Proteotranscriptomic assisted gene annotation and spatial proteomics of the lepidopteran model species Bombyx moriProteotranscriptomic assisted gene annotation and spatial proteomics of the lepidopteran model species Bombyx mori
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ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Bombyx Mori (silk Moth)
SUBMITTER: 
F Butter
LAB HEAD: Falk Butter
PROVIDER: PXD014626 | Pride | 2020-09-16
REPOSITORIES: Pride
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Project description:BackgroundThe process of identifying all coding regions in a genome is crucial for any study at the level of molecular biology, ranging from single-gene cloning to genome-wide measurements using RNA-seq or mass spectrometry. While satisfactory annotation has been made feasible for well-studied model organisms through great efforts of big consortia, for most systems this kind of data is either absent or not adequately precise.ResultsCombining in-depth transcriptome sequencing and high resolution mass spectrometry, we here use proteotranscriptomics to improve gene annotation of protein-coding genes in the Bombyx mori cell line BmN4 which is an increasingly used tool for the analysis of piRNA biogenesis and function. Using this approach we provide the exact coding sequence and evidence for more than 6200 genes on the protein level. Furthermore using spatial proteomics, we establish the subcellular localization of thousands of these proteins. We show that our approach outperforms current Bombyx mori annotation attempts in terms of accuracy and coverage.ConclusionsWe show that proteotranscriptomics is an efficient, cost-effective and accurate approach to improve previous annotations or generate new gene models. As this technique is based on de-novo transcriptome assembly, it provides the possibility to study any species also in the absence of genome sequence information for which proteogenomics would be impossible.
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Project description:Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated techniques depend heavily on sequence context and often underestimate the complexity of the proteome. We developed REPARATION (RibosomeE Profiling Assisted (Re-)AnnotaTION), a de novo algorithm that takes advantage of experimental evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation. Ribo-seq next generation sequencing technique that provides a genome-wide snapshot of the position translating ribosome along an mRNA at the time of the experiment. REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds to screen for spurious ORFs based on a growth curve model. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel ORFs including variants of previously annotated and novel small ORFs (<71 codons). Our predictions were supported by matching mass spectrometry (MS) proteomics data and sequence conservation analysis. REPARATION is unique in that it makes use of experimental Ribo-seq data to perform de novo ORF delineation in bacterial genomes, and thus can identify putative coding ORFs irrespective of the sequence context of the reading frame.
2017-08-09 | PXD005844 | Pride
Project description:Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated techniques depend heavily on sequence context and often underestimate the complexity of the proteome. We developed REPARATION (RibosomeE Profiling Assisted (Re-)AnnotaTION), a de novo algorithm that takes advantage of experimental evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation. Ribo-seq next generation sequencing technique that provides a genome-wide snapshot of the position translating ribosome along an mRNA at the time of the experiment. REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds to screen for spurious ORFs based on a growth curve model. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel ORFs including variants of previously annotated and novel small ORFs (<71 codons). Our predictions were supported by matching mass spectrometry (MS) proteomics data and sequence conservation analysis. REPARATION is unique in that it makes use of experimental Ribo-seq data to perform de novo ORF delineation in bacterial genomes, and thus can identify putative coding ORFs irrespective of the sequence context of the reading frame.
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Project description:The delineation of genes in bacteria has remained an important challenge because prokaryotic genomes are often tightly packed frequently resulting in overlapping genes. We hereby present a de novo approach called REPARATION (RibosomeE Profiling Assisted (Re-)AnnotaTION) to delineate translated open reading frames (ORFs) in bacteria independent of (available) genome annotation. By deep sequencing of ribosome protected mRNA fragments (RPF) to map translating ribosomes across the entire genome, REPARATION takes advantage of the recently developed ribosome profiling (Ribo-seq) technique. REPARATION starts by traversing the entire genome to generate all possible ORFs and then collects their corresponding RPF signal information. Based on a growth curve model to estimate minimum ORF read density and Ribo-seq RPF coverage, thresholds indicative of translation is estimated. Finally, our algorithm applies a random forest model to build a classifier to classify putative protein coding ORFs. We evaluated the performance of REPARATION on 3 annotated bacterial species using in-house generated Ribo-seq data and matching N-terminal and shotgun proteomics data next to publically available Ribo-seq data. In all cases, about 80% of the ORFs predicted by REPARATION were previously annotated as protein coding. While 13-20% were variants of previously annotated ORFs and about 3-4% point to novel translated ORFs within intergenic or other regions previously annotated as non-coding. Without stringent length restrictions REPARATION was able to identify several small ORFs (sORFs). Multiple supportive evidence from matching MS data and sequence conservation analysis was obtained to validate predicted ORFs.
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