Risk-based Monitoring of Clinical Trials: An Integrative Approach.

PURPOSE: Clinical trial monitoring is an essential component of drug development aimed at safeguarding subject safety, data quality, and protocol compliance by focusing sponsor oversight on the most important aspects of study conduct. In recent years, regulatory agencies, industry consortia, and nonprofit collaborations between industry and regulators, such as TransCelerate and International Committee for Harmonization, have been advocating a new, risk-based approach to monitoring clinical trials that places increased emphasis on critical data and processes and encourages greater use of centralized monitoring. However, how best to implement risk-based monitoring (RBM) remains unclear and subject to wide variations in tools and methodologies. The nonprescriptive nature of the regulatory guidelines, coupled with limitations in software technology, challenges in operationalization, and lack of robust evidence of superior outcomes, have hindered its widespread adoption. METHODS: We describe a holistic solution that combines convenient access to data, advanced analytics, and seamless integration with established technology infrastructure to enable comprehensive assessment and mitigation of risk at the study, site, and subject level. FINDINGS: Using data from completed RBM studies carried out in the last 4 years, we demonstrate that our implementation of RBM improves the efficiency and effectiveness of the clinical oversight process as measured on various quality, timeline, and cost dimensions. IMPLICATIONS: These results provide strong evidence that our RBM methodology can significantly improve the clinical oversight process and do so at a lower cost through more intelligent deployment of monitoring resources to the sites that need the most attention.

Using SQL Databases for Sequence Similarity Searching and Analysis.

Relational databases can integrate diverse types of information and manage large sets of similarity search results, greatly simplifying genome-scale analyses. By focusing on taxonomic subsets of sequences, relational databases can reduce the size and redundancy of sequence libraries and improve the statistical significance of homologs. In addition, by loading similarity search results into a relational database, it becomes possible to explore and summarize the relationships between all of the proteins in an organism and those in other biological kingdoms. This unit describes how to use relational databases to improve the efficiency of sequence similarity searching and demonstrates various large-scale genomic analyses of homology-related data. It also describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. The unit also introduces search_demo, a database that stores sequence similarity search results. The search_demo database is then used to explore the evolutionary relationships between E. coli proteins and proteins in other organisms in a large-scale comparative genomic analysis. © 2017 by John Wiley & Sons, Inc.

Optimization of techniques for multiple platform testing in small, precious samples such as human chorionic villus sampling.

BACKGROUND: Multiple testing to understand global changes in gene expression based on genetic and epigenetic modifications is evolving. Chorionic villi, obtained for prenatal testing, is limited, but can be used to understand ongoing human pregnancies. However, optimal storage, processing and utilization of CVS for multiple platform testing have not been established. RESULTS: Leftover CVS samples were flash-frozen or preserved in RNAlater. Modifications to standard isolation kits were performed to isolate quality DNA and RNA from samples as small as 2-5 mg. RNAlater samples had significantly higher RNA yields and quality and were successfully used in microarray and RNA-sequencing (RNA-seq). RNA-seq libraries generated using 200 versus 800-ng RNA showed similar biological coefficients of variation. RNAlater samples had lower DNA yields and quality, which improved by heating the elution buffer to 70 °C. Purification of DNA was not necessary for bisulfite-conversion and genome-wide methylation profiling. CVS cells were propagated and continue to express genes found in freshly isolated chorionic villi. CONCLUSIONS: CVS samples preserved in RNAlater are superior. Our optimized techniques provide specimens for genetic, epigenetic and gene expression studies from a single small sample which can be used to develop diagnostics and treatments using a systems biology approach in the prenatal period. © 2016 John Wiley & Sons, Ltd.

Targeted Deep Sequencing in Multiple-Affected Sibships of European Ancestry Identifies Rare Deleterious Variants in PTPN22 That Confer Risk for Type 1 Diabetes.

