Standardized annotation of translated open reading frames.
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ABSTRACT:
SUBMITTER: Mudge JM
PROVIDER: S-EPMC9757701 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
Publications
Standardized annotation of translated open reading frames.
Mudge Jonathan M JM Ruiz-Orera Jorge J Prensner John R JR Brunet Marie A MA Calvet Ferriol F Jungreis Irwin I Gonzalez Jose Manuel JM Magrane Michele M Martinez Thomas F TF Schulz Jana Felicitas JF Yang Yucheng T YT Albà M Mar MM Aspden Julie L JL Baranov Pavel V PV Bazzini Ariel A AA Bruford Elspeth E Martin Maria Jesus MJ Calviello Lorenzo L Carvunis Anne-Ruxandra AR Chen Jin J Couso Juan Pablo JP Deutsch Eric W EW Flicek Paul P Frankish Adam A Gerstein Mark M Hubner Norbert N Ingolia Nicholas T NT Kellis Manolis M Menschaert Gerben G Moritz Robert L RL Ohler Uwe U Roucou Xavier X Saghatelian Alan A Weissman Jonathan S JS van Heesch Sebastiaan S
Nature biotechnology 20220701 7
Similar Datasets
Project description:Open reading frames (ORFs) are the genomic DNA sequences that have the potential to be translated. Genome annotation pipelines dismiss translation products of small ORFs (smORFs) of less than 100 codons (<300 nucleotides) as being unlikely to have a biological function. In this study, we systematically characterized smORFs in mouse B and T cells under different conditions and predicted a total of 5744 unique actively translated smORFs. We then extended our analysis to ORFs of 101-200 codons in length and predicted 945 of such longer translation products. Additionally, our results have suggested the existence of candidate secreted micropeptides. Furthermore, verifying their existence and identifying their functions will be essential and potentially lead to useful applications.
2021-04-28 | GSE154491 | GEO
Project description:Ribosome profiling identifies RNA fragments associated with translating ribosomes. The technology provides an opportunity to examine genome-wide translation events at single-nucleotide resolution and in an unbiased manner. Here I present a computational pipeline named RibORF to systematically identify translated open reading frames (ORFs), based on read distribution features representing active translation, including 3-nt periodicity and uniformness across codons. Analyses using the computational tool revealed pervasive translation in putative 'noncoding' regions, such as lncRNAs, pseudogenes, and 5'UTRs. The computational tool is useful for studying functional roles of non-canonical translation events in various biological processes. © 2018 by John Wiley & Sons, Inc.
| S-EPMC6168376 | biostudies-literature
Project description:Protein-coding small open reading frames (smORFs) are emerging as an important class of genes, however, the coding capacity of smORFs in the human genome is unclear. By integrating de novo transcriptome assembly and Ribo-Seq, we confidently annotate thousands of novel translated smORFs in three human cell lines. We find that smORF translation prediction is noisier than for annotated coding sequences, underscoring the importance of analyzing multiple experiments and footprinting conditions. These smORFs are located within non-coding and antisense transcripts, the UTRs of mRNAs, and unannotated transcripts. Analysis of RNA levels and translation efficiency during cellular stress identifies regulated smORFs and provides an approach for identifying smORFs for further investigation. Sequence conservation and signatures of positive selection indicate that encoded microproteins are likely functional. Additionally, proteomics data from enriched human leukocyte antigen complexes validates the translation of hundreds of smORFs and positions them as a source of novel antigens. Thus, smORFs represent a significant number of important, yet unexplored human genes.
2019-07-03 | GSE125218 | GEO
Project description:Genomes contain millions of short (<100 codons) open reading frames (sORFs), which are usually dismissed during gene annotation. Nevertheless, peptides encoded by such sORFs can play important biological roles, and their impact on cellular processes has long been underestimated. Here, we analyzed approximately 70,000 transcribed sORFs in the model plant Physcomitrella patens (moss). Several distinct classes of sORFs that differ in terms of their position on transcripts and the level of evolutionary conservation are present in the moss genome. Over 5000 sORFs were conserved in at least one of 10 plant species examined. Mass spectrometry analysis of proteomic and peptidomic data sets suggested that tens of sORFs located on distinct parts of mRNAs and long noncoding RNAs (lncRNAs) are translated, including conserved sORFs. Translational analysis of the sORFs and main ORFs at a single locus suggested the existence of genes that code for multiple proteins and peptides with tissue-specific expression. Functional analysis of four lncRNA-encoded peptides showed that sORFs-encoded peptides are involved in regulation of growth and differentiation in moss. Knocking out lncRNA-encoded peptides resulted in a decrease of moss growth. In contrast, the overexpression of these peptides resulted in a diverse range of phenotypic effects. Our results thus open new avenues for discovering novel, biologically active peptides in the plant kingdom.
| S-EPMC6724668 | biostudies-literature
Project description:Functional protein-coding small open reading frames (smORFs) are emerging as an important class of genes. However, the number of translated smORFs in the human genome is unclear because proteogenomic methods are not sensitive enough, and, as we show, Ribo-seq strategies require additional measures to ensure comprehensive and accurate smORF annotation. Here, we integrate de novo transcriptome assembly and Ribo-seq into an improved workflow that overcomes obstacles with previous methods, to more confidently annotate thousands of smORFs. Evolutionary conservation analyses suggest that hundreds of smORF-encoded microproteins are likely functional. Additionally, many smORFs are regulated during fundamental biological processes, such as cell stress. Peptides derived from smORFs are also detectable on human leukocyte antigen complexes, revealing smORFs as a source of antigens. Thus, by including additional validation into our smORF annotation workflow, we accurately identify thousands of unannotated translated smORFs that will provide a rich pool of unexplored, functional human genes.
