ZCURVE_V: a new self-training system for recognizing protein-coding genes in viral and phage genomes

Background  

It necessary to use highly accurate and statistics-based systems for viral and phage genome annotations. The GeneMark systems for gene-finding in virus and phage genomes suffer from some basic drawbacks. This paper puts forward an alternative approach for viral and phage gene-finding to improve the quality of annotations, particularly for newly sequenced genomes.     

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation

Characterising gene function for the ever-increasing number and diversity of species with annotated genomes relies almost entirely on computational prediction methods. These software are also numerous and diverse, each with different strengths and weaknesses as revealed through community benchmarking efforts. Meta-predictors that assess consensus and conflict from individual algorithms should deliver enhanced functional annotations. To exploit the benefits of meta-approaches, we developed CrowdGO, an open-source consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected annotations. Applying CrowdGO to results from a deep learning-based, a sequence similarity-based, and two protein domain-based methods, delivers consensus annotations with improved precision and recall. Furthermore, using standard evaluation measures CrowdGO performance matches that of the community’s best performing individual methods. CrowdGO therefore offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations.     

Phylogenetic Profiling: How Much Input Data Is Enough?

Phylogenetic profiling is a well-established approach for predicting gene function based on patterns of gene presence and absence across species. Much of the recent developments have focused on methodological improvements, but relatively little is known about the effect of input data size on the quality of predictions. In this work, we ask: how many genomes and functional annotations need to be considered for phylogenetic profiling to be effective? Phylogenetic profiling generally benefits from an increased amount of input data. However, by decomposing this improvement in predictive accuracy in terms of the contribution of additional genomes and of additional annotations, we observed diminishing returns in adding more than ∼100 genomes, whereas increasing the number of annotations remained strongly beneficial throughout. We also observed that maximising phylogenetic diversity within a clade of interest improves predictive accuracy, but the effect is small compared to changes in the number of genomes under comparison. Finally, we show that these findings are supported in light of the Open World Assumption, which posits that functional annotation databases are inherently incomplete. All the tools and data used in this work are available for reuse from http://lab.dessimoz.org/14_phylprof. Scripts used to analyse the data are available on request from the authors.     

Clustering of main orthologs for multiple genomes

The identification of orthologous genes shared by multiple genomes is critical for both functional and evolutionary studies in comparative genomics. While it is usually done by sequence similarity search and reconciled tree construction in practice, recently a new combinatorial approach and high-throughput system MSOAR for ortholog identification between closely related genomes based on genome rearrangement and gene duplication has been proposed in Fu et al. MSOAR assumes that orthologous genes correspond to each other in the most parsimonious evolutionary scenario, minimizing the number of genome rearrangement and (postspeciation) gene duplication events. However, the parsimony approach used by MSOAR limits it to pairwise genome comparisons. In this paper, we extend MSOAR to multiple (closely related) genomes and propose an ortholog clustering method, called MultiMSOAR, to infer main orthologs in multiple genomes. As a preliminary experiment, we apply MultiMSOAR to rat, mouse, and human genomes, and validate our results using gene annotations and gene function classifications in the public databases. We further compare our results to the ortholog clusters predicted by MultiParanoid, which is an extension of the well-known program InParanoid for pairwise genome comparisons. The comparison reveals that MultiMSOAR gives more detailed and accurate orthology information, since it can effectively distinguish main orthologs from inparalogs.     

Accessing the SEED Genome Databases via Web Services API: Tools for Programmers

Background  

The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups.     

原核生物蛋白质基因组学研究进展

随着基因组测序技术的不断发展,大量微生物基因组序列可以在短时间内得以准确鉴定。为了进一步探究基因组的结构与功能,基于序列特征与同源特征的基因组注释算法广泛应用于新测序物种。然而受基因组测序质量以及算法本身准确性偏低等问题的影响,现有的基因组注释存在着相当比例的假基因以及注释错误,尤其是蛋白质N端的注释错误。为了弥补基因组注释的不足,以基因芯片或RNA-seq为核心的转录组测序技术和以串联质谱为核心的蛋白质组测序技术可以高通量地对基因的转录和翻译产物进行精确测定,进而实现预测基因结构的实验验证。然而,原核生物细胞中存在的大量非编码RNA给转录组测序技术引入了污染数据,限制了其对基因组注释的应用。相对而言,以串联质谱技术为核心的蛋白质组学测序可以在短时间内鉴定到生物体内大量的蛋白质,实现注释基因的验证甚至校准。已成为基因组注释和重注释的重要依据,并因而衍生了"蛋白质基因组学"的新研究方向。文中首先介绍传统的基于序列预测和同源比对的基因组注释算法,指出其中存在的不足。在此基础上,结合转录组学与蛋白质组学的技术特点,分析蛋白质组学对于原核生物基因组注释的优势,总结现阶段大规模蛋白质基因组学研究的进展情况。最后从信息学角度指出当前蛋白质组数据进行基因组重注释存在的问题与相应的解决方案,进而探讨未来蛋白质基因组学的发展方向。     

