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The history of lamprey evolution has been contentious due to limited morphological differentiation and limited genetic data. Available data has produced inconsistent results, including in the relationship among northern and southern species and the monophyly of putative clades. Here we use whole genome sequence data sourced from a public database to identify orthologs for 11 lamprey species from across the globe and build phylogenies. The phylogeny showed a clear separation between northern and southern lamprey species, which contrasts with some prior work. We also find that the phylogenetic relationships of our samples of two genera, Lethenteron and Eudontomyzon, deviate from the taxonomic classification of these species, suggesting that they require reclassification. 

Abstract Many evolutionary relationships remain controversial despite whole-genome sequencing data. These controversies arise, in part, due to challenges associated with accurately modeling the complex phylogenetic signal coming from genomic regions experiencing distinct evolutionary forces. Here, we examine how different regions of the genome support or contradict well-established relationships among three mammal groups using millions of orthologous parsimony-informative biallelic sites (PIBS) distributed across primate, rodent, and Pecora genomes. We compared PIBS concordance percentages among locus types (e.g. coding sequences (CDS), introns, intergenic regions), and contrasted PIBS utility over evolutionary timescales. Sites derived from noncoding sequences provided more data and proportionally more concordant sites compared with those from CDS in all clades. CDS PIBS were also predominant drivers of tree incongruence in two cases of topological conflict. PIBS derived from most locus types provided surprisingly consistent support for splitting events spread across the timescales we examined, although we find evidence that CDS and intronic PIBS may, respectively and to a limited degree, inform disproportionately about older and younger splits. In this era of accessible wholegenome sequence data, these results:1) suggest benefits to more intentionally focusing on noncoding loci as robust data for tree inference and 2) reinforce the importance of accurate modeling, especially when using CDS data. 

Many biologists are interested in teaching computing skills or using computing in the classroom, despite not being formally trained in these skills themselves. Thus biologists may find themselves researching how to teach these skills, and therefore many individuals are individually attempting to discover resources and methods to do so. Recent years have seen an expansion of new technologies to assist in delivering course content interactively. Educational research provides insights into how learners absorb and process information during interactive learning. In this review, we discuss the value of teaching foundational computing skills to biologists, and strategies and tools to do so. Additionally, we review the literature on teaching practices to support the development of these skills. We pay special attention to meeting the needs of diverse learners, and consider how different ways of delivering course content can be leveraged to provide a more inclusive classroom experience. Our goal is to enable biologists to teach computational skills and use computing in the classroom successfully. 

Abstract MotivationAccurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others. ResultsWe modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. Availability and ImplementationMethods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). Supplementary informationSupplementary data is available at Bioinformatics online.