Crime and Building Rehabilitation or Demolition: A Dose-Response Analysis.

Thousands of buildings in Cleveland, Ohio were demolished or rehabilitated since the Great Recession in the 2000s. Recent evidence suggests removing vacant and decaying buildings reduces violent and firearm-involved crime. This study examines the dose-response relationship between demolitions, rehabilitations, and crime. We use Bayesian spatiotemporal models to estimate the association of interest for five types of crime outcomes: violent crimes, violent crimes involving a firearm, drug crimes, and crimes often associated with building vacancy. We estimate associations in quarterly time periods from 2012 through 2017 in 569 hexagons approximately the size of a neighborhood (2000 feet, approximately 610 m, in diameter), stratified by vacancy level. Across vacancy levels, the majority of our models do not identify statistically significant associations between demolition and rehabilitation dose and crime incidence. However, in some cases, we identify positive associations between demolition and crime. These associations generally appeared at higher levels of demolition (2 or 3 or more demolitions) in areas characterized by medium to high levels of vacancy. We also find that the presence of a property rehabilitation is associated with an increase in drug crimes in areas with medium levels of vacancy.

Space-filling and benthic competition on coral reefs.

Reef-building corals are ecosystem engineers that compete with other benthic organisms for space and resources. Corals harvest energy through their surface by photosynthesis and heterotrophic feeding, and they divert part of this energy to defend their outer colony perimeter against competitors. Here, we hypothesized that corals with a larger space-filling surface and smaller perimeters increase energy gain while reducing the exposure to competitors. This predicted an association between these two geometric properties of corals and the competitive outcome against other benthic organisms. To test the prediction, fifty coral colonies from the Caribbean island of Curaçao were rendered using digital 3D and 2D reconstructions. The surface areas, perimeters, box-counting dimensions (as a proxy of surface and perimeter space-filling), and other geometric properties were extracted and analyzed with respect to the percentage of the perimeter losing or winning against competitors based on the coral tissue apparent growth or damage. The increase in surface space-filling dimension was the only significant single indicator of coral winning outcomes, but the combination of surface space-filling dimension with perimeter length increased the statistical prediction of coral competition outcomes. Corals with larger surface space-filling dimensions (Ds > 2) and smaller perimeters displayed more winning outcomes, confirming the initial hypothesis. We propose that the space-filling property of coral surfaces complemented with other proxies of coral competitiveness, such as life history traits, will provide a more accurate quantitative characterization of coral competition outcomes on coral reefs. This framework also applies to other organisms or ecological systems that rely on complex surfaces to obtain energy for competition.

Multi-dimensional clinical phenotyping of a national cohort of adult cystic fibrosis patients.

BACKGROUND: Cystic Fibrosis (CF) is a multi-systemic disorder resulting from genetic variation in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene which can result in bronchiectasis, chronic sinusitis, pancreatic malabsorption, cholestatic liver disease and distal intestinal obstructive syndrome. This study generates multi-dimensional clinical phenotypes that capture the complexity and spectrum of the disease manifestations seen in adult CF patients using statistically robust techniques. METHODS: Pre-transplant clinical data from adult (age ≥18 years) CF patients (n = 992) seen in six regionally distinct US CF centers between 1/1/2014 and 6/30/2015 were included. Demographic, spirometry, nutritional, microbiological and therapy data were used to generate clusters using the Random Forests statistical-learning and Partitioning around Medoids (PAM) clustering algorithms. Five commonly measured demographic, physiological and nutritional parameters were needed to create the final phenotypes that are highly similar to a regionally matched group of patients from the CF Foundation Patient Registry RESULTS: This approach identified high-risk phenotypes with expected characteristics including high rates of pancreatic insufficiency, diabetes and Pseudomonas aeruginosa colonization. It also identified unexpected populations including a) a male-dominated, well-nourished group with good lung function with a high prevalence of severe genotypes (i.e. 60% subjects had two minimal function CFTR variations), b) and an older, "survivor" phenotype that had high rates of chronic P. aeruginosa infection. CONCLUSIONS: This study identified recognizable phenotypes that capture the clinical complexity in a statistically robust manner and which may aide in the identification of specific genetic and environmental factors responsible for these disease manifestation patterns.

A Distinct Contractile Injection System Gene Cluster Found in a Majority of Healthy Adult Human Microbiomes.

