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About 10,000 papers for “ORFS”

ORFs sequences

Machado, Miguel P., Pinho, Joana, Grosso, Ana R. et al. · DRYAD · 2015

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Ratio of annotated ORFs to non-annotated ORFs.

Katharina Mir (134139), Klaus Neuhaus (134143), Siegfried Scherer (72669) et al. · Figshare · 2015

The ratio predicted by the mixture model is compared to the ratio observed in bacterial genomes (orange triangles) and Rcodon (blue open dots), respectively. The observable slight difference between natural genomes and Rcodon is due the fact that the expected number of short coding ORFs in Rcodon deviates from the natural genomes (compare to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045103#pone-0045103-g007" target="_blank">Figure 7</a>). Some individual data points are labeled with an abbreviated species name and the corresponding GC-content according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045103#pone.0045103.s001" target="_blank">Table S1</a>.

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Length distributions of () annotated ORFs and () random ORFs

Noguchi, Hideki, Jungho Park, Toshihisa Takagi · Figshare · 2011

Copyright information:Taken from "MetaGene: prokaryotic gene finding from environmental genome shotgun sequences"Nucleic Acids Research 2006;34(19):5623-5630.Published online 05 Oct 2006PMCID:PMC1636498.© 2006 The Author(s) ORFs are classified by the GC% of their genomes, and then the length distributions of each class are calculated. Distributions of distances between () leftmost start codons and annotated start codons and () leftmost start codons and incorrect start codons. Zero means the leftmost start codons are used as the start codons of the ORFs. () Orientation-dependent distributions of distances between neighboring ORFs. () The background distributions. Negative values mean overlapping of ORFs.

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Length distributions of () annotated ORFs and () random ORFs

Noguchi, Hideki, Jungho Park, Toshihisa Takagi · Figshare · 2011

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and their ORFs

Naama Eldad, Yahav Yosefzon, Yoav Arava · 2008

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and their ORFs

Naama Eldad, Yahav Yosefzon, Yoav Arava · 2008

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and their ORFs

Naama Eldad, Yahav Yosefzon, Yoav Arava · 2008

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YC02_Transcriptome translated ORFs

Armengaud, Jean · Figshare · 2019

We sequenced the transcriptomes of a male individual and a female individual for seven different taxonomical groups: Gammarus fossarum A, G. fossarum B, G. fossarum C, Gammarus wautieri, Gammarus pulex, Echinogammarus berilloni and Echinogammarus marinus. Translations of coding sequence regions were produced for each transcriptome by Transdecoder from stop to stop codons, analysing only top strand with 500 top longest ORFs used for training, and retaining 600 base pair ORFs and only proteins with a minimum length of 50 amino acids.

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YC02_Transcriptome translated ORFs

Jean Armengaud · Figshare · 2019

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N315 ORFs BLASTn

Alice Viana · Mendeley Data · 2022

We performed a whole-genome comparison based on the nucleotide identity between ORFs from a reference genome and genomes from CC5 to identify differences between the RdJ clone and related lineages.

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Choanoflagellate intron ORFs

Leonard, Guy, Wideman, Jeremy · Figshare · 2019

Choanoflagellate intron ORFs

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Leishmania infantum_All-ORFs

Jose M. Requena · Mendeley Data · 2022

This dataset contains all possible amino acids sequences, longer than 20 amino acids in lenght, derived from the reading of the Leishmania infantum (JPCM5 strain) genome in all six frames. This dataset was used for the analysis of proteomic data derived from L. infantum promastigotes as described elsewhere (Sanchiz, Á., Morato, E., Rastrojo, A., Camacho, E., González-de la Fuente, S., Marina, A., Aguado, B., and Requena, J.M. (2020). The Experimental Proteome of Leishmania infantum Promastigote and Its Usefulness for Improving Gene Annotations. Genes (Basel). 11, E1036; PMID: 32887454). The entry names contain the chromosome and coordinates in which a given amino acid sequence is encoded. For instance, the first entry is LinJ.01:197..358:r. This means that the amino acid sequence was read in the minus strand, between positions 197 and 358 in chromosome 1. The genome sequence used for creating this dataset may be accessed at: https://www.wikidata.org/wiki/Q97597959 Also, a dataset for the L. infantum genome sequence may be downloaded through the following Mendeley data entry: Requena, Jose M. (2021), “LINF_Genome sequence”, Mendeley Data, V1, doi: 10.17632/rb34cg9xk7.1 The assembly of this genome is described in the article: Gonzalez-de la Fuente, S., Peiro-Pastor, R., Rastrojo, A., Moreno, J., Carrasco-Ramiro, F., Requena, J.M., and Aguado, B. (2017). Resequencing of the Leishmania infantum (strain JPCM5) genome and de novo assembly into 36 contigs. Sci Rep 7, 18050 (PMID: 29273719).

