Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4–5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching.

Fastai: A Layered API for Deep Learning

Examples are discussed to show the differences among discriminant analysis, logistic regression, and multiple regression. Chapter 6, “Multivariate Analysis of Variance,” presents advantages of multivariate analysis of variance (MANOVA) over univariate analysis of variance (ANOVA), discusses assumptions of MANOVA, and assesses validations of MANOVA assumptions and model estimation. The authors also discuss post hoc tests of MANOVA and multivariate analysis of covariance. Chapter 7, “Conjoint Analysis,” explains what conjoint analysis does and how it is different from other multivariate techniques. Guidelines of selecting attributes, models, and methods of data collection are presented. Chapter 8, “Cluster Analysis,” studies objectives, roles, and limitations of cluster analysis. Two basic concepts: similarity and distance are discussed. The authors also discuss details of five most popular hierarchical algorithms (singlelinkage, complete-linkage, average-linkage, centroid method, Ward’s method) and three nonhierarchical algorithms (the sequential threshold method, the parallel threshold method, and the optimizing procedure). Profiles of clusters and guidelines for cluster validation are studied as well. Chapter 9, “Multidimensional Scaling and Correspondence Analysis,” introduces two interdependence techniques to display the relationships in the data. The book describes clearly and intuitively the differences between the two techniques and how these two techniques are performed. Chapters 10–12 cover topics in SEM. Chapter 10, “Structural Equation Modeling: An Introduction,” introduces SEM and related concepts such as exogenous, endogenous constructs, and so on, points out the differences between SEM and other multivariate techniques, overviews the decision process of SEM. Chapter 11, “Confirmatory Factor Analysis,” explains the differences between exploratory and confirmatory factor analysis, discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factor analysis. Chapter 12, “Testing a Structural Model,” presents some methods of SEM in examining the relationships between latent constructs. The book is an excellent book for people in management and marketing. For the Technometrics audience, this book does not have much flavor of physical, chemical, and engineering sciences. For example, partial least squares, a very popular method in Chemometrics, is discussed but not as detailed as other techniques in the book. Furthermore, due to the amount of materials covered in the book, it might be inappropriate for someone who is new to multivariate analysis.

The Grammar of Graphics

Tidy data sets allow manipulation with a standard set of “tidy” tools, including popular packages such as dplyr (Wickham, Francois, and RStudio 2015), ggplot2 (Wickham, Chang, and RStudio 2016), and broom (Robinson et al. 2015). These tools do not yet, however, have the infrastructure to work fluently with text data and natural language processing tools. In developing this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.

/pdf/tidytext-text-mining-and-analysis-using-tidy-data-principles-34wkfek5jf.pdf

tidytext: Text Mining and Analysis Using Tidy Data Principles in R

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) coronavirus is a major public health challenge. Rapid tests for detecting existing SARS-CoV-2 infections and assessing virus spread are critical. Approaches to detect viral RNA based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) have potential as simple, scalable, and broadly applicable testing methods. Compared to RT quantitative polymerase chain reaction (RT-qPCR)-based methods, RT-LAMP assays require incubation at a constant temperature, thus eliminating the need for sophisticated instrumentation. Here, we tested a two-color RT-LAMP assay protocol for detecting SARS-CoV-2 viral RNA using a primer set specific for the N gene. We tested our RT-LAMP assay on surplus RNA samples isolated from 768 pharyngeal swab specimens collected from individuals being tested for COVID-19. We determined the sensitivity and specificity of the RT-LAMP assay for detecting SARS-CoV-2 viral RNA. Compared to an RT-qPCR assay using a sensitive primer set, we found that the RT-LAMP assay reliably detected SARS-CoV-2 RNA with an RT-qPCR cycle threshold (CT) number of up to 30, with a sensitivity of 97.5% and a specificity of 99.7%. We also developed a swab-to-RT-LAMP assay that did not require a prior RNA isolation step, which retained excellent specificity (99.5%) but showed lower sensitivity (86% for CT < 30) than the RT-LAMP assay. In addition, we developed a multiplexed sequencing protocol (LAMP-sequencing) as a diagnostic validation procedure to detect and record the outcome of RT-LAMP reactions.

/pdf/a-colorimetric-rt-lamp-assay-and-lamp-sequencing-for-h88h9zodh3.pdf

A colorimetric RT-LAMP assay and LAMP-sequencing for detecting SARS-CoV-2 RNA in clinical samples.

Hadley Wickham1, Mara Averick1, Jennifer Bryan1, Winston Chang1, Lucy D’Agostino McGowan8, Romain François1, Garrett Grolemund1, Alex Hayes12, Lionel Henry1, Jim Hester1, Max Kuhn1, Thomas Lin Pedersen1, Evan Miller13, Stephan Milton Bache3, Kirill Müller2, Jeroen Ooms14, David Robinson5, Dana Paige Seidel10, Vitalie Spinu4, Kohske Takahashi9, Davis Vaughan1, Claus Wilke6, Kara Woo7, and Hiroaki Yutani11

/pdf/welcome-to-the-tidyverse-2gqeyze158.pdf

Welcome to the Tidyverse

Tools to Make Developing R Packages Easier [R package devtools version 2.3.2]