Many commensal bacteria antagonize each other or their host by producing syringe-like secretion systems called contractile injection systems (CIS). Members of the Bacteroidales family have been shown to produce only one type of CIS-a contact-dependent type 6 secretion system that mediates bacterium-bacterium interactions. Here, we show that a second distinct cluster of genes from Bacteroidales bacteria from the human microbiome may encode yet-uncharacterized injection systems that we term Bacteroidales injection systems (BIS). We found that BIS genes are present in the gut microbiomes of 99% of individuals from the United States and Europe and that BIS genes are more prevalent in the gut microbiomes of healthy individuals than in those individuals suffering from inflammatory bowel disease. Gene clusters similar to that of the BIS mediate interactions between bacteria and diverse eukaryotes, like amoeba, insects, and tubeworms. Our findings highlight the ubiquity of the BIS gene cluster in the human gut and emphasize the relevance of the gut microbiome to the human host. These results warrant investigations into the structure and function of the BIS and how they might mediate interactions between Bacteroidales bacteria and the human host or microbiome.IMPORTANCE To engage with host cells, diverse pathogenic bacteria produce syringe-like structures called contractile injection systems (CIS). CIS are evolutionarily related to the contractile tails of bacteriophages and are specialized to puncture membranes, often delivering effectors to target cells. Although CIS are key for pathogens to cause disease, paradoxically, similar injection systems have been identified within healthy human microbiome bacteria. Here, we show that gene clusters encoding a predicted CIS, which we term Bacteroidales injection systems (BIS), are present in the microbiomes of nearly all adult humans tested from Western countries. BIS genes are enriched within human gut microbiomes and are expressed both in vitro and in vivo Further, a greater abundance of BIS genes is present within healthy gut microbiomes than in those humans with with inflammatory bowel disease (IBD). Our discovery provides a potentially distinct means by which our microbiome interacts with the human host or its microbiome.

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics.

BACKGROUND: Microbial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to human behavior. METHODS: Here, we demonstrate a novel application of SourceTracker to work with metagenomic datasets and tested this approach using sink samples from a study of coastal marine environments. Source environment metagenomes were obtained from metagenomics studies of gut, freshwater, marine, sand and soil environments. As part of this effort, we implemented features for determining the stability of source proportion estimates, including precision visualizations for performance optimization, and performed domain-specific source-tracking analyses (i.e., Bacteria, Archaea, Eukaryota and viruses). We also applied SourceTracker to metagenomic libraries generated from samples collected from the International Space Station (ISS). RESULTS: SourceTracker proved highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of environmental source origins for both the coastal marine and ISS samples. These findings indicated that applying SourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.

Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism.

Machine learning techniques have been implemented to reveal brain features that distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) peers. However, it remains unknown whether different neuroimaging modalities are equally informative for diagnostic classification. We combined anatomical magnetic resonance imaging (aMRI), diffusion weighted imaging (DWI), and functional connectivity MRI (fcMRI) using conditional random forest (CRF) for supervised learning to compare how informative each modality was in diagnostic classification. In-house data (N = 93) included 47 TD and 46 ASD participants, matched on age, motion, and nonverbal IQ. Four main analyses consistently indicated that fcMRI variables were significantly more informative than anatomical variables from aMRI and DWI. This was found (1) when the top 100 variables from CRF (run separately in each modality) were combined for multimodal CRF; (2) when only 19 top variables reaching >67% accuracy in each modality were combined in multimodal CRF; and (3) when the large number of initial variables (before dimension reduction) potentially biasing comparisons in favor of fcMRI was reduced using a less granular region of interest scheme. Consistent superiority of fcMRI was even found (4) when 100 variables per modality were randomly selected, removing any such potential bias. Greater informative value of functional than anatomical modalities may relate to the nature of fcMRI data, reflecting more closely behavioral condition, which is also the basis of diagnosis, whereas brain anatomy may be more reflective of neurodevelopmental history.

Cardiomyocyte cell cycle dynamics and proliferation revealed through cardiac-specific transgenesis of fluorescent ubiquitinated cell cycle indicator (FUCCI).

