Large-scale prokaryotic gene prediction and comparison to genome annotation

MOTIVATION: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome comparison either on a large or small scale would be facilitated by using a single standard for annotation, which incorporates a transparency of why an open reading frame (ORF) is considered to be a gene. RESULTS: A total of 143 prokaryotic genomes were scored with an updated version of the prokaryotic genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to approximately 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms that too many short genes are annotated in numerous organisms. Furthermore, genes might be missing in the annotation of some of the genomes. We predict 41 of 143 genomes to be over-annotated by >5%, meaning that too many ORFs are annotated as genes. We also predict that 12 of 143 genomes are under-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank. We argue that the average performance of our standardized and fully automated method is slightly better than the annotation.     

Automated bacterial genome analysis and annotation

More than 300 bacterial genome sequences are publicly available, and many more are scheduled to be completed and released in the near future. Converting this raw sequence information into a better understanding of the biology of bacteria involves the identification and annotation of genes, proteins and pathways. This processing is typically done using sequence annotation pipelines comprised of a variety of software modules and, in some cases, human experts. The reference databases, computational methods and knowledge that form the basis of these pipelines are constantly evolving, and thus there is a need to reprocess genome annotations on a regular basis. The combined challenge of revising existing annotations and extracting useful information from the flood of new genome sequences will necessitate more reliance on completely automated systems.     

Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis

Single-cell RNA sequencing is a powerful technique that continues to expand across various biological applications. However, incomplete 3′-UTR annotations can impede single-cell analysis resulting in genes that are partially or completely uncounted. Performing single-cell RNA sequencing with incomplete 3′-UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single-cell isoform sequencing in tandem with single-cell RNA sequencing can rapidly improve 3′-UTR annotations. Using threespine stickleback fish (Gasterosteus aculeatus), we show that gene models resulting from a minimal embryonic single-cell isoform sequencing dataset retained 26.1% greater single-cell RNA sequencing reads than gene models from Ensembl alone. Furthermore, pooling our single-cell sequencing isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the single-cell isoform sequencing only dataset. In addition, isoforms identified by single-cell isoform sequencing included thousands of new splicing variants. The improved gene models obtained using single-cell isoform sequencing led to successful identification of cell types and increased the reads identified of many genes in our single-cell RNA sequencing stickleback dataset. Our work illuminates single-cell isoform sequencing as a cost-effective and efficient mechanism to rapidly annotate genomes for single-cell RNA sequencing.     

Automated genome sequence analysis and annotation.

MOTIVATION: Large-scale genome projects generate a rapidly increasing number of sequences, most of them biochemically uncharacterized. Research in bioinformatics contributes to the development of methods for the computational characterization of these sequences. However, the installation and application of these methods require experience and are time consuming. RESULTS: We present here an automatic system for preliminary functional annotation of protein sequences that has been applied to the analysis of sets of sequences from complete genomes, both to refine overall performance and to make new discoveries comparable to those made by human experts. The GeneQuiz system includes a Web-based browser that allows examination of the evidence leading to an automatic annotation and offers additional information, views of the results, and links to biological databases that complement the automatic analysis. System structure and operating principles concerning the use of multiple sequence databases, underlying sequence analysis tools, lexical analyses of database annotations and decision criteria for functional assignments are detailed. The system makes automatic quality assessments of results based on prior experience with the underlying sequence analysis tools; overall error rates in functional assignment are estimated at 2.5-5% for cases annotated with highest reliability ('clear' cases). Sources of over-interpretation of results are discussed with proposals for improvement. A conservative definition for reporting 'new findings' that takes account of database maturity is presented along with examples of possible kinds of discoveries (new function, family and superfamily) made by the system. System performance in relation to sequence database coverage, database dynamics and database search methods is analysed, demonstrating the inherent advantages of an integrated automatic approach using multiple databases and search methods applied in an objective and repeatable manner. AVAILABILITY: The GeneQuiz system is publicly available for analysis of protein sequences through a Web server at http://www.sander.ebi.ac. uk/gqsrv/submit     

GPAC: benchmarking the sensitivity of genome informatics analysis to genome annotation completeness

In view of the recent explosion in genome sequence data, and the 200 or more complete genome sequences currently available, the importance of genome-scale bioinformatics analysis is increasing rapidly. However, computational genome informatics analyses often lack a statistical assessment of their sensitivity to the completeness of the functional annotation. Therefore, a pre-analysis method to automatically validate the sensitivity of computational genome analyses with regard to genome annotation completeness is useful for this purpose. In this report we developed the Gene Prediction Accuracy Classification (GPAC) test, which provides statistical evidence of sensitivity by repeating the same analysis for five different gene groups (classified according to annotation accuracy level), and for randomly sampled gene groups, with the same number of genes as each of the five classified groups. Variability in these results is then assessed, and if the results vary significantly with different data subsets, the analysis is considered "sensitive" to annotation completeness, and careful selection of data is advised prior to the actual in silico analysis. The GPAC test has been applied to the analyses of Sakai et al., 2001, and Ohno et al., 2001, and it revealed that the analysis of Ohno et al. was more sensitive to annotation completeness. It showed that GPAC could be employed to ascertain the sensitivity of an analysis. The GPAC bendhmarking software is freely available in the latest G-language Genome Analysis Environment package, at http://www.g-language.org/.     

