orfipy: a fast and flexible tool for extracting ORFs

De Novo Transcriptome ◽

Flexible Tool ◽

Protein Coding ◽

Coding Regions ◽

Downstream Analysis ◽

Fine Tune ◽

Abstract Summary Searching for open reading frames is a routine task and a critical step prior to annotating protein coding regions in newly sequenced genomes or de novo transcriptome assemblies. With the tremendous increase in genomic and transcriptomic data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in genomic and transcriptomic sequences. The search is rapid and is fully customizable, with a choice of FASTA and BED output formats. Availability and implementation orfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy). Supplementary information Supplementary data are available at Bioinformatics online.

orfipy: a fast and flexible tool for extracting ORFs

10.1101/2020.10.20.348052 ◽

2020 ◽

Author(s):

Urminder Singh ◽

Eve Syrkin Wurtele

Keyword(s):

Open Reading Frames ◽

Rna Seq ◽

Flexible Tool ◽

Coding Regions ◽

Link Type ◽

Alternative Reading Frames ◽

Downstream Analysis ◽

Fine Tune ◽

SummarySearching for ORFs in transcripts is a critical step prior to annotating coding regions in newly-sequenced genomes and to search for alternative reading frames within known genes. With the tremendous increase in RNA-Seq data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in fasta sequences. The search is rapid and is fully customizable, with a choice of Fasta and BED output formats.Availability and implementationorfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy)[email protected], [email protected] informationSupplementary data are available at https://github.com/urmi-21/orfipy

Distribution of cp32 Prophages among Lyme Disease-Causing Spirochetes and Natural Diversity of Their Lipoprotein-EncodingerpLoci

Applied and Environmental Microbiology ◽

10.1128/aem.00817-13 ◽

2013 ◽

Vol 79 (13) ◽

pp. 4115-4128 ◽

Cited By ~ 15

Author(s):

Dustin Brisson ◽

Wei Zhou ◽

Brandon L. Jutras ◽

Sherwood Casjens ◽

Brian Stevenson

Keyword(s):

Lyme Disease ◽

Surface Proteins ◽

Open Reading Frames ◽

Protein Coding ◽

Content Type ◽

Coding Regions ◽

Natural Diversity ◽

Different Strains ◽

Main Chromosome ◽

ABSTRACTLyme disease spirochetes possess complex genomes, consisting of a main chromosome and 20 or more smaller replicons. Among those small DNAs are the cp32 elements, a family of prophages that replicate as circular episomes. All complete cp32s contain anerplocus, which encodes surface-exposed proteins. Sequences were compared for all 193erpalleles carried by 22 different strains of Lyme disease-causing spirochete to investigate their natural diversity and evolutionary histories. These included multiple isolates from a focus where Lyme disease is endemic in the northeastern United States and isolates from across North America and Europe. Bacteria were derived from diseased humans and from vector ticks and included members of 5 differentBorreliagenospecies. Allerpoperon 5′-noncoding regions were found to be highly conserved, as were the initial 70 to 80 bp of allerpopen reading frames, traits indicative of a common evolutionary origin. However, the majority of the protein-coding regions are highly diverse, due to numerous intra- and intergenic recombination events. Mosterpalleles are chimeras derived from sequences of closely related and distantly relatederpsequences and from unknown origins. Since known functions of Erp surface proteins involve interactions with various host tissue components, this diversity may reflect both their multiple functions and the abilities of Lyme disease-causing spirochetes to successfully infect a wide variety of vertebrate host species.

Thousands of novel unannotated proteins expand the MHC I immunopeptidome in cancer

10.1101/2020.02.12.945840 ◽

2020 ◽

Cited By ~ 6

Author(s):

Tamara Ouspenskaia ◽

Travis Law ◽

Karl R. Clauser ◽

Susan Klaeger ◽

Siranush Sarkizova ◽

...