| S-EPMC7085969 | biostudies-literature
Project description:Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into function. We describe riboHMM, a new method that uses ribosome footprint data to accurately infer translated sequences. Applying riboHMM to human lymphoblastoid cell lines, we identified 7273 novel coding sequences, including 2442 translated upstream open reading frames. We observed an enrichment of footprints at inferred initiation sites after drug-induced arrest of translation initiation, validating many of the novel coding sequences. The novel proteins exhibit significant selective constraint in the inferred reading frames, suggesting that many are functional. Moreover, ~40% of bicistronic transcripts showed negative correlation in the translation levels of their two coding sequences, suggesting a potential regulatory role for these novel regions. Despite known limitations of mass spectrometry to detect protein expressed at low level, we estimated a 14% validation rate. Our work significantly expands the set of known coding regions in humans.
| S-EPMC4940163 | biostudies-literature
Project description:BackgroundRecent technological advances have revealed thousands of functional open reading frames (ORF) that have eluded reference genome annotations. These overlooked ORFs are found throughout the genome, in any reading frame of transcripts, mature or non-coding, and can overlap annotated ORFs in a different reading frame. The exploration of these novel ORFs in genomic datasets and of their role in genetic traits is hindered by a lack of software.ResultsHere, we present OpenVar, a genomic variant annotator that mends that gap and fosters meaningful discoveries. To illustrate the potential of OpenVar, we analysed all variants within SynMicDB, a database of cancer-associated synonymous mutations. By including non-canonical ORFs in the analysis, OpenVar yields a 33.6-fold, 13.8-fold and 8.3-fold increase in high impact variants over Annovar, SnpEff and VEP respectively. We highlighted an overlapping non-canonical ORF in the HEY2 gene where variants significantly clustered.ConclusionsOpenVar integrates non-canonical ORFs in the analysis of genomic variants, unveiling new research avenues to better understand the genotype-phenotype relationships.
| S-EPMC9375913 | biostudies-literature
Project description:Accurate annotations of protein coding regions are essential for understanding how genetic information is translated into biological functions. The recent development of ribosome footprint profiling provides an important new tool for measuring translation. Here we describe riboHMM, a new method that uses ribosome footprint data along with gene expression and sequence information to accurately infer translated sequences. We applied our method to human lymphoblastoid cell lines and identified 7,863 previously unannotated coding sequences, including 445 translated sequences in pseudogenes and 2,442 translated upstream open reading frames. We observed an enrichment of harringtonine-treated ribosome footprints at the inferred initiation sites, validating many of the novel coding sequences. In aggregate, the novel sequences exhibit significant signatures of purifying selection indicative of protein-coding function, suggesting that many of the novel sequences are functional. We observed that nearly 40% of bicistronic transcripts showed significant negative correlation in the levels of translation of their two coding sequences, suggesting a key regulatory role for these novel translated sequences. Despite evidence for their functional importance, the novel peptide sequences were detected by mass spectrometry at a lower rate than predicted based on data from annotated proteins, thus suggesting that many of the novel peptide products may be relatively short-lived. Our work illustrates the value of ribosome profiling for improving coding annotations, and significantly expands the set of known coding regions.
2015-12-25 | GSE75290 | GEO
Project description:Accurate and comprehensive annotation of microprotein-coding small open reading frames (smORFs) is critical to our understanding of normal physiology and disease. Empirical identification of translated smORFs is carried out primarily using ribosome profiling (Ribo-seq). While effective, published Ribo-seq datasets can vary drastically in quality and different analysis tools are frequently employed. Here, we examine the impact of these factors on identifying translated smORFs. We compared five commonly used software tools that assess open reading frame translation from Ribo-seq (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH) and found surprisingly low agreement across all tools. Only ~2% of smORFs were called translated by all five tools, and ~15% by three or more tools when assessing the same high-resolution Ribo-seq dataset. For larger annotated genes, the same analysis showed ~74% agreement across all five tools. We also found that some tools are strongly biased against low-resolution Ribo-seq data, while others are more tolerant. Analyzing Ribo-seq coverage revealed that smORFs detected by more than one tool tend to have higher translation levels and higher fractions of in-frame reads, consistent with what was observed for annotated genes. Together these results support employing multiple tools to identify the most confident microprotein-coding smORFs and choosing the tools based on the quality of the dataset and the planned downstream characterization experiments of the predicted smORFs.
| S-EPMC11155197 | biostudies-literature
Project description:Accurate and comprehensive annotation of microprotein-coding small open reading frames (smORFs) is critical to our understanding of normal physiology and disease. Empirical identification of translated smORFs is carried out primarily using ribosome profiling (Ribo-seq). While effective, published Ribo-seq datasets can vary drastically in quality and different analysis tools are frequently employed. Here, we examine the impact of these factors on identifying translated smORFs. We compared five commonly used software tools that assess ORF translation from Ribo-seq (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH), and found surprisingly low agreement across all tools. Only ~2% of smORFs were called translated by all five tools and ~15% by three or more tools when assessing the same high-resolution Ribo-seq dataset. For larger annotated genes, the same analysis showed ~72% agreement across all five tools. We also found that some tools are strongly biased against low-resolution Ribo-seq data, while others are more tolerant. Analyzing Ribo-seq coverage as a proxy for translation levels revealed that highly translated smORFs are more likely to be detected by more than one tool. Together these results support employing multiple tools to identify the most confident microprotein-coding smORFs, and choosing the tools based on the quality of the dataset and planned downstream characterization experiments of predicted smORFs.
| S-EPMC10793472 | biostudies-literature