REPARATION: Ribosome Profiling Assisted (Re-)Annotation of Bacterial genomes

Ndah, Elvis; Jonckheere, Veronique; Giess, Adam; Valen, Eivind; Menschaert, Gerben; Damme, Petra Van

doi:10.1101/113530

Cited by 18 publications

(52 citation statements)

References 53 publications

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“…For complementary evidence of protein synthesis, we relied on our previously published ribosome profiling (ribo-seq) data acquired in similar growth conditions at an O.D. of 0.5 Ndah et al 2017). Striving to search the full complement of possible genomic ORFs, all theoretical ORFs with minimal length of 30 nt.…”

Section: Maximizing Peptide Identification Using An Iterative Search mentioning

confidence: 99%

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Willems

Fijałkowski

Damme

2019

Preprint

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Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribo-seq and proteomic data of Salmonella Typhiumurium to identify unannotated proteins or alternative protein forms raised upon alternative translation initiation (i.e. N-terminal proteoforms). This data analysis encompasses the searching of cofragmenting peptides and post-processing with extended peptide-to-spectrum quality features including comparison to predicted fragment ion intensities. When applying this strategy, an enhanced proteome-depth is achieved as well as greater confidence for unannotated peptide hits.We demonstrate the general applicability of our pipeline by re-analyzing public Deinococcus radiodurans datasets. Taken together, systematic re-analysis using available prokaryotic (proteome) datasets holds great promise to assist in experimentally-based genome annotation.

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Section: Maximizing Peptide Identification Using An Iterative Search mentioning

confidence: 99%

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Willems

Fijałkowski

Damme

2019

Preprint

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“…Prediction of translated open reading frames was performed with REPARATION, a ribosomal profiling assisted reannotation tool [53]. The REPARATION workflow was adjusted by replacing UBLAST with DIAMOND for choosing the training set, after ORF prediction with prodigal.…”

Section: Bioinformatic Data Analysismentioning

confidence: 99%

Improving Bacterial Ribosome Profiling Data Quality

Glaub

Huptas

Neuhaus

et al. 2019

Preprint

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Ribosome profiling (RIBO-seq) in prokaryotes has the potential to facilitate accurate detection of translation initiation sites, to increase understanding of translational dynamics, and has already allowed detection of many unannotated genes. However, protocols for ribosome profiling and corresponding data analysis are not yet standardized. To better understand the influencing factors, we analysed 48 ribosome profiling samples from 9 studies on E. coli K12 grown in LB medium. We particularly investigated the size selection step in each experiment since the selection for ribosome-protected footprints (RPFs) has been performed at various read lengths. We suggest choosing a size range between 22-30 nucleotides in order to obtain protein-coding fragments. In order to use RIBO-seq data for improving gene annotation of weakly expressed genes, the total amount of reads mapping to protein-coding sequences and not rRNA or tRNA is important, but no consensus about the appropriate sequencing depth has been reached. Again, this causes significant variation between studies. Our analysis suggests that 20 million non rRNA/tRNA mapping reads are required for global detection of translated annotated genes. Further, we highlight the influence of drug induced ribosome stalling, causing bias at translation start sites. Drug induced stalling may be especially useful for detecting weakly expressed genes. These suggestions should improve both gene detection and the comparability of resulting ribosome profiling datasets.

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“…al. [13] has been applied. The method is based upon threshold dose-response estimation done by [30].…”

Section: Sample Selection Using the Four Parameter S-curvementioning

confidence: 99%

“…chloramphenicol or tetracycline) preceding the ribo-seq protocol enriching for initiating ribosomes [12]. Recently, prediction methods based on machine learning algorithms have been devised to either delineate the ORF [13] or predict the TIS [14] based on a combination of ribosome profiling and sequence features for prokaryotic genomes. A multitude of tools are also available for eukaryotic samples [15,16,17,18,19,20].…”

Section: Introductionmentioning

confidence: 99%

DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data

Clauwaert

Menschaert

Waegeman

2018

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Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network applying ribosome profiling data that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and Shine-Dalgarno region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, and is therefore evaluated on independent datasets. Through extensive validation of the model, including the use of multiple species sequence similarity and mass spectrometry, the effectiveness of the model is highlighted.

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REPARATION: Ribosome Profiling Assisted (Re-)Annotation of Bacterial genomes

Cited by 18 publications

References 53 publications

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Improving Bacterial Ribosome Profiling Data Quality

DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data

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