CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction

CONTRAST is a gene predictor that directly incorporates information from multiple alignments and uses discriminative machine learning techniques to give large improvements in prediction over previous methods

Genetic and Computational Identification of a Conserved Bacterial Metabolic Module

We have experimentally and computationally defined a set of genes that form a conserved metabolic module in the α-proteobacterium Caulobacter crescentus and used this module to illustrate a schema for the propagation of pathway-level annotation across bacterial genera. Applying comprehensive forward and reverse genetic methods and genome-wide transcriptional analysis, we (1) confirmed the presence of genes involved in catabolism of the abundant environmental sugar myo-inositol, (2) defined an operon encoding an ABC-family myo-inositol transmembrane transporter, and (3) identified a novel myo-inositol regulator protein and cis-acting regulatory motif that control expression of genes in this metabolic module. Despite being encoded from non-contiguous loci on the C. crescentus chromosome, these myo-inositol catabolic enzymes and transporter proteins form a tightly linked functional group in a computationally inferred network of protein associations. Primary sequence comparison was not sufficient to confidently extend annotation of all components of this novel metabolic module to related bacterial genera. Consequently, we implemented the Graemlin multiple-network alignment algorithm to generate cross-species predictions of genes involved in myo-inositol transport and catabolism in other α-proteobacteria. Although the chromosomal organization of genes in this functional module varied between species, the upstream regions of genes in this aligned network were enriched for the same palindromic cis-regulatory motif identified experimentally in C. crescentus. Transposon disruption of the operon encoding the computationally predicted ABC myo-inositol transporter of Sinorhizobium meliloti abolished growth on myo-inositol as the sole carbon source, confirming our cross-genera functional prediction. Thus, we have defined regulatory, transport, and catabolic genes and a cis-acting regulatory sequence that form a conserved module required for myo-inositol metabolism in select α-proteobacteria. Moreover, this study describes a forward validation of gene-network alignment, and illustrates a strategy for reliably transferring pathway-level annotation across bacterial species.</p

Genetic and Computational Identification of a Conserved Bacterial Metabolic Module

We have experimentally and computationally defined a set of genes that form a conserved metabolic module in the α-proteobacterium Caulobacter crescentus and used this module to illustrate a schema for the propagation of pathway-level annotation across bacterial genera. Applying comprehensive forward and reverse genetic methods and genome-wide transcriptional analysis, we (1) confirmed the presence of genes involved in catabolism of the abundant environmental sugar myo-inositol, (2) defined an operon encoding an ABC-family myo-inositol transmembrane transporter, and (3) identified a novel myo-inositol regulator protein and cis-acting regulatory motif that control expression of genes in this metabolic module. Despite being encoded from non-contiguous loci on the C. crescentus chromosome, these myo-inositol catabolic enzymes and transporter proteins form a tightly linked functional group in a computationally inferred network of protein associations. Primary sequence comparison was not sufficient to confidently extend annotation of all components of this novel metabolic module to related bacterial genera. Consequently, we implemented the Graemlin multiple-network alignment algorithm to generate cross-species predictions of genes involved in myo-inositol transport and catabolism in other α-proteobacteria. Although the chromosomal organization of genes in this functional module varied between species, the upstream regions of genes in this aligned network were enriched for the same palindromic cis-regulatory motif identified experimentally in C. crescentus. Transposon disruption of the operon encoding the computationally predicted ABC myo-inositol transporter of Sinorhizobium meliloti abolished growth on myo-inositol as the sole carbon source, confirming our cross-genera functional prediction. Thus, we have defined regulatory, transport, and catabolic genes and a cis-acting regulatory sequence that form a conserved module required for myo-inositol metabolism in select α-proteobacteria. Moreover, this study describes a forward validation of gene-network alignment, and illustrates a strategy for reliably transferring pathway-level annotation across bacterial species