BugView: a browser for comparing genomes

BugView is a Java application for visualizing homologous genes on a pair of related genomes, and can also be used to view individual genomes. It accepts files of prokaryotic or eukaryotic genomes in GenBank format and allows users to assign homologous pairs, and make and save annotations. AVAILABILITY: http://www.gla.ac.uk/~dpl1n/BugView/     

Tree House Explorer: A Novel Genome Browser for Phylogenomics

Tree House Explorer (THEx) is a genome browser that integrates phylogenomic data and genomic annotations into a single interactive platform for combined analysis. THEx allows users to visualize genome-wide variation in evolutionary histories and genetic divergence on a chromosome-by-chromosome basis, with continuous sliding window comparisons to gene annotations, recombination rates, and other user-specified, highly customizable feature annotations. THEx provides a new platform for interactive phylogenomic data visualization to analyze and interpret the diverse evolutionary histories woven throughout genomes. Hosted on Conda, THEx integrates seamlessly into new or pre-existing workflows.     

Questionable 16S ribosomal RNA gene annotations are frequent in completed microbial genomes

According to recent reports, many ribosomal RNA gene annotations are still questionable, and the use of inappropriate tools for annotation has been blamed. However, we believe that the abundant 16S rRNA partial sequence in the databases, mainly created by culture-independent PCR methods, is another main cause of the ambiguous annotations of 16S rRNA. To examine the current status of 16S rRNA gene annotations in complete microbial genomes, we used as a criterion the conserved anti-SD sequence, located at the 3′ end of the 16S rRNA gene, which is commonly overlooked by culture-independent PCR methods. In our large survey, 859 16S rRNA gene sequences from 252 different species of the microbial complete genomes were inspected. 67 species (234 genes) were detected with ambiguous annotations. The common anti-SD sequence and other conserved 16S rRNA sequence features could be detected in the downstream-intergenic regions for almost every questionable sequence, indicating that many of the 16S rRNA genes were annotated incorrectly. Furthermore, we found that more than 91.5% of the 93,716 sequences of the available 16S rRNA in the main databases are partial sequences. We also performed BLAST analysis for every questionable rRNA sequence, and most of the best hits in the analysis were rRNA partial sequences. This result indicates that partial sequences are prevalent in the databases, and that these sequences have significantly affected the accuracy of microbial genomic annotation. We suggest that the annotation of 16S rRNA genes in newly complete microbial genomes must be done in more detail, and that revision of questionable rRNA annotations should commence as soon as possible.     

Calculating orthologs in bacteria and Archaea: a divide and conquer approach

Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or more on a typical server). Here, we present xBASE-Orth, a system for ongoing ortholog annotation, which applies a "divide and conquer" approach and adopts a pragmatic scheme that trades accuracy for speed. Starting at species level, xBASE-Orth carefully constructs and uses pan-genomes as proxies for the full collections of coding sequences at each level as it progressively climbs the taxonomic tree using the previously computed data. This leads to a significant decrease in the number of alignments that need to be performed, which translates into faster computation, making ortholog computation possible on a global scale. Using xBASE-Orth, we analyzed an NCBI collection of 1,288 bacterial and 94 archaeal complete genomes with more than 4 million coding sequences in 5 weeks and predicted more than 700 million ortholog pairs, clustered in 175,531 orthologous groups. We have also identified sets of highly conserved bacterial and archaeal orthologs and in so doing have highlighted anomalies in genome annotation and in the proposed composition of the minimal bacterial genome. In summary, our approach allows for scalable and efficient computation of the bacterial and archaeal ortholog annotations. In addition, due to its hierarchical nature, it is suitable for incorporating novel complete genomes and alternative genome annotations. The computed ortholog data and a continuously evolving set of applications based on it are integrated in the xBASE database, available at http://www.xbase.ac.uk/.     

COCO-CL: hierarchical clustering of homology relations based on evolutionary correlations

MOTIVATION: Determining orthology relations among genes across multiple genomes is an important problem in the post-genomic era. Identifying orthologous genes can not only help predict functional annotations for newly sequenced or poorly characterized genomes, but can also help predict new protein-protein interactions. Unfortunately, determining orthology relation through computational methods is not straightforward due to the presence of paralogs. Traditional approaches have relied on pairwise sequence comparisons to construct graphs, which were then partitioned into putative clusters of orthologous groups. These methods do not attempt to preserve the non-transitivity and hierarchic nature of the orthology relation. RESULTS: We propose a new method, COCO-CL, for hierarchical clustering of homology relations and identification of orthologous groups of genes. Unlike previous approaches, which are based on pairwise sequence comparisons, our method explores the correlation of evolutionary histories of individual genes in a more global context. COCO-CL can be used as a semi-independent method to delineate the orthology/paralogy relation for a refined set of homologous proteins obtained using a less-conservative clustering approach, or as a refiner that removes putative out-paralogs from clusters computed using a more inclusive approach. We analyze our clustering results manually, with support from literature and functional annotations. Since our orthology determination procedure does not employ a species tree to infer duplication events, it can be used in situations when the species tree is unknown or uncertain. CONTACT: jothi@mail.nih.gov, przytyck@mail.nih.gov SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.     

