scholarly journals Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Li ◽  
Urminder Singh ◽  
Zebulun Arendsee ◽  
Eve Syrkin Wurtele
Keyword(s):  
Open Reading Frames ◽  
Rna Seq ◽  
Protein Coding ◽  
Interactive Analysis ◽  
Yeast Data ◽  
Specific Proteins ◽  
Species Specific ◽  
Developmental Conditions ◽  
Reading Frames ◽  
Expression Matrix

The “dark transcriptome” can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins (“orphan-ORFs”); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

scholarly journals Landscape of the Dark Transcriptome Revealed through Re-mining Massive RNA-Seq Data

2019 ◽  
Author(s):  
Jing Li ◽  
Urminder Singh ◽  
Zebulun Arendsee ◽  
Eve Syrkin Wurtele
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Rna Seq ◽  
Protein Coding ◽  
Interactive Analysis ◽  
Protein Coding Genes ◽  
Yeast Data ◽  
Specific Proteins ◽  
Species Specific ◽  
Developmental Conditions ◽  
Expression Matrix

AbstractThe “dark transcriptome” can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated ORFs in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 48% of the transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins (“orphan-ORFs”); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph, a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally-testable hypotheses about candidate genes.

Predicting the Function of Hypothetical Genes in Genomes of Bioleaching Microorganisms

2009 ◽  
Vol 71-73 ◽  
pp. 203-206
Author(s):  
F.J. Ossandón ◽  
G. Rivera ◽  
F. Lazo ◽  
David S. Holmes
Keyword(s):  
Open Reading Frames ◽  
Ecological Niches ◽  
Metabolic Potential ◽  
Orphan Genes ◽  
Picrophilus Torridus ◽  
Specific Proteins ◽  
Species Specific ◽  
Hypothetical Genes ◽  
Database Match ◽  
Reading Frames

A particularly challenging problem in genome annotation is to attribute function to genes annotated as “hypothetical, no known function”. These typically account for about 40% of all genes regardless of the genome. Some of these are “orphan” genes and are not found in any other genome. Some of these could encode species specific proteins and so are particularly interesting for evaluating novel metabolic potential and for understanding the evolution of genes and genomes. Several similarity and non-similarity bioinformatics tools exist that help predict function of hypotheticals, but none are able to suggest function for more than a few percent and the annotation of the others remains a formidable task. We have developed a bioinformatics tool called AlterORF (www.AlterORF.cl) that is able to identify alternate open reading frames (ORFs) embedded within annotated genes. Analysis of over 2 million genes in over 700 completely sequenced genomes reveals that alternate ORFs of substantial length (potentially encoding 70 amino acids or more) are surprisingly common, especially in G+C rich genomes. During our examination of these alternate ORFs, we uncovered hundreds of examples where the alternate ORF has a significant hit with databases of motifs and domains (e.g. CDD, Pfam) and where the actual annotated gene is described as hypothetical and has no database match. This strongly suggests that the annotated gene has been incorrectly identified and that the alternate ORF is the real gene. We describe the evaluation of the following genomes of bioleaching microorganisms and others that reside in similar ecological niches using AlterORF: Acidithiobacillus ferrooxidans (2 strains), Leptospirillum type II, Methylacidiphilum infernorum, Picrophilus torridus, Sulfolobus acidocaldarius, S. solfataricus, S. tokodaii, Thermodesulfovibrio yellowstonii, Thermoplasma acidophilum and T. volcanium. Examples of novel genes from these microorganisms and their suggested roles in metabolism will be described.

scholarly journals Use of short-read RNA-Seq data to identify transcripts that can translate novel ORFs

2020 ◽  
Author(s):  
Chaitanya Erady ◽  
Shraddha Puntambekar ◽  
Sudhakaran Prabakaran
Keyword(s):  
Cichlid Fish ◽  
Open Reading Frames ◽  
Rna Seq ◽  
Naive Mouse ◽  
Protein Coding ◽  
Short Read ◽  
Noncoding Regions ◽  
Post Mortem Brain ◽  
Transcript Identification ◽  
Reading Frames

