High-Density Amplicon Sequencing Identifies Community Spread and Ongoing Evolution of SARS-CoV-2 in the Southern United States

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is constantly evolving. Prior studies focused on high-case-density locations, such as the northern and western metropolitan areas of the United States. This study demonstrates continued SARS-CoV-2 evolution in a suburban southern region of the United States by high-density amplicon sequencing of symptomatic cases. 57% of strains carry the spike D614G variant, which is associated with higher genome copy numbers, and its prevalence expands with time. Four strains carry a deletion in a predicted stem loop of the 3′ UTR. The data are consistent with community spread within local populations and the larger continental United States. The data instill confidence in current testing sensitivity and validate “testing by sequencing” as an option to uncover cases, particularly nonstandard coronavirus disease 2019 (COVID-19) clinical presentations. This study contributes to the understanding of COVID-19 through an extensive set of genomes from a non-urban setting and informs vaccine design by defining D614G as a dominant and emergent SARS-CoV-2 isolate in the United States

SeqAn An efficient, generic C++ library for sequence analysis

<p>Abstract</p> <p>Background</p> <p>The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.</p> <p>Results</p> <p>To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use.</p> <p>Conclusion</p> <p>We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms.</p

Coffee agroforestry systems in Central America: I. A review of quantitative information on physiological and ecological processes

Coffee is widely grown across Central America at altitudes between 600 and 2500 m, mostly in association with trees that provide shade and other services. Research on coffee agroforestry systems has identified many environmental factors, management strategies and plant characteristics that affect growth, yield and environmental impact of the system. Much of this literature only presents qualitative estimates of the importance of the different growth determining factors, or highly site-specific estimates. Quantitative information is required to allow statistical analysis or the construction of process-based models of the system. Here, we review the available quantitative information for the latter purpose, with emphasis on the data needs for modelling agroforestry systems common in Central America. Process-based models require environmental data—weather, soil—and data on the physiological characteristics of the coffee plants and trees. Our review showed that the current literature is insufficient to allow full parameterisation of a process-based model for any coffee-tree combination. Information on weather, coffee and trees is highly limited, but soil information seems more adequate. A regional network of replicated multi-factorial experiments, focusing on the interactive effects of different environmental factors, may help address the main knowledge gaps