Thus, our results provide the evidence that FMNL3 regulates FASCIN for actin-mediated spindle migration and cytokinesis during mouse oocyte meiosis. © The Author(s) 2020. Published by Oxford University Press on behalf of Society for the Study of Reproduction.At labor, the myometrium is infiltrated by a massive influx of macrophages that secrete high levels of pro-inflammatory cytokines inducing the expression of specific labor-associated markers. However, the interactions between myocytes and macrophages and the role of macrophages in the myometrium at labor remain to be elucidated. In this work, we studied the role of myometrium-infiltrated macrophages and their interaction with myocytes in lipopolysaccharide-induced preterm labor. A co-culture model of human primary myometrial cells and macrophages was developed and validated. Collagen lattices were used to evaluate myocyte contraction. Differentiation steps were assessed by i) phalloidin and vinculin staining for cytoskeleton reorganization, ii) gap junction protein alpha 1 expression and scrape loading/dye transfer with Lucifer Yellow for gap junction intercellular communication, and iii) calcium imaging for cell excitability. We demonstrated that macrophages favored lipopolysaccharide-induced contraction and early differentiation of myometrial cells. Transwell assays showed that previous activation of macrophages by lipopolysaccharide was essential for this differentiation and that macrophage/myocyte interactions involved macrophage release of reactive oxygen species (ROS). The effects of macrophage-released ROS in myometrial cell transactivation were mimicked by H2O2, suggesting that superoxide anion is a major intermediate messenger in macrophage/myocyte crosstalk during labor. These novel findings provide the foundation for innovative approaches to managing preterm labor, specifically the use of antioxidants to inhibit the initial stages of labor before the contractile phenotype has been acquired. In addition, the co-culture model developed by our team could be used in future research to decipher pathophysiological signaling pathways or screen/develop new tocolytics. © The Author(s) 2020. Published by Oxford University Press on behalf of Society for the Study of Reproduction.MOTIVATION Technological advances in metatranscriptomics have enabled a deeper understanding of the structure and function of microbial communities. "Total RNA" metatranscriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA (SSU rRNA). However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments. RESULTS In this work we introduce MetaRib, a new tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data. MetaRib is based on the popular rRNA assembly program EMIRGE (Miller et al., 2013), together with several improvements. We address the challenge posed by large complex datasets by integrating sub-assembly, dereplication and mapping in an iterative approach, with additional post-processing steps. We applied the method to both simulated and real-world datasets. Our results show that MetaRib can deal with larger data sets and recover more rRNA genes, which achieve around 60 times speedup and higher F1 score compared to EMIRGE in simulated datasets. In the real-world dataset, it shows similar trends but recovers more contigs compared with a previous analysis based on random sub-sampling, while enabling the comparison of individual contig abundances across samples for the first time. AVAILABILITY The source code of MetaRib is freely available at https//github.com/yxxue/MetaRib. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.MOTIVATION In the analysis of high throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy. RESULTS We introduce TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell type specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods. AVAILABILITY The proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https//bioconductor.org/packages/TOAST. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs (bp). These long reads are used to construct an assembly (i.e., the subject's genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of bps in these long reads are incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e., read-to-assembly alignment information). A-1210477 solubility dmso However, current assembly polishing algorithms can only polish an assembly using reads either from a certain sequencing technology or from a small assembly. Such technology-dependency and assembly-size dependency require researchers to 1) run multiple polishing algorithms and 2) use small chunks of a large genome to use all available read sets and polish large genomes, respectively.