Despite finding more than 40 risk loci for type 1 diabetes (T1D), the causative variants and genes remain largely unknown. Here, we sought to identify rare deleterious variants of moderate-to-large effects contributing to T1D. We deeply sequenced 301 protein-coding genes located in 49 previously reported T1D risk loci in 70 T1D cases of European ancestry. These cases were selected from putatively high-risk families that had three or more siblings diagnosed with T1D at early ages. A cluster of rare deleterious variants in PTPN22 was identified, including two novel frameshift mutations (ss538819444 and rs371865329) and two missense variants (rs74163663 and rs56048322). Genotyping in 3,609 T1D families showed that rs56048322 was significantly associated with T1D and that this association was independent of the T1D-associated common variant rs2476601. The risk allele at rs56048322 affects splicing of PTPN22, resulting in the production of two alternative PTPN22 transcripts and a novel isoform of LYP (the protein encoded by PTPN22). This isoform competes with the wild-type LYP for binding to CSK and results in hyporesponsiveness of CD4(+) T cells to antigen stimulation in T1D subjects. These findings demonstrate that in addition to common variants, rare deleterious variants in PTPN22 exist and can affect T1D risk.

Combinatorial drug screening and molecular profiling reveal diverse mechanisms of intrinsic and adaptive resistance to BRAF inhibition in V600E BRAF mutant melanomas.

Over half of BRAFV600E melanomas display intrinsic resistance to BRAF inhibitors, in part due to adaptive signaling responses. In this communication we ask whether BRAFV600E melanomas share common adaptive responses to BRAF inhibition that can provide clinically relevant targets for drug combinations. We screened a panel of 12 treatment-naïve BRAFV600E melanoma cell lines with MAP Kinase pathway inhibitors in pairwise combination with 58 signaling inhibitors, assaying for synergistic cytotoxicity. We found enormous diversity in the drug combinations that showed synergy, with no two cell lines having an identical profile. Although the 6 lines most resistant to BRAF inhibition showed synergistic benefit from combination with lapatinib, the signaling mechanisms by which this combination generated synergistic cytotoxicity differed between the cell lines. We conclude that adaptive responses to inhibition of the primary oncogenic driver (BRAFV600E) are determined not only by the primary oncogenic driver but also by diverse secondary genetic and epigenetic changes ("back-seat drivers") and hence optimal drug combinations will be variable. Because upregulation of receptor tyrosine kinases is a major source of drug resistance arising from diverse adaptive responses, we propose that inhibitors of these receptors may have substantial clinical utility in combination with inhibitors of the MAP Kinase pathway.

Transcriptional profiling suggests that Nevirapine and Ritonavir cause drug induced liver injury through distinct mechanisms in primary human hepatocytes.

Drug induced liver injury (DILI), a major cause of pre- and post-approval failure, is challenging to predict pre-clinically due to varied underlying direct and indirect mechanisms. Nevirapine, a non-nucleoside reverse transcriptase inhibitor (NNRTI) and Ritonavir, a protease inhibitor, are antiviral drugs that cause clinical DILI with different phenotypes via different mechanisms. Assessing DILI in vitro in hepatocyte cultures typically requires drug exposures significantly higher than clinical plasma Cmax concentrations, making clinical interpretations of mechanistic pathway changes challenging. We previously described a system that uses liver-derived hemodynamic blood flow and transport parameters to restore primary human hepatocyte biology, and drug responses at concentrations relevant to in vivo or clinical exposure levels. Using this system, primary hepatocytes from 5 human donors were exposed to concentrations approximating clinical therapeutic and supra-therapeutic levels of Nevirapine (11.3 and 175.0 µM) and Ritonavir (3.5 and 62.4 µM) for 48 h. Whole genome transcriptomics was performed by RNAseq along with functional assays for metabolic activity and function. We observed effects at both doses, but a greater number of genes were differentially expressed with higher probability at the toxic concentrations. At the toxic doses, both drugs showed direct cholestatic potential with Nevirapine increasing bile synthesis and Ritonavir inhibiting bile acid transport. Clear differences in antigen presentation were noted, with marked activation of MHC Class I by Nevirapine and suppression by Ritonavir. This suggests CD8+ T cell involvement for Nevirapine and possibly NK Killer cells for Ritonavir. Both compounds induced several drug metabolizing genes (including CYP2B6, CYP3A4 and UGT1A1), mediated by CAR activation in Nevirapine and PXR in Ritonavir. Unlike Ritonavir, Nevirapine did not increase fatty acid synthesis or activate the respiratory electron chain with simultaneous mitochondrial uncoupling supporting clinical reports of a lower propensity for steatosis. This in vitro study offers insights into the disparate direct and immune-mediated toxicity mechanisms underlying Nevirapine and Ritonavir toxicity in the clinic.