Recent sequencing of Plasmodium vivax field isolates and monkey-adapted strains enabled characterization of SNPs throughout the genome. These analyses relied on mapping short reads onto the P. vivax reference genome that was generated using DNA from the monkey-adapted strain Salvador I. Any genomic locus deleted in this strain would be lacking in the reference genome sequence and missed in previous analyses. Here, we report de novo assembly of a P. vivax field isolate genome. Out of 2,857 assembled contigs, we identify 362 contigs, each containing more than 5 kb of contiguous DNA sequences absent from the reference genome sequence. These novel P. vivax DNA sequences account for 3.8 million nucleotides and contain 792 predicted genes. Most of these contigs contain members of multigene families and likely originate from telomeric regions. Interestingly, we identify two contigs containing predicted protein coding genes similar to known Plasmodium red blood cell invasion proteins. One gene encodes the reticulocyte-binding protein gene orthologous to P. cynomolgi RBP2e and P. knowlesi NBPXb. The second gene harbors all the hallmarks of a Plasmodium erythrocyte-binding protein, including conserved Duffy-binding like and C-terminus cysteine-rich domains. Phylogenetic analysis shows that this novel gene clusters separately from all known Plasmodium Duffy-binding protein genes. Additional analyses showing that this gene is present in most P. vivax genomes and transcribed in blood-stage parasites suggest that P. vivax red blood cell invasion mechanisms may be more complex than currently understood. The strategy employed here complements previous genomic analyses and takes full advantage of next-generation sequencing data to provide a comprehensive characterization of genetic variations in this important malaria parasite. Further analyses of the novel protein coding genes discovered through de novo assembly have the potential to identify genes that influence key aspects of P. vivax biology, including alternative mechanisms of human erythrocyte invasion.

/pdf/de-novo-assembly-of-a-field-isolate-genome-reveals-novel-105a9ipzgh.pdf

De Novo Assembly of a Field Isolate Genome Reveals Novel Plasmodium vivax Erythrocyte Invasion Genes

Analysis of environmental DNA (eDNA) enables the detection of species of interest from water and soil samples, typically using species-specific PCR. Here, we describe a method to characterize the biodiversity of a given environment by amplifying eDNA using primer pairs targeting a wide range of taxa and high-throughput sequencing for species identification. We tested this approach on 91 water samples of 40 mL collected along the Cuyahoga River (Ohio, USA). We amplified eDNA using 12 primer pairs targeting mammals, fish, amphibians, birds, bryophytes, arthropods, copepods, plants and several microorganism taxa and sequenced all PCR products simultaneously by high-throughput sequencing. Overall, we identified DNA sequences from 15 species of fish, 17 species of mammals, 8 species of birds, 15 species of arthropods, one turtle and one salamander. Interestingly, in addition to aquatic and semi-aquatic animals, we identified DNA from terrestrial species that live near the Cuyahoga River. We also identified DNA from one Asian carp species invasive to the Great Lakes but that had not been previously reported in the Cuyahoga River. Our study shows that analysis of eDNA extracted from small water samples using wide-range PCR amplification combined with high-throughput sequencing can provide a broad perspective on biological diversity.

/pdf/in-silico-assessment-of-primers-for-edna-studies-using-13vok2egk3.pdf

In silico assessment of primers for eDNA studies using PrimerTree and application to characterize the biodiversity surrounding the Cuyahoga River

Aims 
Epidemiological and animal studies have shown that maternal diet can influence metabolism in adult offspring. However, the molecular mechanisms underlying these changes remain poorly understood. Here, we characterize the phenotypes induced by maternal obesity in a mouse model and examine gene expression and epigenetic changes induced by maternal diet in adult offspring.


Methods 
We analyzed genetically identical male mice born from dams fed a high- or low-fat diet throughout pregnancy and until day 21 postpartum. After weaning, half of the males of each group were fed a high-fat diet, the other half a low-fat diet. We first characterized the genome-wide gene expression patterns of six tissues of adult offspring - liver, pancreas, white adipose, brain, muscle and heart. We then measured DNA methylation patterns in liver at selected loci and throughout the genome.


Results 
Maternal diet had a significant effect on the body weight of the offspring when they were fed an obesogenic diet after weaning. Our analyses showed that maternal diet had a pervasive effect on gene expression, with a pronounced effect in liver where it affected many genes involved in inflammation, cholesterol synthesis and RXR activation. We did not detect any effect of the maternal diet on DNA methylation in the liver.


Conclusions 
Overall, our findings highlighted the persistent influence of maternal diet on adult tissue regulation and suggested that the transcriptional changes were unlikely to be caused by DNA methylation differences in adult liver.

/pdf/maternal-nutrition-induces-pervasive-gene-expression-changes-4yqq1mvr87.pdf

Jim Hester

Papers

Welcome to the Tidyverse

Tools to Make Developing R Packages Easier [R package devtools version 2.3.2]

De Novo Assembly of a Field Isolate Genome Reveals Novel Plasmodium vivax Erythrocyte Invasion Genes

In silico assessment of primers for eDNA studies using PrimerTree and application to characterize the biodiversity surrounding the Cuyahoga River

Maternal Nutrition Induces Pervasive Gene Expression Changes but No Detectable DNA Methylation Differences in the Liver of Adult Offspring