DOUTfinder—identification of distant domain outliers using subsignificant sequence similarity

DOUTfinder is a web-based tool facilitating protein domain detection among related protein sequences in the twilight zone of sequence similarity. The sequence set required for this analysis can be provided by the user or will be collected using PSI-BLAST if a single sequence is given as an input. The obtained sequence family is analyzed for known Pfam and SMART domains, and the thereby identified subsignificant domain similarities are evaluated further. Domains with several subthreshold hits in the query set are ranked based on a sum-score function and likely homologous domains are suggested according to established cut-offs. By providing a post-filtering procedure for subsignificant domain hits DOUTfinder allows the detection of non-trivial domain relationships and can thereby lead to new insights into the function and evolution of distantly related sequence families. DOUTfinder is available at

Genome-wide signatures of complex introgression and adaptive evolution in the big cats.

The great cats of the genus Panthera comprise a recent radiation whose evolutionary history is poorly understood. Their rapid diversification poses challenges to resolving their phylogeny while offering opportunities to investigate the historical dynamics of adaptive divergence. We report the sequence, de novo assembly, and annotation of the jaguar (Panthera onca) genome, a novel genome sequence for the leopard (Panthera pardus), and comparative analyses encompassing all living Panthera species. Demographic reconstructions indicated that all of these species have experienced variable episodes of population decline during the Pleistocene, ultimately leading to small effective sizes in present-day genomes. We observed pervasive genealogical discordance across Panthera genomes, caused by both incomplete lineage sorting and complex patterns of historical interspecific hybridization. We identified multiple signatures of species-specific positive selection, affecting genes involved in craniofacial and limb development, protein metabolism, hypoxia, reproduction, pigmentation, and sensory perception. There was remarkable concordance in pathways enriched in genomic segments implicated in interspecies introgression and in positive selection, suggesting that these processes were connected. We tested this hypothesis by developing exome capture probes targeting ~19,000 Panthera genes and applying them to 30 wild-caught jaguars. We found at least two genes (DOCK3 and COL4A5, both related to optic nerve development) bearing significant signatures of interspecies introgression and within-species positive selection. These findings indicate that post-speciation admixture has contributed genetic material that facilitated the adaptive evolution of big cat lineages

Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species

Background As global change and anthropogenic pressures continue to increase, conservation and management increasingly needs to consider species’ potential to adapt to novel environmental conditions. Therefore, it is imperative to characterise the main selective forces acting on ecosystems, and how these may influence the evolutionary potential of populations and species. Using a multi-model seascape genomics approach, we compare putative environmental drivers of selection in three sympatric southern African marine invertebrates with contrasting ecology and life histories: Cape urchin (Parechinus angulosus), Common shore crab (Cyclograpsus punctatus), and Granular limpet (Scutellastra granularis). Results Using pooled (Pool-seq), restriction-site associated DNA sequencing (RAD-seq), and seven outlier detection methods, we characterise genomic variation between populations along a strong biogeographical gradient. Of the three species, only S. granularis showed significant isolation-by-distance, and isolation-by-environment driven by sea surface temperatures (SST). In contrast, sea surface salinity (SSS) and range in air temperature correlated more strongly with genomic variation in C. punctatus and P. angulosus. Differences were also found in genomic structuring between the three species, with outlier loci contributing to two clusters in the East and West Coasts for S. granularis and P. angulosus, but not for C. punctatus. Conclusion The findings illustrate distinct evolutionary potential across species, suggesting that species-specific habitat requirements and responses to environmental stresses may be better predictors of evolutionary patterns than the strong environmental gradients within the region. We also found large discrepancies between outlier detection methodologies, and thus offer a novel multi-model approach to identifying the principal environmental selection forces acting on species. Overall, this work highlights how adding a comparative approach to seascape genomics (both with multiple models and species) can elucidate the intricate evolutionary responses of ecosystems to global change

Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage

Background: Bathycoccus prasinos is an extremely small cosmopolitan marine green alga whose cells are covered with intricate spider's web patterned scales that develop within the Golgi cisternae before their transport to the cell surface. The objective of this work is to sequence and analyze its genome, and to present a comparative analysis with other known genomes of the green lineage. Research: Its small genome of 15 Mb consists of 19 chromosomes and lacks transposons. Although 70% of all B. prasinos genes share similarities with other Viridiplantae genes, up to 428 genes were probably acquired by horizontal gene transfer, mainly from other eukaryotes. Two chromosomes, one big and one small, are atypical, an unusual synapomorphic feature within the Mamiellales. Genes on these atypical outlier chromosomes show lower GC content and a significant fraction of putative horizontal gene transfer genes. Whereas the small outlier chromosome lacks colinearity with other Mamiellales and contains many unknown genes without homologs in other species, the big outlier shows a higher intron content, increased expression levels and a unique clustering pattern of housekeeping functionalities. Four gene families are highly expanded in B. prasinos, including sialyltransferases, sialidases, ankyrin repeats and zinc ion-binding genes, and we hypothesize that these genes are associated with the process of scale biogenesis. Conclusion: The minimal genomes of the Mamiellophyceae provide a baseline for evolutionary and functional analyses of metabolic processes in green plants