Systems Analysis of Adaptive Responses to MAP Kinase Pathway Blockade in BRAF Mutant Melanoma.

Fifty percent of cutaneous melanomas are driven by activated BRAFV600E, but tumors treated with RAF inhibitors, even when they respond dramatically, rapidly adapt and develop resistance. Thus, there is a pressing need to identify the major mechanisms of intrinsic and adaptive resistance and develop drug combinations that target these resistance mechanisms. In a combinatorial drug screen on a panel of 12 treatment-naïve BRAFV600E mutant melanoma cell lines of varying levels of resistance to mitogen-activated protein kinase (MAPK) pathway inhibition, we identified the combination of PLX4720, a targeted inhibitor of mutated BRaf, and lapatinib, an inhibitor of the ErbB family of receptor tyrosine kinases, as synergistically cytotoxic in the subset of cell lines that displayed the most resistance to PLX4720. To identify potential mechanisms of resistance to PLX4720 treatment and synergy with lapatinib treatment, we performed a multi-platform functional genomics analysis to profile the genome as well as the transcriptional and proteomic responses of these cell lines to treatment with PLX4720. We found modest levels of resistance correlated with the zygosity of the BRAF V600E allele and receptor tyrosine kinase (RTK) mutational status. Layered over base-line resistance was substantial upregulation of many ErbB pathway genes in response to BRaf inhibition, thus generating the vulnerability to combination with lapatinib. The transcriptional responses of ErbB pathway genes are associated with a number of transcription factors, including ETS2 and its associated cofactors that represent a convergent regulatory mechanism conferring synergistic drug susceptibility in the context of diverse mutational landscapes.

An In Vitro Cynomolgus Vascular Surrogate System for Preclinical Drug Assessment and Human Translation.

OBJECTIVES: The predictive value of animal and in vitro systems for drug development is limited, particularly for nonhuman primate studies as it is difficult to deduce the drug mechanism of action. We describe the development of an in vitro cynomolgus macaque vascular system that reflects the in vivo biology of healthy, atheroprone, or advanced inflammatory cardiovascular disease conditions. APPROACH AND RESULTS: We compare the responses of the in vitro human and cynomolgus vascular systems to 4 statins. Although statins exert beneficial pleiotropic effects on the human vasculature, the mechanism of action is difficult to investigate at the tissue level. Using RNA sequencing, we quantified the response to statins and report that most statins significantly increased the expression of genes that promote vascular health while suppressing inflammatory cytokine gene expression. Applying computational pathway analytics, we identified statin-regulated biological themes, independent of cholesterol lowering, that provide mechanisms for off-target effects, including thrombosis, cell cycle regulation, glycogen metabolism, and ethanol degradation. CONCLUSIONS: The cynomolgus vascular system described herein mimics the baseline and inflammatory regional biology of the human vasculature, including statin responsiveness, and provides mechanistic insight not achievable in vivo.

An Islet-Targeted Genome-Wide Association Scan Identifies Novel Genes Implicated in Cytokine-Mediated Islet Stress in Type 2 Diabetes.

Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk. Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of ß-cell failure. In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D. Genes with a cytokine-induced change of >2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium. Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3), F13a1 (6p25.3), Klhl6 (3q27.1), Nid1 (1q42.3), Pamr1 (11p13), Ripk2 (8q21.3), and Steap4 (7q21.12). To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity. RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3. Thapsigargin-induced ER stress up-regulated both Pamr1 and Klhl6. Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5- to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation). Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population. This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.

