Kumarhebert6052

With the generation of a large amount of sequencing data, different assemblers have emerged to perform de novo genome assembly. As a single strategy is hard to fit various biases of datasets, none of these tools outperforms the others on all species. The process of assembly reconciliation is to merge multiple assemblies and generate a high-quality consensus assembly. Several assembly reconciliation tools have been proposed. However, the existing reconciliation tools cannot produce a merged assembly which has better contiguity and contains less errors simultaneously, and the results of these tools usually depend on the ranking of input assemblies. In this study, we propose a novel assembly reconciliation tool MAC, which merges assemblies by using the adjacency algebraic model and classification. In order to solve the problem of uneven sequencing depth and sequencing errors, MAC identifies consensus blocks between contig sets to construct an adjacency graph. To solve the problem of repetitive region, MAC employs classification to optimize the adjacency algebraic model. What's more, MAC designs an overall scoring function to solve the problem of unknown ranking of input assembly sets. The experimental results from four species of GAGE-B demonstrate that MAC outperforms other assembly reconciliation tools. Copyright © 2020 Tang, Li, Wu, Pan and Wang.RNase H1 is able to recognize DNA/RNA heteroduplexes and to degrade their RNA component. As a consequence, it has been implicated in different aspects of mtDNA replication such as primer formation, primer removal, and replication termination, and significant differences have been reported between control and mutant RNASEH1 skin fibroblasts from patients. However, neither mtDNA depletion nor the presence of deletions have been described in skin fibroblasts while still presenting signs of mitochondrial dysfunction (lower mitochondrial membrane potential, reduced oxygen consumption, slow growth in galactose). Here, we show that RNase H1 has an effect on mtDNA transcripts, most likely through the regulation of 7S RNA and other R-loops. The observed effect on both mitochondrial mRNAs and 16S rRNA results in decreased mitochondrial translation and subsequently mitochondrial dysfunction in cells carrying mutations in RNASEH1. Copyright © 2020 Reyes, Rusecka, Tońska and Zeviani.Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional "shape-space" describing distinct types of coexpression patterns. buy GSK583 We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions. Copyright © 2020 Gallivan, Ren and Read.Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance. Copyright © 2020 Peng, Liu, Yang, Liu, Meng, Deng, Peng, Tian and Zhou.Epidemiological evidence has shown an association between prenatal malnutrition and a higher risk of developing metabolic disease in adult life. An inadequate intrauterine milieu affects both growth and development, leading to a permanent programming of endocrine and metabolic functions. Programming may be due to the epigenetic modification of genes implicated in the regulation of key metabolic mechanisms, including DNA methylation, histone modifications, and microRNAs (miRNAs). The expression of miRNAs in organs that play a key role in metabolism is influenced by in utero programming, as demonstrated by both experimental and human studies. miRNAs modulate multiple pathways such as insulin signaling, immune responses, adipokine function, lipid metabolism, and food intake. Liver is one of the main target organs of programming, undergoing structural, functional, and epigenetic changes following the exposure to a suboptimal intrauterine environment. The focus of this review is to provide an overview of the effects of exposure to an adverse in utero milieu on epigenome with a focus on the molecular mechanisms involved in liver programming. Copyright © 2020 Deodati, Inzaghi and Cianfarani.Soybean is a major crop that is used as a source of vegetable oil for human use. To develop transgenic soybean with high α-linolenic acid (ALA; 183) content, the FAD3-1 gene isolated from lesquerella (Physaria fendleri) was used to construct vectors with two different seed-specific promoters, soybean β-conglycinin (Pβ-con) and kidney bean phaseolin (Pphas), and one constitutive cauliflower mosaic virus 35S promoter (P35S). The corresponding vectors were used for Agrobacterium-mediated transformation of imbibed mature half seeds. The transformation efficiency was approximately 2%, 1%, and 3% and 21, 7, and 17 transgenic plants were produced, respectively. T-DNA insertion and expression of the transgene were confirmed from most of the transgenic plants by polymerase chain reaction (PCR), quantitative real-time PCR (qPCR), reverse transcription PCR (RT-PCR), and Southern blot analysis. The fatty acid composition of soybean seeds was analyzed by gas chromatography. The 183 content in the transgenic generation T1 seeds was increased 7-fold in Pβ-conPfFAD3-1, 4-fold in Pphas PfFAD3-1, and 1.