DNA modifications such as methylation and DNA damage can play critical regulatory roles in biological systems. Single molecule, real time (SMRT) sequencing technology generates DNA sequences as well as DNA polymerase kinetic information that can be used for the direct detection of DNA modifications. We demonstrate that local sequence context has a strong impact on DNA polymerase kinetics in the neighborhood of the incorporation site during the DNA synthesis reaction, allowing for the possibility of estimating the expected kinetic rate of the enzyme at the incorporation site using kinetic rate information collected from existing SMRT sequencing data (historical data) covering the same local sequence contexts of interest. We develop an Empirical Bayesian hierarchical model for incorporating historical data. Our results show that the model could greatly increase DNA modification detection accuracy, and reduce requirement of control data coverage. For some DNA modifications that have a strong signal, a control sample is not even needed by using historical data as alternative to control. Thus, sequencing costs can be greatly reduced by using the model. We implemented the model in a R package named seqPatch, which is available at https://github.com/zhixingfeng/seqPatch.
DNA modifications have been found in a wide range of living organisms, from bacteria to human. Many existing studies have shown that they play important roles in development, disease, bacteria virulence, etc. However, for many types of DNA modification, for example N6-methyladenine and 8-oxoG, there is not an efficient and accurate detection method. Single molecule real time (SMRT) sequencing not only generates DNA sequences, but also generates DNA polymerase kinetic information. The kinetic information is sensitive to DNA modifications in the sequenced DNA template, and therefore can be used for detecting a wide range of DNA modification types. The usual detection strategy is a case-control method, which compare kinetic information between native sample and a control sample whose modifications have been removed. However, generating a control sample doubles the cost. We proposed a hierarchical model, which can incorporate existing SMRT sequencing data to increase detection accuracy and reduce coverage requirement of control sample or even avoid the need of a control sample in some cases. We tested our method on SMRT sequencing data of plasmids with known modified sites and E. coli K-12 strain to demonstrate our method can greatly increase detection accuracy and reduce sequencing cost.
A classical example of repeated speciation coupled with ecological diversification is the evolution of 14 closely related species of Darwin’s (Galápagos) finches (Thraupidae, Passeriformes). Their adaptive radiation in the Galápagos archipelago took place in the last 2–3 million years and some of the molecular mechanisms that led to their diversification are now being elucidated. Here we report evolutionary analyses of genome of the large ground finch, Geospiza magnirostris.
13,291 protein-coding genes were predicted from a 991.0 Mb G. magnirostris genome assembly. We then defined gene orthology relationships and constructed whole genome alignments between the G. magnirostris and other vertebrate genomes. We estimate that 15% of genomic sequence is functionally constrained between G. magnirostris and zebra finch. Genic evolutionary rate comparisons indicate that similar selective pressures acted along the G. magnirostris and zebra finch lineages suggesting that historical effective population size values have been similar in both lineages. 21 otherwise highly conserved genes were identified that each show evidence for positive selection on amino acid changes in the Darwin's finch lineage. Two of these genes (Igf2r and Pou1f1) have been implicated in beak morphology changes in Darwin’s finches. Five of 47 genes showing evidence of positive selection in early passerine evolution have cilia related functions, and may be examples of adaptively evolving reproductive proteins.
These results provide insights into past evolutionary processes that have shaped G. magnirostris genes and its genome, and provide the necessary foundation upon which to build population genomics resources that will shed light on more contemporaneous adaptive and non-adaptive processes that have contributed to the evolution of the Darwin’s finches.
Genomics; Evolution; Darwin’s finches; Large ground finch; Geospiza magnirostris
With more chromosomes than any other sequenced genome, the macronuclear genome of Oxytricha trifallax has a unique and complex architecture, including alternative fragmentation and predominantly single-gene chromosomes.
The macronuclear genome of the ciliate Oxytricha trifallax displays an extreme and unique eukaryotic genome architecture with extensive genomic variation. During sexual genome development, the expressed, somatic macronuclear genome is whittled down to the genic portion of a small fraction (∼5%) of its precursor “silent” germline micronuclear genome by a process of “unscrambling” and fragmentation. The tiny macronuclear “nanochromosomes” typically encode single, protein-coding genes (a small portion, 10%, encode 2–8 genes), have minimal noncoding regions, and are differentially amplified to an average of ∼2,000 copies. We report the high-quality genome assembly of ∼16,000 complete nanochromosomes (∼50 Mb haploid genome size) that vary from 469 bp to 66 kb long (mean ∼3.2 kb) and encode ∼18,500 genes. Alternative DNA fragmentation processes ∼10% of the nanochromosomes into multiple isoforms that usually encode complete genes. Nucleotide diversity in the macronucleus is very high (SNP heterozygosity is ∼4.0%), suggesting that Oxytricha trifallax may have one of the largest known effective population sizes of eukaryotes. Comparison to other ciliates with nonscrambled genomes and long macronuclear chromosomes (on the order of 100 kb) suggests several candidate proteins that could be involved in genome rearrangement, including domesticated MULE and IS1595-like DDE transposases. The assembly of the highly fragmented Oxytricha macronuclear genome is the first completed genome with such an unusual architecture. This genome sequence provides tantalizing glimpses into novel molecular biology and evolution. For example, Oxytricha maintains tens of millions of telomeres per cell and has also evolved an intriguing expansion of telomere end-binding proteins. In conjunction with the micronuclear genome in progress, the O. trifallax macronuclear genome will provide an invaluable resource for investigating programmed genome rearrangements, complementing studies of rearrangements arising during evolution and disease.
The macronuclear genome of the ciliate Oxytricha trifallax, contained in its somatic nucleus, has a unique genome architecture. Unlike its diploid germline genome, which is transcriptionally inactive during normal cellular growth, the macronuclear genome is fragmented into at least 16,000 tiny (∼3.2 kb mean length) chromosomes, most of which encode single actively transcribed genes and are differentially amplified to a few thousand copies each. The smallest chromosome is just 469 bp, while the largest is 66 kb and encodes a single enormous protein. We found considerable variation in the genome, including frequent alternative fragmentation patterns, generating chromosome isoforms with shared sequence. We also found limited variation in chromosome amplification levels, though insufficient to explain mRNA transcript level variation. Another remarkable feature of Oxytricha's macronuclear genome is its inordinate fondness for telomeres. In conjunction with its possession of tens of millions of chromosome-ending telomeres per macronucleus, we show that Oxytricha has evolved multiple putative telomere-binding proteins. In addition, we identified two new domesticated transposase-like protein classes that we propose may participate in the process of genome rearrangement. The macronuclear genome now provides a crucial resource for ongoing studies of genome rearrangement processes that use Oxytricha as an experimental or comparative model.
Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.
Recent work has suggested that individuals can be classified into ‘enterotypes’ based on the abundance of key bacterial taxa in gut microbial communities. However, the generality of enterotypes across populations, and the existence of similar cluster types for other body sites, remains to be evaluated. We combined the Human Microbiome Project 16S rRNA gene sequence data and metagenomes with similar published data to assess the existence of enterotypes across body sites. We found that rather than forming enterotypes (note we use this term for clusters in all body sites), most samples fell into gradients based on taxonomic abundances of bacteria such as Bacteroides, although in some body sites there is a bi/multi modal distribution of samples across gradients. Furthermore, many of the methods used in the analysis (e.g., distance metrics and clustering approaches) affected the likelihood of identifying enterotypes in particular body habitats. We recommend that multiple approaches be used and compared when testing for enterotypes.
Niabella soli Weon et al. 2008 is a member of the Chitinophagaceae, a family within the class Sphingobacteriia that is poorly characterized at the genome level, thus far. N. soli strain JS13-8T is of interest for its ability to produce a variety of glycosyl hydrolases. The genome of N. soli strain JS13-8T is only the second genome sequence of a type strain from the family Chitinophagaceae to be published, and the first one from the genus Niabella. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 4,697,343 bp long chromosome with its 3,931 protein-coding and 49 RNA genes is a part of the Genomic
aerobic; non-motile; Gram-negative; mesophilic; chemoorganotrophic; glycosyl hydrolases; soil; Chitinophagaceae; GEBA
The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.
Microbial ecologists cannot observe their study organisms directly, so they use molecular sequencing to measure the abundance of different microbes living in the wild. The most commonly used method for measuring the abundance of different microbes is to collect a DNA sample from an environment and sequence a particular gene, the 16S SSU rRNA gene (“16S”) from those samples. The abundance of 16S sequences from different microbes is then used as a surrogate measure of the abundance of the microbial taxa in the community. One problem with the use of the 16S gene as a measure of microbial abundance is that many microbes have multiple copies of the gene in their genome. Thus, variation in 16S gene abundances can be caused by both genomic copy number variation and variation in the abundance of organisms. In this study we present a computational method that allows estimation of the abundance and genomic 16S copy number of microbes based on environmental sequencing of the 16S gene. We use simulations and analysis of microbial community data sets to demonstrate that estimating the abundance of organisms from 16S data improves our ability to accurately measure the diversity and abundance of microbial communities.