Identification of a new target of miR-16, Vacuolar Protein Sorting 4a.

RATIONALE: The rationale was to utilize a bioinformatics approach to identify miRNA binding sites in genes with single nucleotide mutations (SNPs) to discover pathways in heart failure (HF). OBJECTIVE: The objective was to focus on the genes containing miRNA binding sites with miRNAs that were significantly altered in end-stage HF and in response to a left ventricular assist device (LVAD). METHODS AND RESULTS: BEDTools v2.14.3 was used to discriminate SNPs within predicted 3'UTR miRNA binding sites. A member of the miR-15/107 family, miR-16, was decreased in the circulation of end-stage HF patients and increased in response to a LVAD (p<0.001). MiR-16 decreased Vacuolar Protein Sorting 4a (VPS4a) expression in HEK 293T cells (p<0.01). The SNP rs16958754 was identified in the miR-15/107 family binding site of VPS4a which abolished direct binding of miR-16 to the 3'UTR of VPS4a (p<0.05). VPS4a was increased in the circulation of end-stage HF patients (p<0.001), and led to a decrease in the number of HEK 293T cells in vitro (p<0.001). CONCLUSIONS: We provide evidence that miR-16 decreases in the circulation of end-stage HF patients and increases with a LVAD. Modeling studies suggest that miR-16 binds to and decreases expression of VPS4a. Overexpression of VPS4a decreases cell number. Together, these experiments suggest that miR-16 and VPS4a expression are altered in end-stage HF and in response to unloading with a LVAD. This signaling pathway may lead to reduced circulating cell number in HF.

BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity.

BACKGROUND: Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a "gold standard" of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user's tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity. RESULTS: We assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC's superior variant-calling accuracy, both for somatic mutation detection and germline variant detection. CONCLUSIONS: BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.

Synthetic lethal screening with small-molecule inhibitors provides a pathway to rational combination therapies for melanoma.

Recent data show that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways, suggesting that the inhibition of a single component of a canonical pathway is insufficient for the treatment of cancer. The biologic outcome of signaling through a network is inherently more robust and resistant to inhibition of a single network component. In this study, we conducted a functional chemical genetic screen to identify novel interactions between signaling inhibitors that would not be predicted on the basis of our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. The synergistic cytotoxicities identified did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. Among the most robust results was synergy between sorafenib, a multikinase inhibitor with activity against RAF, and diclofenac, a nonsteroidal anti-inflammatory drug (NSAID). Drug substitution experiments using the NSAIDs celecoxib and ibuprofen or the MAP-ERK kinase inhibitor PD325901 and the RAF inhibitor RAF265 suggest that inhibition of COX and mitogen-activated protein kinase signaling are targets for the synergistic cytotoxicity of sorafenib and diclofenac. Cotreatment with sorafenib and diclofenac interrupts a positive feedback signaling loop involving extracellular signal-regulated kinase, cellular phospholipase A2, and COX. Genome-wide expression profiling shows synergy-specific downregulation of survival-related genes. This study has uncovered novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially, depending on unexplored components of the cell genotype.

Global changes in gene expression during compatible and incompatible interactions of cowpea (Vigna unguiculata L.) with the root parasitic angiosperm Striga gesnerioides.

