RESUMO
In this chapter, we outline an approach to analyzing metatranscriptomic data, focusing on the assessment of differential enzyme expression and metabolic pathway activities using a novel bioinformatics software tool, EMPathways2. The analysis pipeline commences with raw data originating from a sequencer and concludes with an output of enzyme expressions and an estimate of metabolic pathway activities. The initial step involves aligning specific transcriptomes assembled from RNA-Seq data using Bowtie2 and acquiring gene expression data with IsoEM2. Subsequently, the pipeline proceeds to quality assessment and preprocessing of the input data, ensuring accurate estimates of enzymes and their differential regulation. Upon completion of the preprocessing stage, EMPathways2 is employed to decipher the intricate relationships between genes, enzymes, and pathways. An online repository containing sample data has been made available, alongside custom Python scripts designed to modify the output of the programs within the pipeline for diverse downstream analyses. This chapter highlights the technical aspects and practical applications of using EMPathways2, which facilitates the advancement of transcriptome data analysis and contributes to a deeper understanding of the complex regulatory mechanisms underlying living systems.
Assuntos
Biologia Computacional , Perfilação da Expressão Gênica , Redes e Vias Metabólicas , RNA-Seq , Software , RNA-Seq/métodos , Redes e Vias Metabólicas/genética , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Transcriptoma , Humanos , Análise de Sequência de RNA/métodosRESUMO
Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate these methods on four benchmarks: two SARS-CoV-2 interhost data sets and two HIV intrahost data sets. A parallelized version of our tool is scalable to very large data sets. We show that both entropy and Hamming distance-based MC clusterings discern the meaningful information from sequencing data. The proposed clustering methods consistently converge to similar clusterings across different runs. Finally, we show that MC clustering improves reconstruction of intrahost viral population from sequencing data.
Assuntos
COVID-19 , Humanos , COVID-19/genética , SARS-CoV-2/genética , Benchmarking , Análise por Conglomerados , Progressão da DoençaRESUMO
This article presents a novel scalable character-based phylogeny algorithm for dense viral sequencing data called SPHERE (Scalable PHylogEny with REcurrent mutations). The algorithm is based on an evolutionary model where recurrent mutations are allowed, but backward mutations are prohibited. The algorithm creates rooted character-based phylogeny trees, wherein all leaves and internal nodes are labeled by observed taxa. We show that SPHERE phylogeny is more stable than Nextstrain's, and that it accurately infers known transmission links from the early pandemic. SPHERE is a fast algorithm that can process >200,000 sequences in <2 hours, which offers a compact phylogenetic visualization of Global Initiative on Sharing All Influenza Data (GISAID).
Assuntos
Mutação , Filogenia , SARS-CoV-2/genética , Algoritmos , COVID-19/transmissão , COVID-19/virologia , Bases de Dados Genéticas , HumanosRESUMO
The ß1-adrenergic signaling system is one of the most important protein signaling systems in cardiac cells. It regulates cardiac action potential duration, intracellular Ca(2+) concentration ([Ca(2+)]i) transients, and contraction force. In this paper, a comprehensive experimentally based mathematical model of the ß1-adrenergic signaling system for mouse ventricular myocytes is explored to simulate the effects of moderate stimulations of ß1-adrenergic receptors (ß1-ARs) on the action potential, Ca(2+) and Na(+) dynamics, as well as the effects of inhibition of protein kinase A (PKA) and phosphodiesterase of type 4 (PDE4). Simulation results show that the action potential prolongations reach saturating values at relatively small concentrations of isoproterenol (â¼0.01 µM), while the [Ca(2+)]i transient amplitude saturates at significantly larger concentrations (â¼0.1-1.0 µM). The differences in the response of Ca(2+) and Na(+) fluxes to moderate stimulation of ß1-ARs are also observed. Sensitivity analysis of the mathematical model is performed and the model limitations are discussed. The investigated model reproduces most of the experimentally observed effects of moderate stimulation of ß1-ARs, PKA, and PDE4 inhibition on the L-type Ca(2+) current, [Ca(2+)]i transients, and the sarcoplasmic reticulum Ca(2+) load and makes testable predictions for the action potential duration and [Ca(2+)]i transients as functions of isoproterenol concentration.