One of the hallmarks of the Gram-negative bacterium Pseudomonas aeruginosa is its ability to thrive in diverse environments that includes humans with a variety of debilitating diseases or immune deficiencies. Here we report the complete sequence and comparative analysis of the genomes of two representative P. aeruginosa strains isolated from cystic fibrosis (CF) patients whose genetic disorder predisposes them to infections by this pathogen. The comparison of the genomes of the two CF strains with those of other P. aeruginosa presents a picture of a mosaic genome, consisting of a conserved core component, interrupted in each strain by combinations of specific blocks of genes. These strain-specific segments of the genome are found in limited chromosomal locations, referred to as regions of genomic plasticity. The ability of P. aeruginosa to shape its genomic composition to favor survival in the widest range of environmental reservoirs, with corresponding enhancement of its metabolic capacity is supported by the identification of a genomic island in one of the sequenced CF isolates, encoding enzymes capable of degrading terpenoids produced by trees. This work suggests that niche adaptation is a major evolutionary force influencing the composition of bacterial genomes. Unlike genome reduction seen in host-adapted bacterial pathogens, the genetic capacity of P. aeruginosa is determined by the ability of individual strains to acquire or discard genomic segments, giving rise to strains with customized genomic repertoires. Consequently, this organism can survive in a wide range of environmental reservoirs that can serve as sources of the infecting organisms.

Motivation Bacterial metagenomics profiling for metagenomic whole sequencing (mWGS) usually starts by aligning sequencing reads to a collection of reference genomes. Current profiling tools are designed to work against a small representative collection of genomes, and do not scale very well to larger reference genome collections. However, large reference genome collections are capable of providing a more complete and accurate profile of the bacterial population in a metagenomics dataset. In this paper, we discuss a scalable, efficient and affordable approach to this problem, bringing big data solutions within the reach of laboratories with modest resources. Results We developed Flint, a metagenomics profiling pipeline that is built on top of the Apache Spark framework, and is designed for fast real-time profiling of metagenomic samples against a large collection of reference genomes. Flint takes advantage of Spark’s built-in parallelism and streaming engine architecture to quickly map reads against a large (170 GB) reference collection of 43 552 bacterial genomes from Ensembl. Flint runs on Amazon’s Elastic MapReduce service, and is able to profile 1 million Illumina paired-end reads against over 40 K genomes on 64 machines in 67 s—an order of magnitude faster than the state of the art, while using a much larger reference collection. Streaming the sequencing reads allows this approach to sustain mapping rates of 55 million reads per hour, at an hourly cluster cost of $8.00 USD, while avoiding the necessity of storing large quantities of intermediate alignments. Availability and implementation Flint is open source software, available under the MIT License (MIT). Source code is available at https://github.com/camilo-v/flint. Supplementary information Supplementary data are available at Bioinformatics online.

Cerebellar neuronal progenitors undergo a series of divisions before irreversibly exiting the cell cycle and differentiating into neurons. Dysfunction of this process underlies many neurological diseases including ataxia and the most common pediatric brain tumor, medulloblastoma. To better define the pathways controlling the most abundant neuronal cells in the mammalian cerebellum, cerebellar granule cell progenitors (GCPs), we performed RNA-sequencing of GCPs exiting the cell cycle. Time-series modeling of GCP cell cycle exit identified downregulation of activity of the epigenetic reader protein Brd4. Brd4 binding to the Gli1 locus is controlled by Casein Kinase 1δ (CK1 δ)-dependent phosphorylation during GCP proliferation, and decreases during GCP cell cycle exit. Importantly, conditional deletion of Brd4 in vivo in the developing cerebellum induces cerebellar morphological deficits and ataxia. These studies define an essential role for Brd4 in cerebellar granule cell neurogenesis and are critical for designing clinical trials utilizing Brd4 inhibitors in neurological indications.

Nicotinic acid adenine dinucleotide phosphate regulates skeletal muscle differentiation via action at two-pore channels. Proc Natl Acad Sci USA 107:19927-32 Billington RA, Bellomo EA, Floriddia EM et al. (2006) A transport mechanism for NAADP in a rat basophilic cell line. FASEB J 20:521-3 Churchill GC, Okada Y, Thomas JM et al. (2002) NAADP mobilizes Ca 2+ from reserve granules, lysosome-related organelles, in Sea Urchin eggs. Cell 111:703-8 Greig AV, Linge C, Terenghi G et al. (2003) Purinergic receptors are part of a functional signaling system for proliferation and differentiation of human epidermal keratinocytes.

The detection of transcripts and the measurement of their associated activity at the pseudogene scale have recently become important topics of research. Being integral part of many recent studies aimed at establishing a role for a variety of noncoding RNA structures, pseudogenes' popularity has substantially increased due to the discovery of regulatory properties and complex mechanisms of action that, while requiring further investigation, analysis, and validation, promise as well to have a broad impact on human disease. Currently, there are relatively few methodologies specifically designed to accomplish the detection of pseudogene transcripts and tools that either replace or integrate manual annotation procedures are very much needed. In particular, it seems to us justified that we engage in advancing the computational treatment of pseudogenes at the whole transcriptome level. Catalogs of human pseudogenes have started to be delivered, through RNA-Seq technologies. However, just a certain number of transcriptomes has been covered. Furthermore, while most proposals have led to the production of a targeted algorithm, especially used for detection, few computational pipelines were designed following a comprehensive approach addressing identification and quantification of transcriptional activity within a unifying methodological frame. Given the currently incomplete evidence, the limitations of the impacts due to the lack of extensive testing, and the presence of unsolved uncertainties affecting the reproducibility of results, our motivation for the proposal of a new computational approach is high and timely. We have considered a hybrid approach, based on the assembly of a variety of computational tools, including RNA-Seq methods and machine learning applications, all applied to transcriptome data of various complexities. Our initial strategy is to provide lists of pseudogenes to be validated against the currently known examples, in order to extend our knowledge further. An ultimate goal that is naturally linked to this work is to provide an automatic approach that analyzes transcriptomes with the goal of detecting candidate pseudogenes through characteristic features and that allows efficient and reproducible pseudogene classification models.