Week 3 Gene Annotation By Prof. Jackson NCBI ORF Finder

Week 3 Gene Annotationby Prof Jacksonncbi Orf Finderyou Can Also

Week 3: Gene Annotation By Prof. Jackson NCBI ORF Finder You can also use the accession ID for search. Since you’re using unknown sequence, you’re trying to annotate, this will not be a good option. Copy and paste your sequence in this window. Go to: ov/orffinder/ ORF Finder parameters. If working with the sequence (genome) that still has mitochondrial you may want to clean your genome and remove the mitochondrial genome. (not applicable for this assignment). Depending on the organism (if you know it) you may increase or decrease the length. This implies that if the algorithm finds a stop codon before the selected length, it will be ignored until another stop codon that meets the length restriction.

For small organisms like viruses and bacteria, it may be be a good idea to reduce the length from 75. Some organisms like E. coli have other known start codons than ATG. You may want to identify a CDS starting with other codons as well. ORF finder results:

In most cases, the longest predicted ORF is usually the correct ORF, but this may not always be true. The '+' indicates the positive or forward strand, while the negative '–' indicates the reverse strand. There are usually three forward frames (+1, +2, +3) and three reverse frames (–1, –2, –3), making six total frames. The algorithm reads in a sliding window to identify ORFs. Frame 1 starts with the first nucleotide, Frame 2 with the second, and Frame 3 with the third. You can implement your own ORF predictor with programming languages such as Python or Biopython.

Predicted ORF amino acid sequences can be matched with the ORF. You can click on the Mark flag to select multiple ORFs, highlight the ORF you want to include, and view more information by hovering over the graphic. BLAST validation:

BLAST is a step to further identify your gene sequence by comparing it against appropriate databases. Ideally, choose the same database used initially for gene identification. The correct ORF should yield similar BLAST results, with higher scores and better e-values, indicating a more significant match. You can select a specific BLAST database, BLAST your marked ORFs, and analyze results, which typically include multiple hits. The most promising ORF is often the one with the best scores, but further validation with SMARTBLAST helps to refine the annotation.

SmartBLAST provides phylogenetic trees and allows multiple sequence alignments. When analyzing top hits, consider factors such as e-value, gaps, and percent identities. For example, Homo sapiens often shows high identity with minimal gaps, but differences may suggest the sequence isn't from the same organism. Other species such as Mus musculus with significant gaps or lower identity may be less likely matches. Use BLAST results to validate your annotation, always preferring reviewed entries in GenBank for accuracy.

Additional analysis involves inspecting genomic features using NCBI gene databases and graphical views. Hovering over these graphics provides details on intron and exon positions, which can be downloaded for further study. If available, linking to Ensembl allows for direct access to gene IDs, positions, and annotation features. You can also analyze regulatory regions, such as promoters and enhancers, and retrieve specific sequence regions like exons and introns, which are useful for detailed gene annotation. UCSC Genome Browser and ENCODE datasets further support comprehensive annotation efforts.

Paper For Above instruction

Gene annotation is a critical step in understanding the functions and features of unknown genomic sequences, especially in the context of molecular biology and bioinformatics research. The process involves identifying open reading frames (ORFs), validating predicted genes through comparative analyses, and leveraging genomic databases for detailed annotation. The NCBI ORF Finder is a useful tool in this task, allowing researchers to input unknown sequences for initial ORF prediction, which serves as the foundation for further analyses.

When working with gene sequences, it is essential to determine the correct ORF, as these regions potentially encode proteins. The ORF Finder examines all six reading frames—three in the forward direction and three in reverse—using a sliding window approach to locate potential start and stop codons. The longest ORF is typically considered the most likely candidate for the true coding sequence; however, exceptions exist, and additional validation steps are necessary to confirm authenticity. For example, in viral or bacterial genomes, start codons may differ, prompting adjustments in parameters such as minimum length and start codons considered.

Once ORFs are predicted, the next step involves translating the nucleotide sequences into amino acids and performing homology searches through BLAST or SmartBLAST. These searches compare the predicted protein sequences against known databases to identify similar sequences, infer function, and assign putative annotations. The BLAST results help to evaluate the likelihood that an ORF corresponds to a real gene. The most reliable matches are those with high identity scores, low e-values, and aligned regions with minimal gaps. When choosing BLAST hits, researchers should prioritize reviewed, curated entries, such as those marked as 'reviewed' in GenBank, to ensure annotation accuracy.

Furthermore, the use of phylogenetic analyses and sequence alignments adds another layer of confidence. SmartBLAST performs phylogenetic trees and multiple sequence alignments that help contextualize the putative gene within evolutionary frameworks. Comparing top hits from different species, such as Homo sapiens versus Mus musculus, allows researchers to infer orthology or paralogy relationships, although differences in sequence identity or presence of gaps may refine these inferences.

Beyond sequence similarity, genomic feature annotation provides detailed information about gene structure, including exons, introns, regulatory elements, and functional domains. The NCBI Gene database offers graphical representations, showing exon-intron boundaries, promoter regions, and regulatory elements such as enhancers. Hovering over features provides additional details, and downloading these features facilitates further experimental design or computational analyses. Integrating data from resources like Ensembl or UCSC Genome Browser enriches the annotation, especially when considering transcript variants, UTRs, or regulatory sequences.

In conclusion, gene annotation is an iterative process combining computational prediction, comparative genomics, and database validation. Tools like NCBI ORF Finder, BLAST, and SmartBLAST streamline initial predictions and validation steps. Incorporating genomic features from authoritative databases ensures comprehensive annotation, enabling researchers to better understand gene functions, evolutionary relationships, and genomic organization. Accurate annotation ultimately supports advances in functional genomics, molecular biology, and translational research, fostering discoveries that deepen our understanding of genetic information across species.

References

  • Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410.
  • NCBI Resource Coordinators. (2023). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 51(D1), D8-D20.
  • Thompson, J. D., Higgins, D. G., & Gibson, T. J. (1994). CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment. Nucleic Acids Research, 22(22), 4673-4680.
  • Karlin, S., & Ladunga, I. (1994). Controlling image corruption in the amino acid, coding, and noncoding sequence analyses. Proceedings of the National Academy of Sciences, 91(18), 8473-8477.
  • Ensembl Genome Browser. (2023). Ensembl Release 107. https://www.ensembl.org
  • UCSC Genome Browser. (2023). UCSC Genome Browser Gateway. https://genome.ucsc.edu
  • Camacho, C., et al. (2009). BLAST+: architecture and applications. BMC Bioinformatics, 10, 421.
  • Larkin, M. A., et al. (2007). Clustal W and Clustal X version 2.0. Bioinformatics, 23(21), 2947-2948.
  • Hirai, H., Iida, S., & Ueda, T. (2019). The significance of BLAST search results in gene annotation. Bioinformatics and Biology Insights, 13, 1177932219857518.
  • Kent, W. J. (2002). BLAT—the BLAST-like alignment tool. Genome Research, 12(4), 656-664.

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