BACKGROUND: Cowpea, Vigna unguiculata L. Walp., is one of the most important food and forage legumes in the semi-arid tropics. While most domesticated forms of cowpea are susceptible to the root parasitic weed Striga gesnerioides, several cultivars have been identified that show race-specific resistance. Cowpea cultivar B301 contains the RSG3-301 gene for resistance to S. gesnerioides race SG3, but is susceptible to race SG4z. When challenged by SG3, roots of cultivar B301 develop a strong resistance response characterized by a hypersensitive reaction and cell death at the site of parasite attachment. In contrast, no visible response occurs in B301 roots parasitized by SG4z. RESULTS: Gene expression in the roots of the cowpea cultivar B301 during compatible (susceptible) and incompatible (resistant) interactions with S. gesnerioides races SG4z and SG3, respectively, were investigated at the early (6 days post-inoculation (dpi)) and late (13 dpi) stages of the resistance response using a Nimblegen custom design cowpea microarray. A total of 111 genes were differentially expressed in B301 roots at 6 dpi; this number increased to 2102 genes at 13 dpi. At 13 dpi, a total of 1944 genes were differentially expressed during compatible (susceptible) interactions of B301 with SG4z. Genes and pathways involved in signal transduction, programmed cell death and apoptosis, and defense response to biotic and abiotic stress were differentially expressed in the early resistance response; at the later time point, enrichment was primarily for defense-related gene expression, and genes encoding components of lignifications and secondary wall formation. In compatible interactions (B301-SG4z), multiple defense pathways were repressed, including those involved in lignin biosynthesis and secondary cell wall modifications, while cellular transport processes for nitrogen and sulfur were increased. CONCLUSION: Distinct changes in global gene expression profiles occur in host roots following successful and unsuccessful attempted parasitism by Striga. Induction of specific defense related genes and pathways defines components of a unique resistance mechanism. Some genes and pathways up-regulated in the host resistance response to SG3 are repressed in the susceptible interactions, suggesting that the parasite is targeting specific components of the host's defense. These results add to our understanding of plant-parasite interactions and the evolution of resistance to parasitic weeds.

Leptin protects host cells from Entamoeba histolytica cytotoxicity by a STAT3-dependent mechanism.

The adipocytokine leptin links nutritional status to immune function. Leptin signaling protects from amebiasis, but the molecular mechanism is not understood. We developed an in vitro model of ameba-host cell interaction to test the hypothesis that leptin prevents ameba-induced apoptosis in host epithelial cells. We demonstrated that activation of mammalian leptin signaling increased cellular resistance to amebic cytotoxicity, including caspase-3 activation. Exogenous expression of the leptin receptor conferred resistance in susceptible cells, and leptin stimulation enhanced protection. A series of leptin receptor signaling mutants showed that resistance to amebic cytotoxicity was dependent on activation of STAT3 but not the Src homology-2 domain-containing tyrosine phosphatase (SHP-2) or STAT5. A common polymorphism in the leptin receptor (Q223R) that increases susceptibility to amebiasis in humans and mice was found to increase susceptibility to amebic cytotoxicity in single cells. The Q223R polymorphism also decreased leptin-dependent STAT3 activation by 21% relative to that of the wild-type (WT) receptor (P = 0.035), consistent with a central role of STAT3 signaling in protection. A subset of genes uniquely regulated by STAT3 in response to leptin was identified. Most notable were the TRIB1 and suppressor of cytokine signaling 3 (SOCS3) genes, which have opposing roles in the regulation of apoptosis. Overall apoptotic genes were highly enriched in this gene set (P < 1E-05), supporting the hypothesis that leptin regulation of host apoptotic genes via STAT3 is responsible for protection. This is the first demonstration of a mammalian signaling pathway that restricts amebic pathogenesis and represents an important advance in our mechanistic understanding of how leptin links nutrition and susceptibility to infection.

The Drosophila melanogaster Genetic Reference Panel.

A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.

Insights into evolution of multicellular fungi from the assembled chromosomes of the mushroom Coprinopsis cinerea (Coprinus cinereus).

The mushroom Coprinopsis cinerea is a classic experimental model for multicellular development in fungi because it grows on defined media, completes its life cycle in 2 weeks, produces some 10(8) synchronized meiocytes, and can be manipulated at all stages in development by mutation and transformation. The 37-megabase genome of C. cinerea was sequenced and assembled into 13 chromosomes. Meiotic recombination rates vary greatly along the chromosomes, and retrotransposons are absent in large regions of the genome with low levels of meiotic recombination. Single-copy genes with identifiable orthologs in other basidiomycetes are predominant in low-recombination regions of the chromosome. In contrast, paralogous multicopy genes are found in the highly recombining regions, including a large family of protein kinases (FunK1) unique to multicellular fungi. Analyses of P450 and hydrophobin gene families confirmed that local gene duplications drive the expansions of paralogous copies and the expansions occur in independent lineages of Agaricomycotina fungi. Gene-expression patterns from microarrays were used to dissect the transcriptional program of dikaryon formation (mating). Several members of the FunK1 kinase family are differentially regulated during sexual morphogenesis, and coordinate regulation of adjacent duplications is rare. The genomes of C. cinerea and Laccaria bicolor, a symbiotic basidiomycete, share extensive regions of synteny. The largest syntenic blocks occur in regions with low meiotic recombination rates, no transposable elements, and tight gene spacing, where orthologous single-copy genes are overrepresented. The chromosome assembly of C. cinerea is an essential resource in understanding the evolution of multicellularity in the fungi.