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Choanoflagellate intron ORFs

Guy Leonard, Jeremy G. Wideman · Figshare · 2018

Choanoflagellate intron ORFs

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ORFs, StORFs and Pseudogenes

Nick · 2026

Often viewed as simplistic when compared to eukaryotic genomics, the multifaceted processes behind prokaryotic genome annotation have been re-evaluated in this thesis. In order to undertake this re-evaluation, both contemporary and novel methods of characterising prokaryotic genomic data were thoroughly investigated and developed to further our understanding of these organisms. In Chapter 2, historic and contemporary prokaryotic genome annotation techniques are evaluated via the development of a novel genome annotation comparison and improvement platform, ORForise (https://github.com/NickJD/ORForise). The results of ORForise outlined that these techniques are effective at identifying genes that are similar to those in existing genomic databases. However, there are two key findings in Chapter 2 which point to notable inadequacies. Firstly, no single annotation tool performed best for all genomes studied, with the type of gene and organism being annotated being the most important criteria when choosing a genome annotation tool. Secondly, taking into account many of the limitations consistent among the annotation tools considered in this study, there were an unexpected number of large regions of each genome which were consistently labelled as‘intergenic’ or without annotation. In Chapter 3, a thorough investigation of many of the specific weaknesses identified in the annotation tools from Chapter 2 was performed. This resulted in the identification of a set of full-length CDS gene sequences in these ‘intergenic’ regions which formed part of known and novel core and soft-core gene families in the E. coli pangenome. Additionally, a large number of highly conserved gene families were found in ‘intergenic regions’ across multiple genera. This adds evidence to the contention that regions of DNA labelled as ’intergenic’ by existing annotation tools contain real genes and, as such, these regions were renamed ‘unannotated regions’. This discovery and the redefinition of ‘intergenic’ regions was possible via the development and modification of two novel techniques and software platforms, ORForise and StORF-Reporter (https://github.com/NickJD/StORF-Reporter). These allowed for the extraction of additional and novel genomic information from existing genomic databases. Additionally, as StORF-Reporter found a number of putative CDS gene fragments in these unannotated regions, Chapter 4, focuses on the absence of pseudogenes in canonical genome annotations. This uncovered thousands of potential pseudogenised and functional genes that were missed by annotation tools due to either terminating in-frame stop codons or alternative use of stop codons to code for amino acids. The results from Chapters 3 and 4 have led to the redefinition of not only the gene collection of the E. coli pangenome and many of the studied genera, but also may impact our understanding of their phylogeny. To enable the discoveries and analysis in Chapters 2, 3 and 4, a number of passive computational approaches (those which can only operate alongside a rigid set of predefined rules) were used and developed. The majority of rules or parameters in these approaches were tuned through a thorough investigation of genomic features identified manually. However, biology as a domain has too many exceptions and too many rules for a passive computational approach to be universally tractable. The scale of this problem is only matched by the genomic data that we now have available. To overcome this, machine learning methodologies were investigated during a research visit to King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Specifically, the growing affinity between machine learning and biology was investigated in Chapter 5 with a novel neural network algorithm named FrameRate (https://github.com/NickJD/FrameRate). FrameRate was developed to offer insight into the coding potential of unassembled DNA sequences without the need for sequence homology or assembly. Lastly, at the beginning of the current SARS-CoV-2 pandemic, an opportunity was presented to apply the skills and knowledge gained throughout the development of this thesis to the novel SARS-CoV-2 genome. Chapter 6, describes how a novel hybrid genome annotation approach which combined ab initio gene prediction and sequence alignment techniques was developed and used to annotate coronavirus genomes found in human, bat and pangolin hosts. Additionally, unlike other contemporary gene prediction tools, StORF-Reporter was able to identify the enigmatic ORF10 Open Reading Frame in SARS-CoV-2 without sequence alignment or RNA-Seq analysis.

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GCN periodicity in ORFs.

