here, we show that these proteins are required for modeling the axonal ER network in Drosophila. Loss of reticulon or REEP proteins can lead to expansion of ER sheets, and to partial loss of ER from distal motor axons. Ultrastructural analysis reveals an extensive ER network in every axon of peripheral nerves, which is reduced in larvae that lack reticulon and REEP proteins, with defects including larger and fewer tubules, and occasional gaps in the ER network, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of the axonal ER network, suggesting an important role for ER modeling in axon maintenance and function.

Cell Biology↗

bioRxiv2016

Bartender: an ultrafast and accurate clustering algorithm to count barcode and amplicon reads

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing - from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs, sgRNAs, or transposon insertion libraries, to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq have not been well developed. Available methods, which use prior information and/or simple brute-force comparisons, are slow and often result in overclustering artifacts that group distinct barcodes together. Here, we developed Bartender: an ultrafast and accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data. To improve speed and reduce unnecessary pairwise comparisons, Bartender employs a divide-and-conquer strategy that intelligently sorts barcode reads into distinct bins before performing comparisons. To improve accuracy and reduce over-clustering artifacts, Bartender employs a modified two-sample proportion test that uses information on both the cluster sequence distances and cluster sizes to make merging decisions. Additionally, Bartender includes a \"multiple time point\" mode, which matches barcode clusters between different clustering runs for seamless handling of time course data. For both simulated and real data, Bartender clusters millions of unique barcodes in a few minutes at high accuracy (>99.9%), and is ~100-fold faster than previous methods. Bartender is a set of simple-to-use command line tools that can be performed on a laptop.\n\nAvailabilityBartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.

Bioinformatics↗