The combination of ecological diversity with genetic and experimental tractability makes Drosophila a powerful model for the study of animal-associated microbial communities. Despite the known importance of yeasts in Drosophila physiology, behavior, and fitness, most recent work has focused on Drosophila-bacterial interactions. In order to get a more complete understanding of the Drosophila microbiome, we characterized the yeast communities associated with different Drosophila species collected around the world. We focused on the phylum Ascomycota because it constitutes the vast majority of the Drosophila-associated yeasts. Our sampling strategy allowed us to compare the distribution and structure of the yeast and bacterial communities in the same host populations. We show that yeast communities are dominated by a small number of abundant taxa, that the same yeast lineages are associated with different host species and populations, and that host diet has a greater effect than host species on yeast community composition. These patterns closely parallel those observed in Drosophila bacterial communities. However, we do not detect a significant correlation between the yeast and bacterial communities of the same host populations. Comparative analysis of different symbiont groups provides a more comprehensive picture of host-microbe interactions. Future work on the role of symbiont communities in animal physiology, ecological adaptation, and evolution would benefit from a similarly holistic approach.
New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences.
We implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as “Sifting Families,” or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology–based analyses.
We describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/).
Gillisia limnaea Van Trappen et al. 2004 is the type species of the genus Gillisia, which is a member of the well characterized family Flavobacteriaceae. The genome of G. limnea R-8282T is the first sequenced genome (permanent draft) from a type strain of the genus Gillisia. Here we describe the features of this organism, together with the permanent-draft genome sequence and annotation. The 3,966,857 bp long chromosome (two scaffolds) with its 3,569 protein-coding and 51 RNA genes is a part of the Genomic
Encyclopedia of Bacteria and Archaea project.
aerobic; motile; rod-shaped; moderately halotolerant; psychrophilic; chemoheterotrophic; proteorhodopsin; microbial mat; yellow-pigmented; Flavobacteriaceae; GEBA
Owenweeksia hongkongensis Lau et al. 2005 is the sole member of the monospecific genus Owenweeksia in the family Cryomorphaceae, a poorly characterized family at the genome level thus far. This family comprises seven genera within the class Flavobacteria. Family members are known to be psychrotolerant, rod-shaped and orange pigmented (β-carotene), typical for Flavobacteria. For growth, seawater and complex organic nutrients are necessary. The genome of O. hongkongensis UST20020801T is only the second genome of a member of the family Cryomorphaceae whose sequence has been deciphered. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 4,000,057 bp long chromosome with its 3,518 protein-coding and 45 RNA genes is a part of the Genomic
aerobic; motile; rod-shaped; mesophilic; non-fermentative; Gram-negative; orange-pigmented sea water; Bacteroidetes; Flavobacteria; Cryomorphaceae; GEBA
The direct “metagenomic” sequencing of genomic material from complex assemblages of bacteria, archaea, viruses and microeukaryotes has yielded new insights into the structure of microbial communities. For example, analysis of metagenomic data has revealed the existence of previously unknown microbial taxa whose spatial distributions are limited by environmental conditions, ecological competition, and dispersal mechanisms. However, differences in genotypes that might lead biologists to designate two microbes as taxonomically distinct need not necessarily imply differences in ecological function. Hence, there is a growing need for large-scale analysis of the distribution of microbial function across habitats. Here, we present a framework for investigating the biogeography of microbial function by analyzing the distribution of protein families inferred from environmental sequence data across a global collection of sites. We map over 6,000,000 protein sequences from unassembled reads from the Global Ocean Survey dataset to protein families, generating a protein family relative abundance matrix that describes the distribution of each protein family across sites. We then use non-negative matrix factorization (NMF) to approximate these protein family profiles as linear combinations of a small number of ecological components. Each component has a characteristic functional profile and site profile. Our approach identifies common functional signatures within several of the components. We use our method as a filter to estimate functional distance between sites, and find that an NMF-filtered measure of functional distance is more strongly correlated with environmental distance than a comparable PCA-filtered measure. We also find that functional distance is more strongly correlated with environmental distance than with geographic distance, in agreement with prior studies. We identify similar protein functions in several components and suggest that functional co-occurrence across metagenomic samples could lead to future methods for de-novo functional prediction. We conclude by discussing how NMF, and other dimension reduction methods, can help enable a macroscopic functional description of marine ecosystems.
Remarkable advances in DNA sequencing technology have created a need for de novo genome assembly methods tailored to work with the new sequencing data types. Many such methods have been published in recent years, but assembling raw sequence data to obtain a draft genome has remained a complex, multi-step process, involving several stages of sequence data cleaning, error correction, assembly, and quality control. Successful application of these steps usually requires intimate knowledge of a diverse set of algorithms and software. We present an assembly pipeline called A5 (Andrew And Aaron's Awesome Assembly pipeline) that simplifies the entire genome assembly process by automating these stages, by integrating several previously published algorithms with new algorithms for quality control and automated assembly parameter selection. We demonstrate that A5 can produce assemblies of quality comparable to a leading assembly algorithm, SOAPdenovo, without any prior knowledge of the particular genome being assembled and without the extensive parameter tuning required by the other assembly algorithm. In particular, the assemblies produced by A5 exhibit 50% or more reduction in broken protein coding sequences relative to SOAPdenovo assemblies. The A5 pipeline can also assemble Illumina sequence data from libraries constructed by the Nextera (transposon-catalyzed) protocol, which have markedly different characteristics to mechanically sheared libraries. Finally, A5 has modest compute requirements, and can assemble a typical bacterial genome on current desktop or laptop computer hardware in under two hours, depending on depth of coverage.
The human gut microbiota comprise a complex and dynamic ecosystem that profoundly affects host development and physiology. Standard approaches for analyzing time-series data of the microbiota involve computation of measures of ecological community diversity at each time-point, or measures of dissimilarity between pairs of time-points. Although these approaches, which treat data as static snapshots of microbial communities, can identify shifts in overall community structure, they fail to capture the dynamic properties of individual members of the microbiota and their contributions to the underlying time-varying behavior of host ecosystems. To address the limitations of current methods, we present a computational framework that uses continuous-time dynamical models coupled with Bayesian dimensionality adaptation methods to identify time-dependent signatures of individual microbial taxa within a host as well as across multiple hosts. We apply our framework to a publicly available dataset of 16S rRNA gene sequences from stool samples collected over ten months from multiple human subjects, each of whom received repeated courses of oral antibiotics. Using new diversity measures enabled by our framework, we discover groups of both phylogenetically close and distant bacterial taxa that exhibit consensus responses to antibiotic exposure across multiple human subjects. These consensus responses reveal a timeline for equilibration of sub-communities of micro-organisms with distinct physiologies, yielding insights into the successive changes that occur in microbial populations in the human gut after antibiotic treatments. Additionally, our framework leverages microbial signatures shared among human subjects to automatically design optimal experiments to interrogate dynamic properties of the microbiota in new studies. Overall, our approach provides a powerful, general-purpose framework for understanding the dynamic behaviors of complex microbial ecosystems, which we believe will prove instrumental for future studies in this field.
Microbes colonize the human body soon after birth and propagate to form rich ecosystems. These ecosystems play essential roles in health and disease. Recent advances in DNA sequencing technologies make possible comprehensive studies of the time-dependent behavior of microbes throughout the body. Sophisticated computer-based methods are essential for the analysis and interpretation of these complex datasets. We present a computational method that models how human microbial ecosystems respond over time to perturbations, such as when subjects in a study are treated with a drug. When applied to a large publicly available dataset, our method yields new insights into the diversity of dynamic responses to antibiotics among microbes in the human body. We find that within an individual, sub-populations of microbes that share certain physiological roles also share coordinated responses. Moreover, we find that these responses are similar across different people. We use this information to improve the experimental design of the previously conducted study, and to develop strategies for optimal design of future studies. Our work provides an integrated computer-based method for automatically discovering patterns of change over time in the microbiota, and for designing future experiments to identify changes that impact human health and disease.
