Dataset Information

Achieving Accurate Sequence and Annotation Data for Caulobacter vibrioides CB13.

ABSTRACT: Annotated sequence data are instrumental in nearly all realms of biology. However, the advent of next-generation sequencing has rapidly facilitated an imbalance between accurate sequence data and accurate annotation data. To increase the annotation accuracy of the Caulobacter vibrioides CB13b1a (CB13) genome, we compared the PGAP and RAST annotations of the CB13 genome. A total of 64 unique genes were identified in the PGAP annotation that were either completely or partially absent in the RAST annotation, and a total of 16 genes were identified in the RAST annotation that were not included in the PGAP annotation. Moreover, PGAP identified 73 frameshifted genes and 22 genes with an internal stop. In contrast, RAST annotated the larger segment of these frameshifted genes without indicating a change in reading frame may have occurred. The RAST annotation did not include any genes with internal stop codons, since it chose start codons that were after the internal stop. To confirm the discrepancies between the two annotations and verify the accuracy of the CB13 genome sequence data, we re-sequenced and re-annotated the entire genome and obtained an identical sequence, except in a small number of homopolymer regions. A genome sequence comparison between the two versions allowed us to determine the correct number of bases in each homopolymer region, which eliminated frameshifts for 31 genes annotated as frameshifted genes and removed 24 pseudogenes from the PGAP annotation. Both annotation systems correctly identified genes that were missed by the other system. In addition, PGAP identified conserved gene fragments that represented the beginning of genes, but it employed no corrective method to adjust the reading frame of frameshifted genes or the start sites of genes harboring an internal stop codon. In doing so, the PGAP annotation identified a large number of pseudogenes, which may reflect evolutionary history but likely do not produce gene products. These results demonstrate that re-sequencing and annotation comparisons can be used to increase the accuracy of genomic data and the corresponding gene annotation.

SUBMITTER: Berrios L

PROVIDER: S-EPMC6232633 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Achieving Accurate Sequence and Annotation Data for Caulobacter vibrioides CB13.

Berrios Louis L Ely Bert B

Current microbiology 20180926 12

Annotated sequence data are instrumental in nearly all realms of biology. However, the advent of next-generation sequencing has rapidly facilitated an imbalance between accurate sequence data and accurate annotation data. To increase the annotation accuracy of the Caulobacter vibrioides CB13b1a (CB13) genome, we compared the PGAP and RAST annotations of the CB13 genome. A total of 64 unique genes were identified in the PGAP annotation that were either completely or partially absent in the RAST a ...[more]

PMID: 30259084

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Project description:BackgroundMicrobiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis.ResultsHerein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample.ConclusionsOverall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. Video Abstract.

Project description:Non-sequence gene data (images, literature, etc.) can be found in many different public databases. Access to these data is mostly by text based methods using gene names; however, gene annotation is neither complete, nor fully systematic between organisms, and is also not generally stable over time. This provides some challenges for text based access, especially for cross-species searches. We propose a method for non-sequence data retrieval based on sequence similarity, which removes dependence on annotation and text searches. This work was motivated by the need to provide better access to large numbers of in situ images, and the observation that such image data were usually associated with a specific gene sequence. Sequence similarity searches are found in existing gene oriented databases, but mostly give indirect access to non-sequence data via navigational links.Three applications were built to explore the proposed method: accessing image data, literature and gene names. Searches are initiated with the sequence of the user's gene of interest, which is searched against a database of sequences associated with the target data. The matching (non-sequence) target data are returned directly to the user's browser, organised by sequence similarity. The method worked well for the intended application in image data management. Comparison with text based searches of the image data set showed the accuracy of the method. Applied to literature searches it facilitated retrieval of mostly high relevance references. Applied to gene name data it provided a useful analysis of name variation of related genes within and between species.This method makes a powerful and useful addition to existing methods for searching gene data based on text retrieval or curated gene lists. In particular the method facilitates cross-species comparisons, and enables the handling of novel or otherwise un-annotated genes. Applications using the method are quick and easy to build, and the data require little maintenance. This approach largely circumvents the need for annotation, which can be a major obstacle to the development of genomic scale data resources.

Dataset Information

Achieving Accurate Sequence and Annotation Data for Caulobacter vibrioides CB13.

Publications

Achieving Accurate Sequence and Annotation Data for Caulobacter vibrioides CB13.

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