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BackgroundMethylotrophy describes the ability of organisms to grow on reduced organic compounds without carbon-carbon bonds. The genomes of two pink-pigmented facultative methylotrophic bacteria of the Alpha-proteobacterial genus Methylobacterium, the reference species Methylobacterium extorquens strain AM1 and the dichloromethane-degrading strain DM4, were compared.Methodology/Principal FindingsThe 6.88 Mb genome of strain AM1 comprises a 5.51 Mb chromosome, a 1.26 Mb megaplasmid and three plasmids, while the 6.12 Mb genome of strain DM4 features a 5.94 Mb chromosome and two plasmids. The chromosomes are highly syntenic and share a large majority of genes, while plasmids are mostly strain-specific, with the exception of a 130 kb region of the strain AM1 megaplasmid which is syntenic to a chromosomal region of strain DM4. Both genomes contain large sets of insertion elements, many of them strain-specific, suggesting an important potential for genomic plasticity. Most of the genomic determinants associated with methylotrophy are nearly identical, with two exceptions that illustrate the metabolic and genomic versatility of Methylobacterium. A 126 kb dichloromethane utilization (dcm) gene cluster is essential for the ability of strain DM4 to use DCM as the sole carbon and energy source for growth and is unique to strain DM4. The methylamine utilization (mau) gene cluster is only found in strain AM1, indicating that strain DM4 employs an alternative system for growth with methylamine. The dcm and mau clusters represent two of the chromosomal genomic islands (AM1: 28; DM4: 17) that were defined. The mau cluster is flanked by mobile elements, but the dcm cluster disrupts a gene annotated as chelatase and for which we propose the name “island integration determinant” (iid).Conclusion/SignificanceThese two genome sequences provide a platform for intra- and interspecies genomic comparisons in the genus Methylobacterium, and for investigations of the adaptive mechanisms which allow bacterial lineages to acquire methylotrophic lifestyles.

A recent normative turn in computer science has brought concerns about fairness, bias, and accountability to the core of the field. Yet recent scholarship has warned that much of this technical work treats problematic features of the status quo as fixed, and fails to address deeper patterns of injustice and inequality. While acknowledging these critiques, we posit that computational research has valuable roles to play in addressing social problems -roles whose value can be recognized even from a perspective that aspires toward fundamental social change. In this paper, we articulate four such roles, through an analysis that considers the opportunities as well as the significant risks inherent in such work. Computing research can serve as a diagnostic, helping us to understand and measure social problems with precision and clarity. As a formalizer, computing shapes how social problems are explicitly defined -changing how those problems, and possible responses to them, are understood. Computing serves as rebuttal when it illuminates the boundaries of what is possible through technical means. And computing acts as synecdoche when it makes long-standing social problems newly salient in the public eye. We offer these paths forward as modalities that leverage the particular strengths of computational work in the service of social change, without overclaiming computing's capacity to solve social problems on its own. CCS CONCEPTS• Social and professional topics → Computing / technology policy; • Applied computing → Computers in other domains.

High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

Motivation: Next-generation sequencing experiments, such as RNA-Seq, play an increasingly important role in biological research. One complication is that the power and accuracy of such experiments depend substantially on the number of reads sequenced, so it is important and challenging to determine the optimal read depth for an experiment or to verify whether one has adequate depth in an existing experiment.Results: By randomly sampling lower depths from a sequencing experiment and determining where the saturation of power and accuracy occurs, one can determine what the most useful depth should be for future experiments, and furthermore, confirm whether an existing experiment had sufficient depth to justify its conclusions. We introduce the subSeq R package, which uses a novel efficient approach to perform this subsampling and to calculate informative metrics at each depth.Availability and Implementation: The subSeq R package is available at http://github.com/StoreyLab/subSeq/.Contact: dgrtwo@princeton.edu or jstorey@princeton.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Insertion sequences (ISs) are simple transposable elements present in most bacterial and archaeal genomes and play an important role in genomic evolution. The recent expansion of sequenced genomes offers the opportunity to study ISs comprehensively, but this requires efficient and accurate tools for IS annotation. We have developed an open-source program called OASIS, or Optimized Annotation System for Insertion Sequences, which automatically annotates ISs within sequenced genomes. OASIS annotations of 1737 bacterial and archaeal genomes offered an unprecedented opportunity to examine IS evolution. At a broad scale, we found that most IS families are quite widespread; however, they are not present randomly across taxa. This may indicate differential loss, barriers to exchange and/or insufficient time to equilibrate across clades. The number of ISs increases with genome length, but there is both tremendous variation and no increase in IS density for genomes >2 Mb. At the finer scale of recently diverged genomes, the proportion of shared IS content falls sharply, suggesting loss and/or emergence of barriers to successful cross-infection occurs rapidly. Surprisingly, even after controlling for 16S rRNA sequence divergence, the same ISs were more likely to be shared between genomes labeled as the same species rather than as different species.