Large-scale analysis of pseudogenes in the human genome

Pseudogenes are considered as genomic fossils: disabled copies of functional genes that were once active in the ancient genome. Recently, whole-genome computational approaches have revealed thousands of pseudogenes in the genomes of the human and other eukaryotes. Identification of these pseudogenes can improve the accuracy of gene annotation. It also offers new insight on the evolutionary history and the stability of the genome as a whole.     

Applications of InterPro in protein annotation and genome analysis

The applications of InterPro span a range of biologically important areas that includes automatic annotation of protein sequences and genome analysis. In automatic annotation of protein sequences InterPro has been utilised to provide reliable characterisation of sequences, identifying them as candidates for functional annotation. Rules based on the InterPro characterisation are stored and operated through a database called RuleBase. RuleBase is used as the main tool in the sequence database group at the EBI to apply automatic annotation to unknown sequences. The annotated sequences are stored and distributed in the TrEMBL protein sequence database. InterPro also provides a means to carry out statistical and comparative analyses of whole genomes. In the Proteome Analysis Database, InterPro analyses have been combined with other analyses based on CluSTr, the Gene Ontology (GO) and structural information on the proteins.     

Towards multidimensional genome annotation

Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.     

Enzyme-specific profiles for genome annotation: PRIAM

The advent of fully sequenced genomes opens the ground for the reconstruction of metabolic pathways on the basis of the identification of enzyme-coding genes. Here we describe PRIAM, a method for automated enzyme detection in a fully sequenced genome, based on the classification of enzymes in the ENZYME database. PRIAM relies on sets of position-specific scoring matrices ('profiles') automatically tailored for each ENZYME entry. Automatically generated logical rules define which of these profiles is required in order to infer the presence of the corresponding enzyme in an organism. As an example, PRIAM was applied to identify potential metabolic pathways from the complete genome of the nitrogen-fixing bacterium Sinorhizobium meliloti. The results of this automated method were compared with the original genome annotation and visualised on KEGG graphs in order to facilitate the interpretation of metabolic pathways and to highlight potentially missing enzymes.     

草地贪夜蛾基因组注释及分析

草地贪夜蛾Spodoptera frugiperda近年来在我国迅速扩散,造成了重大的经济损失,引起社会关注。草地贪夜蛾基因组序列对深入研究其迁飞、入侵和抗药性等特性具有十分重要的作用。目前,已有5个版本的基因组序列被公开报道,但3个版本无基因组注释信息。除以Sf 9细胞系为DNA来源的基因组版本外,其他版本的scaffold N50过小,拼接质量偏低。为此,本研究选取了scaffold N50最大的草地贪夜蛾Sf 9细胞系基因组进行了蛋白编码基因注释。该版本的基因组重复序列占比28.1%。CEGMA评估显示该本版本基因组可覆盖93.6%的核心基因,BUSCO评估显示可覆盖90.8%的核心基因。利用OMIGA注释流程预测到25 699个蛋白质编码基因,详细的基因序列可从InsectBase网站获得(http://www.insect-genome.com/FAW/),其中具有GO注释的基因为15 623个,具有KEGG注释的基因共有9 213个。选取了12个鳞翅目昆虫进行比较基因组学分析,发现草地贪夜蛾与斜纹夜蛾的亲缘关系最近,两者分化时间大约在1 284万年前。对12个鳞翅目昆虫蛋白质编码基因进行同源分析,在草地贪夜蛾中发现了2 490个单拷贝基因、891个鳞翅目特有基因、2 360个物种特异扩增基因和4 180个物种特异基因。GO富集分析显示,2 360个物种特异扩增基因主要参与DNA整合、代谢相关的生物过程;4 180个物种特异基因主要参与酶活性、光感受、糖代谢等,KEGG通路富集发现草地贪夜蛾特异基因主要参与氨基酸代谢、糖代谢和Wnt信号通路。本研究结果丰富了草地贪夜蛾的基因信息,对进一步了解其生物学特性、开发新型绿色防控方法具有指导意义。     

Computational methods for gene annotation: the Arabidopsis genome

Since the structure of the DNA molecule was identified half a century ago, the complete genome sequence has been determined for 37 prokaryotes and several eukaryotes. With the exponential growth of genetic information, bioinformatics has attempted to predict gene locations and functions in cyberspace prior to experimental confirmation at the bench.     

Intrinsic errors in genome annotation

Genome sequencing is usually followed by routine annotation of protein function based on the assumption that similar sequences will have similar functions. Here, we introduce a simple calculation to estimate the magnitude of any possible annotation errors. We counted the number of discrepancies in the annotation of well-established sets of similar proteins and extrapolated these values to the pairs of similar sequences used for the annotation of different microbial genomes. We conclude that the number of potential errors in the prediction of detailed functions is higher than is usually believed.