He, W. — Page 10 | bioRxiv Science

bioRxiv2018

Topological segregation of functional networks increases in developing brains

A growing literature conceptualises human brain development from a network perspective, but it remains unknown how functional brain networks are refined during the preschool years. The extant literature diverges in its characterisation of functional network development, with little agreement between haemodynamic- and electrophysiology-based measures. In children aged from 4 to 12 years, as well as adults, age appropriate magnetoencephalography was used to estimate unbiased network topology, using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series. During childhood, network topology becomes increasingly segregated, while cortical regions decrease in centrality. We propose a heuristic MST model, in which a clear developmental trajectory for the emergence of complex brain networks is delineated. Our results resolve topological reorganisation of functional networks across temporal and special scales in youth and fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years.

neuroscience↗

bioRxiv2018

Phenotypic expansion in DDX3X -- a common cause of intellectual disability in females

De novo variants in DDX3X account for 1-3% of unexplained intellectual disability (ID), one of the most common causes of ID, in females. Forty-seven patients (44 females, 3 males) have been described. We identified 29 additional individuals carrying 27 unique DDX3X variants in the setting of complex clinical presentations including developmental delay or ID. In addition to previously reported manifestations, rare or novel phenotypes were identified including respiratory problems, congenital heart disease, skeletal muscle mitochondrial DNA depletion, and late-onset neurologic decline. Our findings expand the spectrum of DNA variants and phenotypes associated with DDX3X disorders.

genetics↗

bioRxiv2017

QuickRNASeq: Guide For Pipeline Implementation And For Interactive Results Visualization

i.Summary/AbstractSequencing of transcribed RNA molecules (RNA-seq) has been used wildly for studying cell transcriptomes in bulk or at the single-cell level (1, 2, 3) and is becoming the de facto technology for investigating gene expression level changes in various biological conditions, on the time course, and under drug treatments. Furthermore, RNA-Seq data helped identify fusion genes that are related to certain cancers (4). Differential gene expression before and after drug treatments provides insights to mechanism of action, pharmacodynamics of the drugs, and safety concerns (5). Because each RNA-seq run generates tens to hundreds of millions of short reads with size ranging from 50bp-200bp, a tool that deciphers these short reads to an integrated and digestible analysis report is in high demand. QuickRNASeq (6) is an application for large-scale RNA-seq data analysis and real-time interactive visualization of complex data sets. This application automates the use of several of the best open-source tools to efficiently generate user friendly, easy to share, and ready to publish report. Figure 1 illustrates some of the interactive plots produced by QuickRNASeq. The visualization features of the application have been further improved since its first publication in early 2016. The original QuickRNASeq publication (6) provided details of background, software selection, and implementation. Here, we outline the steps required to implement QuickRNASeq in users own environment, as well as demonstrate some basic yet powerful utilities of the advanced interactive visualization modules in the report.\n\nO_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC=\"FIGDIR/small/125856_fig1.gif\" ALT=\"Figure 1\">\nView larger version (59K):\norg.highwire.dtl.DTLVardef@1f6fb70org.highwire.dtl.DTLVardef@1f5a748org.highwire.dtl.DTLVardef@b990fborg.highwire.dtl.DTLVardef@dd5336_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 1C_FLOATNO Interactive plots from QuickRNASeq report. Figures (a, b, c) can be retrieved by clicking on the pointing hands as shown in figure Id. On any of these interactive plots, mouse over each sample displays associated sample QC metrics, (a) Read mapping summary in the expanded display mode, (b) SNP concordance matrix of 48 samples from 5 donors. Samples from the same donor should be highly concordant, (c) Gene expression chart, which shows the number of genes past various expression thresholds, (d) Center portion of the QuickRNASeq report, (e) Parallel plot linking multiple QC measures for the same samples plus table of multi-dimensional QC measures.\n\nC_FIG

bioinformatics↗