scholarly journals Common and phylogenetically widespread coding for peptides by bacterial small RNAs

2015 ◽  
Author(s):  
Robin C Friedman ◽  
Stefan Kalkhof ◽  
Olivia Doppelt-Azeroual ◽  
Stephan Mueller ◽  
Martina Chovancova ◽  
...  
Keyword(s):  
Small Rnas ◽  
Shotgun Proteomics ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Machine Learning Techniques ◽  
Type I ◽  
Protein Coding ◽  
Bacterial Transcription ◽  
Small Proteins ◽  
User Friendly

While eukaryotic noncoding RNAs have recently received intense scrutiny, it is becoming clear that bacterial transcription is at least as pervasive. Bacterial small RNAs and antisense RNAs (sRNAs) are often assumed to be noncoding, due to their lack of long open reading frames (ORFs). However, there are numerous examples of sRNAs encoding for small proteins, whether or not they also have a regulatory role at the RNA level. Here, we apply flexible machine learning techniques based on sequence features and comparative genomics to quantify the prevalence of sRNA ORFs under natural selection to maintain protein-coding function in phylogenetically diverse bacteria. A majority of annotated sRNAs have at least one ORF between 10 and 50 amino acids long, and we conservatively predict that 188 ± 25.5 unannotated sRNA ORFs are under selection to maintain coding, an average of 13 per species considered here. This implies that overall at least 7.5 ± 0.3% of sRNAs have a coding ORF, and in some species at least 20% do. 84 ± 9.8 of these novel coding ORFs have some antisense overlap to annotated ORFs. As experimental validation, many of our predictions are translated according to ribosome profiling data and are identified via mass spectrometry shotgun proteomics. B. subtilis sRNAs with coding ORFs are enriched for high expression in biofilms and confluent growth, and two S. pneumoniae sRNAs with coding ORFs are involved in virulence. sRNA coding ORFs are enriched for transmembrane domains and many are novel components of type I toxin/antitoxin systems. Our predictions for sRNA coding ORFs, including novel type I toxins, are freely available in a user-friendly format at http://disco-bac.web.pasteur.fr.

scholarly journals Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Sondos Samandi ◽  
Annie V Roy ◽  
Vivian Delcourt ◽  
Jean-François Lucier ◽  
Jules Gagnon ◽  
...  
Keyword(s):  
Mitochondrial Fission ◽  
Functional Characterization ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Gene Encoding ◽  
Protein Coding ◽  
Small Proteins ◽  
Conservation Patterns ◽  
Extreme Conservation

Recent functional, proteomic and ribosome profiling studies in eukaryotes have concurrently demonstrated the translation of alternative open-reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by these altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and contain functional domains. Evolutionary analyses indicate that altORFs often show more extreme conservation patterns than their CDSs. Thousands of alternative proteins are detected in proteomic datasets by reanalysis using a database containing predicted alternative proteins. This is illustrated with specific examples, including altMiD51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission. Our results suggest that many genes are multicoding genes and code for a large protein and one or several small proteins.

scholarly journals ORFik: a comprehensive R toolkit for the analysis of translation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Håkon Tjeldnes ◽  
Kornel Labun ◽  
Yamila Torres Cleuren ◽  
Katarzyna Chyżyńska ◽  
Michał Świrski ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Sequencing Data ◽  
Protein Coding ◽  
High Throughput Sequencing Data ◽  
Upstream Open Reading Frames ◽  
Many Core ◽  
User Friendly

Abstract Background With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. Results Here, we introduce ORFik, a user-friendly R/Bioconductor API and toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5′UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames (uORFs). As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5′ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. Conclusion In summary, ORFik introduces hundreds of tested, documented and optimized methods. ORFik is designed to be easily customizable, enabling users to create complete workflows from raw data to publication-ready figures for several types of sequencing data. Finally, by improving speed and scope of many core Bioconductor functions, ORFik offers enhancement benefiting the entire Bioconductor environment. Availability http://bioconductor.org/packages/ORFik.

scholarly journals ORFik: a comprehensive R toolkit for the analysis of translation

2021 ◽  
Author(s):  
Håkon Tjeldnes ◽  
Kornel Labun ◽  
Yamila Torres Cleuren ◽  
Katarzyna Chyżyńska ◽  
Michał Świrski ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Sequencing Data ◽  
Protein Coding ◽  
Coding Regions ◽  
High Throughput Sequencing Data ◽  
Upstream Open Reading Frames ◽  
User Friendly

ABSTRACT•BackgroundWith the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays.•ResultsHere, we introduce ORFik, a user-friendly R/Bioconductor toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5’UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames. As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5’ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions.•Availabilityhttp://bioconductor.org/packages/ORFik

scholarly journals Disrupting upstream translation in mRNAs is associated with human disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David S. M. Lee ◽  
Joseph Park ◽  
Andrew Kromer ◽  
Aris Baras ◽  
Daniel J. Rader ◽  
...  
Keyword(s):  
Protein Expression ◽  
Biological Significance ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Protein Coding ◽  
Stop Codons ◽  
Human Genes ◽  
Strong Negative Selection ◽  
Disease Associations ◽  
Reading Frames

AbstractRibosome-profiling has uncovered pervasive translation in non-canonical open reading frames, however the biological significance of this phenomenon remains unclear. Using genetic variation from 71,702 human genomes, we assess patterns of selection in translated upstream open reading frames (uORFs) in 5’UTRs. We show that uORF variants introducing new stop codons, or strengthening existing stop codons, are under strong negative selection comparable to protein-coding missense variants. Using these variants, we map and validate gene-disease associations in two independent biobanks containing exome sequencing from 10,900 and 32,268 individuals, respectively, and elucidate their impact on protein expression in human cells. Our results suggest translation disrupting mechanisms relating uORF variation to reduced protein expression, and demonstrate that translation at uORFs is genetically constrained in 50% of human genes.

scholarly journals OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Fabio R. Cerqueira ◽  
Ana Tereza Ribeiro Vasconcelos
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Open Reading Frames ◽  
Machine Learning Techniques ◽  
Bacterial Genomes ◽  
Small Proteins ◽  
Learning Techniques ◽  
Speed Up ◽  
Computational Procedures ◽  
Small Orfs

Abstract Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

scholarly journals When Long Noncoding Becomes Protein Coding

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Corrine Corrina R. Hartford ◽  
Ashish Lal
Keyword(s):  
Cell Division ◽  
Cell Signaling ◽  
Transcription Regulation ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Open Reading Frames ◽  
Protein Coding ◽  
Small Proteins ◽  
Coding Potential ◽  
Reading Frames

ABSTRACT Recent advancements in genetic and proteomic technologies have revealed that more of the genome encodes proteins than originally thought possible. Specifically, some putative long noncoding RNAs (lncRNAs) have been misannotated as noncoding. Numerous lncRNAs have been found to contain short open reading frames (sORFs) which have been overlooked because of their small size. Many of these sORFs encode small proteins or micropeptides with fundamental biological importance. These micropeptides can aid in diverse processes, including cell division, transcription regulation, and cell signaling. Here we discuss strategies for establishing the coding potential of putative lncRNAs and describe various functions of known micropeptides.

scholarly journals Genome-wide identification of Arabidopsis non-AUG-initiated upstream ORFs with evolutionarily conserved regulatory sequences that control protein expression levels

2021 ◽  
Author(s):  
Yuta Hiragori ◽  
Hiro Takahashi ◽  
Noriya Hayashi ◽  
Shun Sasaki ◽  
Kodai Nakao ◽  
...  
Keyword(s):  
Regulatory Peptides ◽  
Inhibitory Effect ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Regulatory Sequences ◽  
Dependent Manner ◽  
Protein Coding ◽  
Genome Wide ◽  
Evolutionarily Conserved ◽  
Upstream Open Reading Frames

Upstream open reading frames (uORFs) are short ORFs found in the 5′-UTRs of many eukaryotic transcripts and can influence the translation of protein-coding main ORFs (mORFs). Recent genome-wide ribosome profiling studies have revealed that thousands of uORFs initiate translation at non-AUG start codons. However, the physiological significance of these non-AUG uORFs has so far been demonstrated for only a few of them. It is conceivable that physiologically important non-AUG uORFs are evolutionarily conserved across species. In this study, using a combination of bioinformatics and experimental approaches, we searched the Arabidopsis genome for non-AUG-initiated uORFs with conserved sequences that control the expression of the mORF-encoded proteins. As a result, we identified four novel regulatory non-AUG uORFs. Among these, two exerted repressive effects on mORF expression in an amino acid sequence-dependent manner. These two non-AUG uORFs are likely to encode regulatory peptides that cause ribosome stalling, thereby enhancing their repressive effects. In contrast, one of the identified regulatory non-AUG uORFs promoted mORF expression by alleviating the inhibitory effect of a downstream AUG-initiated uORF. These findings provide insights into the mechanisms that enable non-AUG uORFs to play regulatory roles despite their low translation initiation efficiencies.

scholarly journals Identifying small proteins by ribosome profiling with stalled initiation complexes