Autoimmune Disease Classification by Inverse Association with SNP Alleles

With multiple genome-wide association studies (GWAS) performed across autoimmune diseases, there is a great opportunity to study the homogeneity of genetic architectures across autoimmune disease. Previous approaches have been limited in the scope of their analysis and have failed to properly incorporate the direction of allele-specific disease associations for SNPs. In this work, we refine the notion of a genetic variation profile for a given disease to capture strength of association with multiple SNPs in an allele-specific fashion. We apply this method to compare genetic variation profiles of six autoimmune diseases: multiple sclerosis (MS), ankylosing spondylitis (AS), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), Crohn's disease (CD), and type 1 diabetes (T1D), as well as five non-autoimmune diseases. We quantify pair-wise relationships between these diseases and find two broad clusters of autoimmune disease where SNPs that make an individual susceptible to one class of autoimmune disease also protect from diseases in the other autoimmune class. We find that RA and AS form one such class, and MS and ATD another. We identify specific SNPs and genes with opposite risk profiles for these two classes. We furthermore explore individual SNPs that play an important role in defining similarities and differences between disease pairs. We present a novel, systematic, cross-platform approach to identify allele-specific relationships between disease pairs based on genetic variation as well as the individual SNPs which drive the relationships. While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways

Sequencing of \u3ci\u3eAspergillus nidulans\u3c/i\u3e and comparative analysis with \u3ci\u3eA. fumigatus\u3c/i\u3e and \u3ci\u3eA. oryzae\u3c/i\u3e

The aspergilli comprise a diverse group of filamentous fungi spanning over 200 million years of evolution. Here we report the genome sequence of the model organism Aspergillus nidulans, and a comparative study with Aspergillus fumigatus, a serious human pathogen, and Aspergillus oryzae, used in the production of sake, miso, and soy sauce. Our analysis of genome structure provided a quantitative evaluation of forces driving long-term eukaryotic genome evolution. It also led to an experimentally validated model of mating-type locus evolution, suggesting the potential for sexual reproduction in A. fumigatus and A. oryzae. Our analysis of sequence conservation revealed over 5,000 non-coding regions actively conserved across all three species. Within these regions, we identified potential functional elements including a previously uncharacterized TPP riboswitch and motifs suggesting regulation in filamentous fungi by Puf family genes. We further obtained comparative and experimental evidence indicating widespread translational regulation by upstream open reading frames. These results enhance our understanding of these widely studied fungi as well as provide new insight into eukaryotic genome evolution and gene regulation. Document includes all supplementary information (820 pages). Supplementary files are also attached below as Related files. THERE IS NO SUPPLEMENTARY FILE #7. PDF file size (with supplementary files included) is 10 Mbytes. An optimized version of the ARTICLE ONLY is attached as a Related File and is 1.9 Mbytes

Sequencing of \u3ci\u3eAspergillus nidulans\u3c/i\u3e and comparative analysis with \u3ci\u3eA. fumigatus\u3c/i\u3e and \u3ci\u3eA. oryzae\u3c/i\u3e

The aspergilli comprise a diverse group of filamentous fungi spanning over 200 million years of evolution. Here we report the genome sequence of the model organism Aspergillus nidulans, and a comparative study with Aspergillus fumigatus, a serious human pathogen, and Aspergillus oryzae, used in the production of sake, miso, and soy sauce. Our analysis of genome structure provided a quantitative evaluation of forces driving long-term eukaryotic genome evolution. It also led to an experimentally validated model of mating-type locus evolution, suggesting the potential for sexual reproduction in A. fumigatus and A. oryzae. Our analysis of sequence conservation revealed over 5,000 non-coding regions actively conserved across all three species. Within these regions, we identified potential functional elements including a previously uncharacterized TPP riboswitch and motifs suggesting regulation in filamentous fungi by Puf family genes. We further obtained comparative and experimental evidence indicating widespread translational regulation by upstream open reading frames. These results enhance our understanding of these widely studied fungi as well as provide new insight into eukaryotic genome evolution and gene regulation. Document includes all supplementary information (820 pages). Supplementary files are also attached below as Related files. THERE IS NO SUPPLEMENTARY FILE #7. PDF file size (with supplementary files included) is 10 Mbytes. An optimized version of the ARTICLE ONLY is attached as a Related File and is 1.9 Mbytes

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases