Identification Of Open Reading Frames Research Articles

Three-nucleotide periodicity of nucleotide diversity in a population enables the identification of open reading frames.

Accurate prediction of open reading frames (ORFs) is important for studying and using genome sequences. Ribosomes move along mRNA strands with a step of three nucleotides and datasets carrying this information can be used to predict ORFs. The ribosome-protected footprints (RPFs) feature a significant 3-nt periodicity on mRNAs and are powerful in predicting translating ORFs, including small ORFs (sORFs), but the application of RPFs is limited because they are too short to be accurately mapped in complex genomes. In this study, we found a significant 3-nt periodicity in the datasets of populational genomic variants in coding sequences, in which the nucleotide diversity increases every three nucleotides. We suggest that this feature can be used to predict ORFs and develop the Python package ‘OrfPP’, which recovers ~83% of the annotated ORFs in the tested genomes on average, independent of the population sizes and the complexity of the genomes. The novel ORFs, including sORFs, identified from single-nucleotide polymorphisms are supported by protein mass spectrometry evidence comparable to that of the annotated ORFs. The application of OrfPP to tetraploid cotton and hexaploid wheat genomes successfully identified 76.17% and 87.43% of the annotated ORFs in the genomes, respectively, as well as 4704 sORFs, including 1182 upstream and 2110 downstream ORFs in cotton and 5025 sORFs, including 232 upstream and 234 downstream ORFs in wheat. Overall, we propose an alternative and supplementary approach for ORF prediction that can extend the studies of sORFs to more complex genomes.

ORFhunteR: An accurate approach to the automatic identification and annotation of open reading frames in human mRNA molecules

<h2>Abstract</h2> The coding potential of RNA molecules can be estimated using algorithms that find open reading frames (ORFs). However, previously developed algorithms show limited performance. We developed a computational approach dedicated to the automatic identification of ORFs in a large set of human mRNA molecules. It is based on the vectorization of nucleotide sequences followed by classification using a random forest. The predictive model was validated on human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. Our method is implemented into a powerful R/Bioconductor package ORFhunteR.

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data.

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological contexts, is critical forcomprehensive annotations of context-dependent translatomes. Ribosome profiling, a technique for detecting the binding locations and densities of translating ribosomes on RNA, offers an avenue to rapidly discover where translation is occurring at the genome-wide scale. However, it is not a trivial task in bioinformatics to efficiently and comprehensively identify the translating ORFs for ribosome profiling. Described here is an easy-to-use package, named RiboCode, designed to search for actively translating ORFs of any size from distorted and ambiguous signals in ribosome profiling data. Taking our previously published dataset as an example, this article provides step-by-step instructions for the entire RiboCode pipeline, from preprocessing of the raw data to interpretation of the final output result files. Furthermore, for evaluating the translation rates of the annotated ORFs, procedures for visualization and quantification of ribosome densities on each ORF are also described in detail. In summary, the present article is a useful and timely instruction for the research fields related to translation, small ORFs, and peptides.

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria.

Small proteins encoded by short open reading frames (ORFs) with 50 codons or fewer are emerging as an important class of cellular macromolecules in diverse organisms. However, they often evade detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation in genomic regions previously thought to be non-coding, driving the development of ORF detection tools using Ribo-seq data. However, only a handful of tools have been designed for bacteria, and these have not yet been systematically compared. Here, we aimed to identify tools that use Ribo-seq data to correctly determine the translational status of annotated bacterial ORFs and also discover novel translated regions with high sensitivity. To this end, we generated a large set of annotated ORFs from four diverse bacterial organisms, manually labeled for their translation status based on Ribo-seq data, which are available for future benchmarking studies. This set was used to investigate the predictive performance of seven Ribo-seq-based ORF detection tools (REPARATION_blast, DeepRibo, Ribo-TISH, PRICE, smORFer, ribotricer and SPECtre), as well as IRSOM, which uses coding potential and RNA-seq coverage only. DeepRibo and REPARATION_blast robustly predicted translated ORFs, including sORFs, with no significant difference for ORFs in close proximity to other genes versus stand-alone genes. However, no tool predicted a set of novel, experimentally verified sORFs with high sensitivity. Start codon predictions with smORFer show the value of initiation site profiling data to further improve the sensitivity of ORF prediction tools in bacteria. Overall, we find that bacterial tools perform well for sORF detection, although there is potential for improving their performance, applicability, usability and reproducibility.

BEtarget: A versatile web-based tool to design guide RNAs for base editing in plants

CRISPR-dependent base editors enable direct nucleotide conversion without the introduction of double-strand DNA break or donor DNA template, thus expanding the CRISPR toolbox for genetic manipulation. However, designing guide RNAs (gRNAs) for base editors to enable gene correction or inactivation is more complicated than using the CRISPR system for gene disruption. Here, we present a user-friendly web tool named BEtarget dedicated to the design of gRNA for base editing. It is currently supported by 46 plant reference genomes and 5 genomes of non-plant model organisms. BEtarget supports the design of gRNAs with different types of protospacer adjacent motifs (PAM) and integrates various functions, including automatic identification of open reading frame, prediction of potential off-target sites, annotation of codon change, and assessment of gRNA quality. Moreover, the program provides an interactive interface for users to selectively display information about the desired target sites. In brief, we have developed a flexible and versatile web-based tool to simplify complications associated with the design of base editing technology. BEtarget is freely accessible at https://skl.scau.edu.cn/betarget/.

Comparative transcriptome profiling provides insights into plant salt tolerance in seashore paspalum (Paspalum vaginatum)

BackgroundSeashore paspalum (Paspalum vaginatum), a halophytic warm-seasoned perennial grass, is tolerant of many environmental stresses, especially salt stress. To investigate molecular mechanisms underlying salinity tolerance in seashore paspalum, physiological characteristics and global transcription profiles of highly (Supreme) and moderately (Parish) salinity-tolerant cultivars under normal and salt stressed conditions were analyzed.ResultsPhysiological characterization comparing highly (Supreme) and moderately (Parish) salinity-tolerant cultivars revealed that Supreme’s higher salinity tolerance is associated with higher Na+ and Ca2+ accumulation under normal conditions and further increase of Na+ under salt-treated conditions (400 mM NaCl), possibly by vacuolar sequestration. Moreover, K+ retention under salt treatment occurs in both cultivars, suggesting that it may be a conserved mechanism for prevention of Na+ toxicity. We sequenced the transcriptome of the two cultivars under both normal and salt-treated conditions (400 mM NaCl) using RNA-seq. De novo assembly of about 153 million high-quality reads and identification of Open Reading Frames (ORFs) uncovered a total of 82,608 non-redundant unigenes, of which 3250 genes were identified as transcription factors (TFs). Gene Ontology (GO) annotation revealed the presence of genes involved in diverse cellular processes in seashore paspalum’s transcriptome. Differential expression analysis identified a total of 828 and 2222 genes that are responsive to high salinity for Supreme and Parish, respectively. “Oxidation-reduction process” and “nucleic acid binding” are significantly enriched GOs among differentially expressed genes in both cultivars under salt treatment. Interestingly, compared to Parish, a number of salt stress induced transcription factors are enriched and show higher abundance in Supreme under normal conditions, possibly due to enhanced Ca2+ signaling transduction out of Na+ accumulation, which may be another contributor to Supreme’s higher salinity tolerance.ConclusionPhysiological and transcriptome analyses of seashore paspalum reveal major molecular underpinnings contributing to plant response to salt stress in this halophytic warm-seasoned perennial grass. The data obtained provide valuable molecular resources for functional studies and developing strategies to engineer plant salinity tolerance.

The Zymoseptoria tritici ORFeome: A Functional Genomics Community Resource.

Libraries of protein-encoding sequences can be generated by identification of open reading frames (ORFs) from a genome of choice that are then assembled into collections of plasmids termed ORFeome libraries. These represent powerful resources to facilitate functional genomic characterization of genes and their encoded products. Here, we report the generation of an ORFeome for Zymoseptoria tritici, which causes the most serious disease of wheat in temperate regions of the world. We screened the genome of strain IP0323 for high confidence gene models, identifying 4,075 candidates from 10,933 predicted genes. These were amplified from genomic DNA, were cloned into the Gateway entry vector pDONR207, and were sequenced, providing a total of 3,022 quality-controlled plasmids. The ORFeome includes genes predicted to encode effectors (n = 410) and secondary metabolite biosynthetic proteins (n = 171) in addition to genes residing at dispensable chromosomes (n = 122) or those that are preferentially expressed during plant infection (n = 527). The ORFeome plasmid library is compatible with our previously developed suite of Gateway destination vectors, which have various combinations of promoters, selection markers, and epitope tags. The Z. tritici ORFeome constitutes a powerful resource for functional genomics and offers unparalleled opportunities to understand the biology of Z. tritici.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

EffectR: An Expandable R Package to Predict Candidate RxLR and CRN Effectors in Oomycetes Using Motif Searches.

Effectors are small, secreted proteins that facilitate infection of host plants by all major groups of plant pathogens. Effector protein identification in oomycetes relies on identification of open reading frames with certain amino acid motifs among additional minor criteria. To date, identification of effectors relies on custom scripts to identify motifs in candidate open reading frames. Here, we developed the R package effectR, which provides a convenient tool for rapid prediction of effectors in oomycete genomes, or with custom scripts for any genome, in a reproducible way. The effectR package relies on a combination of regular expressions statements and hidden Markov model approaches to predict candidate RxLR and crinkler effectors. Other custom motifs for novel effectors can easily be implemented and added to package updates. The effectR package has been validated with published oomycete genomes. This package provides a convenient tool for wet lab researchers interested in reproducible identification of candidate effectors in oomycete genomes.

GFACs: Gene Filtering, Analysis, and Conversion to Unify Genome Annotations Across Alignment and Gene Prediction Frameworks

Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames, start sites, splice sites, and related structural features. The source of these inconsistencies is often traced back to integration across text file formats designed to describe long read alignments and predicted gene structures. In addition, the majority of gene prediction frameworks do not provide robust downstream filtering to remove problematic gene annotations, nor do they represent these annotations in a format consistent with current file standards. These frameworks also lack consideration for functional attributes, such as the presence or absence of protein domains that can be used for gene model validation. To provide oversight to the increasing number of published genome annotations, we present a software package, the Gene Filtering, Analysis, and Conversion (gFACs), to filter, analyze, and convert predicted gene models and alignments. The software operates across a wide range of alignment, analysis, and gene prediction files with a flexible framework for defining gene models with reliable structural and functional attributes. gFACs supports common downstream applications, including genome browsers, and generates extensive details on the filtering process, including distributions that can be visualized to further assess the proposed gene space. gFACs is freely available and implemented in Perl with support from BioPerl libraries at https://gitlab.com/PlantGenomicsLab/gFACs.

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns.

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and ribosome binding translation initiation sequence region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, used for the identification of open reading frames in prokaryotes without a priori knowledge of the translational landscape. Through extensive validation of the model trained on various sets of data, multiple species sequence similarity, mass spectrometry and Edman degradation verified proteins, the effectiveness of DeepRibo is highlighted.

Efficient algorithms for biological stems search.

BackgroundMotifs are significant patterns in DNA, RNA, and protein sequences, which play an important role in biological processes and functions, like identification of open reading frames, RNA transcription, protein binding, etc. Several versions of the motif search problem have been studied in the literature. One such version is called the Planted Motif Search (PMS)or (l, d)-motif Search. PMS is known to be NP complete. The time complexities of most of the planted motif search algorithms depend exponentially on the alphabet size. Recently a new version of the motif search problem has been introduced by Kuksa and Pavlovic. We call this version as the Motif Stems Search (MSS) problem. A motif stem is an l-mer (for some relevant value of l)with some wildcard characters and hence corresponds to a set of l-mers (without wildcards), some of which are (l, d)-motifs. Kuksa and Pavlovic have presented an efficient algorithm to find motif stems for inputs from large alphabets. Ideally, the number of stems output should be as small as possible since the stems form a superset of the motifs.ResultsIn this paper we propose an efficient algorithm for MSS and evaluate it on both synthetic and real data. This evaluation reveals that our algorithm is much faster than Kuksa and Pavlovic’s algorithm.ConclusionsOur MSS algorithm outperforms the algorithm of Kuksa and Pavlovic in terms of the run time as well as the number of stems output. Specifically, the stems output by our algorithm form a proper (and much smaller)subset of the stems output by Kuksa and Pavlovic’s algorithm.

FragIdent – Automatic identification and characterisation of cDNA-fragments

BackgroundMany genetic studies and functional assays are based on cDNA fragments. After the generation of cDNA fragments from an mRNA sample, their content is at first unknown and must be assigned by sequencing reactions or hybridisation experiments.Even in characterised libraries, a considerable number of clones are wrongly annotated. Furthermore, mix-ups can happen in the laboratory. It is therefore essential to the relevance of experimental results to confirm or determine the identity of the employed cDNA fragments. However, the manual approach for the characterisation of these fragments using BLAST web interfaces is not suited for larger number of sequences and so far, no user-friendly software is publicly available.ResultsHere we present the development of FragIdent, an application for the automatic identification of open reading frames (ORFs) within cDNA-fragments. The software performs BLAST analyses to identify the genes represented by the sequences and suggests primers to complete the sequencing of the whole insert. Gene-specific information as well as the protein domains encoded by the cDNA fragment are retrieved from Internet-based databases and included in the output. The application features an intuitive graphical interface and is designed for researchers without any bioinformatics skills. It is suited for projects comprising up to several hundred different clones.ConclusionWe used FragIdent to identify 84 cDNA clones from a yeast two-hybrid experiment. Furthermore, we identified 131 protein domains within our analysed clones. The source code is freely available from our homepage at .

ORF30 and ORF34 Are Essential for Expression of Late Genes in Murine Gammaherpesvirus 68

A hallmark of productive infection by DNA viruses is the coupling of viral late gene expression to genome replication. Here we report the identification of open reading frame 30 (ORF30) and ORF34 as viral trans factors crucial for activating late gene transcription following viral DNA replication during lytic infection of murine gammaherpesvirus 68 (MHV-68). The mutant virus lacking either ORF30 or ORF34 underwent normal DNA replication but failed to express viral late gene transcripts, leading to nonproductive infection. In a reporter assay system, ORF30 and ORF34 were required for MHV-68 to activate the viral late gene promoters. Furthermore, studies using chromatin immunoprecipitation assays showed that the recruitment of RNA polymerase II to the viral late promoters during lytic infection was significantly reduced in the absence of ORF30 or ORF34. Together, the results suggest that ORF30 and ORF34 may play an important role in the assembly of the transcription initiation complex at the late gene promoters. Our discovery of the viral mutants that uncouple late gene transcription from DNA replication lays an important foundation to dissect the mechanism of this critical step of gene expression regulation.

Genome annotation past, present, and future: How to define an ORF at each locus

Driven by competition, automation, and technology, the genomics community has far exceeded its ambition to sequence the human genome by 2005. By analyzing mammalian genomes, we have shed light on the history of our DNA sequence, determined that alternatively spliced RNAs and retroposed pseudogenes are incredibly abundant, and glimpsed the apparently huge number of non-coding RNAs that play significant roles in gene regulation. Ultimately, genome science is likely to provide comprehensive catalogs of these elements. However, the methods we have been using for most of the last 10 years will not yield even one complete open reading frame (ORF) for every gene--the first plateau on the long climb toward a comprehensive catalog. These strategies--sequencing randomly selected cDNA clones, aligning protein sequences identified in other organisms, sequencing more genomes, and manual curation--will have to be supplemented by large-scale amplification and sequencing of specific predicted mRNAs. The steady improvements in gene prediction that have occurred over the last 10 years have increased the efficacy of this approach and decreased its cost. In this Perspective, I review the state of gene prediction roughly 10 years ago, summarize the progress that has been made since, argue that the primary ORF identification methods we have relied on so far are inadequate, and recommend a path toward completing the Catalog of Protein Coding Genes, Version 1.0.

Bioinformatics Methods for the Analysis of Hepatitis Viruses

HBV and HCV are the only hepatotropic viruses capable of establishing chronic infections. More than 500 million people worldwide are estimated to have chronic infections with HBV and/or HCV, and they have an increased risk of developing liver complications, such as cirrhosis or hepatocellular carcinoma. During the past decade, several antiviral agents including immune-modulatory drugs and nucleoside/nucleotide analogues have been approved for the treatment of HBV and HCV infections. In recent years, the focus has been on the development of new and better therapeutic agents for management of chronic HCV infections. Bioinformatics has only been applied recently to the field of viral hepatitis research. In addition to the wide range of general tools freely available for identification of open reading frames, gene prediction, homology searching, sequence alignment, and motif and epitope recognition, several public database systems designed specifically for HBV and HCV research have now been developed. The focus of these databases ranged from being viral sequence repositories for the provision of bioinformatics tools for viral genome analysis, as well as HBV or HCV drug resistance prediction. This review provides an overview of these public databases, which have integrated bioinformatics tools for HBV and HCV research. Properly managed and developed, these databases have the potential to have a broad effect on hepatitis research and treatment strategies. However, the effect will depend on the comprehensive collection of not only molecular sequence data, but also anonymous patient clinical and treatment data.

A Complete Set of Escherichia coli Open Reading Frames in Mobile Plasmids Facilitating Genetic Studies

To facilitate genetic studies of Escherichia coli, we constructed a complete set of mobile plasmid clones of intact open reading frames (ORFs). Their expression is strictly controlled by Ptac / lacI(q). The plasmids carrying each ORF were introduced into an F+ recA strain and stored in 96-well microtiter plates. In this way, 96 clones can be transferred simultaneously to F- bacteria using the conjugative system. This provides a convenient procedure for systematic identification of ORFs that suppress or complement mutations. We created two types of clone sets: the original set contained individual clones in 45 microtiter plates, and a second set contained pools of 48 clones stored in a single microtiter plate. Using these clone sets, we have identified 403 genes that can correct in trans the temperature-sensitive defect of cell division mutants, which would suggest multiple global regulators for bacterial cell division.

Selective identification of new therapeutic targets of Mycobacterium tuberculosis by IVIAT approach

The in vivo induced antigen technology (IVIAT) 1 has been used for the identification of open reading frames (ORFs) which could be possible therapeutic targets. A recombinant λgt11:: Mycobacterium tuberculosis H37Rv expression library was screened with pooled TB patient sera preabsorbed with in vitro grown M. tuberculosis H37Rv. Preabsorption of pooled TB patient sera allowed identification of antigens specifically expressed or upregulated during infection and growth in vivo. Six ORFs were identified, of which four ( rv0287, rv2402, rv3878 and rv1045) were of hypothetical functions. Rv0287 is a probable regulatory protein. Rv3878 is present uniquely in M. tuberculosis H37Rv and is a part of RDI deletion region of M. bovis BCG, which includes esat 6 region. This could be exploited as a tool for diagnosis. Two ORFs were assigned function solely on the basis of homology, dnaQ ( rv3711c) and lpdA ( rv3303c). dnaQ codes for the epsilon subunit of DNA polymerase III, which is responsible for the proofreading activity of the complex. lpdA codes for dihydrolipoamide dehydrogenase, which is a part of many multienzyme complexes such as pyruvate dehydrogenase, keto-acid dehydrogenase and α-ketoglutarate dehydrogenase. These two enzymes appear to be potential targets for drug development.

STRUCTURAL FEATURES OF NUCLEAR GENES IN THE CENTRIC DIATOM THALASSIOSIRA WEISSFLOGII (BACILLARIOPHYCEAE)

Thalassiosira weissflogii (Grun.) Fryxell et Hasle is one of the more commonly studied centric diatoms, and yet molecular studies of this organism are still in their infancy. The ability to identify open reading frames and thus distinguish between introns and exons, coding and noncoding sequence is essential to move from nuclear DNA sequences to predicted amino acid sequences. To facilitate the identification of open reading frames in T. weissflogii, two newly identified nuclear genes encoding β‐tubulin and t ‐complex polypeptide (TCP)‐γ, along with six previously published nuclear DNA sequences, were examined for general structural features. The coding region of the nuclear open reading frames had a G + C content of about 49% and could readily be distinguished from noncoding sequence due to a significant difference in G + C content. The introns were uniformly small, about 100 base pairs in size. Furthermore, the 5′ and 3′ splice sites of introns displayed the canonical GT/AG sequence, further facilitating recognition of noncoding regions. Six of the nuclear open reading frames displayed relatively little bias in the use of synonymous codons, as exemplified by the cDNAs encoding β‐tubulin and TCP‐γ. Two open reading frames displayed strong bias in the use of particular codons (although the codons used were different), as exemplified by the cDNA encoding fucoxanthin chlorophyll a/c binding protein. Knowledge of codon bias should facilitate, for example, design of degenerate PCR primers and potential heterologous reporter gene constructs.

New doors to open...and so many!

New doors to open...and so many!

Complete Nucleotide Sequence of a Cryptic Plasmid from the Marine Bacterium Vibrio splendidus and Identification of Open Reading Frames

A 2.3-kb replication-proficient fragment was previously obtained from a cryptic plasmid (pPS41) isolated from a marine Vibrio splendidus isolate (P. A. Sobecky, T. J. Mincer, M. C. Chang, A. Toukdarian, and D. R. Helinski, 1998, Appl. Environ. Microbiol. 64, 2822–2830). Analysis of the complete nucleotide sequence of plasmid pPS41 revealed two additional open reading frames (ORFs). Analysis of ORF-1 revealed that its translated product has 125 amino acids with a predicted MW of 16,978 and ORF-2 encodes a putative protein of 151 amino acids with a predicted MW of 19,802. The ORF-2 encoded protein showed 31 to 35% sequence homology to proteins identified to have a role in plasmid mobilization. These proteins are encoded on plasmids found in Escherichia coli and Pasteurella multocida. Plasmid pPS41 could be mobilized by a conjugative plasmid at frequencies of 1 × 10−2 to 2 × 10−2.