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We have designed and implemented a system to carry out cross-genome comparisons of open reading frames (ORFs) from multiple genomes. This implementation includes a genome profiling system that allows us to explore pairwise comparisons at different levels of match similarity and ask biologically motivated queries involving number and identity of ORFs, their function, functional category, distribution in genomes or in biological domains, and statistics on their matches and match families. This analysis required precise definition of new classification terms and concepts. We define the terms genomic signature, summary signature, biologic domain signature, domain class, match level, match family, and extended match family, then use these terms to define concepts, including genomically universal proteins and proteins characteristics of sets of genomes. We initiate an analysis based on automated FASTA (Pearson, 1996) comparison of 22,419 conceptually translated protein sequences from nine microbial genomes.

Background: Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions. Therefore, complexes aggregate the effect of individual genes (proteins) and can be used to gain a better understanding of cancer mechanisms. Here, we observe that complexes show considerable changes in their expression, in turn directed by the concerted action of transcription factors (TFs), across cancer conditions. We seek to gain novel insights into cancer mechanisms through a systematic analysis of complexes and their transcriptional regulation. Results: We integrated large-scale protein-interaction (PPI) and gene-expression datasets to identify complexes that exhibit significant changes in their expression across different conditions in cancer. We devised a log-linear model to relate these changes to the differential regulation of complexes by TFs. The application of our model on two case studies involving pancreatic and familial breast tumour conditions revealed: (i) complexes in core cellular processes, especially those responsible for maintaining genome stability and cell proliferation (e.g. DNA damage repair and cell cycle) show considerable changes in expression; (ii) these changes include decrease and countering increase for different sets of complexes indicative of compensatory mechanisms coming into play in tumours; and (iii) TFs work in cooperative and counteractive ways to regulate these mechanisms. Such aberrant complexes and their regulating TFs play vital roles in the initiation and progression of cancer., Comment: 22 pages, BMC Systems Biology

The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChlPseq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au .

Genetic analyses of 15 species of yeast have shed new light on the divergence of gene regulation during evolution, with significant changes occurring after an event in which a whole genome was duplicated.

Sequences of nuclear-encoded small-subunit rRNA genes have been determined for representatives of the enigmatic genera Dermocystidium, Ichthyophonus, and Psorospermium, protistan parasites of fish and crustaceans. The small-subunit rRNA genes from these parasites and from the "rosette agent" (also a parasite of fish) together form a novel, statistically supported clade. Phylogenetic analyses demonstrate this clade to diverge near the animal-fungal dichotomy, although more precise resolution is problematic. In the most parsimonious and maximally likely phylogenetic frameworks inferred from the most stably aligned sequence regions, the clade constitutes the most basal branch of the metazoa; but within a limited range of model parameters, and in some analyses that incorporate less well-aligned sequence regions, an alternative topology in which it diverges immediately before the animal-fungal dichotomy was recovered. Mitochondrial cristae of Dermocystidium spp. are flat, whereas those of Ichthyophonus hoferi appear tubulovesiculate. These results extend our understanding of the types of organisms from which metazoa and fungi may have evolved.

We have determined the nucleotide sequence of the nuclear gene encoding small-subunit ribosomal ribonucleic acid of the ciliate Anophryoides haemophila, a parasite of the American lobster Homarus americanus. The gene is 1763 bp in length, and has a guanosine-plus-cytosine content of 43.9%. Inferred phylogenetic frameworks strongly support the monophyly of the scuticociliates, and suggest that order Scuticociliatida should be elevated to at least subclass rank. Oligonucleotide probes based on A. haemophila ssurDNA can discriminate between DNAs of A. haemophila and other investigated hymenostome ciliates, and effectively prime polymerase chain reaction-based detection of A. haemophila deoxyribonucleic acid against at least a 1600-fold excess of total deoxyribonucleic acid from H. americanus. GENBANK/U51554

Over 800 kbp of the 3-Mbp genome of Sulfolobus solfataricus have been sequenced to date. Our approach is to sequence subclones of mapped cosmids, followed by sequencing directly on cosmid templates with custom primers. Using a prototype automated system for genome-scale analysis, known as MAGPIE, along with other tools, we have discovered one open reading frame of at least 100 amino acids per kbp of sequence, and have been able to associate 50% of these with known genes through database searches. An examination of completely sequenced cosmids suggests a clustering of genes by function in the S. solfataricus genome.

Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques.

Cancer forms a robust system and progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. Here, we propose a novel model of the 'cancer system' as a Boolean state space in which a Boolean network, built from protein interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. The application of our model (called BoolSpace) on three case studies - pancreatic and breast tumours in human and post spinal-cord injury in rats - reveals valuable insights into the phenomenon of cancer progression. In particular, we notice that several of the genes flipped are serine/threonine kinases which act as natural cellular switches and that different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. We hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages, and therefore an effective therapy should target a "cover set" of these genes. A C/C++ implementation of BoolSpace is freely available at: http://www.bioinformatics.org.au/tools-data, Comment: 13 pages, 4 figures, Genome Informatics (GIW) Singapore, December 2013. To appear in IEEE/ACM Trans. Computational Biology and Bioinformatics

Each step in the temporal process of lateral genetic transfer (LGT) presents barriers and biases that, together with contingent processes in the environment, determine the success of transfer events. Appropriate interaction with existing host systems, together with regulated expression of gene products, are particularly important for the long-term establishment of transferred DNA. Large datasets of biological interactions can be abstracted to construct graphical abstractions of cellular networks. Phyletic distributions and other metadata can then be mapped onto these networks to examine hypotheses of network properties that contribute to successful LGT. Here we examine the organising principles that govern the integration of laterally transferred DNA with existing host cellular networks.

The nuclear genes encoding small-subunit ribosomal RNAs (ssu-rDNAs) of the carrageenophytes Eucheuma denticulatum, E. isiforme and Kappaphycus alvarezii were amplified by the polymerase chain reaction, cloned and sequenced. The sequences range from 1767 (K. alvarezii) to 1781 (E. isiforme) nucleotides in length, and have guanine+cytosine contents between 51.2% (E. isiforme) and 51.5% (E. denticulatum). Pairwise sequence identities among these sequences ranged from 97.6% to 98.5%, levels comparable to some intergeneric identities within Gracilariales. In phylogenetic analyses, the two Eucheuma ssu-rDNAs group stably together vis-a-vis the ssu-rDNA of K. alvarezii, and these three ssu-rDNAs form a monophyletic group within a larger grouping of other carrageenophytes. The results demonstrate quantitatively that analysis of nuclear-encoded ssu-rDNA sequences is likely to be useful in resolving taxonomic, phylogenetic and biogeographic questions among tribe Eucheumatoideae Doty.

We have cloned and sequenced the single-copy nuclear gene (GapC) encoding the complete 335-amino acid cytosolic glyceraldehyde-3-phosphate dehydrogenase (GAPC) from the red alga Gracilaria verrucosa. The proline residue which contributes to the specificity of NAD+ binding in other GAPC-like proteins is present. Putative regulatory regions, including GC-rich regions, a GATA element, and 11-base T- and T/G-clusters, but excluding TATA- and CCAAT-boxes, were identified upstream. Two types of GapC cDNAs differing in polyadenylation site were characterized. An 80-bp phase-two spliceosomal intron was identified in a novel position interrupting the highly conserved cofactor-coding region I. The G. verrucosa GAPC was easily aligned with other known GAPC-type sequences. Inferred phylogenetic trees place red algae among the eukaryote crown taxa, although with modest bootstrap support and without stable resolution among related GAPC lineages.

Nucleotide sequences of the small-subunit ribosomal RNA gene (18S rDNA) were newly determined for four members of the related rhodophyte orders Palmariales and Acrochaetiales and were compared by neighbor-joining and parsimony analyses with Sour previously published sequences from these orders. A published rDNA sequence from Nemalion helminthoides (Velley in Withering) Batters (Nemaliales) and a gene sequence determined herein for Nothocladus nodosus Skuja (Batrachospermales) were used as outgroups. Sequences were very similar within the Palmariales with a maximum difference of 13 nucleotides out of 1772 between members of the families Palmariaceae and Rhodophysemataceae. Despite similarities in life history, the acrochaetioid algae Rhodechorton purpureum (Lightfoot) Rosenvinge and Rhodothamniella floridula (Dillwyn) J. Feldmann in Christensen were phylogenetically separated with R. purpureum affiliated weakly with Acrochaetiales, and R. floridula forming a sister branch unequivocally to palmarialean algae of the families Rhodophysemataceae and Palmariaceae. Rhodothamniella floridula is postulated on both molecular and phenotypic grounds (encompassing biochemical and anatomical characters, and life history features) to represent an early lineage, Rhodethamniellaceae fam, nov., in the Palmariales. The family Rhodophysemataceae appeared paraphyletic in the sequence analyses, but a monophyletic Rhodophysemataceae could not be rejected by Templeton-Felsenstein or Kishino-Hasegawa tests, and the family was clearly monophyletic in analysis of the phenotypic data. The earlier tentative placement of Rhodophysemataceae in the Palmariales rather than Acrochaetiales was confirmed.

We have cloned a nuclear gene from the marine red alga Gracilaria verrucosa that encodes the complete 779 amino-acid mitochondrial aconitase (m-ACN), the first characterized from a photosynthetic organism. The N-terminal 28 deduced amino acids are predicted to constitute the mitochondrial transit peptide, the first described from a red alga. Putative transcriptional cis-acting elements were identified in the upstream untranslated region. The G. verrucosa m-ACN gene (m-ACN) is present in a single copy and is located ca. 1.5 kb upstream from the single-copy polyubiquitin gene. The single spliceosomal intron is located near the 5' end of the region encoding the mature m-ACN in precisely the same location and phase as intron 2 in Caenorhabditis elegans m-ACN; sequences at its 3' and 5' splice junctions and at the predicted lariat branch point conform well to the eukaryote consensus sequences. Multiple protein-sequence alignment of m-ACN, bacterial aconitase (b-ACN) and iron-responsive element-binding protein (IRE-BP), and phylogenetic analyses, revealed that m-ACN does not share a recent common ancestry with either b-ACN or IRE-BP.

