Phylogenetic Tree Building Research Articles

Phylogenetic monitoring of Escherichia coli in medical wastewater/Baghdad City

Clean drinking water access is a main factor that supports general health worldwide, where important investment is made to maintain water quality. The gene sequence of 16S rRNA was applied to study bacterial phylogenesis and classification. This studyaimed to isolate new bacterial strains from wastewater environments. Thisstudy collected water samples from differentwastewater sites for Baghdad Medical City’s Hospital. The results of Vitek 2Compact were with a probability of99% forEscherichia coli isolates .The bacterial isolates were identified using Polymerase chain reaction (PCR) based on the universaldiagnostic gene 16SrRNA, and the PCR product was obtained with a molecular weight of 1250bp. The PCR productsequencingshowed 166 isolates of E. coli different from the isolates registered in the NCBI database of E. coli after alignment between them.The present study reported 16 Iraqi isolates in NCBI with an accession number: OM032663.1; OM 032664.1; OM294659.2;ON724178.1; ON724264.1; ON724331.1; ON725076.1; ON725091.1; ON725139.1; ON725141.1; ON739200.1; ON739201.1;ON739207.1; ON739208.1; ON739209.1; ON739211.1. The identity was then observed(96-100%) with strains in Gene Bank.There were many different sequence position substitutions (transition, deletion, transversionfor the registered local isolates whenconducting multiple alignments and comparing themwith the isolates in the Gene Bank. According to the phylogenetic treebuilding by joining-the neighbor method, the local Iraqi isolates clustered into sub-descents. Moreover, local isolates of E.coliappeared more convergent to Saudi Arabia isolates (KY780347.1). This may be due to the geographical proximity of the two countries to the same Arab Gulf region .The present study identified 16 isolates of E. coli from the Iraqi aquatic environment (medical wastewater). The novelty of this study was represented to monitoring the evolution of E. coli in medical wastewater.

Taxonomic and nomenclatural notes on Geodiaprialongiceps Kieffer, 1911 (Hymenoptera, Diapriidae) and synonymy of the genus Geodiapria Kieffer, 1910.

This paper reviews the status of Geodiapria and its nominotypical and only included species G.longiceps. Geodiapria was previously understood to be very similar to, and doubtfully separated from the genus Basalys. We use integrative taxonomy (morphology, DNA-barcoding, phylogenetic tree building) to show that the valid name for what was G.longiceps Kieffer, 1911 is now Basalysrufocinctus (Kiefer, 1911) and that Geodiapria is consequently a junior synonym of Basalyssyn. nov. The following taxa are new synonyms of B.rufocinctus: Loxotropalongiceps Wasmann, 1909, syn. nov., G.longiceps Kieffer, 1911, syn. nov., L.rufosignata Kieffer, 1911, syn. nov. Basalysrufocinctus is newly reported from Corsica, Germany, Norway and Spain.

The pear genomics database (PGDB): a comprehensive multi-omics research platform for Pyrus spp.

BackgroundPears are among the most important temperate fruit trees in the world, with significant research efforts increasing over the last years. However, available omics data for pear cannot be easily and quickly retrieved to enable further studies using these biological data.DescriptionHere, we present a publicly accessible multi-omics pear resource platform, the Pear Genomics Database (PGDB). We collected and collated data on genomic sequences, genome structure, functional annotation, transcription factor predictions, comparative genomics, and transcriptomics. We provide user-friendly functional modules to facilitate querying, browsing and usage of these data. The platform also includes basic and useful tools, including JBrowse, BLAST, phylogenetic tree building, and additional resources providing the possibility for bulk data download and quick usage guide services.ConclusionsThe Pear Genomics Database (PGDB, http://pyrusgdb.sdau.edu.cn) is an online data analysis and query resource that integrates comprehensive multi-omics data for pear. This database is equipped with user-friendly interactive functional modules and data visualization tools, and constitutes a convenient platform for integrated research on pear.

The mitogenome of Phytophthora agathidicida: Evidence for a not so recent arrival of the "kauri killing" Phytophthora in New Zealand.

Phytophthora agathidicida is associated with a root rot that threatens the long-term survival of the iconic New Zealand kauri. Although it is widely assumed that this pathogen arrived in New Zealand post-1945, this hypothesis has yet to be formally tested. Here we describe evolutionary analyses aimed at evaluating this and two alternative hypotheses. As a basis for our analyses, we assembled complete mitochondrial genome sequences from 16 accessions representing the geographic range of P. agathidicida as well as those of five other members of Phytophthora clade 5. All 21 mitogenome sequences were very similar, differing little in size with all sharing the same gene content and arrangement. We first examined the temporal origins of genetic diversity using a pair of calibration schemes. Both resulted in similar age estimates; specifically, a mean age of 303.0-304.4 years and 95% HPDs of 206.9-414.6 years for the most recent common ancestor of the included isolates. We then used phylogenetic tree building and network analyses to investigate the geographic distribution of the genetic diversity. Four geographically distinct genetic groups were recognised within P. agathidicida. Taken together the inferred age and geographic distribution of the sampled mitogenome diversity suggests that this pathogen diversified following arrival in New Zealand several hundred to several thousand years ago. This conclusion is consistent with the emergence of kauri dieback disease being a consequence of recent changes in the relationship between the pathogen, host, and environment rather than a post-1945 introduction of the causal pathogen into New Zealand.

Murine models of IDH-wild-type glioblastoma exhibit spatial segregation of tumor initiation and manifestation during evolution

Recent characterization of spatiotemporal genomic architecture of IDH-wild-type multifocal glioblastomas (M-GBMs) suggests a clinically unobserved common-ancestor (CA) with a less aggressive phenotype, generating highly genetically divergent malignant gliomas/GBMs in distant brain regions. Using serial MRI/3D-reconstruction, whole-genome sequencing and spectral karyotyping-based single-cell phylogenetic tree building, we show two distinct types of tumor evolution in p53-mutant driven mouse models. Malignant gliomas/GBMs grow as a single mass (Type 1) and multifocal masses (Type 2), respectively, despite both exhibiting loss of Pten/chromosome 19 (chr19) and PI3K/Akt activation with sub-tetraploid/4N genomes. Analysis of early biopsied and multi-segment tumor tissues reveals no evidence of less proliferative diploid/2N lesions in Type 1 tumors. Strikingly, CA-derived relatively quiescent tumor precursors with ancestral diploid/2N genomes and normal Pten/chr19 are observed in the subventricular zone (SVZ), but are distantly segregated from multi focal Type 2 tumors. Importantly, PI3K/Akt inhibition by Rictor/mTORC2 deletion blocks distant dispersal, restricting glioma growth in the SVZ.

Molecular typing of conjunctivitis-causing adenoviruses in Hanoi, Vietnam from 2017 to 2019 and complete genome analysis of the most prevalent type (HAdV-8).

Adenoviral conjunctivitis is a common epidemic worldwide. In Vietnam, up to 80,000 patients are infected with adenoviral conjunctivitis annually. However, there are few investigations on the pathogenic adenoviruses that cause conjunctivitis. In total, 120 eye-swab samples were collected from patients with viral conjunctivitis symptoms in Hanoi, Vietnam from 2017 to 2019. Human adenoviruse (HAdV) was detected in 67 samples (55.83%) using polymerase chain reaction amplification of at least one of three HAdV-specific marker genes (hexon, penton, and fiber). Of the 67 HAdV samples, 46 samples could be analyzed by all three marker genes. DNA sequence analysis and phylogenetic tree building based on the three marker genes from the 46 HAdV samples revealed five different HAdV types associated with conjunctivitis in Hanoi, including HAdV-3 (4.3%), HAdV-4 (2.2%), HAdV-8 (89.1%), HAdV-37 (2.2%), and a potential recombinant type between types HAdV-8 and HAdV-3 (2.2%). This showed that HAdV-8 was the most common type identified in Hanoi. Complete genome analysis of HAdV-8 isolated from a Vietnamese patient (VN2017) using Sanger sequencing revealed 34 unique nucleotide changes, indicating that the adenovirus continuously accumulates new mutations. Hence, continuous surveillance of HAdV-8 changes in Vietnam is necessary in the future.

Phylogenetic tree building in the genomic age.

Knowing phylogenetic relationships among species is fundamental for many studies in biology. An accurate phylogenetic tree underpins our understanding of the major transitions in evolution, such as the emergence of new body plans or metabolism, and is key to inferring the origin of new genes, detecting molecular adaptation, understanding morphological character evolution and reconstructing demographic changes in recently diverged species. Although data are ever more plentiful and powerful analysis methods are available, there remain many challenges to reliable tree building. Here, we discuss the major steps of phylogenetic analysis, including identification of orthologous genes or proteins, multiple sequence alignment, and choice of substitution models and inference methodologies. Understanding the different sources of errors and the strategies to mitigate them is essential for assembling an accurate tree of life.

16S rRNA Gene Analysis with QIIME2.

Microbial marker-gene sequence data can be used to generate comprehensive taxonomic profiles of the microorganisms present in a given community and for other community diversity analyses. The process of going from raw gene sequences to taxonomic profiles or diversity measures involves a series of data transformations performed by numerous computational tools. This includes tools for sequence quality checking, denoising, taxonomic classification, alignment, and phylogenetic tree building. In this chapter, we demonstrate how the Quantitative Insights Into Microbial Ecology version 2 (QIIME2) software suite can simplify 16S rRNA marker-gene analysis. We walk through an example data set extracted from the guts of bumblebees in order to show how QIIME2 can transform raw sequences into taxonomic bar plots, phylogenetic trees, principal co-ordinates analyses, and other visualizations of microbial diversity.

Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes.

Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of nonexpert students, or "citizen scientists." Crowdsourcing, or data collection by nonexperts, frequently via the internet, has enabled scientists to tackle some large-scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by nonexpert crowds is, however, often questioned and little data have been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 nonexperts and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared with scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a nonexpert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large-scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and nonexperts. We show that there are important instances in which crowd consensus is not a good proxy for correctness.

Molecular systematics and DNA barcoding of Altai osmans, oreoleuciscus (pisces, cyprinidae, and leuciscinae), and their nearest relatives, inferred from sequences of cytochrome b (Cyt-b), cytochrome oxidase c (Co-1), and complete mitochondrial genome

Mitochondrial DNA (mtDNA) at the protein-coding Cyt-b gene along with data retrieved from GenBank for Co-1 gene fragments and complete mitochondrial genome (mitogenome) of Altai osmans and the nearest relatives of Leuciscinae fish species were compared for the estimation of variability and phylogenetic tree building. Phylogenetic trees were built by four techniques: Bayesian (BA), maximum likelihood (ML), maximum parsimony (MP), and neighbor-joining (NJ). Resolution of Cyt-b trees for species of two genera (Oreoleuciscus and Phoxinus) was quite distinct at all the approaches. For Tribolodon, the single gene trees were not well resolved; however, the mitogenome tree was resolved. Species identification on per individual basis (DNA barcoding) was high for both Cyt-b and Co-1 genes. The trees built using the data for 13 protein mitochondrial genes revealed a complicated phylogenetic pattern within the subfamily Leuciscinae. Scores of the average p-distances at three taxonomic levels were considerably different: (1) 1.16 ± 0.96, (2) 8.21 ± 1.01, and (3) 16.41 ± 0.85 for Cyt-b and (1) 1.04 ± 0.78, (2) 8.30 ± 0.92, and (3) 10.74 ± 0.79 for 13 protein genes of mitogenome, where (1) is intraspecies, (2) is intragenus, and (3) is intrasubfamily levels. Data on mitogenome distances were summarized for the taxonomic hierarchy for the first time. A concordant increase in distance score with growth of the rank of taxa (having the minimum score at the intraspecies level), both for a single gene and the whole mitogenome, substantiates the concept that speciation in the subfamily Leuciscinae in most cases follows the geographic mode. The distinct clustering of Altai osmans, Oreoleuciscus potanini and O. humilis, in the Cyt-b and Co-1 gene trees with small overall genetic distances, obtained for both genes, allows us to consider these taxa as separate but genetically sister species.

Whole genome single-nucleotide variation profile-based phylogenetic tree building methods for analysis of viral, bacterial and human genomes

Next-generation sequencing data can be mapped to a reference genome to identify single-nucleotide polymorphisms/variations (SNPs/SNVs; called SNPs hereafter). In theory, SNPs can be compared across several samples and the differences can be used to create phylogenetic trees depicting relatedness among the samples. However, in practice this is difficult because currently there is no stand-alone tool that takes SNP data directly as input and produces phylogenetic trees. In response to this need, PhyloSNP application was created with two analysis methods 1) a quantitative method that creates the presence/absence matrix which can be directly used to generate phylogenetic trees or creates a tree from a shrunk genome alignment (includes additional bases surrounding the SNP position) and 2) a qualitative method that clusters samples based on the frequency of different bases found at a particular position. The algorithms were used to generate trees from Poliovirus, Burkholderia and human cancer genomics NGS datasets.Availability: PhyloSNP is freely available for download at http://hive.biochemistry.gwu.edu/dna.cgi?cmd=phylosnp.

Virus discovery in sandflies, from field studies to phylogenetic tree building: classic methods versus novel methods, what do we need to address public health impact?

Phlebotomine sandflies are vectors of a large variety of arboviruses, mostly in the genus Phlebovirus. Many were discovered during the last decade, but were only partially sequenced because of extremely high genetic diversity. Next-Generation sequencing (NGS) is a key tool to achieve rapid sequencing of these viruses. Here we present the results obtained with a custom-made automated platform for high throughput processing (HTP) of field-trapped sandflies. The aim is to present an integrated approach to discover new/existing viruses vectored by sandflies, to characterize these viruses by complete genome sequencing, and to study their potential public health impact for humans and non human vertebrates. Sandflies were collected in France, Tunisia, Algeria, Turkey and Iran. They were processed on a custom-made automated HTP platform. RT-PCR assays targeting conserved regions within the genus Phlebovirus were performed both directly on sandfly extracted RNA and on supernatant of Vero cells, and further sequenced. Sandflies and cell culture that were PCR-positive, as well as supernatant of flasks with cytopathic effect were sequenced using a de novo protocol onto a Ion Torrent PGM sequencer. A total of 29,664 sandflies were collected and processed onto the platform. Origin and number of specimens are as follows: France (n = 3,087), Tunisia (n = 8,206), Algeria (n = 1,283), Turkey (n = 12,000), Iran (n = 5,089). We isolated 9 virus strains in France (2 Toscana, 7 Massilia), 10 strains in Tunisia (2 Toscana, 5 Punique, 3 new viruses still unnamed), 1 strain in Algeria (1 Toscana), 2 strains in Turkey (new phlebovirus, provisionally named Damyeri), 1 strain in Iran (new phlebovirus, provisionally named Dashli). Analysis using complete gene sequences allowed to produce full protein phylograms for isolated virus strains. Seroprevalence studies were performed to define whether these viruses infect humans and non human vertebrates in regions where sandflies were trapped or not. Seroprevalence results will be presented for France and Tunisia. A total of 23 strains were isolated, 6 of which correspond to new sandfly-borne phleboviruses that have been completely sequenced through NGS. Screening field-trapped sandflies is suitable to inventory recognized and new viruses. Combining cell culture for isolation and NGS for sequencing is cost-effective and adapted to process high quantities of insects. Serological tools are a rapid and easy method to address potential public health impact which must be confirmed by molecular diagnostic derived from complete sequence data obtained through NGS.

Comparative Analysis of Proteomes and Functionomes Provides Insights into Origins of Cellular Diversification

Reconstructing the evolutionary history of modern species is a difficult problem complicated by the conceptual and technical limitations of phylogenetic tree building methods. Here, we propose a comparative proteomic and functionomic inferential framework for genome evolution that allows resolving the tripartite division of cells and sketching their history. Evolutionary inferences were derived from the spread of conserved molecular features, such as molecular structures and functions, in the proteomes and functionomes of contemporary organisms. Patterns of use and reuse of these traits yielded significant insights into the origins of cellular diversification. Results uncovered an unprecedented strong evolutionary association between Bacteria and Eukarya while revealing marked evolutionary reductive tendencies in the archaeal genomic repertoires. The effects of nonvertical evolutionary processes (e.g., HGT, convergent evolution) were found to be limited while reductive evolution and molecular innovation appeared to be prevalent during the evolution of cells. Our study revealed a strong vertical trace in the history of proteins and associated molecular functions, which was reliably recovered using the comparative genomics approach. The trace supported the existence of a stem line of descent and the very early appearance of Archaea as a diversified superkingdom, but failed to uncover a hidden canonical pattern in which Bacteria was the first superkingdom to deploy superkingdom-specific structures and functions.

Surprising results on phylogenetic tree building methods based on molecular sequences.

BackgroundWe analyze phylogenetic tree building methods from molecular sequences (PTMS). These are methods which base their construction solely on sequences, coding DNA or amino acids.ResultsOur first result is a statistically significant evaluation of 176 PTMSs done by comparing trees derived from 193138 orthologous groups of proteins using a new measure of quality between trees. This new measure, called the Intra measure, is very consistent between different groups of species and strong in the sense that it separates the methods with high confidence.The second result is the comparison of the trees against trees derived from accepted taxonomies, the Taxon measure. We consider the NCBI taxonomic classification and their derived topologies as the most accepted biological consensus on phylogenies, which are also available in electronic form. The correlation between the two measures is remarkably high, which supports both measures simultaneously.ConclusionsThe big surprise of the evaluation is that the maximum likelihood methods do not score well, minimal evolution distance methods over MSA-induced alignments score consistently better. This comparison also allows us to rank different components of the tree building methods, like MSAs, substitution matrices, ML tree builders, distance methods, etc. It is also clear that there is a difference between Metazoa and the rest, which points out to evolution leaving different molecular traces. We also think that these measures of quality of trees will motivate the design of new PTMSs as it is now easier to evaluate them with certainty.

No so HoT - heads or tails is not able to reliably compare multiple sequence alignments.

Most phylogenetic-tree building applications use multiple sequence alignments as a starting point. A recent meta-level methodology, called Heads or Tails, aims to reveal the quality of multiple sequence alignments by comparing alignments taken in the forward direction with the alignments of the same sequences when the sequences are reversed. Through an examination of a special case for multiple sequence alignment - pair-wise alignments, where an optimal algorithm exists - and the use of a modified global-alignment application, it is shown that the forward and reverse alignments, even when they are the same, do not capture all the possible variations in the alignments and when the forward and reverse alignments differ there may be other alignments that remain unaccounted for. The implication is that comparing just the forward and (biologically irrelevant) reverse alignments is not sufficient to capture the variability in multiple sequence alignments, and the Heads or Tails methodology is therefore not suitable as a method for investigating multiple sequence alignment accuracy. Part of the reason is the inability of individual multiple sequence alignment applications to adequately sample the space of possible alignments. A further implication is that the Hall [Hall, B.G., 2008. Mol. Biol. Evol. 25, 1576-1580] methodology may create optimal synthetic multiple sequence alignments that extant aligners will be unable to completely recover ab initio due to alternative alignments being possible at particular sites. In general, it is shown that more divergent sequences will give rise to an increased number of alternative alignments, so sequence sets with a higher degree of similarity are preferable to sets with lower similarity as the starting point for phylogenetic tree building. © The Willi Hennig Society 2009.

The Gene Ontology's Reference Genome Project: *A Unified Framework for Functional Annotation across Species