RATIONALE: Understanding and manipulating the cardiomyocyte cell cycle has been the focus of decades of research, however the ultimate goal of activating mitotic activity in adult mammalian cardiomyocytes remains elusive and controversial. The relentless pursuit of controlling cardiomyocyte mitosis has been complicated and obfuscated by a multitude of indices used as evidence of cardiomyocyte cell cycle activity that lack clear identification of cardiomyocyte "proliferation" versus cell cycle progression, endoreplication, endomitosis, and even DNA damage. Unambiguous appreciation of the complexity of cardiomyocyte replication that avoids oversimplification and misinterpretation is desperately needed. OBJECTIVE: Track cardiomyocyte cell cycle activity and authenticate fidelity of proliferation markers as indicators of de novo cardiomyogenesis in post-mitotic cardiomyocytes. METHODS AND RESULTS: Cardiomyocytes expressing the FUCCI construct driven by the α-myosin heavy chain promoter were readily and uniformly detected through the myocardium of transgenic mice. Cardiomyocyte cell cycle activity peaks at postnatal day 2 and rapidly declines thereafter with almost all cardiomyocytes arrested at the G1/S cell cycle transition. Myocardial infarction injury in adult hearts prompts transient small increases in myocytes progressing through cell cycle without concurrent mitotic activity, indicating lack of cardiomyogenesis. In comparison, cardiomyogenic activity during early postnatal development correlated with coincidence of FUCCI and cKit+ cells that were undetectable in the adult myocardium. CONCLUSIONS: Cardiomyocyte-specific expression of Fluorescence Ubiquitination-based Cell Cycle Indicators (FUCCI) reveals previously unappreciated aspects of cardiomyocyte cell cycle arrest and biological activity in postnatal development and in response to pathologic damage. Compared to many other methods and model systems, the FUCCI transgenic (FUCCI-Tg) mouse represents a valuable tool to unambiguously track cell cycle and proliferation of the entire cardiomyocyte population in the adult murine heart. FUCCI-Tg provides a desperately needed novel approach in the armamentarium of tools to validate cardiomyocyte proliferative activity that will reveal cell cycle progression, discriminate between cycle progression, DNA replication, and proliferation, and provide important insight for enhancing cardiomyocyte proliferation in the context of adult myocardial tissue.

Cardiac c-Kit Biology Revealed by Inducible Transgenesis.

RATIONALE: Biological significance of c-Kit as a cardiac stem cell marker and role(s) of c-Kit+ cells in myocardial development or response to pathological injury remain unresolved because of varied and discrepant findings. Alternative experimental models are required to contextualize and reconcile discordant published observations of cardiac c-Kit myocardial biology and provide meaningful insights regarding clinical relevance of c-Kit signaling for translational cell therapy. OBJECTIVE: The main objectives of this study are as follows: demonstrating c-Kit myocardial biology through combined studies of both human and murine cardiac cells; advancing understanding of c-Kit myocardial biology through creation and characterization of a novel, inducible transgenic c-Kit reporter mouse model that overcomes limitations inherent to knock-in reporter models; and providing perspective to reconcile disparate viewpoints on c-Kit biology in the myocardium. METHODS AND RESULTS: In vitro studies confirm a critical role for c-Kit signaling in both cardiomyocytes and cardiac stem cells. Activation of c-Kit receptor promotes cell survival and proliferation in stem cells and cardiomyocytes of either human or murine origin. For creation of the mouse model, the cloned mouse c-Kit promoter drives Histone2B-EGFP (enhanced green fluorescent protein; H2BEGFP) expression in a doxycycline-inducible transgenic reporter line. The combination of c-Kit transgenesis coupled to H2BEGFP readout provides sensitive, specific, inducible, and persistent tracking of c-Kit promoter activation. Tagging efficiency for EGFP+/c-Kit+ cells is similar between our transgenic versus a c-Kit knock-in mouse line, but frequency of c-Kit+ cells in cardiac tissue from the knock-in model is 55% lower than that from our transgenic line. The c-Kit transgenic reporter model reveals intimate association of c-Kit expression with adult myocardial biology. Both cardiac stem cells and a subpopulation of cardiomyocytes express c-Kit in uninjured adult heart, upregulating c-Kit expression in response to pathological stress. CONCLUSIONS: c-Kit myocardial biology is more complex and varied than previously appreciated or documented, demonstrating validity in multiple points of coexisting yet heretofore seemingly irreconcilable published findings.