BackgroundIn response to a wound, fibroblasts are activated to migrate toward the wound, to proliferate and to contribute to the wound healing process. We hypothesize that changes in pre-mRNA processing occurring as fibroblasts enter the proliferative cell cycle are also important for promoting their migration.ResultsRNA sequencing of fibroblasts induced into quiescence by contact inhibition reveals downregulation of genes involved in mRNA processing, including splicing and cleavage and polyadenylation factors. These genes also show differential exon use, especially increased intron retention in quiescent fibroblasts compared to proliferating fibroblasts. Mapping the 3′ ends of transcripts reveals that longer transcripts from distal polyadenylation sites are more prevalent in quiescent fibroblasts and are associated with increased expression and transcript stabilization based on genome-wide transcript decay analysis. Analysis of dermal excisional wounds in mice reveals that proliferating cells adjacent to wounds express higher levels of cleavage and polyadenylation factors than quiescent fibroblasts in unwounded skin. Quiescent fibroblasts contain reduced levels of the cleavage and polyadenylation factor CstF-64. CstF-64 knockdown recapitulates changes in isoform selection and gene expression associated with quiescence, and results in slower migration.ConclusionsOur findings support cleavage and polyadenylation factors as a link between cellular proliferation state and migration.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1551-9) contains supplementary material, which is available to authorized users.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue, benign and malignant tumors from each patient. We demonstrated human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We demonstrated that multiple-histological-samples per patient is necessary to effectively capture these progressionrelated signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework. molecular progression signal (Crawford and Oleksiak 2007;Storey et al. 2007;Hughes et al. 2015). In vitro approaches using human cell lines have been used to control for this sample heterogeneity; however, they are not fully reflective of in vivo tumor progression, including the effects of the microenvironment and the immune system (Gillet et al. 2011;Stein et al. 2004).The (in vivo) murine model of breast cancer has proven incredibly useful in deciphering cancer mechanisms, however, it requires experimental modification of the host via genetic modification (transgenic mice) or the transplantation of foreign tissue (xenografts) (Rangarajan and Weinberg 2003; Boone et al. 2015), which alters the tumor dynamics (Ben-David et al. 2017).Canine mammary tumor (CMT) is a promising emerging model for studying naturally occurring breast tumors (Klopfleisch et al. 2011;Liu et al. 2014;Pinho et al. 2012). CMTs and human breast cancer (BRCA) have similar histopathological profiles, including incidence rates, relationship with age and body mass index, hormonal influence, and clinical presentation as demonstrated in many clinical and smaller scale studies (Cekanova and Rathore 2014;Paoloni and Khanna 2008;Rowell et al. 2011;Kol et al. 2015;Kristiansen et al. 2016). Canine simple carcinomas share especially strong similarities with human breast cancer in terms of both histological and genetic features (Liu et al. 2014). Additionally, BRCA and CMT share chromosomal abnormalities such as copy number variations in several key breast cancer marker genes like MYC and PTEN (Borge et al. 2015). A significant advantage of the canine model is the high incidence of multiple naturally occurring tumors in the same patient (Sorenmo et al. ...