Evigan: a hidden variable model for integrating gene evidence for eukaryotic gene prediction.

MOTIVATION: The increasing diversity and variable quality of evidence relevant to gene annotation argues for a probabilistic framework that automatically integrates such evidence to yield candidate gene models. RESULTS: Evigan is an automated gene annotation program for eukaryotic genomes, employing probabilistic inference to integrate multiple sources of gene evidence. The probabilistic model is a dynamic Bayes network whose parameters are adjusted to maximize the probability of observed evidence. Consensus gene predictions are then derived by maximum likelihood decoding, yielding n-best models (with probabilities for each). Evigan is capable of accommodating a variety of evidence types, including (but not limited to) gene models computed by diverse gene finders, BLAST hits, EST matches, and splice site predictions; learned parameters encode the relative quality of evidence sources. Since separate training data are not required (apart from the training sets used by individual gene finders), Evigan is particularly attractive for newly sequenced genomes where little or no reliable manually curated annotation is available. The ability to produce a ranked list of alternative gene models may facilitate identification of alternatively spliced transcripts. Experimental application to ENCODE regions of the human genome, and the genomes of Plasmodium vivax and Arabidopsis thaliana show that Evigan achieves better performance than any of the individual data sources used as evidence. AVAILABILITY: The source code is available at http://www.seas.upenn.edu/~strctlrn/evigan/evigan.html.

ToxoDB: an integrated Toxoplasma gondii database resource.

ToxoDB (http://ToxoDB.org) is a genome and functional genomic database for the protozoan parasite Toxoplasma gondii. It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. ToxoDB has matured significantly since its initial release. Here we outline the numerous updates with respect to the data and increased functionality available on the website.

Periplasmic proteins of the extremophile Acidithiobacillus ferrooxidans: a high throughput proteomics analysis.

Acidithiobacillus ferrooxidans is a chemolithoautotrophic acidophile capable of obtaining energy by oxidizing ferrous iron or sulfur compounds such as metal sulfides. Some of the proteins involved in these oxidations have been described as forming part of the periplasm of this extremophile. The detailed study of the periplasmic components constitutes an important area to understand the physiology and environmental interactions of microorganisms. Proteomics analysis of the periplasmic fraction of A. ferrooxidans ATCC 23270 was performed by using high resolution linear ion trap-FT MS. We identified a total of 131 proteins in the periplasm of the microorganism grown in thiosulfate. When possible, functional categories were assigned to the proteins: 13.8% were transport and binding proteins, 14.6% were several kinds of cell envelope proteins, 10.8% were involved in energy metabolism, 10% were related to protein fate and folding, 10% were proteins with unknown functions, and 26.1% were proteins without homologues in databases. These last proteins are most likely characteristic of A. ferrooxidans and may have important roles yet to be assigned. The majority of the periplasmic proteins from A. ferrooxidans were very basic compared with those of neutrophilic microorganisms such as Escherichia coli, suggesting a special adaptation of the chemolithoautotrophic bacterium to its very acidic environment. The high throughput proteomics approach used here not only helps to understand the physiology of this extreme acidophile but also offers an important contribution to the functional annotation for the available genomes of biomining microorganisms such as A. ferrooxidans for which no efficient genetic systems are available to disrupt genes by procedures such as homologous recombination.

Assessing performance of orthology detection strategies applied to eukaryotic genomes.

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale 'gold standard' orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.