Median regression spline modeling of longitudinal FEV1 measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients.

RATIONALE: Clinical phenotyping, therapeutic investigations as well as genomic, airway secretion metabolomic and metagenomic investigations can benefit from robust, nonlinear modeling of FEV1 in individual subjects. We demonstrate the utility of measuring FEV1 dynamics in representative cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) populations. METHODS: Individual FEV1 data from CF and COPD subjects were modeled by estimating median regression splines and their predicted first and second derivatives. Classes were created from variables that capture the dynamics of these curves in both cohorts. RESULTS: Nine FEV1 dynamic variables were identified from the splines and their predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three FEV1 dynamic classes (i.e. stable, intermediate and hypervariable) were generated and described using these variables from both cohorts. In the CF cohort, the FEV1 hypervariable class (HV) was associated with a clinically unstable, female-dominated phenotypes while stable FEV1 class (S) individuals were highly associated with the male-dominated milder clinical phenotype. In the COPD cohort, associations were found between the FEV1 dynamic classes, the COPD GOLD grades, with exacerbation frequency and symptoms. CONCLUSION: Nonlinear modeling of FEV1 with splines provides new insights and is useful in characterizing CF and COPD clinical phenotypes.

Meta-mass shift chemical profiling of metabolomes from coral reefs.

Untargeted metabolomics of environmental samples routinely detects thousands of small molecules, the vast majority of which cannot be identified. Meta-mass shift chemical (MeMSChem) profiling was developed to identify mass differences between related molecules using molecular networks. This approach illuminates metabolome-wide relationships between molecules and the putative chemical groups that differentiate them (e.g., H2, CH2, COCH2). MeMSChem profiling was used to analyze a publicly available metabolomic dataset of coral, algal, and fungal mat holobionts (i.e., the host and its associated microbes and viruses) sampled from some of Earth's most remote and pristine coral reefs. Each type of holobiont had distinct mass shift profiles, even when the analysis was restricted to molecules found in all samples. This result suggests that holobionts modify the same molecules in different ways and offers insights into the generation of molecular diversity. Three genera of stony corals had distinct patterns of molecular relatedness despite their high degree of taxonomic relatedness. MeMSChem profiles also partially differentiated between individuals, suggesting that every coral reef holobiont is a potential source of novel chemical diversity.

Characterizing Lung Disease in Cystic Fibrosis with Magnetic Resonance Imaging and Airway Physiology.

Translational investigations in cystic fibrosis (CF) have a need for improved quantitative and longitudinal measures of disease status. To establish a non-invasive quantitative MRI technique to monitor lung health in patients with CF and correlate MR metrics with airway physiology as measured by multiple breath washout (MBW). Data were collected in 12 CF patients and 12 healthy controls. Regional (central and peripheral lung) measures of fractional lung water density (FLD: air to 100% fluid) were acquired both at FRC and TLC on a 1.5T MRI. The median FLD (mFLD) and the FRC-to-TLC mFLD ratio were calculated for each region at both lung volumes. Spirometry and MBW data were also acquired for each subject. Ventilation inhomogeneities were quantified by the lung clearance index (LCI) and by indices Scond* and Sacin* that assess inhomogeneities in the conducting (central) and acinar (peripheral) lung regions, respectively. MBW indices and mFLD at TLC (both regions) were significantly elevated in CF (p<0.01) compared to controls. The mFLD at TLC (central: R = 0.82) and the FRC-to-TLC mFLD ratio (peripheral: R = -0.77) were strongly correlated with Scond* and LCI. CF patients had high lung water content at TLC when compared to controls. This is likely due to the presence of retained airway secretions and airway wall edema (more water) and to limited expansions of air trapping areas (less air) in CF subjects. FRC-to-TLC ratios of mFLD strongly correlated with central ventilation inhomogeneities. These combined measures may provide a useful marker of both retained mucus and air trapping in CF lungs.

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

BACKGROUND: Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. METHODS: First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. RESULTS: A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. CONCLUSIONS: The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome.

