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Open reading frame

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This is an old revision of this page, as edited by Yahargen (talk | contribs) at 18:07, 25 August 2021 (Two of the stop codons listed were incorrect; UGG and UUA are not stop codons. Source changed from a generic non-specific National Library of Medicine page to a specific web article from NIH with author.). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Sample sequence showing three different possible reading frames. Start codons are highlighted in purple, and stop codons are highlighted in red.

In molecular genetics, an open reading frame (ORF) is the part of a reading frame that has the ability to be translated. An ORF is a continuous stretch of codons that may[1] begin with a start codon (usually AUG) and ends at a stop codon (usually UAA, UAG or UGA).[2] An ATG codon (AUG in terms of RNA) within the ORF (not necessarily the first) may indicate where translation starts. The transcription termination site is located after the ORF, beyond the translation stop codon. If transcription were to cease before the stop codon, an incomplete protein would be made during translation.[3] In eukaryotic genes with multiple exons, introns are removed and exons are then joined together after transcription to yield the final mRNA for protein translation. In the context of gene finding, the start-stop definition of an ORF therefore only applies to spliced mRNAs, not genomic DNA, since introns may contain stop codons and/or cause shifts between reading frames. An alternative definition says that an ORF is a sequence that has a length divisible by three and is bounded by stop codons.[4][5] This more general definition can also be useful in the context of transcriptomics and/or metagenomics, where start and/or stop codon may not be present in the obtained sequences. Such an ORF corresponds to parts of a gene rather than the complete gene.

Biological significance

One common use of open reading frames (ORFs) is as one piece of evidence to assist in gene prediction. Long ORFs are often used, along with other evidence, to initially identify candidate protein-coding regions or functional RNA-coding regions in a DNA sequence.[6] The presence of an ORF does not necessarily mean that the region is always translated. For example, in a randomly generated DNA sequence with an equal percentage of each nucleotide, a stop-codon would be expected once every 21 codons.[6] A simple gene prediction algorithm for prokaryotes might look for a start codon followed by an open reading frame that is long enough to encode a typical protein, where the codon usage of that region matches the frequency characteristic for the given organism's coding regions.[6] Therefore, some authors say that an ORF should have a minimal length, e.g. 100 codons[7] or 150 codons.[6] By itself even a long open reading frame is not conclusive evidence for the presence of a gene.[6] On the other hand, it has been proven that some short ORFs (sORFs) that lack the classical hallmarks of protein-coding genes (both from ncRNAs and mRNAs) can produce functional peptides.[8] 5’-UTR of about 50% of mammal mRNAs are known to contain one or several sORFs.[9] 64–75% of experimentally found translation initiation sites of sORFs are conserved in the genomes of human and mouse and may indicate that these elements have function.[10] However, sORFs can often be found only in the minor forms of mRNAs and avoid the selection; the high conservatism of initiation sites may be connected with their location inside promoters of the relevant genes. This is characteristic of SLAMF1 gene, for example.[11]

Six-frame translation

Since DNA is interpreted in groups of three nucleotides (codons), a DNA strand has three distinct reading frames.[12] The double helix of a DNA molecule has two anti-parallel strands; with the two strands having three reading frames each, there are six possible frame translations.[12]

Example of a six-frame translation. The nucleotide sequence is shown in the middle with forward translations above and reverse translations below. Two possible open reading frames with the sequences are highlighted.

ORF finding tools

ORF Finder

The ORF Finder (Open Reading Frame Finder)[13] is a graphical analysis tool which finds all open reading frames of a selectable minimum size in a user's sequence or in a sequence already in the database. This tool identifies all open reading frames using the standard or alternative genetic codes. The deduced amino acid sequence can be saved in various formats and searched against the sequence database using the basic local alignment search tool (BLAST) server. The ORF Finder should be helpful in preparing complete and accurate sequence submissions. It is also packaged with the Sequin sequence submission software (sequence analyser).

ORF Investigator

ORF Investigator[14] is a program which not only gives information about the coding and non coding sequences but also can perform pairwise global alignment of different gene/DNA regions sequences. The tool efficiently finds the ORFs for corresponding amino acid sequences and converts them into their single letter amino acid code, and provides their locations in the sequence. The pairwise global alignment between the sequences makes it convenient to detect the different mutations, including single nucleotide polymorphism. Needleman–Wunsch algorithms are used for the gene alignment. The ORF Investigator is written in the portable Perl programming language, and is therefore available to users of all common operating systems.

ORF Predictor

OrfPredictor[15] is a web server designed for identifying protein-coding regions in expressed sequence tag (EST)-derived sequences. For query sequences with a hit in BLASTX, the program predicts the coding regions based on the translation reading frames identified in BLASTX alignments, otherwise, it predicts the most probable coding region based on the intrinsic signals of the query sequences. The output is the predicted peptide sequences in the FASTA format, and a definition line that includes the query ID, the translation reading frame and the nucleotide positions where the coding region begins and ends. OrfPredictor facilitates the annotation of EST-derived sequences, particularly, for large-scale EST projects.

ORF Predictor uses a combination of the two different ORF definitions mentioned above. It searches stretches starting with a start codon and ending at a stop codon. As an additional criterion, it searches for a stop codon in the 5' untranslated region (UTR or NTR, nontranslated region[16]).

ORFik

ORFik is a R-package in Bioconductor for finding open reading frames and using Next generation sequencing technologies for justification of ORFs.[17]

orfipy

orfipy is a tool written in Python/Cython to extract ORFs in an extremely and fast and flexible manner.[18] orfipy can work with plain or gzipped FASTA and FASTQ sequences, and provides several options to fine-tune ORF searches; these include specifying the start and stop codons, reporting partial ORFs, and using custom translation tables. The results could be saved in multiple formats, including the space-efficient BED format. orfipy is particularly faster for data containing multiple smaller FASTA sequences such as de-novo transcriptome assemblies.[19]

See also

References

  1. ^ Sieber, Patricia; Platzer, Matthias; Schuster, Stefan (March 2018). "The Definition of Open Reading Frame Revisited". Trends in Genetics. 34 (3): 167–170. doi:10.1016/j.tig.2017.12.009. ISSN 0168-9525.
  2. ^ Brody, Lawrence C. (2021-08-25). "Stop Codon". National Human Genome Research Institute. National Institutes of Health. Retrieved 2021-08-25.{{cite web}}: CS1 maint: url-status (link)
  3. ^ Slonczewski, Joan; John Watkins Foster (2009). Microbiology: An Evolving Science. New York: W.W. Norton & Co. ISBN 978-0-393-97857-5. OCLC 185042615.
  4. ^ Claverie, J.-M. (1997) Computational methods for the identification of genes in vertebrate genomic sequences. Hum. Mol. Genet. 6, 1735–1744.
  5. ^ P. Sieber, M. Platzer, S. Schuster (2018) The definition of open reading frame revisited. Trends Genet. 34, 167-170.
  6. ^ a b c d e Deonier, Richard; Simon Tavaré; Michael Waterman (2005). Computational Genome Analysis: an introduction. Springer-Verlag. p. 25. ISBN 978-0-387-98785-9.
  7. ^ Claverie, J.-M., Poirot, O., Lopez, F. (1997) The difficulty of identifying genes in anonymous vertebrate sequences. Comput. Chem. 21 203-214
  8. ^ Zanet, J.; Benrabah, E.; Li, T.; Pelissier-Monier, A.; Chanut-Delalande, H.; Ronsin, B.; Bellen, H. J.; Payre, F.; Plaza, S. (2015). "Pri sORF peptides induce selective proteasome-mediated protein processing". Science. 349 (6254): 1356–1358. Bibcode:2015Sci...349.1356Z. doi:10.1126/science.aac5677. ISSN 0036-8075. PMID 26383956. S2CID 206639549.
  9. ^ Wethmar, Klaus; Barbosa-Silva, Adriano; Andrade-Navarro, Miguel A.; Leutz, Achim (2014-01-01). "uORFdb—a comprehensive literature database on eukaryotic uORF biology". Nucleic Acids Research. 42 (D1): D60–D67. doi:10.1093/nar/gkt952. ISSN 0305-1048. PMC 3964959. PMID 24163100.
  10. ^ Lee, Sooncheol; Liu, Botao; Lee, Soohyun; Huang, Sheng-Xiong; Shen, Ben; Qian, Shu-Bing (2012-09-11). "Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution". Proceedings of the National Academy of Sciences. 109 (37): E2424–E2432. doi:10.1073/pnas.1207846109. ISSN 0027-8424. PMC 3443142. PMID 22927429.
  11. ^ Schwartz, Anton M.; Putlyaeva, Lidia V.; Covich, Milica; Klepikova, Anna V.; Akulich, Kseniya A.; Vorontsov, Ilya E.; Korneev, Kirill V.; Dmitriev, Sergey E.; Polanovsky, Oleg L. (2016-10-01). "Early B-cell factor 1 (EBF1) is critical for transcriptional control of SLAMF1 gene in human B cells". Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1859 (10): 1259–1268. doi:10.1016/j.bbagrm.2016.07.004. PMID 27424222.
  12. ^ a b Pearson, William R.; Wood, Todd; Zhang, Zheng; Miller, Webb (1997-11-15). "Comparison of DNA Sequences with Protein Sequences". Genomics. 46 (1): 24–36. doi:10.1006/geno.1997.4995. ISSN 0888-7543. PMID 9403055. S2CID 6413018.
  13. ^ "ORFfinder". www.ncbi.nlm.nih.gov.
  14. ^ Dwivedi, Vivek Dhar; Mishra, Sarad Kumar (2012). "ORF Investigator: A New ORF finding tool combining Pairwise Global Gene Alignment". Research Journal of Recent Sciences. 1 (11): 32–35. {{cite journal}}: |first1= has generic name (help); External link in |first1= (help)CS1 maint: numeric names: authors list (link)
  15. ^ "OrfPredictor". bioinformatics.ysu.edu.
  16. ^ J. C. Carrington, D. D. Freed (1990): Cap-independent enhancement of translation by a plant potyvirus 5’ nontranslated region. In: J. Virol., 64: 1590-1597. doi:10.1128/JVI.64.4.1590-1597.1990. , PMID 2319646, PMC PMC249294
  17. ^ "ORFik - Open reading frames in genomics". bioconductor.org.
  18. ^ Singh, Urminder; Wurtele, Eve Syrkin. "orfipy: a fast and flexible tool for extracting ORFs". Bioinformatics. doi:10.1093/bioinformatics/btab090.
  19. ^ Singh, Urminder (2021-02-13), urmi-21/orfipy, retrieved 2021-02-13
  • Translation and Open Reading Frames
  • hORFeome V5.1 - A web-based interactive tool for CCSB Human ORFeome Collection
  • ORF Marker - A free, fast and multi-platform desktop GUI tool for predicting and analyzing ORFs
  • StarORF - A multi-platform, java-based, GUI tool for predicting and analyzing ORFs and obtaining reverse complement sequence
  • ORFPredictor - A webserver designed for ORF prediction and translation of a batch of EST or cDNA sequences
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