William A. Barr (9396098), Ruchi B. Sheth (9396101), Jack Kwon (9396104) et al. · Figshare · 2020

(A) Three-nucleotide periodicity spans ORFs but not UTRs. ORFs of 5791 yeast genes were aligned at their AUG start codon (positions 1, 2, 3 in first line) or their stop codon (positions -3, -2, -1 in second line). The log-odds ratio graphs of log2(freqobs/freqbackground) show at each nucleotide position the deviations from the background nucleotide frequencies in ORFs (A: 0.326; C: 0.192; G: 0.204; U: 0.278). A 3-nucleotide periodicity is detected throughout the ORFs characterized by depression of G at the second nucleotide of codons (*) and enhancement of G and C at positions 1 and 2 respectively. The periodicity is not seen in the 5’UTR or 3’UTR. The yeast Kozak sequence surrounding the start codon (AAA(A/G)AAAUGCG) has pronounced depression of C/U at position -3. (B) A cartoon depiction of an mRNA at the beginning of translation. Initial codons in the ramp region show log-odds ratios illustrating a GCN periodicity and depression of G at position 2. (C) Amino termini of 1027 annotated yeast proteins were detected by peptide mass spectrometry [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0233197#pone.0233197.ref032" target="_blank">32</a>]. ORFs aligned at their start codons (nucleotides 1, 2, 3) are displayed to show log-odds ratio deviations from the background nucleotide frequencies in ORFs. Pronounced depression of G at position 2 of codons is observed between nucleotides 10 and 20 (*) when compared to the full set of 5791 verified and uncharacterized yeast genes (in panel A). (D) The 3-nucleotide periodicity is pronounced in genes with high protein expression. Protein expression levels were determined previously in genomic-scale Western analysis of TAP-epitope tagged ORFs [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0233197#pone.0233197.ref033" target="_blank">33</a>]. Periodicity was examined for 3838 verified and uncharacterized genes with detectable protein expression (protein expression levels > 0). These genes were partitioned into 10% bins, and superimposed codons between nucleotides 7 and 22 for all genes in each bin are illustrated. The periodicity increases progressively in strength as illustrated by the G2 depression. Bootstrap analysis indicates that compared to all genes, the average G2 depression between positions 7 and 22 is significantly enhanced in the MS/MS detected genes (C; P < 0.001) and top 10% protein expression bin (D; P < 0.001).

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Salamanadra salamandra Predicted-ORFs

Libro, Pietro, Chiocchio, Andrea, De Risky, Erika et al. · Figshare · 2023

Dispersal is a key process in ecology and evolutionary biology, as it shapes biodiversity patterns over space and time. Mounting evidence is showing that attitude to disperse is unevenly distributed among individuals within animal populations, and that individual personality can have pivotal roles in the shaping of this attitude. Here, we contribute to the study of the genetic underpinnings of dispersal-related behavioral variation by assembling and annotating the first de novo transcriptome of the head tissues of Salamandra salamandra. Although dispersal occurs mostly during the post-metamorphic terrestrial life stage in this species, variation in dispersal-related personality traits is already evident at the aquatic larval stage, making it an excellent candidate species for studies of carry-over effects. We employed a structured pipeline to de-novo assemble and annotate transcripts from RNA-Seq in a set of individuals representative of distinct behavioral profiles. We obtained 1,153,432,918 reads, which were successfully de novo assembled and annotated. The high-quality assembly was confirmed by comparing BUSCO, DETONATE and TransRate with the S. salamandra dataset. By aligning the contigs against the de novo transcriptome we obtained a percentage of mapping higher than 94%. The homology annotation performed with DIAMOND led to 153,048 (blastx) and 95,942 (blastp) shared contigs, annotated on NCBI nr, Swiss-Prot and TrEMBL. The domain and site protein prediction made with InterProScan led to 9850 GO-annotated contigs. This de-novo transcriptome represents reliable reference for whole transcriptome and proteome studies of other amphibians, for designing primers to clone specific genes or monitor their expression, and for analyzing comparative gene expression profiles between alternative behavioral types.

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Sources of human ORFs.

Darach Miller (5244443), Adam Dziulko (20577109), Sasha Levy (20577112) · Figshare · 2025

The sources of each human ORF used for initial verification that modified plasmids and modified strains still reproduce the mDHFR assay results. (CSV)

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5. Prokka-annotated ORFs

LesleyAnn Giddings · Figshare · 2020

Differentially expressed Prokka-annotated open reading frames.

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ORFs blast hits (1)

Machado, Miguel P., Pinho, Joana, Grosso, Ana R. et al. · DRYAD · 2015

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A. simplex predicted ORFs

Libro, Pietro · Figshare · 2023

Understanding the genomic underpinnings of thermal adaptation is a hot topic in eco-evolutionary studies of parasites. Marine heteroxenous parasites have complex life cycles encompassing a free-living larval stage, an ectothermic intermediate host and a homeothermic definitive host, thus representing compelling systems for the study of thermal adaptation. The Antarctic anisakid Contracaecum osculatum sp. D is a marine parasite able to survive and thrive both at very cold and warm temperatures within the environment and its hosts. Here, a de novo transcriptome of C. osculatum sp. D was generated for the first time, by performing RNA-Seq experiments on a set of individuals exposed to temperatures experienced by the nematode during its life cycle.

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