AbstractComplete functional annotation of genomes is a powerful tool for researchers; however, such annotation is a time-consuming task limited by the availability of experimental data. The function of genes for which there is no experimental data can often be predicted via comparison to related, annotated sequences of known function. We describe here the Reference Genome project, an effort from the Gene Ontology (GO) Consortium to fully annotate twelve genomes to rigorous standards: human, plus eleven organisms that are important models in biomedical research, including mouse, fly, zebrafish, yeast and E. coli. To achieve this, we examine existing experimentally based annotations in a phylogenetic context in order to infer the function(s) of ancestral proteins and propagate these annotations to their descendants. This endeavor faces many difficult challenges, such as: the determination and provision of reference protein sets for each genome; the identification of gene families for curation; the application of consistent best practices for annotation; the development of methodologies for evaluating progress towards our goal; and the development of software tools to support this effort. Annotated genomes are greatly valuable to the research community and will provide the basis for using sequence similarity to annotate further genomes. An overview of the project as well as links to all resources described below can be found at "http://geneontology.org/GO.refgenome.shtml":http://geneontology.org/GO.refgenome.shtmlThis work is supported by NHGRI grant HG002273 (Gene Ontology Consortium) and NIGMS GM081084-01A1 (Phylogenetic tree building and annotation software development). Pascale Gaudet for the Reference Genome Group of the Gene Ontology Consortium. The Reference Genome project is overseen by Pascale Gaudet (dictyBase), Rex Chisholm (dictyBase) and includes these representatives from the curatorial staff: Tanya Berardini (TAIR), Emily Dimmer (GOA), Stacia R. Engel (SGD), Petra Fey (dictyBase), David P. Hill (MGI), Doug Howe (ZFIN), Jim Hu (EcoliWiki), Rachael Huntley (GOA), Varsha K. Khodiyar (UCL), Ranjana Kishore (WormBase), Donghui Li (TAIR), Ruth C. Lovering (UCL), Fiona McCarthy (AgBase), Li Ni (MGI), Victoria Petri (RGD), Deborah A. Siegel (EcoliWiki), Susan Tweedie (FlyBase), Kimberly Van Auken (WormBase), and Valerie Wood (GeneDB)—as well as the following computational staff representatives: Siddhartha Basu (dictyBase), Seth Carbon (BBOP), Mary Dolan (MGI), and Christopher J. Mungall (BBOP)—those establishing the protein families to be annotated: Kara Dolinski (PPOD), Michael S. Livstone (PPOD), and Paul Thomas (PANTHER)—and, the four PIs of the GO Consortium: Michael Ashburner (FlyBase), Judith A. Blake (MGI), J. Michael Cherry (SGD), and Suzanna E. Lewis (BBOP).

The Gene Ontology’s Reference Genome Project: *A Unified Framework for Functional Annotation across Species

Complete functional annotation of genomes is a powerful tool for researchers; however, such annotation is a time-consuming task limited by the availability of experimental data. The function of genes for which there is no experimental data can often be predicted via comparison to related, annotated sequences of known function. We describe here the Reference Genome project, an effort from the Gene Ontology (GO) Consortium to fully annotate twelve genomes to rigorous standards: human, plus eleven organisms that are important models in biomedical research, including mouse, fly, zebrafish, yeast and E. coli. To achieve this, we examine existing experimentally based annotations in a phylogenetic context in order to infer the function(s) of ancestral proteins and propagate these annotations to their descendants. This endeavor faces many difficult challenges, such as: the determination and provision of reference protein sets for each genome; the identification of gene families for curation; the application of consistent best practices for annotation; the development of methodologies for evaluating progress towards our goal; and the development of software tools to support this effort. Annotated genomes are greatly valuable to the research community and will provide the basis for using sequence similarity to annotate further genomes. An overview of the project as well as links to all resources described below can be found at "http://geneontology.org/GO.refgenome.shtml":http://geneontology.org/GO.refgenome.shtmlThis work is supported by NHGRI grant HG002273 (Gene Ontology Consortium) and NIGMS GM081084-01A1 (Phylogenetic tree building and annotation software development). Pascale Gaudet for the Reference Genome Group of the Gene Ontology Consortium. The Reference Genome project is overseen by Pascale Gaudet (dictyBase), Rex Chisholm (dictyBase) and includes these representatives from the curatorial staff: Tanya Berardini (TAIR), Emily Dimmer (GOA), Stacia R. Engel (SGD), Petra Fey (dictyBase), David P. Hill (MGI), Doug Howe (ZFIN), Jim Hu (EcoliWiki), Rachael Huntley (GOA), Varsha K. Khodiyar (UCL), Ranjana Kishore (WormBase), Donghui Li (TAIR), Ruth C. Lovering (UCL), Fiona McCarthy (AgBase), Li Ni (MGI), Victoria Petri (RGD), Deborah A. Siegel (EcoliWiki), Susan Tweedie (FlyBase), Kimberly Van Auken (WormBase), and Valerie Wood (GeneDB)—as well as the following computational staff representatives: Siddhartha Basu (dictyBase), Seth Carbon (BBOP), Mary Dolan (MGI), and Christopher J. Mungall (BBOP)—those establishing the protein families to be annotated: Kara Dolinski (PPOD), Michael S. Livstone (PPOD), and Paul Thomas (PANTHER)—and, the four PIs of the GO Consortium: Michael Ashburner (FlyBase), Judith A. Blake (MGI), J. Michael Cherry (SGD), and Suzanna E. Lewis (BBOP).