2019 ◽  
Author(s):  
Jeremy Weaver ◽  
Fuad Mohammad ◽  
Allen R. Buskirk ◽  
Gisela Storz
Keyword(s):  
Amino Acids ◽  
Model Organism ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Genomic Context ◽  
True Prevalence ◽  
New Genes ◽  
Small Proteins ◽  
Intergenic Regions ◽  
Reading Frames

ABSTRACTSmall proteins consisting of 50 or fewer amino acids have been identified as regulators of larger proteins in bacteria and eukaryotes. Despite the importance of these molecules, the true prevalence of small proteins remains unknown because conventional annotation pipelines usually exclude small open reading frames (smORFs). We previously identified several dozen small proteins in the model organism Escherichia coli using theoretical bioinformatic approaches based on sequence conservation and matches to canonical ribosome binding sites. Here, we present an empirical approach for discovering new proteins, taking advantage of recent advances in ribosome profiling in which antibiotics are used to trap newly-initiated 70S ribosomes at start codons. This approach led to the identification of many novel initiation sites in intergenic regions in E. coli. We tagged 41 smORFs on the chromosome and detected protein synthesis for all but three. The corresponding genes are not only intergenic, but are also found antisense to other genes, in operons, and overlapping other open reading frames (ORFs), some impacting the translation of larger downstream genes. These results demonstrate the utility of this method for identifying new genes, regardless of their genomic context.IMPORTANCEProteins comprised of 50 or fewer amino acids have been shown to interact with and modulate the function of larger proteins in a range of organisms. Despite the possible importance of small proteins, the true prevalence and capabilities of these regulators remain unknown as the small size of the proteins places serious limitations on their identification, purification and characterization. Here, we present a ribosome profiling approach with stalled initiation complexes that led to the identification of 38 new small proteins.

scholarly journals Deep transcriptome annotation suggests that small and large proteins encoded in the same genes often cooperate

2017 ◽  
Author(s):  
Sondos Samandi ◽  
Annie V. Roy ◽  
Vivian Delcourt ◽  
Jean-François Lucier ◽  
Jules Gagnon ◽  
...  
Keyword(s):  
Functional Relationship ◽  
Mitochondrial Fission ◽  
Open Reading Frames ◽  
Gene Encoding ◽  
Evolutionary Patterns ◽  
Protein Coding ◽  
Coding Sequences ◽  
Large Proteins ◽  
Small Proteins ◽  
Reading Frames

AbstractRecent studies in eukaryotes have demonstrated the translation of alternative open reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and evolutionary patterns indicate that altORFs are particularly constrained in CDSs that evolve slowly. Thousands of predicted alternative proteins are detected in proteomic datasets by reanalysis using a database containing predicted alternative proteins. Protein domains and co-conservation analyses suggest a potential functional relationship between small and large proteins encoded in the same genes. This is illustrated with specific examples, including altMiD51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission. Our results suggest that many coding genes code for more than one protein that are often functionally related.

scholarly journals uORF-Tools – Workflow for the determination of translation-regulatory upstream open reading frames

2018 ◽  
Author(s):  
Anica Scholz ◽  
Florian Eggenhofer ◽  
Rick Gelhausen ◽  
Björn Grüning ◽  
Kathi Zarnack ◽  
...  
Keyword(s):  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Annotation File ◽  
Inhibitory Effects ◽  
Protein Coding ◽  
Reading Frame ◽  
Upstream Open Reading Frames ◽  
Induced Changes ◽  
Reading Frames

AbstractRibosome profiling (ribo-seq) provides a means to analyze active translation by determining ribosome occupancy in a transcriptome-wide manner. The vast majority of ribosome protected fragments (RPFs) resides within the protein-coding sequence of mRNAs. However, commonly reads are also found within the transcript leader sequence (TLS) (aka 5’ untranslated region) preceding the main open reading frame (ORF), indicating the translation of regulatory upstream ORFs (uORFs). Here, we present a workflow for the identification of translation-regulatory uORFs. Specifically, uORF-Tools identifies uORFs within a given dataset and generates a uORF annotation file. In addition, a comprehensive human uORF annotation file, based on 35 ribo-seq files, is provided, which can serve as an alternative input file for the workflow. To assess the translation-regulatory activity of the uORFs, stimulus-induced changes in the ratio of the RPFs residing in the main ORFs relative to those found in the associated uORFs are determined. The resulting output file allows for the easy identification of candidate uORFs, which have translation-inhibitory effects on their associated main ORFs. uORF-Tools is available as a free and open Snakemake workflow at https://github.com/Biochemistry1-FFM/uORF-Tools. It is easily installed and all necessary tools are provided in a version-controlled manner, which also ensures lasting usability. uORF-Tools is designed for intuitive use and requires only limited computing times and resources.

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