Proper phosphate signaling is essential for robust growth of Escherichia coli and many other bacteria. The phosphate signal is mediated by a classic two component signal system composed of PhoR and PhoB. The PhoR histidine kinase is responsible for phosphorylating/dephosphorylating the response regulator, PhoB, which controls the expression of genes that aid growth in low phosphate conditions. The mechanism by which PhoR receives a signal of environmental phosphate levels has remained elusive. A transporter complex composed of the PstS, PstC, PstA, and PstB proteins as well as a negative regulator, PhoU, have been implicated in signaling environmental phosphate to PhoR.
This work confirms that PhoU and the PstSCAB complex are necessary for proper signaling of high environmental phosphate. Also, we identify residues important in PhoU/PhoR interaction with genetic analysis. Using protein modeling and docking methods, we show an interaction model that points to a potential mechanism for PhoU mediated signaling to PhoR to modify its activity. This model is tested with direct coupling analysis.
These bioinformatics tools, in combination with genetic and biochemical analysis, help to identify and test a model for phosphate signaling and may be applicable to several other systems.
Analyzing next-generation sequencing data is difficult because datasets are large, second generation sequencing platforms have high error rates, and because each position in the target genome (exome, transcriptome, etc.) is sequenced multiple times. Given these challenges, numerous bioinformatic algorithms have been developed to analyze these data. These algorithms aim to find an appropriate balance between data loss, errors, analysis time, and memory footprint. Typical analysis pipelines require multiple steps. If one or more of these steps is unnecessary, it would significantly decrease compute time and data manipulation to remove the step. One step in many pipelines is PCR duplicate removal, where PCR duplicates arise from multiple PCR products from the same template molecule binding on the flowcell. These are often removed because there is concern they can lead to false positive variant calls. Picard (MarkDuplicates) and SAMTools (rmdup) are the two main softwares used for PCR duplicate removal.
Approximately 92 % of the 17+ million variants called were called whether we removed duplicates with Picard or SAMTools, or left the PCR duplicates in the dataset. There were no significant differences between the unique variant sets when comparing the transition/transversion ratios (p = 1.0), percentage of novel variants (p = 0.99), average population frequencies (p = 0.99), and the percentage of protein-changing variants (p = 1.0). Results were similar for variants in the American College of Medical Genetics genes. Genotype concordance between NGS and SNP chips was above 99 % for all genotype groups (e.g., homozygous reference).
Our results suggest that PCR duplicate removal has minimal effect on the accuracy of subsequent variant calls.
Human viruses have codon usage biases that match highly expressed proteins in the tissues they infect.
It is well-documented that codon usage biases affect gene translational efficiency; however, it is less known if viruses share their host’s codon usage motifs. We
determined that human-infecting viruses share similar codon usage biases as proteins that are expressed in tissues the viruses infect. By performing 7,052,621
pairwise comparisons of genes from humans versus genes from 113 viruses that infect humans, we determined which codon usage motifs were most highly
correlated. We found that 16 viruses averaged a significant correlation in codon usage with over 500 human genes per viral gene, 58 viruses were highly
correlated with an average of at least 100 human genes per viral gene, and 37 viruses were significantly correlated with an average of at least one human gene
per viral gene at an alpha level of 7.09 x (0.05 alpha / 7,052,621 comparisons). Only two viruses were not highly correlated with an average of one human
gene per viral gene. While relatively few of the interactions were previously documented, the high statistical correlations suggest that researchers may be able
to determine which tissues a virus is most likely to infect by analyzing codon usage biases.
Although many studies have documented codon usage bias in different species, the importance of codon usage in a phylogenetic framework remains largely unknown. We demonstrate that a phylogenetic signal is present in the codon usage and non-usage biases of 17 717 orthologues evaluated across 72 tetrapod species using a simple parsimony analysis of a binary matrix of codon characters. Phylogenies estimated using stop codons were more congruent with previous hypotheses than phylogenies based on any other single codon or a combination of codons. Although each codon is present in every species, specific genes have different codon preferences and may or may not use every possible codon. This observation allowed us to map the pattern of codon usage and non-usage across the topology. These results suggest that codon usage is phylogenetically conserved across shallow and deep levels within tetrapods.
Motivation: One of the main challenges with bioinformatics software is that the size and complexity of datasets necessitate trading speed for accuracy, or completeness. To combat this problem of computational complexity, a plethora of heuristic algorithms have arisen that report a ‘good enough’ solution to biological questions. However, in instances such as Simple Sequence Repeats (SSRs), a ‘good enough’ solution may not accurately portray results in population genetics, phylogenetics and forensics, which require accurate SSRs to calculate intra- and inter-species interactions.
Results: We present Kmer-SSR, which finds all SSRs faster than most heuristic SSR identification algorithms in a parallelized, easy-to-use manner. The exhaustive Kmer-SSR option has 100% precision and 100% recall and accurately identifies every SSR of any specified length. To identify more biologically pertinent SSRs, we also developed several filters that allow users to easily view a subset of SSRs based on user input. Kmer-SSR, coupled with the filter options, accurately and intuitively identifies SSRs quickly and in a more user-friendly manner than any other SSR identification algorithm.
Availability and implementation: The source code is freely available on GitHub at
Introduction: Mitochondrial genetics are an important but largely neglected area of research in Alzheimer’s disease. A major impediment is the lack of data sets.
Methods: We used an innovative, rigorous approach, combining several existing tools with our own, to accurately assemble and call variants in 809 whole mitochondrial genomes.
Results: To help address this impediment, we prepared a data set that consists of 809 complete and annotated mitochondrial genomes with samples from the Alzheimer’s Disease Neuroimaging Initiative. These whole mitochondrial genomes include rich phenotyping, such as clinical, fluid biomarker, and imaging data, all of which is available through the Alzheimer’s Disease Neuroimaging Initiative website. Genomes are cleaned, annotated, and prepared for analysis.
Discussion: These data provide an important resource for investigating the impact of mitochondrial genetic variation on risk for Alzheimer’s disease and other phenotypes that have been measured in the Alzheimer’s Disease Neuroimaging Initiative samples.