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Genomic analysis of H. salinarum indicated that the de novo pathway for aromatic amino acid (AroAA) biosynthesis does not follow the classical pathway but begins from non-classical precursors, as is the case for M. jannaschii. The first two steps in the pathway were predicted to be carried out by genes OE1472F and OE1475F, while the 3rd step follows the canonical pathway involving gene OE1477R. The functions of these genes and their products were tested by biochemical and genetic methods. In this study, we provide evidence that supports the role of proteins OE1472F and OE1475F catalyzing consecutive enzymatic reactions leading to the production of 3-dehydroquinate (DHQ), after which AroAA production proceeds via the canonical pathway starting with the formation of DHS (dehydroshikimate), catalyzed by the product of ORF OE1477R. Nutritional requirements and AroAA uptake studies of the mutants gave results that were consistent with the proposed roles of these ORFs in AroAA biosynthesis. DNA microarray data indicated that the 13 genes of the canonical pathway appear to be utilised for AroAA biosynthesis in H. salinarum, as they are differentially expressed when cells are grown in medium lacking AroAA. [ABSTRACT FROM AUTHOR]

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set. [ABSTRACT FROM AUTHOR]