yrGATE: a web-based gene-structure annotation tool for the identification and dissemination of eukaryotic genes

Your Gene structure Annotation Tool for Eukaryotes (yrGATE) provides an Annotation Tool and Community Utilities for worldwide web-based community genome and gene annotation. Annotators can evaluate gene structure evidence derived from multiple sources to create gene structure annotations. Administrators regulate the acceptance of annotations into published gene sets. yrGATE is designed to facilitate rapid and accurate annotation of emerging genomes as well as to confirm, refine, or correct currently published annotations. yrGATE is highly portable and supports different standard input and output formats. The yrGATE software and usage cases are available at .     

Quality of computationally inferred gene ontology annotations

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon-an important outcome given that >98% of all annotations are inferred without direct curation.     

Assessing strategies for improved superfamily recognition

There are more than 200 completed genomes and over 1 million nonredundant sequences in public repositories. Although the structural data are more sparse (approximately 13,000 nonredundant structures solved to date), several powerful sequence-based methodologies now allow these structures to be mapped onto related regions in a significant proportion of genome sequences. We review a number of publicly available strategies for providing structural annotations for genome sequences, and we describe the protocol adopted to provide CATH structural annotations for completed genomes. In particular, we assess the performance of several sequence-based protocols employing Hidden Markov model (HMM) technologies for superfamily recognition, including a new approach (SAMOSA [sequence augmented models of structure alignments]) that exploits multiple structural alignments from the CATH domain structure database when building the models. Using a data set of remote homologs detected by structure comparison and manually validated in CATH, a single-seed HMM library was able to recognize 76% of the data set. Including the SAMOSA models in the HMM library showed little gain in homolog recognition, although a slight improvement in alignment quality was observed for very remote homologs. However, using an expanded 1D-HMM library, CATH-ISL increased the coverage to 86%. The single-seed HMM library has been used to annotate the protein sequences of 120 genomes from all three major kingdoms, allowing up to 70% of the genes or partial genes to be assigned to CATH superfamilies. It has also been used to recruit sequences from Swiss-Prot and TrEMBL into CATH domain superfamilies, expanding the CATH database eightfold.     

Systematic identification of mammalian regulatory motifs' target genes and functions

We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.     

A new comprehensive annotation of leucine-rich repeat-containing receptors in rice

Oryza sativa (rice) plays an essential food security role for more than half of the world’s population. Obtaining crops with high levels of disease resistance is a major challenge for breeders, especially today, given the urgent need for agriculture to be more sustainable. Plant resistance genes are mainly encoded by three large leucine-rich repeat (LRR)-containing receptor (LRR-CR) families: the LRR-receptor-like kinase (LRR-RLK), LRR-receptor-like protein (LRR-RLP) and nucleotide-binding LRR receptor (NLR). Using lrrprofiler , a pipeline that we developed to annotate and classify these proteins, we compared three publicly available annotations of the rice Nipponbare reference genome. The extended discrepancies that we observed for LRR-CR gene models led us to perform an in-depth manual curation of their annotations while paying special attention to nonsense mutations. We then transferred this manually curated annotation to Kitaake, a cultivar that is closely related to Nipponbare, using an optimized strategy. Here, we discuss the breakthrough achieved by manual curation when comparing genomes and, in addition to ‘functional’ and ‘structural’ annotations, we propose that the community adopts this approach, which we call ‘comprehensive’ annotation. The resulting data are crucial for further studies on the natural variability and evolution of LRR-CR genes in order to promote their use in breeding future resilient varieties.     

BEACON: automated tool for Bacterial GEnome Annotation ComparisON

Background

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).

Results

The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.

Conclusions

We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users.     

Automatic annotation of organellar genomes with DOGMA

The Dual Organellar GenoMe Annotator (DOGMA) automates the annotation of organellar (plant chloroplast and animal mitochondrial) genomes. It is a Web-based package that allows the use of BLAST searches against a custom database, and conservation of basepairing in the secondary structure of animal mitochondrial tRNAs to identify and annotate genes. DOGMA provides a graphical user interface for viewing and editing annotations. Annotations are stored on our password-protected server to enable repeated sessions of working on the same genome. Finished annotations can be extracted for direct submission to GenBank.