RESUMO
The amount of SARS-CoV-2 in a sample is often measured using Ct values. However, the same Ct value may correspond to different viral loads on different platforms and assays, making them difficult to compare from study to study. To address this problem, we developed ct2vl, a Python package that converts Ct values to viral loads for any RT-qPCR assay/platform. The method is novel in that it is based on determining the maximum PCR replication efficiency, as opposed to fitting a sigmoid (S-shaped) curve relating signal to cycle number. We calibrated ct2vl on two FDA-approved platforms and validated its performance using reference-standard material, including sensitivity analysis. We found that ct2vl-predicted viral loads were highly accurate across five orders of magnitude, with 1.6-fold median error (for comparison, viral loads in clinical samples vary over 10 orders of magnitude). The package has 100% test coverage. We describe installation and usage both from the Unix command-line and from interactive Python environments. ct2vl is freely available via the Python Package Index (PyPI). It facilitates conversion of Ct values to viral loads for clinical investigators, basic researchers, and test developers for any RT-qPCR platform. It thus facilitates comparison among the many quantitative studies of SARS-CoV-2 by helping render observations in a natural, universal unit of measure.
Assuntos
COVID-19 , SARS-CoV-2 , Carga Viral , Humanos , SARS-CoV-2/genética , COVID-19/virologia , Reação em Cadeia da Polimerase em Tempo Real/métodos , Software , Teste de Ácido Nucleico para COVID-19/métodos , Sensibilidade e EspecificidadeRESUMO
Genomes sometimes undergo large-scale rearrangements. Programmed genome rearrangements in ciliates offer an extreme example, making them a compelling model system to study DNA rearrangements. Currently, available methods for genome annotation are not adequate for highly scrambled genomes. We present a theoretical framework and software implementation for the systematic extraction and analysis of DNA rearrangement annotations from pairs of genome assemblies corresponding to precursor and product versions. The software makes no assumptions about the structure of the rearrangements, and permits the user to select parameters to suit the data. Compared to previous approaches, this work achieves more complete precursor-product mappings, allows for full transparency and reproducibility, and can be adapted to genomic data from different sources.
RESUMO
Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed greylock, a Python package that calculates diversity measures and is tailored to large datasets. greylock can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). greylock also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe greylock's key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating greylock's applicability across a range of dataset types and fields.
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Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function (Z), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately 1010 sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating Z this way for only a few models: it then uses these expensive estimates to estimate Z more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally.
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Ciliates are microbial eukaryotes that undergo extensive programmed genome rearrangement, a natural genome editing process that converts long germline chromosomes into smaller gene-rich somatic chromosomes. Three well-studied ciliates include Oxytricha trifallax, Tetrahymena thermophila, and Paramecium tetraurelia, but only the Oxytricha lineage has a massively scrambled genome, whose assembly during development requires hundreds of thousands of precisely programmed DNA joining events, representing the most complex genome dynamics of any known organism. Here we study the emergence of such complex genomes by examining the origin and evolution of discontinuous and scrambled genes in the Oxytricha lineage. This study compares six genomes from three species, the germline and somatic genomes for Euplotes woodruffi, Tetmemena sp., and the model ciliate O. trifallax. We sequenced, assembled, and annotated the germline and somatic genomes of E. woodruffi, which provides an outgroup, and the germline genome of Tetmemena sp. We find that the germline genome of Tetmemena is as massively scrambled and interrupted as Oxytricha's: 13.6% of its gene loci require programmed translocations and/or inversions, with some genes requiring hundreds of precise gene editing events during development. This study revealed that the earlier diverged spirotrich, E. woodruffi, also has a scrambled genome, but only roughly half as many loci (7.3%) are scrambled. Furthermore, its scrambled genes are less complex, together supporting the position of Euplotes as a possible evolutionary intermediate in this lineage, in the process of accumulating complex evolutionary genome rearrangements, all of which require extensive repair to assemble functional coding regions. Comparative analysis also reveals that scrambled loci are often associated with local duplications, supporting a gradual model for the origin of complex, scrambled genomes via many small events of DNA duplication and decay.
Assuntos
Cromossomos , Rearranjo Gênico , DNA de Protozoário/genética , Rearranjo Gênico/genética , Genoma , GenômicaRESUMO
Ciliates have two different types of nuclei per cell, with one acting as a somatic, transcriptionally active nucleus (macronucleus; abbr. MAC) and another serving as a germline nucleus (micronucleus; abbr. MIC). Furthermore, Oxytricha trifallax undergoes extensive genome rearrangements during sexual conjugation and post-zygotic development of daughter cells. These rearrangements are necessary because the precursor MIC loci are often both fragmented and scrambled, with respect to the corresponding MAC loci. Such genome architectures are remarkably tolerant of encrypted MIC loci, because RNA-guided processes during MAC development reorganize the gene fragments in the correct order to resemble the parental MAC sequence. Here, we describe the germline organization of several nested and highly scrambled genes in Oxytricha trifallax These include cases with multiple layers of nesting, plus highly interleaved or tangled precursor loci that appear to deviate from previously described patterns. We present mathematical methods to measure the degree of nesting between precursor MIC loci, and revisit a method for a mathematical description of scrambling. After applying these methods to the chromosome rearrangement maps of O. trifallax we describe cases of nested arrangements with up to five layers of embedded genes, as well as the most scrambled loci in O. trifallax.