Covariance of maximum likelihood evolutionary distances between sequences aligned pairwise

BackgroundThe estimation of a distance between two biological sequences is a fundamental process in molecular evolution. It is usually performed by maximum likelihood (ML) on characters aligned either pairwise or jointly in a multiple sequence alignment (MSA). Estimators for the covariance of pairs from an MSA are known, but we are not aware of any solution for cases of pairs aligned independently. In large-scale analyses, it may be too costly to compute MSAs every time distances must be compared, and therefore a covariance estimator for distances estimated from pairs aligned independently is desirable. Knowledge of covariances improves any process that compares or combines distances, such as in generalized least-squares phylogenetic tree building, orthology inference, or lateral gene transfer detection.ResultsIn this paper, we introduce an estimator for the covariance of distances from sequences aligned pairwise. Its performance is analyzed through extensive Monte Carlo simulations, and compared to the well-known variance estimator of ML distances. Our covariance estimator can be used together with the ML variance estimator to form covariance matrices.ConclusionThe estimator performs similarly to the ML variance estimator. In particular, it shows no sign of bias when sequence divergence is below 150 PAM units (i.e. above ~29% expected sequence identity). Above that distance, the covariances tend to be underestimated, but then ML variances are also underestimated.

SIMPROT: Using an empirically determined indel distribution in simulations of protein evolution

BackgroundGeneral protein evolution models help determine the baseline expectations for the evolution of sequences, and they have been extensively useful in sequence analysis and for the computer simulation of artificial sequence data sets.ResultsWe have developed a new method of simulating protein sequence evolution, including insertion and deletion (indel) events in addition to amino-acid substitutions. The simulation generates both the simulated sequence family and a true sequence alignment that captures the evolutionary relationships between amino acids from different sequences. Our statistical model for indel evolution is based on the empirical indel distribution determined by Qian and Goldstein. We have parameterized this distribution so that it applies to sequences diverged by varying evolutionary times and generalized it to provide flexibility in simulation conditions. Our method uses a Monte-Carlo simulation strategy, and has been implemented in a C++ program named Simprot.ConclusionSimprot will be useful for testing methods of analysis of protein sequence families particularly alignment methods, phylogenetic tree building, detection of recombination and horizontal gene transfer, and homology detection, where knowing the true course of sequence evolution is essential.

DPRml: distributed phylogeny reconstruction by maximum likelihood

In recent years there has been increased interest in producing large and accurate phylogenetic trees using statistical approaches. However for a large number of taxa, it is not feasible to construct large and accurate trees using only a single processor. A number of specialized parallel programs have been produced in an attempt to address the huge computational requirements of maximum likelihood. We express a number of concerns about the current set of parallel phylogenetic programs which are currently severely limiting the widespread availability and use of parallel computing in maximum likelihood-based phylogenetic analysis. We have identified the suitability of phylogenetic analysis to large-scale heterogeneous distributed computing. We have completed a distributed and fully cross-platform phylogenetic tree building program called distributed phylogeny reconstruction by maximum likelihood. It uses an already proven maximum likelihood-based tree building algorithm and a popular phylogenetic analysis library for all its likelihood calculations. It offers one of the most extensive sets of DNA substitution models currently available. We are the first, to our knowledge, to report the completion of a distributed phylogenetic tree building program that can achieve near-linear speedup while only using the idle clock cycles of machines. For those in an academic or corporate environment with hundreds of idle desktop machines, we have shown how distributed computing can deliver a 'free' ML supercomputer.