We report the sequencing of seven genomes from two haloarchaeal genera, Haloferax and Haloarcula. Ease of cultivation and the existence of well-developed genetic and biochemical tools for several diverse haloarchaeal species make haloarchaea a model group for the study of archaeal biology. The unique physiological properties of these organisms also make them good candidates for novel enzyme discovery for biotechnological applications. Seven genomes were sequenced to ∼20×coverage and assembled to an average of 50 contigs (range 5 scaffolds - 168 contigs). Comparisons of protein-coding gene compliments revealed large-scale differences in COG functional group enrichment between these genera. Analysis of genes encoding machinery for DNA metabolism reveals genera-specific expansions of the general transcription factor TATA binding protein as well as a history of extensive duplication and horizontal transfer of the proliferating cell nuclear antigen. Insights gained from this study emphasize the importance of haloarchaea for investigation of archaeal biology.
Sulfobacillus acidophilus Norris et al. 1996 is a member of the genus Sulfobacillus which comprises five species of the order Clostridiales. Sulfobacillus species are of interest for comparison to other sulfur and iron oxidizers and also have biomining applications. This is the first completed genome sequence of a type strain of the genus Sulfobacillus, and the second published genome of a member of the species S. acidophilus. The genome, which consists of one chromosome and one plasmid with a total size of 3,557,831 bp harbors 3,626 protein-coding and 69 RNA genes, and is a part of the Genomic
aerobic; motile; Gram-positive; acidophilic; moderately thermophilic; sulfide- and iron-oxidizing; biomining; autotrophic; mixotrophic; soil; insertis sedis; Clostridiales; GEBA
Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.
The human body is inhabited by trillions of bacteria and other microbes, which have recently been studied in many different habitats (including gut, mouth, skin, and urogenital) by the Human Microbiome Project (HMP). These microbial communities were assayed using high-throughput DNA sequencing, but it can be challenging to determine their biological functions based solely on the resulting short sequences. To reconstruct the metabolic activities of such communities, we have developed HUMAnN, a method to accurately infer community function directly from short DNA reads. The method's accuracy was validated using a collection of synthetic microbial communities. Applying HUMAnN to data from the HMP, we showed that, unlike individual microbial species, many metabolic processes were present among all body habitats. However, the frequencies of these processes varied dramatically, and some were highly enriched within individual habitats to provide niche specialization (e.g. in the gut, which is abundant in food matter but low in oxygen). Other community functions were linked specifically to properties of the human host, such as biochemical processes only present in vaginal habitats with particularly high or low pH. Studying additional environmental or disease-associated communities using HUMAnN will further improve our understanding of how the microbial organisms in a community are linked to the biological processes they carry out.
The function of most proteins is not determined experimentally, but is extrapolated from homologs. According to the “ortholog conjecture”, or standard model of phylogenomics, protein function changes rapidly after duplication, leading to paralogs with different functions, while orthologs retain the ancestral function. We report here that a comparison of experimentally supported functional annotations among homologs from 13 genomes mostly supports this model. We show that to analyze GO annotation effectively, several confounding factors need to be controlled: authorship bias, variation of GO term frequency among species, variation of background similarity among species pairs, and propagated annotation bias. After controlling for these biases, we observe that orthologs have generally more similar functional annotations than paralogs. This is especially strong for sub-cellular localization. We observe only a weak decrease in functional similarity with increasing sequence divergence. These findings hold over a large diversity of species; notably orthologs from model organisms such as E. coli, yeast or mouse have conserved function with human proteins.
To infer the function of an unknown gene, possibly the most effective way is to identify a well-characterized evolutionarily related gene, and assume that they have both kept their ancestral function. If several such homologs are available, all else being equal, it has long been assumed that those that diverged by speciation (“ortholog”) are functionally closer than those that diverged by duplication (“paralogs”); thus function is more reliably inferred from the former. But despite its prevalence, this model mostly rests on first principles, as for the longest time we have not had sufficient data to test it empirically. Recently, some studies began investigating this question and have cast doubt on the validity of this model. Here, we show that by considering a wide range of organisms and data, and, crucially, by correcting for several easily overlooked biases affecting functional annotations, the standard model is corroborated by the presently available experimental data.
Summary: Analysis of microbial genomes often requires the general organization and comparison of tens to thousands of genomes both from public repositories and unpublished sources. MicrobeDB provides a foundation for such projects by the automation of downloading published, completed bacterial and archaeal genomes from key sources, parsing annotations of all genomes (both public and private) into a local database, and allowing interaction with the database through an easy to use programming interface. MicrobeDB creates a simple to use, easy to maintain, centralized local resource for various large-scale comparative genomic analyses and a back-end for future microbial application design.
Availability: MicrobeDB is freely available under the GNU-GPL at: http://github.com/mlangill/microbedb/
Runella slithyformis Larkin and Williams 1978 is the type species of the genus Runella, which belongs to the Cytophagaceae, a family that was only recently classified to the order Cytophagales in the class Cytophagia. The species is of interest because it is able to grow at temperatures as low as 4°C. This is the first completed genome sequence of a member of the genus Runella and the sixth sequence from the family Cytophagaceae. The 6,919,729 bp long genome consists of a 6.6 Mbp circular genome and five circular plasmids of 38.8 to 107.0 kbp length, harboring a total of 5,974 protein-coding and 51 RNA genes and is a part of the Genomic
strictly aerobic; non-motile; Gram-negative; psychrotolerant; chemoorganotrophic; Cytophagaceae; Cytophagia; GEBA
Thermodesulfatator indicus Moussard et al. 2004 is a member of the Thermodesulfobacteriaceae, a family in the phylum Thermodesulfobacteria that is currently poorly characterized at the genome level. Members of this phylum are of interest because they represent a distinct, deep-branching, Gram-negative lineage. T. indicus is an anaerobic, thermophilic, chemolithoautotrophic sulfate reducer isolated from a deep-sea hydrothermal vent. Here we describe the features of this organism, together with the complete genome sequence, and annotation. The 2,322,224 bp long chromosome with its 2,233 protein-coding and 58 RNA genes is a part of the Genomic
strictly anaerobic; motile; Gram-negative; thermophilic; sulfate-reducing; chemolithoautotrophic; black smoker; Thermodesulfobacteria; Thermodesulfobacteriaceae; GEBA
Thermovirga lienii Dahle and Birkeland 2006 is a member of the genus Thermovirga in the genomically moderately well characterized phylum 'Synergistetes'. Members of this relatively recently proposed phylum ‘Synergistetes’ are of interest because of their isolated phylogenetic position and their diverse habitats, e.g. from humans to oil wells. The genome of T. lienii Cas60314T is the fifth genome sequence (third completed) from this phylum to be published. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 1,999,646 bp long genome (including one plasmid) with its 1,914 protein-coding and 59 RNA genes is a part of the Genomic
anaerobic; chemoorganotrophic; Gram-negative; motile; thermophilic; marine oil well; Synergistaceae; GEBA
Holophaga foetida Liesack et al. 1995 is a member of the phylum Acidobacteria and is of interest for its ability to anaerobically degrade aromatic compounds and for its production of volatile sulfur compounds through a unique pathway. The genome of H. foetida strain TMBS4T is the first to be sequenced for a representative of the class Holophagae. Here we describe the features of this organism, together with the complete genome sequence (improved high quality draft), and annotation. The 4,127,237 bp long chromosome with its 3,615 protein-coding and 57 RNA genes is a part of the Genomic
anaerobic; motile; Gram-negative; mesophilic; chemoorganotrophic; sulfide-methylation; fresh water mud; Acidobacteria; Holophagaceae; GEBA
Muricauda ruestringensis Bruns et al. 2001 is the type species of the genus Muricauda, which belongs to the family Flavobacteriaceae in the phylum Bacteroidetes. The species is of interest because of its isolated position in the genomically unexplored genus Muricauda, which is located in a part of the tree of life containing not many organisms with sequenced genomes. The genome, which consists of a circular chromosome of 3,842,422 bp length with a total of 3,478 protein-coding and 47 RNA genes, is a part of the Genomic
facultatively anaerobic; non-motile; Gram-negative; mesophilic; marine; chemoheterotrophic; Flavobacteriaceae; GEBA
Saprospira grandis Gross 1911 is a member of the Saprospiraceae, a family in the class ‘Sphingobacteria’ that remains poorly characterized at the genomic level. The species is known for preying on other marine bacteria via ‘ixotrophy’. S. grandis strain Sa g1 was isolated from decaying crab carapace in France and was selected for genome sequencing because of its isolated location in the tree of life. Only one type strain genome has been published so far from the Saprospiraceae, while the sequence of strain Sa g1 represents the second genome to be published from a non-type strain of S. grandis. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 4,495,250 bp long Improved-High-Quality draft of the genome with its 3,536 protein-coding and 62 RNA genes is a part of the Genomic
strictly aerobic; gliding; Gram-negative; mesophilic; chemoorganotrophic; marine; ixotrophy; Saprospiraceae; GEBA