Ndah, E. | bioRxiv Science

bioRxiv2017

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

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods 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 protein translation evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation. REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. 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 (small) ORFs including variants of previously annotated ORFs. Our predictions were supported by matching mass spectrometry (MS) proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to perform de novo ORF delineation in bacterial genomes irrespective of the sequence context of the reading frame.

microbiology↗

bioRxiv2016

A Unique Ribosome Signature Reveals Bacterial Translation Initiation Sites

ABSTRCTWhile methods for annotation of genes are increasingly reliable the exact identification of the translation initiation site remains a challenging problem. Since the N-termini of proteins often contain regulatory and targeting information developing a robust method for start site identification is crucial. Ribosome profiling reads show distinct patterns of read length distributions around translation initiation sites. These patterns are typically lost in standard ribosome profiling analysis pipelines, when reads from footprints are adjusted to determine the specific codon being translated. Using these unique signatures we build a model capable of predicting translation initiation sites and demonstrate its high accuracy using N-terminal proteomics. Applying this to prokaryotic samples, we re-annotate translation initiation sites and provide evidence of N-terminal truncations and elongations of annotated coding sequences. These re-annotations are supported by the presence of Shine-Dalgarno sequences, structural and sequence based features and N-terminal peptides. Finally, our model identifies 61 novel genes previously undiscovered in the genome.

bioinformatics↗