Kanepayne9040

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.Recent advances in data acquiring technologies in biology have led to major challenges in mining relevant information from large datasets. For example, single-cell RNA sequencing technologies are producing expression and sequence information from tens of thousands of cells in every single experiment. A common task in analyzing biological data is to cluster samples or features (e.g., genes) into groups sharing common characteristics. This is an NP-hard problem for which numerous heuristic algorithms have been developed. However, in many cases, the clusters created by these algorithms do not reflect biological reality. To overcome this, a Networks Based Clustering (NBC) approach was recently proposed, by which the samples or genes in the dataset are first mapped to a network and then community detection (CD) algorithms are used to identify clusters of nodes.Here, we created an open and flexible python-based toolkit for NBC that enables easy and accessible network construction and community detection. We then tested the applicability of NBC for identifying clusters of cells or genes from previously published large-scale single-cell and bulk RNA-seq datasets.We show that NBC can be used to accurately and efficiently analyze large-scale datasets of RNA sequencing experiments.The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels image files, sequence tags, and alignment reads. The sizes of these types of data usually range from several hundreds of gigabytes to several terabytes. PRT543 chemical structure Biostatisticians and bioinformaticians are typically working with the aligned NGS read count data (hence the last level of NGS data) for data modeling and interpretation.To horn in on the use of NGS technology, researchers utilize it to profile the whole genome to study DNA copy number variations (CNVs) for an individual subject (or patient) as well as groups of subjects (or patients). The resulting aligned NGS read count data are then modeled by proper mathematical and statistical approaches so that the loci of CNVs can be accurately detected. In this book chapter, a summary of most popularly used statistical methods for detecting CNVs using NGS data is given. The goal is to provide readers with a comprehensive resource of available statistical approaches for inferring DNA copy number variations using NGS data.Increasingly affordable sequencing technologies are revolutionizing the field of genomic medicine. It is now feasible to interrogate all major classes of variation in an individual across the entire genome for less than $1000 USD. While the generation of patient sequence information using these technologies has become routine, the analysis and interpretation of this data remains the greatest obstacle to widespread clinical implementation. This chapter summarizes the steps to identify, annotate, and prioritize variant information required for clinical report generation. We discuss methods to detect each variant class and describe strategies to increase the likelihood of detecting causal variant(s) in Mendelian disease. Lastly, we describe a sample workflow for synthesizing large amount of genetic information into concise clinical reports.Nature-based solutions (NbS) are increasingly recognized as sustainable approaches to address societal challenges. Disaster risk reduction (DRR) has benefited by moving away from purely 'grey' infrastructure measures towards NbS. However, this shift also furthers an increasing trend of reliance on public acceptance to plan, implement and manage DRR measures. In this review, we examine how unique NbS characteristics relate to public acceptance through a comparison with grey measures, and we identify influential acceptance factors related to individuals, society, and DRR measures. Based on the review, we introduce the PA-NbS model that highlights the role of risk perception, trust, competing societal interests, and ecosystem services. Efforts to increase acceptance should focus on providing and promoting awareness of benefits combined with effective communication and collaboration. Further research is required to understand interconnections among identified factors and how they can be leveraged for the success and further uptake of NbS.Large carnivores are ecologically important, but their behaviour frequently put them in conflict with humans. I suggest that a spatial co-occurrence of suitable habitat and relatively poor socioeconomic conditions in rural areas may contribute to inflated human-carnivore conflict. Here, I test if there is potential for such an explanation for the human-wolf conflict in Sweden, a conflict that is arguably not congruent with the costs and damages imposed by the wolf population. I found negative correlations between wolf habitat suitability within Swedish municipalities and indicators of their relative socioeconomic conditions. I argue that geographic socioeconomic inequality may contribute to the Swedish human-wolf conflict, partly by the use of wolves as symbols for socioeconomic dissent and partly by using them as scapegoats for socioeconomic conditions. Therefore, regional policies aimed at alleviating geographic socioeconomic inequities may create a more favourable environment for solving the human-wolf conflict in Sweden.Like the rest of the world, African countries are reeling from