SUMMARY We investigated phylogenetic relationships among red algae of the order Bangiales by analysis of sequences of the nuclear gene encoding cytosolic small-subunit ribosomal RNA in Bangia atropurpurea (Roth) C. Ag. and eight samples representing seven species of Porphyra. The ssu-rDNA range from 1818 to 1845 nucleotides in length, with guanosine plus cytosine ratios between 47.0% and 48.6%. A group IC1 intron occurs in the B atropurpurea ssu-rDNAs at the same position as in P. spiralis var. amplifolia Oliveira Filho et Coll and several other eukaryote ssu-rDNAs. The nine sequences form a stable monophyletic group upon phylogenetic analysis. The ssu-rDNA from B. atropurpurea nests stably within the Porphyra group and is closely related to P. amplissima (Kjellm.) Setchell et Hus in Hus, making the genus Porphyra paraphyletic. No correlation is seen between phylogenetic position and number of cell layers in the Porphyra thallus. We discuss possible taxonomic and evolutionary implications of these observations.

We have cloned a nuclear gene (UBI6R) and corresponding cDNAs that encode polyubiquitin in the florideophycidean red alga Gracilaria verrucosa. The gene encodes a polyubiquitin composed of six tandem ubiquitin units, followed by a single glutamine residue. The deduced amino acid sequences are identical among all six units, and identical to the ubiquitin of the florideophyte Aglaothamnion neglectum. There is high sequence similarity among the red algal ubiquitins and those of animals, green plants, fungi and several protists. Only one polyubiquitin gene was found by Southern hybridization analysis of G. verrucosa nuclear DNA. The upstream region of the gene is rich in putative cis-acting transcription-regulatory elements, including a putative heat-responsive element. Poly(A) addition to UBI6R mRNA was observed in cDNAs at four different sites, implicating the sequences AATAAA and (or) AGTAAA as poly(A) addition signals. The polyubiquitin genes of red algae show features of concerted evolution, but appear to be subject to less sequence homogenization than those of animals.

The intergenic spacers (IGS) separating the 23S-like and 16S-like rDNAs of the two varieties of the human pathogenic fungus Cryptococcus neoformans were amplified, cloned and sequenced. The C. neoformans var. neoformans IGS was 2421 nt with 5S rRNA at positions 1228-1345 3' of the 23S-like rRNA. The C. neoformans var. gattii IGS was 2480 nt with 5S rRNA at positions 1268-1385 3' of the 23S-like rRNA. For both varieties the 5S rDNA genes were in the same orientation as the 16S-5.8-23S genes and encode a 118 nt molecule of identical sequence. Phylogenetic comparison of C. neoformans 5S rDNA with that of other fungi placed this fungus in close relationship with other basidiomycetes including Tremella mesenterica, Bullera alba, and Cryptococcus laurentii. A secondary structure model for the deduced 5S rRNA was constructed by comparative sequence analysis. Polymerase chain reaction-amplified IGS of 12 C. neoformans var. neoformans strains revealed extensive size variation ranging from 100 to 300 nt. Size variation between strains in the length of the IGS may be useful for distinguishing strains. Structurally, the IGS were characterized by the presence of occasional short direct GC-rich 19-nt repeats. Overall IGS sequence identity between the C. neoformans varieties was only 78.5%, in sharp contrast to the identical or nearly identical sequences for the rDNA genes, and suggests rapid evolution for IGS sequences.

Background Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset. Results We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours. Conclusions We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers

Thanks to advances in next-generation technologies, genome sequences are now being generated at breadth (e.g. across environments) and depth (thousands of closely related strains, individuals or samples) unimaginable only a few years ago. Phylogenomics – the study of evolutionary relationships based on comparative analysis of genome-scale data – has so far been developed as industrial-scale molecular phylogenetics, proceeding in the two classical steps: multiple alignment of homologous sequences, followed by inference of a tree (or multiple trees). However, the algorithms typically employed for these steps scale poorly with number of sequences, such that for an increasing number of problems, high-quality phylogenomic analysis is (or soon will be) computationally infeasible. Moreover, next-generation data are often incomplete and error-prone, and analysis may be further complicated by genome rearrangement, gene fusion and deletion, lateral genetic transfer, and transcript variation. Here we argue that next-generation data require next-generation phylogenomics, including so-called alignment-free approaches. Reviewers Reviewed by Mr Alexander Panchin (nominated by Dr Mikhail Gelfand), Dr Eugene Koonin and Prof Peter Gogarten. For the full reviews, please go to the Reviewers’ comments section.

We have used molecular characteristics to clarify the taxonomic status of three problematic gracilarioid algae, by comparison with existing data for the Gracilariaceae. Nucleotide sequences of nuclear-encoded 18S rDNA were determined for an unidentified species of Gracilariopsis from North Carolina, and two infertile strains of gracilarioid algae from Namibia, tentatively assigned to Gracilariopsis. The sequence from the North Carolinian alga was intermediate between those established previously for Gracilariopsis lemaneiformis and European Gracilariopsis sp., and represents a different, probably undescribed, species. Sequences from the Namibian plants were characteristic of Gracilaria, and very similar to those from Gracilaria verrucosa and two closely related congeners. In parsimony, distance and maximum-likelihood analyses of these sequences and others already published for the Gracilariaceae, the North Carolinian Gracilariopsis sp. consistently grouped with its congeners, and the two Namibian strains ...

The single-copy nuclear gene (GapA), encoding the plastid-localized glyceraldehyde-3-phosphate dehydrogenase (GAPDH) of the marine red alga Gracilaria verrucosa, has been cloned and sequenced. The GapA transcriptional initiation site was located 49 bp upstream of the start codon, and a putative TATA box was found 54 bp farther upstream. A spliceosomal intron was identified in the transit-peptide-encoding region in a position very similar to intron 1 of GapA and GapB of higher plants; no introns occur in the region encoding the mature protein. These observations provisionally suggest that both red algae and higher plants descend from a single ancestral photosynthetic eukaryote, i.e. that a single endosymbiotic event gave rise to red algal and higher-plant plastids.

Nuclear-encoded small-subunit (18S) ribosomal RNA genes of Chara australis R. Brown (C. corallina var. nobilis f. nobilis R.D.W.) and Nitella flexilis (L.) Ag. were amplified by polymerase chain reaction, cloned, and completely sequenced. Using structural criteria, the sequences were aligned with 18S ribosomal DNAs (rDNAs) from 11 other chlorophyll b-containing algae and six higher plants (embryophytes). Phylogenetic trees were inferred by distance, neighbor-joining, parsimony, and maximum-likelihood approaches; confidence intervals were estimated by bootstrapping, and nonrandomness of tree structure was confirmed by permutation tests. 18S rDNAs of c. australis and two Nitella species were found to be specifically related and, together with 18S rDNAs of Chlorokybus atmophyticus Geitler, Klebsormidium flaccidum (A. Br.) Silva, Mattox, et Blackwell, and two Coleochaete species, support a robust monophyletic group (Charaphyceae). Although most trees favored a specific sister-group relationship between Charophyceae and embryophytes, statistical tests revealed that a sister-group relationship between Charophyceae and Chlorophyceae could not be ruled out. Additional complete sequences from 18S rDNAs of lower land plants may be required to resolve phylogenetic relationships among these organisms.

The nucleotide sequences of the 16S-like, 5.8S and 23S-like rDNAs from the two varieties of Cryptococcus neoformans, C. neoformans var. neoformans and C. neoformans var. gattii, were determined. The rRNA locus has the typical eukaryote organization of 16S-5.8S-23S with the 16S-like and 5.8S rRNA genes separated by a 124-nucleotide spacer and the 5.8S and 23S-like rRNA genes separated by a 187-nucleotide spacer in each strain. The C. neoformans var. neoformans and C. neoformans var. gattii 16S-like, 5.8S and 23S-like rRNAs are, respectively 1802, 158, and 3358 nucleotides in length and share > 99% nucleotide sequence identity, a finding which strongly supports the present taxonomic classification of two varieties within a species. Comparative structure analysis was used to construct secondary-structure models for the deduced 16S-like and 5.8S-23S-like rRNA sequences, which are similar to those of other fungal rRNAs. The C. neoformans 16S-like and 23S-like rRNA sequences were aligned with other eukaryote sequences based on secondary and higher-order structures predicted by comparative structure analysis for phylogenetic analysis. There was good correspondence between the 16S-like and 23S-like derived phylogenetic trees. The closest known fungal relative is Trichosporon beigelii. Southern blot analysis revealed one C. neoformans strain with two types of DNA repeats coding for rRNA which differed in size by about 1000 bp. Restriction fragment length polymorphisms in the rDNA locus provide useful markers for the study of epidemiology and pathogenesis of C. neoformans infections.

Background Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. Results We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. Conclusions Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.

Background Changes in transcriptional regulation underlie many of the phenotypic differences observed within and between species of bacteria. Lateral genetic transfer (LGT) can significantly impact the transcription factor (TF) genes which drive these transcriptional changes. Although much emphasis has been placed on LGT of intact genes, the units of transfer and recombination do not necessarily correspond to regions delineated by exact gene boundaries. Here we apply phylogenetic and network-based methods to investigate the relationship between units of lateral transfer and recombination within the Escherichia coli - Shigella clade and the topological properties of genes in the E. coli transcriptional regulatory network (TRN). Results We carried out a systematic phylogenetic study of genetic transfer among 5282 sets of putatively orthologous genes from 27 strains belonging to the E. coli - Shigella clade. We then used these results to examine the evolutionary histories of TF genes, as well as the transcriptional regulation of lateral genes. We found evidence of LGT in 2655 (50.3%) gene sets: 678 (12.8%) show evidence of recombination breakpoints within the gene boundaries. Thus, within- and whole- gene lateral transfer is widespread among strains of E. coli and Shigella. We found that unlike global regulators, which have mostly evolved vertically, neighbour regulators (genes which regulate adjacent genes on the chromosome) have frequently been subject to transfer within the E. coli - Shigella clade. At least 56 (62%) of the 90 neighbour regulator gene sets examined show evidence of LGT, 19 (34%) of which have internal recombination breakpoints. Neighbour regulators show no evidence of co-transfer with their nearby target genes. Rather, the frequency of recombination breakpoints, and conflicting evolutionary histories among neighbour regulators and their target genes, suggest that the genomic regions encoding these genes have been constructed through successive layering of LGT events within the clade. We find no difference in the relative complexity of regulation (i.e. the number of regulators) of lateral versus vertical genes. Conclusions Neighbour regulators show higher frequencies of transfer than other types of regulatory genes. This implicates the topological properties of regulatory genes in the TRN, and their physical proximity to targets on the chromosome, as contributing to successful LGT. The prevalence of recombination breakpoints within regulatory and target gene sets indicates that within-gene transfer has had a significant cumulative effect on the evolution of regulatory interactions in E. coli and Shigella.

Mineralocorticoid receptors (MR), glucocorticoid receptors (GR), progesterone receptors (PR), and androgen receptors (AR) comprise a closely related subfamily within the human 49-member nuclear receptor family. These receptors and their cognate ligands play major roles in homeostasis, reproduction, growth, and development, despite which their evolution and diversification remains incompletely understood. Several conflicting models have been advanced for the evolution of this subfamily. We have thus undertaken Bayesian and maximum likelihood phylogenetic analyses of this subfamily. The Bayesian consensus and maximum likelihood trees support a basal position for MR, with the PR and AR forming a sister clade. We next performed analyses using topological constraints to directly contrast the likelihood of seven phylogenetic models. In these analyses, three models have similar support: one proposes two sister clades (MR and GR, PR and AR); the other two propose a different subfamily member (MR or GR) to be the first to have diverged. Ancestral state reconstructions at sites critical for physiological function show that the S810L mutation in the MR, which results in the MR being similar to estrogen receptors and the more distantly related retinoic acid receptor-α is likely to reflect the ancestral receptor sequence before the divergence of this subfamily and provides further support for MR having been the first of the subfamily to diverge. Finally, we drew on pathophysiological comparisons to help to distinguish the different models. On the basis of our phylogenetic analyses and pathophysiological considerations, we propose that the MR was the first to diverge from the ancestral gene lineage from which this subfamily derived.

Lateral genetic transfer (LGT) is a major source of phenotypic innovation among bacteria. Determinants for antibiotic resistance and other adaptive traits can spread rapidly, particularly by conjugative plasmids, but also phages and natural transformation. Each successive step from the uptake of foreign DNA, its genetic recombination and regulatory integration, to its establishment in the host population presents differential barriers and opportunities. The emergence of successive multidrug-resistant strains of Staphylococcus aureus illustrates the ongoing role of LGT in the combinatorial assembly of pathogens. The dynamic interplay among hosts, vectors, DNA elements, combinations of genetic determinants and environments constructs communities of genetic exchange. These relations can be abstracted as a graph, within which an exchange community might correspond to a path, transitively closed set, clique or near-clique. We provide a set-based definition, and review the features of actual genetic exchange communities (GECs), adopting first a knowledge-driven approach based on literature, and then a synoptic data-centric bioinformatic approach. GECs are diverse, but share some common features. Differential opportunity and barriers to lateral genetic transfer create bacterial communities of exchange.

The heterogeneous nuclear ribonucleoproteins (hnRNPs) A/B are a family of RNA-binding proteins that participate in various aspects of nucleic acid metabolism, including mRNA trafficking, telomere maintenance, and splicing. They are both regulators and targets of alternative splicing, and the patterns of alternative splicing of their transcripts have diverged between paralogs and between orthologs in different species. Surprisingly, the extent of this splicing variation and its implications for post-transcriptional regulation have remained largely unexplored. Here, we conducted a detailed analysis of hnRNP A/B sequences and expression patterns across six vertebrates. Alternative exons emerged via the introduction of new splice sites, changes in the strengths of existing splice sites, and the accumulation of auxiliary splicing regulatory motifs. Observed isoform expression patterns could be attributed to the frequency and strength of cis-elements. We found a trend toward increased splicing variation in mammals and identified novel alternatively spliced isoforms in human and chicken. Pulldown and translational assays demonstrated that the inclusion of alternative exons altered the affinity of hnRNP A/B proteins for their cognate nucleic acids and modified protein expression levels. As the hnRNPs A/B regulate several key steps in mRNA processing, the involvement of diverse hnRNP isoforms in multiple cellular contexts and species implies concomitant differences in the transcriptional output of these systems. We conclude that the emergence of alternative splicing in the hnRNPs A/B has contributed to the diversification of their roles in the regulation of alternative splicing and has thus added an unexpected layer of regulatory complexity to transcription in vertebrates.

Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.

Perhaps the most-surprising discovery of the genome era has been the extent to which prokaryotic and many eukaryotic genomes incorporate genetic material from sources other than their parent(s). Lateral genetic transfer (LGT) among bacteria was first observed about 100 years ago, and is now accepted to underlie important phenomena including the spread of antibiotic resistance and ability to degrade xenobiotics. LGT is invoked, perhaps too readily, to explain a breadth of awkward data including compositional heterogeneity of genomes, disagreement among gene-sequence trees, and mismatch between physiology and systematics. At the same time many details of LGT remain unknown or controversial, and some key questions have scarcely been asked. Here I critically review what we think we know about the existence, extent, mechanism and impact of LGT; identify important open questions; and point to research directions that hold particular promise for elucidating the role of LGT in genome evolution. Evidence for LGT in nature is not only inferential but also direct, and potential vectors are ubiquitous. Genetic material can pass between diverse habitats and be significantly altered during residency in viruses, complicating the inference of donors, In prokaryotes about twice as many genes are interrupted by LGT as are transferred intact, and about 5Short protein domains can be privileged units of transfer. Unresolved phylogenetic issues include the correct null hypothesis, and genes as units of analysis. Themes are beginning to emerge regarding the effect of LGT on cellular networks, but I show why generalization is premature. LGT can associate with radical changes in physiology and ecological niche. Better quantitative models of genome evolution are needed, and theoretical frameworks remain to be developed for some observations including chromosome assembly by LGT.

Motivation: Protein sequences are often composed of regions that have distinct evolutionary histories as a consequence of domain shuffling, recombination or gene conversion. New approaches are required to discover, visualize and analyze these sequence regions and thus enable a better understanding of protein evolution. Results: Here, we have developed an alignment-free and visual approach to analyze sequence relationships. We use the number of shared n-grams between sequences as a measure of sequence similarity and rearrange the resulting affinity matrix applying a spectral technique. Heat maps of the affinity matrix are employed to identify and visualize clusters of related sequences or outliers, while n-gram-based dot plots and conservation profiles allow detailed analysis of similarities among selected sequences. Using this approach, we have identified signatures of domain shuffling in an otherwise poorly characterized family, and homology clusters in another. We conclude that this approach may be generally useful as a framework to analyze related, but highly divergent protein sequences. It is particularly useful as a fast method to study sequence relationships prior to much more time-consuming multiple sequence alignment and phylogenetic analysis. Availability: A software implementation (MOSAIC) of the framework described here can be downloaded from http://bioinformatics.org.au/mosaic/ Contact: m.ragan@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

18S rRNA gene sequences are presented forAhnfeltia plicata, Chondrus crispus, Furcellaria lumbricalis andPalmaria palmata, commercially important marine algae of the North Atlantic. The sequences range from 1765 to 1777 nucleotides in length, with guanine + cytosine content of 50.1% to 52.4%. Sequence divergence between species in different orders was 11.3–12.3%, whereas the variation betweenC. crispus andF. lumbricalis, both from the Gigartinales, was only 3.6%. Based on limited experience with other groups of Rhodophyta, these sequences obtained from single populations are likely to be representative of the species as a whole, with little variation expected among conspecifics regardless of morphological aberration or apparent genetic isolation.

Nucleotide (nt) sequences of the nuclear-encoded small-subunit (18S) rRNA genes are compared in representatives of the non-parasitic genera of the marine red algal order Gracilariales. Sequence div...

Rooted phylogenetic trees can be represented as matrices in which the rows correspond to termini, and columns correspond to internal nodes (elements of the n-tree). Parsimony analysis of such a matrix will fully recover the topology of the original tree. The maximum size of the represented matrix depends only on the number of termini in the tree; for a tree derived from molecular sequences, the represented matrix may be orders of magnitude smaller than the original data matrix. Representations of multiple trees (which may or may not have identical termini) can readily be combined into a single matrix; columns of discrete-character-state data can be added and, if desired, weighted differentially. Parsimony analysis of the resulting composite matrix yields a hybrid supertree which typically provides greater resolution than conventional consensus trees. Use of this method is illustrated with examples involving multiple tRNA genes in organelles and multiple protein-coding genes in eukaryotes.

The application of the method of matrix representation (MRP) addresses several problems associated with reconstructing phylogenetic relationships among composite organisms such as the eukaryotes. By allowing multiple molecular-sequence and non-molecular data sets to be combined into a single, relatively compact matrix for joint analysis, MRP addresses problems associated with the depth, diversification and specialization of eukaryotic lineages. Characters whose distributions may have been affected by endosymbiotic lateral transfer can be identified by compatibility analysis of MRP-generated hybrid matrices. In conjunction with variant-filtering techniques, MRP can be used to map patterns of lateral transfer in a given phylogenetic tree. These applications are illustrated with molecular-sequence and non-molecular data sets for eukaryotes.

It is well-known that Charles Darwin sketched abstract trees of relationship in his 1837 notebook, and depicted a tree in the Origin of Species (1859). Here I attempt to place Darwin's trees in historical context. By the mid-Eighteenth century the Great Chain of Being was increasingly seen to be an inadequate description of order in nature, and by about 1780 it had been largely abandoned without a satisfactory alternative having been agreed upon. In 1750 Donati described aquatic and terrestrial organisms as forming a network, and a few years later Buffon depicted a network of genealogical relationships among breeds of dogs. In 1764 Bonnet asked whether the Chain might actually branch at certain points, and in 1766 Pallas proposed that the gradations among organisms resemble a tree with a compound trunk, perhaps not unlike the tree of animal life later depicted by Eichwald. Other trees were presented by Augier in 1801 and by Lamarck in 1809 and 1815, the latter two assuming a transmutation of species over time. Elaborate networks of affinities among plants and among animals were depicted in the late Eighteenth and very early Nineteenth centuries. In the two decades immediately prior to 1837, so-called affinities and/or analogies among organisms were represented by diverse geometric figures. Series of plant and animal fossils in successive geological strata were represented as trees in a popular textbook from 1840, while in 1858 Bronn presented a system of animals, as evidenced by the fossil record, in a form of a tree. Darwin's 1859 tree and its subsequent elaborations by Haeckel came to be accepted in many but not all areas of biological sciences, while network diagrams were used in others. Beginning in the early 1960s trees were inferred from protein and nucleic acid sequences, but networks were re-introduced in the mid-1990s to represent lateral genetic transfer, increasingly regarded as a fundamental mode of evolution at least for bacteria and archaea. In historical context, then, the Network of Life preceded the Tree of Life and might again supersede it. Reviewers This article was reviewed by Eric Bapteste, Patrick Forterre and Dan Graur.

Perhaps the most-surprising discovery of the genome era has been the extent to which prokaryotic and many eukaryotic genomes incorporate genetic material from sources other than their parent(s). Lateral genetic transfer (LGT) among bacteria was first observed about 100 years ago, and is now accepted to underlie important phenomena including the spread of antibiotic resistance and ability to degrade xenobiotics. LGT is invoked, perhaps too readily, to explain a breadth of awkward data including compositional heterogeneity of genomes, disagreement among gene-sequence trees, and mismatch between physiology and systematics. At the same time many details of LGT remain unknown or controversial, and some key questions have scarcely been asked. Here I critically review what we think we know about the existence, extent, mechanism and impact of LGT; identify important open questions; and point to research directions that hold particular promise for elucidating the role of LGT in genome evolution. Evidence for LGT in nature is not only inferential but also direct, and potential vectors are ubiquitous. Genetic material can pass between diverse habitats and be significantly altered during residency in viruses, complicating the inference of donors, In prokaryotes about twice as many genes are interrupted by LGT as are transferred intact, and about 5Short protein domains can be privileged units of transfer. Unresolved phylogenetic issues include the correct null hypothesis, and genes as units of analysis. Themes are beginning to emerge regarding the effect of LGT on cellular networks, but I show why generalization is premature. LGT can associate with radical changes in physiology and ecological niche. Better quantitative models of genome evolution are needed, and theoretical frameworks remain to be developed for some observations including chromosome assembly by LGT.

The plasma membrane protects a cell and even though it is only about 10nm thick it is an incredibly complex and crowded environment, with ensembles of channels, membrane and trans-membrane proteins and microdomains. Hence modelling transport and dynamical processes on the plasma membrane is computationally demanding and in order for a simulation to model an entire cell membrane for several real-time seconds, a high-performance computing implementation is essential. Here we describe the domain decomposition of a plasma membrane simulation in a grid-computing environment. We discuss the issues that arise in balancing the communication requirements with the computational complexity, in both a master-slave and a slave-slave communication model.We also discuss performance and fidelity limitations arising through the necessity of frequent inter-process communication. This parallel implementation will allow systems biology researchers to analyse computationally the complex dynamical processes taking place on an entire cell membrane over a non-trivial time scale.

Summary: Genome evolution underpins all of biology, yet its principles can be difficult to communicate to the non-specialist. To facilitate broader understanding of genome evolution, we have designed an interactive 3D environment that enables visualization of diverse genome evolution processes. The system can intuitively and interactively animate mutation histories involving genome rearrangement, point mutation, recombination, insertion and deletion. Multiple organisms related by a phylogeny can be visualized simultaneously. As methods to infer evolutionary histories of genomes become increasingly complex, visualization of the evolutionary process will not only be useful for communication, but will also serve as an exploratory tool for discovering new patterns of genome evolution. Availability: The software is licensed under the GNU GPL and available for download from http://seevolution.org. Contact: aarondarling@ucdavis.edu

Phylogenomic methods can be used to investigate the tangled evolutionary relationships among genomes. Building 'all the trees of all the genes' can potentially identify common pathways of horizontal gene transfer (HGT) among taxa at varying levels of phylogenetic depth. Phylogenetic affinities can be aggregated and merged with the information about genetic linkage and biochemical function to examine hypotheses of adaptive evolution via HGT. Additionally, the use of many genetic data sets increases the power of statistical tests for phylogenetic artifacts. However, large-scale phylogenetic analyses pose several challenges, including the necessary abandonment of manual validation techniques, the need to translate inferred phylogenetic discordance into inferred HGT events, and the challenges involved in aggregating results from search-based inference methods. In this chapter we describe a tree search procedure to recover the most parsimonious pathways of HGT, and examine some of the assumptions that are made by this method.

Background: In prokaryotes and some eukaryotes, genetic material can be transferred laterally among unrelated lineages and recombined into new host genomes, providing metabolic and physiological novelty. Although the process is usually framed in terms of gene sharing (e.g. lateral gene transfer, LGT), there is little reason to imagine that the units of transfer and recombination correspond to entire, intact genes. Proteins often consist of one or more spatially compact structural regions (domains) which may fold autonomously and which, singly or in combination, confer the protein's specific functions. As LGT is frequent in strongly selective environments and natural selection is based on function, we hypothesized that domains might also serve as modules of genetic transfer, i.e. that regions of DNA that are transferred and recombined between lineages might encode intact structural domains of proteins. Methodology/Principal Findings: We selected 1,462 orthologous gene sets representing 144 prokaryotic genomes, and applied a rigorous two-stage approach to identify recombination breakpoints within these sequences. Recombination breakpoints are very significantly over-represented in gene sets within which protein domain-encoding regions have been annotated. Within these gene sets, breakpoints significantly avoid the domain-encoding regions (domons), except where these regions constitute most of the sequence length. Recombination breakpoints that fall within longer domons are distributed uniformly at random, but those that fall within shorter domons may show a slight tendency to avoid the domon midpoint. As we find no evidence for differential selection against nucleotide substitutions following the recombination event, any bias against disruption of domains must be a consequence of the recombination event per se. Conclusions/Significance: This is the first systematic study relating the units of LGT to structural features at the protein level. Many genes have been interrupted by recombination following inter-lineage genetic transfer, during which the regions within these genes that encode protein domains have not been preferentially preserved intact. Protein domains are units of function, but domons are not modules of transfer and recombination. Our results demonstrate that LGT can remodel even the most functionally conservative modules within genomes. Copyright: © 2009 Chan et al.

Phylogenomic methods can be used to investigate the tangled evolutionary relationships among genomes. Building ‘all the trees of all the genes’ can potentially identify common pathways of horizontal gene transfer (HGT) among taxa at varying levels of phylogenetic depth. Phylogenetic affinities can be aggregated and merged with the information about genetic linkage and biochemical function to examine hypotheses of adaptive evolution via HGT. Additionally, the use of many genetic data sets increases the power of statistical tests for phylogenetic artifacts. However, large-scale phylogenetic analyses pose several challenges, including the necessary abandonment of manual validation techniques, the need to translate inferred phylogenetic discordance into inferred HGT events, and the challenges involved in aggregating results from search-based inference methods. In this chapter we describe a tree search procedure to recover the most parsimonious pathways of HGT, and examine some of the assumptions that are made by this method.

Motivation: The classification of proteins into homologous groups (families) allows their structure and function to be analysed and compared in an evolutionary context. The modular nature of eukaryotic proteins presents a considerable challenge to the delineation of families, as different local regions within a single protein may share common ancestry with distinct, even mutually exclusive, sets of homologs, thereby creating an intricate web of homologous relationships if full-length sequences are taken as the unit of evolution. We attempt to disentangle this web by developing a fully automated pipeline to delineate protein subsequences that represent sensible units for homology inference, and clustering them into putatively homologous families using the Markov clustering algorithm. Results: Using six eukaryotic proteomes as input, we clustered 162 349 protein sequences into 19 697–77 415 subsequence families depending on granularity of clustering. We validated these Markov clusters of homologous subsequences (MACHOS) against the manually curated Pfam domain families, using a quality measure to assess overlap. Our subsequence families correspond well to known domain families and achieve higher quality scores than do groups generated by fully automated domain family classification methods. We illustrate our approach by analysis of a group of proteins that contains the glutamyl/glutaminyl-tRNA synthetase domain, and conclude that our method can produce high-coverage decomposition of protein sequence space into precise homologous families in a way that takes the modularity of eukaryotic proteins into account. This approach allows for a fine-scale examination of evolutionary histories of proteins encoded in eukaryotic genomes. Contact: m.ragan@imb.uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. MACHOS for the six proteomes are available as FASTA-formatted files: http://research1t.imb.uq.edu.au/ragan/machos

Nucleotide sequences of microbial genomes provide evidence that genes have been shared among organisms, a phenomenon known as lateral genetic transfer (LGT). Hypotheses about the importance of LGT in the evolution and diversification of microbes can be tested by analyzing the extensive quantities of sequence data now available. Some analysis methods identify genes with sequence features that differ from those of the surrounding genome, whereas other methods are based on inference and comparison of phylogenetic trees. A large-scale search for LGT in 144 genomes using phylogenetic methods has revealed that although parent-to-offspring ("vertical") inheritance has been the dominant mode of gene transmission, LGT has nonetheless been frequent, especially among organisms that are closely related or share the same habitat. This chapter outlines how bioinformatic and phylogenetic analyses can be built into a workflow to identify LGT among microbial genomes.

Despite the significance of haploinsufficiency in human disease, no systematic study has been reported into the types of genes that are haploinsufficient in human, or into the mechanisms that commonly lead to their deletion and to the expression of the haploinsufficient phenotype. We have applied a rigorous text-searching and database-mining strategy to extract, as comprehensively as possible, from PubMed and OMIM an annotated list of currently known human haploinsufficient genes, including their functions and associated diseases. Gene-set enrichment analysis shows that genes-encoding transcription factors, and genes that function in development, the cell cycle, and nucleic acid metabolism are overrepresented among haploinsufficient genes in human. Many of the phenotypes associated with loss-of-function or deletion of one copy of a haploinsufficient gene describe mental retardation, developmental or metabolic disorders, or tumourigenesis. We also found that haploinsufficient genes are less likely than the complete set of human genes to be situated between pairs of segmental duplications (SDs) that are in close proximity to each other on the same chromosome. Given that SDs can initiate non-allelic homologous recombination (NAHR) and the deletion of adjacent genomic regions, this suggests that the location of haploinsufficient genes between SD pairs, from whence they may suffer intra-genomic rearrangement and loss, is selectively disadvantageous. We describe a custom-made Java visualization tool, HaploGeneMapper, to aid in visualizing the proximity of human haploinsufficient genes to SDs and to enable identification of haploinsufficient genes that are vulnerable to NAHR-mediated deletion.

Genetic recombination following a genetic transfer event can produce heterogeneous phylogenetic histories within sets of genes that share a common ancestral origin. Delineating recombination events will enhance our understanding in genome evolution. However, the task of detecting recombination is not trivial due to effect of more‐recent evolutionary changes that can obscure such event from detection. In this paper, we demonstrate the use of a two‐phase strategy for detecting recombination events on a large‐scale dataset.