Dataset Information

A kingdom-specific protein domain HMM library for improved annotation of fungal genomes.

ABSTRACT: BACKGROUND: Pfam is a general-purpose database of protein domain alignments and profile Hidden Markov Models (HMMs), which is very popular for the annotation of sequence data produced by genome sequencing projects. Pfam provides models that are often very general in terms of the taxa that they cover and it has previously been suggested that such general models may lack some of the specificity or selectivity that would be provided by kingdom-specific models. RESULTS: Here we present a general approach to create domain libraries of HMMs for sub-taxa of a kingdom. Taking fungal species as an example, we construct a domain library of HMMs (called Fungal Pfam or FPfam) using sequences from 30 genomes, consisting of 24 species from the ascomycetes group and two basidiomycetes, Ustilago maydis, a fungal pathogen of maize, and the white rot fungus Phanerochaete chrysosporium. In addition, we include the Microsporidion Encephalitozoon cuniculi, an obligate intracellular parasite, and two non-fungal species, the oomycetes Phytophthora sojae and Phytophthora ramorum, both plant pathogens. We evaluate the performance in terms of coverage against the original 30 genomes used in training FPfam and against five more recently sequenced fungal genomes that can be considered as an independent test set. We show that kingdom-specific models such as FPfam can find instances of both novel and well characterized domains, increases overall coverage and detects more domains per sequence with typically higher bitscores than Pfam for the same domain families. An evaluation of the effect of changing E-values on the coverage shows that the performance of FPfam is consistent over the range of E-values applied. CONCLUSION: Kingdom-specific models are shown to provide improved coverage. However, as the models become more specific, some sequences found by Pfam may be missed by the models in FPfam and some of the families represented in the test set are not present in FPfam. Therefore, we recommend that both general and specific libraries are used together for annotation and we find that a significant improvement in coverage is achieved by using both Pfam and FPfam.

SUBMITTER: Alam I

PROVIDER: S-EPMC1854895 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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A kingdom-specific protein domain HMM library for improved annotation of fungal genomes.

Alam Intikhab I Hubbard Simon J SJ Oliver Stephen G SG Rattray Magnus M

BMC genomics 20070410

<h4>Background</h4>Pfam is a general-purpose database of protein domain alignments and profile Hidden Markov Models (HMMs), which is very popular for the annotation of sequence data produced by genome sequencing projects. Pfam provides models that are often very general in terms of the taxa that they cover and it has previously been suggested that such general models may lack some of the specificity or selectivity that would be provided by kingdom-specific models.<h4>Results</h4>Here we present ...[more]

PMID: 17425790

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Project description:BackgroundThe genome sequence data of more than 65985 species are publicly available as of October 2021 within the National Center for Biotechnology Information (NCBI) database alone and additional genome sequences are available in other databases and also continue to accumulate at a rapid pace. However, an error-free functional annotation of these genome is essential for the research communities to fully utilize these data in an optimum and efficient manner.ResultsAn analysis of proteome sequence data of 689 fungal species (7.15 million protein sequences) was conducted to identify the presence of functional annotation errors. Proteins associated with calcium signaling events, including calcium dependent protein kinases (CDPKs), calmodulins (CaM), calmodulin-like (CML) proteins, WRKY transcription factors, selenoproteins, and proteins associated with the terpene biosynthesis pathway, were targeted in the analysis. Gene associated with CDPKs and selenoproteins are known to be absent in fungal genomes. Our analysis, however, revealed the presence of proteins that were functionally annotated as CDPK proteins. However, InterproScan analysis indicated that none of the protein sequences annotated as "calcium dependent protein kinase" were found to encode calcium binding EF-hands at the regulatory domain. Similarly, none of a protein sequences annotated as a "selenocysteine" were found to contain a Sec (U) amino acid. Proteins annotated as CaM and CMLs also had significant discrepancies. CaM proteins should contain four calcium binding EF-hands, however, a range of 2-4 calcium binding EF-hands were present in the fungal proteins that were annotated as CaM proteins. Similarly, CMLs should possess four calcium binding EF-hands, but some of the CML annotated fungal proteins possessed either three or four calcium binding EF-hands. WRKY transcription factors are characterized by the presence of a WRKY domain and are confined to the plant kingdom. Several fungal proteins, however, were annotated as WRKY transcription factors, even though they did not contain a WRKY domain.ConclusionThe presence of functional annotation errors in fungal genome and proteome databases is of considerable concern and needs to be addressed in a timely manner.

Project description:Microorganisms are diverse in their genome sequences and subsequently in their encoded metabolic pathways, which enabled them to adapt to numerous environmental conditions. They produce thousands of small molecules, many of which are volatiles in nature and play important roles in signaling in intra- and inter-species to kingdom and domain interactions, survival, or virulence. Many of these compounds have been studied, characterized, and organized in the mVOC 2.0 database. However, such dataset has not been investigated comprehensively in terms of its phylogeny to determine key volatile markers for certain taxa. It was hypothesized that some of the volatiles described in the mVOC 2.0 database could function as a phylogenetic signal since their production is conserved among certain taxa within the microbial evolutionary tree. Our meta-analysis revealed that some volatiles were produced by a large number of bacteria but not in fungal genera such as dimethyl disulfide, acetic acid, 2-nonanone, dimethyl trisulfide, 2-undecanone, isovaleric acid, 2-tridecanone, propanoic acid, and indole (common bacterial compounds). In contrast, 1-octen-3-ol, 3-octanone, and 2-pentylfuran (common fungal compounds) were produced primarily by fungal genera. Such chemical information was further confirmed by investigating genomic data of publicly available databases revealing that bacteria or fungi harbor gene families involved in these volatiles' biosynthesis. Our phylogenetic signal testing identified 61 volatiles with a significant phylogenetic signal as demonstrated by phylogenetic D statistic P-value < 0.05. Thirty-three volatiles were phylogenetically conserved in the bacterial domain (e.g., cyclocitral) compared to 17 volatiles phylogenetically conserved in the fungal kingdom (e.g., aristolochene), whereas 11 volatiles were phylogenetically conserved in genera from both bacteria and fungi (e.g., geosmin). These volatiles belong to different chemical classes such as heterocyclic compounds, long-chain fatty acids, sesquiterpenoids, and aromatics. The performed approaches serve as a starting point to investigate less explored volatiles with potential roles in signaling, antimicrobial therapy, or diagnostics.

Project description:BackgroundMass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.ResultsOf 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.ConclusionsTo facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (15N) or carbon (13C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.

Dataset Information

A kingdom-specific protein domain HMM library for improved annotation of fungal genomes.

Publications

A kingdom-specific protein domain HMM library for improved annotation of fungal genomes.

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