On strongly chordal graphs that are not leaf powers

A common task in phylogenetics is to find an evolutionary tree representing proximity relationships between species. This motivates the notion of leaf powers: a graph G = (V, E) is a leaf power if there exist a tree T on leafset V and a threshold k such that uv is an edge if and only if the distance between u and v in T is at most k. Characterizing leaf powers is a challenging open problem, along with determining the complexity of their recognition. This is in part due to the fact that few graphs are known to not be leaf powers, as such graphs are difficult to construct. Recently, Nevries and Rosenke asked if leaf powers could be characterized by strong chordality and a finite set of forbidden subgraphs. In this paper, we provide a negative answer to this question, by exhibiting an infinite family \G of (minimal) strongly chordal graphs that are not leaf powers. During the process, we establish a connection between leaf powers, alternating cycles and quartet compatibility. We also show that deciding if a chordal graph is \G-free is NP-complete, which may provide insight on the complexity of the leaf power recognition problem

Pseudomonas Genome Database: facilitating user-friendly, comprehensive comparisons of microbial genomes

Pseudomonas aeruginosa is a well-studied opportunistic pathogen that is particularly known for its intrinsic antimicrobial resistance, diverse metabolic capacity, and its ability to cause life threatening infections in cystic fibrosis patients. The Pseudomonas Genome Database (http://www.pseudomonas.com) was originally developed as a resource for peer-reviewed, continually updated annotation for the Pseudomonas aeruginosa PAO1 reference strain genome. In order to facilitate cross-strain and cross-species genome comparisons with other Pseudomonas species of importance, we have now expanded the database capabilities to include all Pseudomonas species, and have developed or incorporated methods to facilitate high quality comparative genomics. The database contains robust assessment of orthologs, a novel ortholog clustering method, and incorporates five views of the data at the sequence and annotation levels (Gbrowse, Mauve and custom views) to facilitate genome comparisons. A choice of simple and more flexible user-friendly Boolean search features allows researchers to search and compare annotations or sequences within or between genomes. Other features include more accurate protein subcellular localization predictions and a user-friendly, Boolean searchable log file of updates for the reference strain PAO1. This database aims to continue to provide a high quality, annotated genome resource for the research community and is available under an open source license

eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges

Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains non-supervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%)

Beyond representing orthology relations by trees

Reconstructing the evolutionary past of a family of genes is an important aspect of many genomic studies. To help with this, simple relations on a set of sequences called orthology relations may be employed. In addition to being interesting from a practical point of view they are also attractive from a theoretical perspective in that e.\,g.\,a characterization is known for when such a relation is representable by a certain type of phylogenetic tree. For an orthology relation inferred from real biological data it is however generally too much to hope for that it satisfies that characterization. Rather than trying to correct the data in some way or another which has its own drawbacks, as an alternative, we propose to represent an orthology relation $\delta$ in terms of a structure more general than a phylogenetic tree called a phylogenetic network. To compute such a network in the form of a level-1 representation for $\delta$, we formalize an orthology relation in terms of the novel concept of a symbolic 3- dissimilarity which is motivated by the biological concept of a ``cluster of orthologous groups'', or COG for short. For such maps which assign symbols rather that real values to elements, we introduce the novel {\sc Network-Popping} algorithm which has several attractive properties. In addition, we characterize an orthology relation $\delta$ on some set $X$ that has a level-1 representation in terms of eight natural properties for $\delta$ as well as in terms of level-1 representations of orthology relations on certain subsets of $X$

The genome and transcriptome of Trichormus sp NMC-1: insights into adaptation to extreme environments on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) has the highest biodiversity for an extreme environment worldwide, and provides an ideal natural laboratory to study adaptive evolution. In this study, we generated a draft genome sequence of cyanobacteria Trichormus sp. NMC-1 in the QTP and performed whole transcriptome sequencing under low temperature to investigate the genetic mechanism by which T. sp. NMC-1 adapted to the specific environment. Its genome sequence was 5.9 Mb with a G+C content of 39.2% and encompassed a total of 5362 CDS. A phylogenomic tree indicated that this strain belongs to the Trichormus and Anabaena cluster. Genome comparison between T. sp. NMC-1 and six relatives showed that functionally unknown genes occupied a much higher proportion (28.12%) of the T. sp. NMC-1 genome. In addition, functions of specific, significant positively selected, expanded orthogroups, and differentially expressed genes involved in signal transduction, cell wall/membrane biogenesis, secondary metabolite biosynthesis, and energy production and conversion were analyzed to elucidate specific adaptation traits. Further analyses showed that the CheY-like genes, extracellular polysaccharide and mycosporine-like amino acids might play major roles in adaptation to harsh environments. Our findings indicate that sophisticated genetic mechanisms are involved in cyanobacterial adaptation to the extreme environment of the QTP

Database resources of the National Center for Biotechnology Information

In addition to maintaining the GenBank(®) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link(BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace and Assembly Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Viral Genotyping Tools, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at

Ontologies in Quantitative Biology: A Basis for Comparison, Integration, and Discovery

As biology is becoming a data-driven discipline, ontologies become increasingly important for systematically capturing the existing knowledge. This essay discusses current trends and how ontologies can also be used for discovery

Algorithm of OMA for large-scale orthology inference

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following

Bidirectional best hit r-window gene clusters

<p>Abstract</p> <p>Background</p> <p><it>Conserved gene clusters </it>are groups of genes that are located close to one another in the genomes of several species. They tend to code for proteins that have a functional interaction. The identification of conserved gene clusters is an important step towards understanding genome evolution and predicting gene function.</p> <p>Results</p> <p>In this paper, we propose a novel pairwise gene cluster model that combines the notion of bidirectional best hits with the <it>r</it>-window model introduced in 2003 by Durand and Sankoff. The bidirectional best hit (BBH) constraint removes the need to specify the minimum number of shared genes in the <it>r</it>-window model and improves the relevance of the results. We design a subquadratic time algorithm to compute the set of BBH <it>r</it>-window gene clusters efficiently.</p> <p>Conclusion</p> <p>We apply our cluster model to the comparative analysis of <it>E. coli </it>K-12 and <it>B. subtilis </it>and perform an extensive comparison between our new model and the gene teams model developed by Bergeron <it>et al</it>. As compared to the gene teams model, our new cluster model has a slightly lower recall but a higher precision at all levels of recall when the results were ranked using statistical tests. An analysis of the most significant BBH <it>r</it>-window gene cluster show that they correspond to known operons.</p

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure