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Abstract Background Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Results Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. Conclusion We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/∼terrapon/HMMfit/

Abstract Background Plasmodium falciparum is responsible for the most acute form of human malaria. Most recent studies demonstrate that it belongs to a monophyletic lineage specialized in the infection of great ape hosts. Several other Plasmodium species cause human malaria. They all belong to another distinct lineage of parasites which infect a wider range of primate species. All known mammalian malaria parasites appear to be monophyletic. Their clade includes the two previous distinct lineages of parasites of primates and great apes, one lineage of rodent parasites, and presumably Hepatocystis species. Plasmodium falciparum and great ape parasites are commonly thought to be the sister-group of all other mammal-infecting malaria parasites. However, some studies supported contradictory origins and found parasites of great apes to be closer to those of rodents, or to those of other primates. Results To distinguish between these mutually exclusive hypotheses on the origin of Plasmodium falciparum and its great ape infecting relatives, we performed a comprehensive phylogenetic analysis based on a data set of three mitochondrial genes from 33 to 84 malaria parasites. We showed that malarial mitochondrial genes have evolved slowly and are compositionally homogeneous. We estimated their phylogenetic relationships using Bayesian and maximum-likelihood methods. Inferred trees were checked for their robustness to the (i) site selection, (ii) assumptions of various probabilistic models, and (iii) taxon sampling. Our results robustly support a common ancestry of rodent parasites and Plasmodium falciparum's relatives infecting great apes. Conclusions Our results refute the most common view of the origin of great ape malaria parasites, and instead demonstrate the robustness of a less well-established phylogenetic hypothesis, under which Plasmodium falciparum and its relatives infecting great apes are closely related to rodent parasites. This study sheds light on the evolutionary history of Plasmodium falciparum, a major issue for human health.

Abstract Background The amino acid substitution model is the core component of many protein analysis systems such as sequence similarity search, sequence alignment, and phylogenetic inference. Although several general amino acid substitution models have been estimated from large and diverse protein databases, they remain inappropriate for analyzing specific species, e.g., viruses. Emerging epidemics of influenza viruses raise the need for comprehensive studies of these dangerous viruses. We propose an influenza-specific amino acid substitution model to enhance the understanding of the evolution of influenza viruses. Results A maximum likelihood approach was applied to estimate an amino acid substitution model (FLU) from ~113, 000 influenza protein sequences, consisting of ~20 million residues. FLU outperforms 14 widely used models in constructing maximum likelihood phylogenetic trees for the majority of influenza protein alignments. On average, FLU gains ~42 log likelihood points with an alignment of 300 sites. Moreover, topologies of trees constructed using FLU and other models are frequently different. FLU does indeed have an impact on likelihood improvement as well as tree topologies. It was implemented in PhyML and can be downloaded from ftp://ftp.sanger.ac.uk/pub/1000genomes/lsq/FLU or included in PhyML 3.0 server at http://www.atgc-montpellier.fr/phyml/. Conclusions FLU should be useful for any influenza protein analysis system which requires an accurate description of amino acid substitutions.

Abstract Background Plasmodium falciparum is the main causative agent of malaria. Of the 5 484 predicted genes of P. falciparum, about 57% do not have sufficient sequence similarity to characterized genes in other species to warrant functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes. Gene expression data have been widely used in the recent years to help functional annotation in an intra-species way via the so-called Guilt By Association (GBA) principle. Results We propose a new method that uses gene expression data to assess inter-species annotation transfers. Our approach starts from a set of likely orthologs between a reference species (here S. cerevisiae and D. melanogaster) and a query species (P. falciparum). It aims at identifying clusters of coexpressed genes in the query species whose coexpression has been conserved in the reference species. These conserved clusters of coexpressed genes are then used to assess annotation transfers between genes with low sequence similarity, enabling reliable transfers of annotations from the reference to the query species. The approach was used with transcriptomic data sets of P. falciparum, S. cerevisiae and D. melanogaster, and enabled us to propose with high confidence new/refined annotations for several dozens hypothetical/putative P. falciparum genes. Notably, we revised the annotation of genes involved in ribosomal proteins and ribosome biogenesis and assembly, thus highlighting several potential drug targets. Conclusions Our approach uses both sequence similarity and gene expression data to help inter-species gene annotation transfers. Experiments show that this strategy improves the accuracy achieved when using solely sequence similarity and outperforms the accuracy of the GBA approach. In addition, our experiments with P. falciparum show that it can infer a function for numerous hypothetical genes.

Abstract Background The Plasmodium falciparum genome (3D7 strain) published in 2002, revealed ~5,400 genes, mostly based on in silico predictions. Experimental data is therefore required for structural and functional assessments of P. falciparum genes and expression, and polymorphic data are further necessary to exploit genomic information to further qualify therapeutic target candidates. Here, we undertook a large scale analysis of a P. falciparum FcB1-schizont-EST library previously constructed by suppression subtractive hybridization (SSH) to study genes expressed during merozoite morphogenesis, with the aim of: 1) obtaining an exhaustive collection of schizont specific ESTs, 2) experimentally validating or correcting P. falciparum gene models and 3) pinpointing genes displaying protein polymorphism between the FcB1 and 3D7 strains. Results A total of 22,125 clones randomly picked from the SSH library were sequenced, yielding 21,805 usable ESTs that were then clustered on the P. falciparum genome. This allowed identification of 243 protein coding genes, including 121 previously annotated as hypothetical. Statistical analysis of GO terms, when available, indicated significant enrichment in genes involved in "entry into host-cells" and "actin cytoskeleton". Although most ESTs do not span full-length gene reading frames, detailed sequence comparison of FcB1-ESTs versus 3D7 genomic sequences allowed the confirmation of exon/intron boundaries in 29 genes, the detection of new boundaries in 14 genes and identification of protein polymorphism for 21 genes. In addition, a large number of non-protein coding ESTs were identified, mainly matching with the two A-type rRNA units (on chromosomes 5 and 7) and to a lower extent, two atypical rRNA loci (on chromosomes 1 and 8), TARE subtelomeric regions (several chromosomes) and the recently described telomerase RNA gene (chromosome 9). Conclusion This FcB1-schizont-EST analysis confirmed the actual expression of 243 protein coding genes, allowing the correction of structural annotations for a quarter of these sequences. In addition, this analysis demonstrated the actual transcription of several remarkable non-protein coding loci: 2 atypical rRNA, TARE region and telomerase RNA gene. Together with other collections of P. falciparum ESTs, usually generated from mixed parasite stages, this collection of FcB1-schizont-ESTs provides valuable data to gain further insight into the P. falciparum gene structure, polymorphism and expression.

Motivation Most evolutionary analyses are based on pre-estimated multiple sequence alignment. Wong et al. established the existence of an uncertainty induced by multiple sequence alignment when reconstructing phylogenies. They were able to show that in many cases different aligners produce different phylogenies, with no simple objective criterion sufficient to distinguish among these alternatives. Results We demonstrate that incorporating MSA induced uncertainty into bootstrap sampling can significantly increase correlation between clade correctness and its corresponding bootstrap value. Our procedure involves concatenating several alternative multiple sequence alignments of the same sequences, produced using different commonly used aligners. We then draw bootstrap replicates while favoring columns of the more unique aligner among the concatenated aligners. We named this concatenation and bootstrapping method, Weighted Partial Super Bootstrap (wpSBOOT). We show on three simulated datasets of 16, 32 and 64 tips that our method improves the predictive power of bootstrap values. We also used as a benchmark an empirical collection of 853 one to one orthologous genes from seven yeast species and found wpSBOOT to significantly improve discrimination capacity between topologically correct and incorrect trees. Bootstrap values of wpSBOOT are comparable to similar readouts estimated using a single method. However, for reduced trees by 50 and 95% bootstrap thresholds, wpSBOOT comes out the lowest Type I error (less FP). Availability and implementation The automated generation of replicates has been implemented in the T-Coffee package, which is available as open source freeware available from www.tcoffee.org. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract Background Of the 5 484 predicted proteins of Plasmodium falciparum, the main causative agent of malaria, about 60% do not have sufficient sequence similarity with proteins in other organisms to warrant provision of functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes. Results We present PlasmoDraft http://atgc.lirmm.fr/PlasmoDraft/, a database of Gene Ontology (GO) annotation predictions for P. falciparum genes based on postgenomic data. Predictions of PlasmoDraft are achieved with a Guilt By Association method named Gonna. This involves (1) a predictor that proposes GO annotations for a gene based on the similarity of its profile (measured with transcriptome, proteome or interactome data) with genes already annotated by GeneDB; (2) a procedure that estimates the confidence of the predictions achieved with each data source; (3) a procedure that combines all data sources to provide a global summary and confidence estimate of the predictions. Gonna has been applied to all P. falciparum genes using most publicly available transcriptome, proteome and interactome data sources. Gonna provides predictions for numerous genes without any annotations. For example, 2 434 genes without any annotations in the Biological Process ontology are associated with specific GO terms (e.g. Rosetting, Antigenic variation), and among these, 841 have confidence values above 50%. In the Cellular Component and Molecular Function ontologies, 1 905 and 1 540 uncharacterized genes are associated with specific GO terms, respectively (740 and 329 with confidence value above 50%). Conclusion All predictions along with their confidence values have been compiled in PlasmoDraft, which thus provides an extensive database of GO annotation predictions that can be achieved with these data sources. The database can be accessed in different ways. A global view allows for a quick inspection of the GO terms that are predicted with high confidence, depending on the various data sources. A gene view and a GO term view allow for the search of potential GO terms attached to a given gene, and genes that potentially belong to a given GO term.

Abstract Background Distance-based phylogeny inference methods first estimate evolutionary distances between every pair of taxa, then build a tree from the so-obtained distance matrix. These methods are fast and fairly accurate. However, they hardly deal with incomplete distance matrices. Such matrices are frequent with recent multi-gene studies, when two species do not share any gene in analyzed data. The few existing algorithms to infer trees with satisfying accuracy from incomplete distance matrices have time complexity in O(n4) or more, where n is the number of taxa, which precludes large scale studies. Agglomerative distance algorithms (e.g. NJ 12) are much faster, with time complexity in O(n3) which allows huge datasets and heavy bootstrap analyses to be dealt with. These algorithms proceed in three steps: (a) search for the taxon pair to be agglomerated, (b) estimate the lengths of the two so-created branches, (c) reduce the distance matrix and return to (a) until the tree is fully resolved. But available agglomerative algorithms cannot deal with incomplete matrices. Results We propose an adaptation to incomplete matrices of three agglomerative algorithms, namely NJ, BIONJ 3 and MVR 4. Our adaptation generalizes to incomplete matrices the taxon pair selection criterion of NJ (also used by BIONJ and MVR), and combines this generalized criterion with that of ADDTREE 5. Steps (b) and (c) are also modified, but O(n3) time complexity is kept. The performance of these new algorithms is studied with large scale simulations, which mimic multi-gene phylogenomic datasets. Our new algorithms – named NJ*, BIONJ* and MVR* – infer phylogenetic trees that are as least as accurate as those inferred by other available methods, but with much faster running times. MVR* presents the best overall performance. This algorithm accounts for the variance of the pairwise evolutionary distance estimates, and is well suited for multi-gene studies where some distances are accurately estimated using numerous genes, whereas others are poorly estimated (or not estimated) due to the low number (absence) of sequenced genes being shared by both species. Conclusion Our distance-based agglomerative algorithms NJ*, BIONJ* and MVR* are fast and accurate, and should be quite useful for large scale phylogenomic studies. When combined with the SDM method 6 to estimate a distance matrix from multiple genes, they offer a relevant alternative to usual supertree techniques 7. Binaries and all simulated data are downloadable from 8.