Improved Algorithms for Discovery of New Genes in Bacterial Genomes

In this dissertation, we describe a new approach for gene finding that can utilize proteomics information in addition to DNA and RNA to identify new genes in prokaryote genomes. Proteomics processing pipelines require identification of small pieces of proteins called peptides. Peptide identification is a very error-prone process and we have developed a new algorithm for validating peptide identifications using a distance-based outlier detection method. We demonstrate that our method identifies more peptides than other popular methods using standard mixtures of known proteins. In addition, our algorithm provides a much more accurate estimate of the false discovery rate than other methods. Once peptides have been identified and validated, we use a second algorithm, proteogenomic mapping (PGM) to map these peptides to the genome to find the genetic signals that allow us to identify potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs). We then collect and combine evidence for ePSTs we generated, and evaluate the likelihood that each ePST represents a true new protein coding gene using supervised machine learning techniques. We use machine learning approaches to evaluate the likelihood that the ePSTs represent new genes. Finally, we have developed new approaches to Bayesian learning that allow us to model the knowledge domain from sparse biological datasets. We have developed two new bootstrap approaches that utilize resampling to build networks with the most robust features that reoccur in many networks. These bootstrap methods yield improved prediction accuracy. We have also developed an unsupervised Bayesian network structure learning method that can be used when training data is not available or when labels may not be reliable

Improved Algorithms for Discovery of New Genes in Bacterial Genomes

In this dissertation, we describe a new approach for gene finding that can utilize proteomics information in addition to DNA and RNA to identify new genes in prokaryote genomes. Proteomics processing pipelines require identification of small pieces of proteins called peptides. Peptide identification is a very error-prone process and we have developed a new algorithm for validating peptide identifications using a distance-based outlier detection method. We demonstrate that our method identifies more peptides than other popular methods using standard mixtures of known proteins. In addition, our algorithm provides a much more accurate estimate of the false discovery rate than other methods. Once peptides have been identified and validated, we use a second algorithm, proteogenomic mapping (PGM) to map these peptides to the genome to find the genetic signals that allow us to identify potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs). We then collect and combine evidence for ePSTs we generated, and evaluate the likelihood that each ePST represents a true new protein coding gene using supervised machine learning techniques. We use machine learning approaches to evaluate the likelihood that the ePSTs represent new genes. Finally, we have developed new approaches to Bayesian learning that allow us to model the knowledge domain from sparse biological datasets. We have developed two new bootstrap approaches that utilize resampling to build networks with the most robust features that reoccur in many networks. These bootstrap methods yield improved prediction accuracy. We have also developed an unsupervised Bayesian network structure learning method that can be used when training data is not available or when labels may not be reliable

Preservation of Genes Involved in Sterol Metabolism in Cholesterol Auxotrophs: Facts and Hypotheses

Background: It is known that primary sequences of enzymes involved in sterol biosynthesis are well conserved in organisms that produce sterols de novo. However, we provide evidence for a preservation of the corresponding genes in two animals unable to synthesize cholesterol (auxotrophs): Drosophila melanogaster and Caenorhabditis elegans. Principal Findings: We have been able to detect bona fide orthologs of several ERG genes in both organisms using a series of complementary approaches. We have detected strong sequence divergence between the orthologs of the nematode and of the fruitfly; they are also very divergent with respect to the orthologs in organisms able to synthesize sterols de novo (prototrophs). Interestingly, the orthologs in both the nematode and the fruitfly are still under selective pressure. It is possible that these genes, which are not involved in cholesterol synthesis anymore, have been recruited to perform different new functions. We propose a more parsimonious way to explain their accelerated evolution and subsequent stabilization. The products of ERG genes in prototrophs might be involved in several biological roles, in addition to sterol synthesis. In the case of the nematode and the fruitfly, the relevant genes would have lost their ancestral function in cholesterogenesis but would have retained the other function(s), which keep them under pressure. Conclusions: By exploiting microarray data we have noticed a strong expressional correlation between the orthologs of ERG24 and ERG25 in D. melanogaster and genes encoding factors involved in intracellular protein trafficking and folding an

Exploring nucleo-cytoplasmic large DNA viruses in Tara Oceans microbial metagenomes

Nucleo-cytoplasmic large DNA viruses (NCLDVs) constitute a group of eukaryotic viruses that can have crucial ecological roles in the sea by accelerating the turnover of their unicellular hosts or by causing diseases in animals. To better characterize the diversity, abundance and biogeography of marine NCLDVs, we analyzed 17 metagenomes derived from microbial samples (0.2–1.6 μm size range) collected during the Tara Oceans Expedition. The sample set includes ecosystems under-represented in previous studies, such as the Arabian Sea oxygen minimum zone (OMZ) and Indian Ocean lagoons. By combining computationally derived relative abundance and direct prokaryote cell counts, the abundance of NCLDVs was found to be in the order of 104–105 genomes ml−1 for the samples from the photic zone and 102–103 genomes ml−1 for the OMZ. The Megaviridae and Phycodnaviridae dominated the NCLDV populations in the metagenomes, although most of the reads classified in these families showed large divergence from known viral genomes. Our taxon co-occurrence analysis revealed a potential association between viruses of the Megaviridae family and eukaryotes related to oomycetes. In support of this predicted association, we identified six cases of lateral gene transfer between Megaviridae and oomycetes. Our results suggest that marine NCLDVs probably outnumber eukaryotic organisms in the photic layer (per given water mass) and that metagenomic sequence analyses promise to shed new light on the biodiversity of marine viruses and their interactions with potential hosts