the health, economic and social effects of COVID-19. The continent's governments have responded by imposing rigorous lockdowns to limit the spread of the virus. The various lockdown measures are undermining food security, because stay at home orders have among others, threatened food production for a continent that relies heavily on agriculture as the bedrock of the economy. This article draws on quantitative data collected by the GeoPoll, and, from these data, assesses the effect of concern about the local spread and economic impact of COVID-19 on food worries. Qualitative data comprising 12 countries south of the Sahara reveal that lockdowns have created anxiety over food security as a health, economic and human rights/well-being issue. By applying a probit model, we find that concern about the local spread of COVID-19 and economic impact of the virus increases the probability of food worries. Governments have responded with various efforts to support the neediest. By evaluating the various policies rolled out we advocate for a feminist economics approach that necessitates greater use of data analytics to predict the likely impacts of intended regulatory relief responses during the recovery process and post-COVID-19.The study of translational regulation requires reliable measurement of both mRNA levels and protein synthesis. Cytoplasmic polyadenylation is a prevalent mode of translational regulation during oogenesis and early embryogenesis. Here the length of the poly(A) tail of an mRNA is coupled to its translatability. We describe a protocol to identify translationally regulated genes and measure their translation rate in the early zebrafish embryo using genome-wide polysome profiling. This protocol relies on the isolation of mRNA by means of an rRNA depletion strategy, which avoids capture bias due to short poly(A) tail that can occur when using conventional oligo(dT)-based methods. We also present a simple PCR-based method to measure the poly(A) tail length of selected mRNAs.The stability of RNA transcripts is regulated by signals within their sequences, but the identity of those signals still remain elusive in many biological systems. Recently introduced massively parallel tools for the analysis of regulatory RNA sequences provide the ability to detect functional cis-regulatory sequences of post-transcriptional RNA regulation at a much larger scale and resolution than before. link2 Their application formulates the underlying sequence-based rules and predicts the impact of genetic variations. Here, we describe the application of UTR-Seq, as a strategy to uncover cis-regulatory signals of RNA stability during early zebrafish embryogenesis. The method combines massively parallel reporter assays (MPRA) with computational regression models. It surveys the effect of tens of thousands of regulatory sequences on RNA stability and analyzes the results via regression models to identify sequence signals that impact RNA stability and to predict the in vivo effect of sequence variations.Many proteins are assumed to mediate post-transcriptional regulation of mRNAs. However, the lack of information about their target mRNAs and functional domains hampers the detailed analysis of their molecular function. Here we describe a method to analyze the post-transcriptional effects of proteins of interest by artificially tethering the protein to a reporter mRNA in zebrafish embryos.In metazoans, fertilization initiates vast remodeling of the embryonic proteome and transcriptome. This is accomplished via complex post-transcriptional regulation of maternal and zygotic RNA. RNA-binding proteins (RBPs) are one of the major mediators of embryonic post-transcriptional RNA regulation. Thus, elucidation of the molecular mechanisms by which maternal and zygotic transcripts change their translational capacities and expression levels requires thorough and precise determination of the targets and binding sites of individual RBPs in embryonic transcriptomes. Here, I provide a detailed protocol for the UV crosslinking-based method, named iCLIP, to study RBP functions during early zebrafish embryogenesis.Activation of the embryonic genome during development represents a major developmental transition in all species. The history of its exploration began in the 1950s-1960s, when this idea was put forward and proven experimentally by Alexander Neyfakh. He observed the aberrant development of fish embryos upon X-ray irradiation and noted the different developmental outcomes depending on the stage when fertilized eggs were subjected to irradiation. Neyfakh also discriminated a regional difference of X-irradiation between the nucleus and the cytoplasm. By selecting the X-ray dose causing nuclear damage, he determined the beginning of zygotic transcription, which at that time became known as the morphogenetic function of nuclei. His team defined the link of zygotic transcription with the asynchronization of cell division and cell migration, the two other hallmarks, which along with the morphogenetic function (or the zygotic genome activation), are at the core of the mid-blastula transition during development. link3 Within this framework, current studies using maternal mutants and application of modern methods of whole-embryo and single-cell transcriptomics begin to decipher the molecular mechanisms of the mid-blastula transition (or the maternal-zygotic transition).