Cystic fibrosis (CF) lungs are filled with thick mucus that obstructs airways and facilitates chronic infections. Pseudomonas aeruginosa is a significant pathogen of this disease that produces a variety of toxic small molecules. We used molecular networking-based metabolomics to investigate the chemistry of CF sputa and assess how the microbial molecules detected reflect the microbiome and clinical culture history of the patients. Metabolites detected included xenobiotics, P. aeruginosa specialized metabolites and host sphingolipids. The clinical culture and microbiome profiles did not correspond to the detection of P. aeruginosa metabolites in the same samples. The P. aeruginosa molecules that were detected in sputum did not match those from laboratory cultures. The pseudomonas quinolone signal (PQS) was readily detectable from cultured strains, but absent from sputum, even when its precursor molecules were present. The lack of PQS production in vivo is potentially due to the chemical nature of the CF lung environment, indicating that culture-based studies of this pathogen may not explain its behavior in the lung. The most differentially abundant molecules between CF and non-CF sputum were sphingolipids, including sphingomyelins, ceramides and lactosylceramide. As these highly abundant molecules contain the inflammatory mediator ceramide, they may have a significant role in CF hyperinflammation. This study demonstrates that the chemical makeup of CF sputum is a complex milieu of microbial, host and xenobiotic molecules. Detection of a bacterium by clinical culturing and 16S rRNA gene profiling do not necessarily reflect the active production of metabolites from that bacterium in a sputum sample.

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.

Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism.

Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagnostic classification have reached only moderate accuracy. We selected 252 low-motion resting-state functional MRI (rs-fMRI) scans from the Autism Brain Imaging Data Exchange (ABIDE) including typically developing (TD) and ASD participants (n = 126 each), matched for age, non-verbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification, implementing several machine learning tools. While support vector machines in combination with particle swarm optimization and recursive feature elimination performed modestly (with accuracies for validation datasets <70%), diagnostic classification reached a high accuracy of 91% with random forest (RF), a nonparametric ensemble learning method. Among the 100 most informative features (connectivities), for which this peak accuracy was achieved, participation of somatosensory, default mode, visual, and subcortical regions stood out. Whereas some of these findings were expected, given previous findings of default mode abnormalities and atypical visual functioning in ASD, the prominent role of somatosensory regions was remarkable. The finding of peak accuracy for 100 interregional functional connectivities further suggests that brain biomarkers of ASD may be regionally complex and distributed, rather than localized.

Multidimensional clinical phenotyping of an adult cystic fibrosis patient population.

BACKGROUND: Cystic Fibrosis (CF) is a multi-systemic disease resulting from mutations in the Cystic Fibrosis Transmembrane Regulator (CFTR) gene and has major manifestations in the sino-pulmonary, and gastro-intestinal tracts. Clinical phenotypes were generated using 26 common clinical variables to generate classes that overlapped quantiles of lung function and were based on multiple aspects of CF systemic disease. METHODS: The variables included age, gender, CFTR mutations, FEV1% predicted, FVC% predicted, height, weight, Brasfield chest xray score, pancreatic sufficiency status and clinical microbiology results. Complete datasets were compiled on 211 subjects. Phenotypes were identified using a proximity matrix generated by the unsupervised Random Forests algorithm and subsequent clustering by the Partitioning around Medoids (PAM) algorithm. The final phenotypic classes were then characterized and compared to a similar dataset obtained three years earlier. FINDINGS: Clinical phenotypes were identified using a clustering strategy that generated four and five phenotypes. Each strategy identified 1) a low lung health scores phenotype, 2) a younger, well-nourished, male-dominated class, 3) various high lung health score phenotypes that varied in terms of age, gender and nutritional status. This multidimensional clinical phenotyping strategy identified classes with expected microbiology results and low risk clinical phenotypes with pancreatic sufficiency. INTERPRETATION: This study demonstrated regional adult CF clinical phenotypes using non-parametric, continuous, ordinal and categorical data with a minimal amount of subjective data to identify clinically relevant phenotypes. These studies identified the relative stability of the phenotypes, demonstrated specific phenotypes consistent with published findings and identified others needing further study.

Multivariate analysis of functional metagenomes.

Metagenomics is a primary tool for the description of microbial and viral communities. The sheer magnitude of the data generated in each metagenome makes identifying key differences in the function and taxonomy between communities difficult to elucidate. Here we discuss the application of seven different data mining and statistical analyses by comparing and contrasting the metabolic functions of 212 microbial metagenomes within and between 10 environments. Not all approaches are appropriate for all questions, and researchers should decide which approach addresses their questions. This work demonstrated the use of each approach: for example, random forests provided a robust and enlightening description of both the clustering of metagenomes and the metabolic processes that were important in separating microbial communities from different environments. All analyses identified that the presence of phage genes within the microbial community was a predictor of whether the microbial community was host-associated or free-living. Several analyses identified the subtle differences that occur with environments, such as those seen in different regions of the marine environment.

