MetamORF: a repository of unique short open reading frames identified by both experimental and computational approaches for gene and metagene analyses

Choteau, Sébastien A.; Wagner, Audrey; Pierre, Philippe; Spinelli, Lionel; Brun, Catherine

doi:10.1093/database/baab032

Cited by 28 publications

(30 citation statements)

References 60 publications

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“…As recent studies found that some putative lncRNAs contain a short open reading frame (sORF) [35], we further analyzed the association of lncRNAs and sORF using the explanation results of Xlnc1DCNN. Some false negative sequences were randomly selected and checked if they contained sORF by using MetamORF [36]. While MetamORF found sORFs in some of these sequences, the reported regions of these sORFs did not correspond to the important regions highlighted by the explanation results.…”

Section: Discussionmentioning

confidence: 99%

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-coding RNA Identification

Lin

Wichadakul

2022

Preprint

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Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial un-annotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments is more time-consuming and expensive than computational approaches. Several tools for identifying long non-coding RNAs are available. These tools, however, did not explain which features in their tools contributed to the prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) using a one-dimensional convolutional neural network with prediction explanations. The evaluation results of the human test set showed that Xlnc1DCNN outperformed other state-of-the-art tools in terms of accuracy and F1-score. The explanation results revealed that lncRNA transcripts were mainly identified as sequences with no conserved regions or with a region of transmembrane helix while protein-coding transcripts were mostly classified by conserved protein domains or families. The explanation results also conveyed the probably inconsistent annotations among the public databases, lncRNA transcripts which contain protein domains or families, as well as protein-coding transcripts which are nonsense-mediated decay or processed transcripts. Xlnc1DCNN is freely available at https://github.com/cucpbioinfo/Xlnc1DCNN.

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Section: Discussionmentioning

confidence: 99%

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-coding RNA Identification

Lin

Wichadakul

2022

Preprint

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“…RibORF was used to systematically detect the actively translated regions in the Ribo-seq datasets ( 37 ). Moreover, the predicted ORFs from MetamORF ( 38 ) and OpenProt ( 39 ) were obtained. The ORFs predicted by at least two methods were remained for further analyses.…”

Section: Methodsmentioning

confidence: 99%

TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome

Chang

Cai

et al. 2021

Nucleic Acids Research

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LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

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“…For detailed query, sORFs.org incorporates a Biomart function, where one can tailor the search for microproteins by customising their queries according to species, chromosome number, start codons and sORFs attributes [ 14 ]. On the other hand, MetamORF is more limited in the sense that it only contains sORF data from Homo sapiens and Mus musculus , but its data has been experimentally and computationally verified, where one can browse the database according to gene locus, ORF, and transcript [ 110 ]. For OpenProt, since it is a large database, sORFs are stored under the heading of altORFs, where there is annotation of sORFs within ncRNA and pseudogene transcripts, hence some data downloaded from OpenProt in FASTA files may contain sequences longer than 100 codons [ 111 , 112 ].…”

Section: Microprotein Databasesmentioning

confidence: 99%

Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures

et al. 2022

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A short open reading frame (sORFs) constitutes ≤ 300 bases, encoding a microprotein or sORF-encoded protein (SEP) which comprises ≤ 100 amino acids. Traditionally dismissed by genome annotation pipelines as meaningless noise, sORFs were found to possess coding potential with ribosome profiling (RIBO-Seq), which unveiled sORF-based transcripts at various genome locations. Nonetheless, the existence of corresponding microproteins that are stable and functional was little substantiated by experimental evidence initially. With recent advancements in multi-omics, the identification, validation, and functional characterisation of sORFs and microproteins have become feasible. In this review, we discuss the history and development of an emerging research field of sORFs and microproteins. In particular, we focus on an array of bioinformatics and OMICS approaches used for predicting, sequencing, validating, and characterizing these recently discovered entities. These strategies include RIBO-Seq which detects sORF transcripts via ribosome footprints, and mass spectrometry (MS)-based proteomics for sequencing the resultant microproteins. Subsequently, our discussion extends to the functional characterisation of microproteins by incorporating CRISPR/Cas9 screen and protein–protein interaction (PPI) studies. Our review discusses not only detection methodologies, but we also highlight on the challenges and potential solutions in identifying and validating sORFs and their microproteins. The novelty of this review lies within its validation for the functional role of microproteins, which could contribute towards the future landscape of microproteomics.

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MetamORF: a repository of unique short open reading frames identified by both experimental and computational approaches for gene and metagene analyses

Cited by 28 publications

References 60 publications

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-coding RNA Identification

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-coding RNA Identification

TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome

Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures

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