Keyword(s):

Somatic Mutations ◽

Tumor Antigens ◽

Ribosome Profiling ◽

Lymphocytic Leukemia ◽

Open Reading Frames ◽

Specific Expression ◽

Protein Coding ◽

Mhc I ◽

Coding Regions ◽

AbstractTumor epitopes – peptides that are presented on surface-bound MHC I proteins - provide targets for cancer immunotherapy and have been identified extensively in the annotated protein-coding regions of the genome. Motivated by the recent discovery of translated novel unannotated open reading frames (nuORFs) using ribosome profiling (Ribo-seq), we hypothesized that cancer-associated processes could generate nuORFs that can serve as a new source of tumor antigens that harbor somatic mutations or show tumor-specific expression. To identify cancer-specific nuORFs, we generated Ribo-seq profiles for 29 malignant and healthy samples, developed a sensitive analytic approach for hierarchical ORF prediction, and constructed a high-confidence database of translated nuORFs across tissues. Peptides from 3,555 unique translated nuORFs were presented on MHC I, based on analysis of an extensive dataset of MHC I-bound peptides detected by mass spectrometry, with >20-fold more nuORF peptides detected in the MHC I immunopeptidomes compared to whole proteomes. We further detected somatic mutations in nuORFs of cancer samples and identified nuORFs with tumor-specific translation in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs thus expand the pool of MHC I-presented, tumor-specific peptides, targetable by immunotherapies.

Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling

10.1101/031617 ◽

2015 ◽

Author(s):

Anil Raj ◽

Sidney H. Wang ◽

Heejung Shim ◽

Arbel Harpak ◽

Yang I. Li ◽

...

Keyword(s):

Significant Negative Correlation ◽

Selective Constraint ◽

Open Reading Frames ◽

Sequence Information ◽

The Novel ◽

Protein Coding ◽

Coding Sequences ◽

Coding Regions ◽

Human Lymphoblastoid Cell ◽

AbstractAccurate annotation of protein coding regions is essential for understanding how genetic information is translated into biological functions. 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 7,273 previously unannotated coding sequences, including 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. The novel sequences exhibit significant signatures of selective constraint in the reading frames of the inferred proteins, suggesting that many of these are functional. 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. Our work significantly expands the set of known coding regions in humans.

A spectral analysis approach to detect actively translated open reading frames in high-resolution ribosome profiling data

10.1101/031625 ◽

2015 ◽

Author(s):

Lorenzo Calviello ◽

Neelanjan Mukherjee ◽

Emanuel Wyler ◽

Henrik Zauber ◽

Antje Hirsekorn ◽

...

Keyword(s):

Spectral Analysis ◽

Gene Expression Regulation ◽

De Novo ◽

Ribosome Profiling ◽

Open Reading Frames ◽

Mass Spectrometry Data ◽

Hek293 Cells ◽

Protein Coding ◽

Reading Frame ◽

RNA sequencing protocols allow for quantifying gene expression regulation at each individual step, from transcription to protein synthesis. Ribosome Profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. Despite its great potential, a rigorous statistical approach to identify translated regions by means of the characteristic three-nucleotide periodicity of Ribo-seq data is not yet available. To fill this gap, we developed RiboTaper, which quantifies the significance of periodic Ribo-seq reads via spectral analysis methods. We applied RiboTaper on newly generated, deep Ribo-seq data in HEK293 cells, to derive an extensive map of translation that covers Open Reading Frame (ORF) annotations for more than 11,000 protein- coding genes. We also find distinct ribosomal signatures for several hundred detected upstream ORFs and ORFs in annotated non-coding genes (ncORFs). Mass spectrometry data confirms that RiboTaper achieves excellent coverage of the cellular proteome and validates dozens of novel peptide products. Collectively, RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/ ) is a powerful method for comprehensive de novo identification of actively used ORFs in the human genome.

Annotating high-impact 5’untranslated region variants with the UTRannotator

10.1101/2020.06.03.132266 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xiaolei Zhang ◽

Matthew Wakeling ◽

James Ware ◽

Nicola Whiffin

Keyword(s):

Open Reading Frames ◽

Untranslated Regions ◽

Protein Coding ◽

Pathogenic Variants ◽

Uncertain Significance ◽

Upstream Open Reading Frames ◽

The Impact ◽

AbstractSummaryCurrent tools to annotate the predicted effect of genetic variants are heavily biased towards protein-coding sequence. Variants outside of these regions may have a large impact on protein expression and/or structure and can lead to disease, but this effect can be challenging to predict. Consequently, these variants are poorly annotated using standard tools. We have developed a plugin to the Ensembl Variant Effect Predictor, the UTRannotator, that annotates variants in 5’untranslated regions (5’UTR) that create or disrupt upstream open reading frames (uORFs). We investigate the utility of this tool using the ClinVar database, providing an annotation for 30.8% of all 5’UTR (likely) pathogenic variants, and highlighting 31 variants of uncertain significance as candidates for further follow-up. We will continue to update the UTR annotator as we gain new knowledge on the impact of variants in UTRs.Availability and implementationUTRannotator is freely available on Github: https://github.com/ImperialCardioGenetics/UTRannotatorSupplementary informationSupplementary data are available at bioRxiv.

Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling

eLife ◽

10.7554/elife.13328 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 66

Author(s):

Anil Raj ◽

Sidney H Wang ◽

Heejung Shim ◽

Arbel Harpak ◽

Yang I Li ◽

...

Keyword(s):

Selective Constraint ◽

Open Reading Frames ◽

The Novel ◽

Drug Induced ◽

Protein Coding ◽

Coding Sequences ◽

Coding Regions ◽

Human Lymphoblastoid Cell ◽

Validation Rate ◽

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.

Annotating high-impact 5′untranslated region variants with the UTRannotator

Bioinformatics ◽

10.1093/bioinformatics/btaa783 ◽

2020 ◽

Author(s):

Xiaolei Zhang ◽

Matthew Wakeling ◽

James Ware ◽

Nicola Whiffin

Keyword(s):

Open Reading Frames ◽

Untranslated Regions ◽

Protein Coding ◽

Pathogenic Variants ◽

Uncertain Significance ◽

Upstream Open Reading Frames ◽

The Impact ◽

Abstract Summary Current tools to annotate the predicted effect of genetic variants are heavily biased towards protein-coding sequence. Variants outside of these regions may have a large impact on protein expression and/or structure and can lead to disease, but this effect can be challenging to predict. Consequently, these variants are poorly annotated using standard tools. We have developed a plugin to the Ensembl Variant Effect Predictor, the UTRannotator, that annotates variants in 5′untranslated regions (5′UTR) that create or disrupt upstream open reading frames. We investigate the utility of this tool using the ClinVar database, providing an annotation for 31.9% of all 5′UTR (likely) pathogenic variants, and highlighting 31 variants of uncertain significance as candidates for further follow-up. We will continue to update the UTRannotator as we gain new knowledge on the impact of variants in UTRs. Availability and implementation UTRannotator is freely available on Github: https://github.com/ImperialCardioGenetics/UTRannotator. Supplementary information Supplementary data are available at Bioinformatics online.

uORF Shuffling Fine-Tunes Gene Expression at a Deep Level of the Process

Plants ◽

10.3390/plants9050608 ◽

2020 ◽

Vol 9 (5) ◽

pp. 608

Author(s):

Yukio Kurihara

Keyword(s):

Gene Expression ◽

Deep Level ◽

Ribosome Profiling ◽

Open Reading Frames ◽

Protein Coding ◽

Polycistronic Mrna ◽

Upstream Open Reading Frames ◽

Fine Tune ◽

Insight Into ◽

Upstream open reading frames (uORFs) are present in the 5’ leader sequences (or 5’ untranslated regions) upstream of the protein-coding main ORFs (mORFs) in eukaryotic polycistronic mRNA. It is well known that a uORF negatively affects translation of the mORF. Emerging ribosome profiling approaches have revealed that uORFs themselves, as well as downstream mORFs, can be translated. However, it has also been revealed that plants can fine-tune gene expression by modulating uORF-mediated regulation in some situations. This article reviews several proposed mechanisms that enable genes to escape from uORF-mediated negative regulation and gives insight into the application of uORF-mediated regulation for precisely controlling gene expression.

Draft Genome Sequence of a Novel Luteimonas sp. Strain from Coral Mucus, Hawai‘i

Genome Announcements ◽

10.1128/genomea.01228-16 ◽

2016 ◽

Vol 4 (6) ◽

Cited By ~ 1

Author(s):

Xuehua Wan ◽

James M. Miller ◽

Sonia J. Rowley ◽

Shaobin Hou ◽

Stuart P. Donachie

Keyword(s):

Genome Sequence ◽

Draft Genome ◽

Open Reading Frames ◽

Draft Genome Sequence ◽

Diguanylate Cyclase ◽

Protein Coding ◽

Coral Mucus ◽

Coding Regions ◽

Porites Lobata ◽

Luteimonas sp. strain JM171 was cultivated from mucus collected around the coral Porites lobata . The JM171 draft genome of 2,992,353 bp contains 2,672 protein-coding open reading frames, 45 tRNA coding regions, and encodes a putative globin-coupled diguanylate cyclase, Jm GReg.