Applying Shannon's information theory to bacterial and phage genomes and metagenomes.

All sequence data contain inherent information that can be measured by Shannon's uncertainty theory. Such measurement is valuable in evaluating large data sets, such as metagenomic libraries, to prioritize their analysis and annotation, thus saving computational resources. Here, Shannon's index of complete phage and bacterial genomes was examined. The information content of a genome was found to be highly dependent on the genome length, GC content, and sequence word size. In metagenomic sequences, the amount of information correlated with the number of matches found by comparison to sequence databases. A sequence with more information (higher uncertainty) has a higher probability of being significantly similar to other sequences in the database. Measuring uncertainty may be used for rapid screening for sequences with matches in available database, prioritizing computational resources, and indicating which sequences with no known similarities are likely to be important for more detailed analysis.

PHACTS, a computational approach to classifying the lifestyle of phages.

MOTIVATION: Bacteriophages have two distinct lifestyles: virulent and temperate. The virulent lifestyle has many implications for phage therapy, genomics and microbiology. Determining which lifestyle a newly sequenced phage falls into is currently determined using standard culturing techniques. Such laboratory work is not only costly and time consuming, but also cannot be used on phage genomes constructed from environmental sequencing. Therefore, a computational method that utilizes the sequence data of phage genomes is needed. RESULTS: Phage Classification Tool Set (PHACTS) utilizes a novel similarity algorithm and a supervised Random Forest classifier to make a prediction whether the lifestyle of a phage, described by its proteome, is virulent or temperate. The similarity algorithm creates a training set from phages with known lifestyles and along with the lifestyle annotation, trains a Random Forest to classify the lifestyle of a phage. PHACTS predictions are shown to have a 99% precision rate. AVAILABILITY AND IMPLEMENTATION: PHACTS was implemented in the PERL programming language and utilizes the FASTA program (Pearson and Lipman, 1988) and the R programming language library 'Random Forest' (Liaw and Weiner, 2010). The PHACTS software is open source and is available as downloadable stand-alone version or can be accessed online as a user-friendly web interface. The source code, help files and online version are available at http://www.phantome.org/PHACTS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational analysis of HIV-1 protease protein binding pockets.

Mutations that arise in HIV-1 protease after exposure to various HIV-1 protease inhibitors have proved to be a difficult aspect in the treatment of HIV. Mutations in the binding pocket of the protease can prevent the protease inhibitor from binding to the protein effectively. In the present study, the crystal structures of 68 HIV-1 proteases complexed with one of the nine FDA approved protease inhibitors from the Protein Data Bank (PDB) were analyzed by (a) identifying the mutational changes with the aid of a developed mutation map and (b) correlating the structure of the binding pockets with the complexed inhibitors. The mutations of each crystal structure were identified by comparing the amino acid sequence of each structure against the HIV-1 wild-type strain HXB2. These mutations were visually presented in the form of a mutation map to analyze mutation patterns corresponding to each protease inhibitor. The crystal structure mutation patterns of each inhibitor (in vitro) were compared against the mutation patterns observed in in vivo data. The in vitro mutation patterns were found to be representative of most of the major in vivo mutations. We then performed a data mining analysis of the binding pockets from each crystal structure in terms of their chemical descriptors to identify important structural features of the HIV-1 protease protein with respect to the binding conformation of the HIV-1 protease inhibitors. Data mining analysis is performed using several classification techniques: Random Forest (RF), linear discriminant analysis (LDA), and logistic regression (LR). We developed two hybrid models, RF-LDA and RF-LR. Random Forest is used as a feature selection proxy, reducing the descriptor space to a few of the most relevant descriptors determined by the classifier. These descriptors are then used to develop the subsequent LDA, LR, and hierarchical classification models. Clustering analysis of the binding pockets using the selected descriptors used to produce the optimal classification models reveals conformational similarities of the ligands in each cluster. This study provides important information in understanding the structural features of HIV-1 protease which cannot be studied by other existing in vivo genomic data sets.