Dataset Information

Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes.

ABSTRACT: Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

SUBMITTER: Prasad TS

PROVIDER: S-EPMC5204337 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes.

Prasad T S Keshava TS Mohanty Ajeet Kumar AK Kumar Manish M Sreenivasamurthy Sreelakshmi K SK Dey Gourav G Nirujogi Raja Sekhar RS Pinto Sneha M SM Madugundu Anil K AK Patil Arun H AH Advani Jayshree J Manda Srikanth S SS Gupta Manoj Kumar MK Dwivedi Sutopa B SB Kelkar Dhanashree S DS Hall Brantley B Jiang Xiaofang X Peery Ashley A Rajagopalan Pavithra P Yelamanchi Soujanya D SD Solanki Hitendra S HS Raja Remya R Sathe Gajanan J GJ Chavan Sandip S Verma Renu R Patel Krishna M KM Jain Ankit P AP Syed Nazia N Datta Keshava K KK Khan Aafaque Ahmed AA Dammalli Manjunath M Jayaram Savita S Radhakrishnan Aneesha A Mitchell Christopher J CJ Na Chan-Hyun CH Kumar Nirbhay N Sinnis Photini P Sharakhov Igor V IV Wang Charles C Gowda Harsha H Tu Zhijian Z Kumar Ashwani A Pandey Akhilesh A

Genome research 20161115 1

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on t ...[more]

PMID: 28003436

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Project description:BackgroundThe procedural aspects of genome sequencing and assembly have become relatively inexpensive, yet the full, accurate structural annotation of these genomes remains a challenge. Next-generation sequencing transcriptomics (RNA-Seq), global microarrays, and tandem mass spectrometry (MS/MS)-based proteomics have demonstrated immense value to genome curators as individual sources of information, however, integrating these data types to validate and improve structural annotation remains a major challenge. Current visual and statistical analytic tools are focused on a single data type, or existing software tools are retrofitted to analyze new data forms. We present Visual Exploration and Statistics to Promote Annotation (VESPA) is a new interactive visual analysis software tool focused on assisting scientists with the annotation of prokaryotic genomes though the integration of proteomics and transcriptomics data with current genome location coordinates.ResultsVESPA is a desktop Java™ application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST. VESPA is demonstrated on two use cases (Yersinia pestis Pestoides F and Synechococcus sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data.ConclusionsVESPA is an interactive visual analytics tool that integrates high-throughput data into a genomic context to facilitate the discovery of structural mis-annotations in prokaryotic genomes. Data is evaluated via visual analysis across multiple levels of genomic resolution, linked searches and interaction with existing bioinformatics tools. We highlight the novel functionality of VESPA and core programming requirements for visualization of these large heterogeneous datasets for a client-side application. The software is freely available at https://www.biopilot.org/docs/Software/Vespa.php.

Dataset Information

Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes.

Publications

Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes.

Similar Datasets

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