AbstractIdentification of as of yet unannotated or undefined novel open reading frames (nORFs) and exploration of their functions in multiple organisms has revealed that vast regions of the genome have remained unexplored or ‘hidden’. Present within both protein-coding and noncoding regions, these nORFs signify the presence of a much more diverse proteome than previously expected. Given the need to study nORFs further, proper identification strategies must be in place, especially because they cannot be identified using conventional gene signatures. Although Ribo-Seq and proteogenomics are frequently used to identify and investigate nORFs, in this study, we propose a workflow for identifying nORF containing transcripts using our precompiled database of nORFs with translational evidence, using sample transcript information. Further, we discuss the potential uses of this identification, the caveats involved in such a transcript identification and finally present a few representative results from our analysis of naive mouse B and T cells, human post-mortem brain and cichlid fish transcriptome. Our proposed workflow can identify noncoding transcripts that can potentially translate intronic, intergenic and several other classes of nORFs.One-line summaryA systematic workflow to identify nORF containing transcripts using sample transcript information.

scholarly journals Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Robin-Lee Troskie ◽  
Yohaann Jafrani ◽  
Tim R. Mercer ◽  
Adam D. Ewing ◽  
Geoffrey J. Faulkner ◽  
...  
Keyword(s):  
Cultured Cells ◽  
Open Reading Frames ◽  
Cdna Sequencing ◽  
Protein Coding ◽  
Dynamic Component ◽  
Gene Copies ◽  
Long Read ◽  
Normal Human ◽  
Reading Frames ◽  
Transcriptional Landscape

AbstractPseudogenes are gene copies presumed to mainly be functionless relics of evolution due to acquired deleterious mutations or transcriptional silencing. Using deep full-length PacBio cDNA sequencing of normal human tissues and cancer cell lines, we identify here hundreds of novel transcribed pseudogenes expressed in tissue-specific patterns. Some pseudogene transcripts have intact open reading frames and are translated in cultured cells, representing unannotated protein-coding genes. To assess the biological impact of noncoding pseudogenes, we CRISPR-Cas9 delete the nucleus-enriched pseudogene PDCL3P4 and observe hundreds of perturbed genes. This study highlights pseudogenes as a complex and dynamic component of the human transcriptional landscape.

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 YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 155 ◽  
Author(s):  
Sandeep Chakraborty ◽  
Monica Britton ◽  
Jill Wegrzyn ◽  
Timothy Butterfield ◽  
Pedro José Martínez-García ◽  
...  
Keyword(s):  
Transition Zone ◽  
Transcript Abundance ◽  
Black Walnut ◽  
Open Reading Frames ◽  
Rna Seq ◽  
Reading Frame ◽  
Homologous Genes ◽  
Associated Proteins ◽  
Molecular Components ◽  
Reading Frames

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software.

scholarly journals Overexpression-based detection of translatable circular RNAs is vulnerable to coexistent linear RNA byproducts

2021 ◽  
Author(s):  
Yanyi Jiang ◽  
Xiaofan Chen ◽  
Wei Zhang
Keyword(s):  
Open Reading Frames ◽  
Systematic Evaluation ◽  
Circular Rnas ◽  
Protein Coding ◽  
Rolling Circle ◽  
Functional Investigation ◽  
Overexpression System ◽  
Translation Signals ◽  
Coding Potential ◽  
Reading Frames

AbstractIn RNA field, the demarcation between coding and non-coding has been negotiated by the recent discovery of occasionally translated circular RNAs (circRNAs). Although absent of 5’ cap structure, circRNAs can be translated cap-independently. Complementary intron-mediated overexpression is one of the most utilized methodologies for circRNA research but not without bearing echoing skepticism for its poorly defined mechanism and latent coexistent side products. In this study, leveraging such circRNA overexpression system, we have interrogated the protein-coding potential of 30 human circRNAs containing infinite open reading frames in HEK293T cells. Surprisingly, pervasive translation signals are detected by immunoblotting. However, intensive mutagenesis reveals that numerous translation signals are generated independently of circRNA synthesis. We have developed a dual tag strategy to isolate translation noise and directly demonstrate that the fallacious translation signals originate from cryptically spliced linear transcripts. The concomitant linear RNA byproducts, presumably concatemers, can be translated to allow pseudo rolling circle translation signals, and can involve backsplicing junction (BSJ) to disqualify the BSJ-based evidence for circRNA translation. We also find non-AUG start codons may engage in the translation initiation of circRNAs. Taken together, our systematic evaluation sheds light on heterogeneous translational outputs from circRNA overexpression vector and comes with a caveat that ectopic overexpression technique necessitates extremely rigorous control setup in circRNA translation and functional investigation.

