Biology subjects

Yao, W.

Publications and source records attributed to Yao, W..

39 records · Page 3Linked to original sources

bioRxiv2018

The Juicebox Assembly Tools module facilitates de novo assembly of mammalian genomes with chromosome-length scaffolds for under $1000

Hi-C contact maps are valuable for genome assembly (Lieberman-Aiden, van Berkum et al. 2009; Burton et al. 2013; Dudchenko et al. 2017). Recently, we developed Juicebox, a system for the visual exploration of Hi-C data (Durand, Robinson et al. 2016), and 3D-DNA, an automated pipeline for using Hi-C data to assemble genomes (Dudchenko et al. 2017). Here, we introduce \"Assembly Tools,\" a new module for Juicebox, which provides a point-and-click interface for using Hi-C heatmaps to identify and correct errors in a genome assembly. Together, 3D-DNA and the Juicebox Assembly Tools greatly reduce the cost of accurately assembling complex eukaryotic genomes. To illustrate, we generated de novo assemblies with chromosome-length scaffolds for three mammals: the wombat, Vombatus ursinus (3.3Gb), the Virginia opossum, Didelphis virginiana (3.3Gb), and the raccoon, Procyon lotor (2.5Gb). The only inputs for each assembly were Illumina reads from a short insert DNA-Seq library (300 million Illumina reads, maximum length 2x150 bases) and an in situ Hi-C library (100 million Illumina reads, maximum read length 2x150 bases), which cost <$1000.

genomics↗

bioRxiv2017

The INO80 Chromatin Remodeler Sustains Metabolic Stability by Promoting TOR Signaling and Regulating Histone Acetylation

Chromatin remodeling complexes are essential for gene expression programs that coordinate cell function with metabolic status. However, how these remodelers are integrated in metabolic stability pathways is not well known. Here, we report an expansive genetic screen with chromatin remodelers and metabolic regulators in Saccharomyces cerevisiae. We found that, unlike the SWR1 remodeler, the INO80 chromatin remodeling complex is composed of multiple distinct functional subunit modules. We identified a strikingly divergent genetic signature for the Ies6 subunit module that links the INO80 complex to metabolic homeostasis, including mitochondrial maintenance. INO80 is also needed to communicate TORC1-mediated signaling to chromatin and maintains histone acetylation at TORC1-responsive genes. Furthermore, computational analysis reveals subunits of INO80 and mTORC1 have high co-occurrence of alterations in human cancers. Collectively, these results demonstrate that the INO80 complex is a central component of metabolic homeostasis that influences histone acetylation and may contribute to disease when disrupted.

genetics↗

bioRxiv2017

YASS: Yet Another Spike Sorter

Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage \"triage-then-cluster-then-pursuit\" approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or \"collided\" events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a \"coreset\" approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The \"triaged\" waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a [~=] 500-electrode dataset, using primarily a single CPU core.

neuroscience↗