scholarly journals A micropeptide encoded by lncRNA MIR155HG suppresses autoimmune inflammation via modulating antigen presentation

2020 ◽  
Vol 6 (21) ◽  
pp. eaaz2059 ◽  
Author(s):  
Liman Niu ◽  
Fangzhou Lou ◽  
Yang Sun ◽  
Libo Sun ◽  
Xiaojie Cai ◽  
...  
Keyword(s):  
Antigen Presentation ◽  
Inflammatory Diseases ◽  
Open Reading Frames ◽  
Protein Coding ◽  
Histocompatibility Complex ◽  
Antigen Trafficking ◽  
Heat Shock Cognate Protein ◽  
Antigen Presenting ◽  
Cognate Protein ◽  
Reading Frames

Many annotated long noncoding RNAs (lncRNAs) harbor predicted short open reading frames (sORFs), but the coding capacities of these sORFs and the functions of the resulting micropeptides remain elusive. Here, we report that human lncRNA MIR155HG encodes a 17–amino acid micropeptide, which we termed miPEP155 (P155). MIR155HG is highly expressed by inflamed antigen-presenting cells, leading to the discovery that P155 interacts with the adenosine 5′-triphosphate binding domain of heat shock cognate protein 70 (HSC70), a chaperone required for antigen trafficking and presentation in dendritic cells (DCs). P155 modulates major histocompatibility complex class II–mediated antigen presentation and T cell priming by disrupting the HSC70-HSP90 machinery. Exogenously injected P155 improves two classical mouse models of DC-driven auto inflammation. Collectively, we demonstrate the endogenous existence of a micropeptide encoded by a transcript annotated as “non-protein coding” and characterize a micropeptide as a regulator of antigen presentation and a suppressor of inflammatory diseases.

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 Identification of Proteins Associated with Murine Cytomegalovirus Virions

2004 ◽  
Vol 78 (20) ◽  
pp. 11187-11197 ◽  
Author(s):  
Lisa M. Kattenhorn ◽  
Ryan Mills ◽  
Markus Wagner ◽  
Alexandre Lomsadze ◽  
Vsevolod Makeev ◽  
...  
Keyword(s):  
Gene Prediction ◽  
Polyacrylamide Gel Electrophoresis ◽  
Sodium Dodecyl ◽  
Open Reading Frames ◽  
Murine Cytomegalovirus ◽  
Prediction Algorithm ◽  
Sequencing Analysis ◽  
Protein Coding ◽  
Coding Potential ◽  
Reading Frames

ABSTRACT Proteins associated with the murine cytomegalovirus (MCMV) viral particle were identified by a combined approach of proteomic and genomic methods. Purified MCMV virions were dissociated by complete denaturation and subjected to either separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and in-gel digestion or treated directly by in-solution tryptic digestion. Peptides were separated by nanoflow liquid chromatography and analyzed by tandem mass spectrometry (LC-MS/MS). The MS/MS spectra obtained were searched against a database of MCMV open reading frames (ORFs) predicted to be protein coding by an MCMV-specific version of the gene prediction algorithm GeneMarkS. We identified 38 proteins from the capsid, tegument, glycoprotein, replication, and immunomodulatory protein families, as well as 20 genes of unknown function. Observed irregularities in coding potential suggested possible sequence errors in the 3′-proximal ends of m20 and M31. These errors were experimentally confirmed by sequencing analysis. The MS data further indicated the presence of peptides derived from the unannotated ORFs ORFc225441-226898 (m166.5) and ORF105932-106072. Immunoblot experiments confirmed expression of m166.5 during viral infection.

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