A Pilot Study on the In Silico Characterization of the rbcL Gene in Rice (Oryza sativa) and Maize (Zea mays) Using BioEdit and EMBOSS.
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866902159771041792 |
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| author | Sharma, Neha |
| author_facet | Sharma, Neha |
| contents | <p>This study presents a comparative bioinformatic analysis of the chloroplast rbcL gene from two monocot species, <em>Oryza sativa</em> (rice) and <em>Zea mays</em> (maize). The rbcL gene encodes the large subunit of RuBisCO, the key enzyme in the Calvin cycle and one of the most abundant proteins on Earth.</p> <p>Using publicly available chloroplast genome sequences, we analyzed the rbcL gene at both the nucleotide and protein levels. BioEdit was used to assess base composition and GC content, while the NCBI ORF Finder identified the coding sequences. Protein-level properties, including amino acid composition, molecular weight, and physicochemical parameters, were characterized using EMBOSS Pepstats. Hydropathy profiles were generated through the Kyte–Doolittle method implemented in EMBOSS Pepwindow. Codon usage was evaluated with EMBOSS Compseq to highlight codon bias between the two species.</p> <p>The results reveal both conserved and divergent features of the rbcL gene in rice and maize. While the overall sequence conservation underscores the evolutionary stability of this photosynthetic gene, subtle differences in codon usage patterns, amino acid composition, and hydropathy profiles point toward species-specific adaptations.</p> <p>This work demonstrates how open-access bioinformatics tools can be applied to perform in-depth comparative genomics without the need for laboratory experiments. The findings may support further evolutionary, phylogenetic, and functional studies of photosynthetic genes in cereals and other plants.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17037041 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Pilot Study on the In Silico Characterization of the rbcL Gene in Rice (Oryza sativa) and Maize (Zea mays) Using BioEdit and EMBOSS. Sharma, Neha bioinformatic Bioedit emboss Zea mays Oryza sativa rbcl <p>This study presents a comparative bioinformatic analysis of the chloroplast rbcL gene from two monocot species, <em>Oryza sativa</em> (rice) and <em>Zea mays</em> (maize). The rbcL gene encodes the large subunit of RuBisCO, the key enzyme in the Calvin cycle and one of the most abundant proteins on Earth.</p> <p>Using publicly available chloroplast genome sequences, we analyzed the rbcL gene at both the nucleotide and protein levels. BioEdit was used to assess base composition and GC content, while the NCBI ORF Finder identified the coding sequences. Protein-level properties, including amino acid composition, molecular weight, and physicochemical parameters, were characterized using EMBOSS Pepstats. Hydropathy profiles were generated through the Kyte–Doolittle method implemented in EMBOSS Pepwindow. Codon usage was evaluated with EMBOSS Compseq to highlight codon bias between the two species.</p> <p>The results reveal both conserved and divergent features of the rbcL gene in rice and maize. While the overall sequence conservation underscores the evolutionary stability of this photosynthetic gene, subtle differences in codon usage patterns, amino acid composition, and hydropathy profiles point toward species-specific adaptations.</p> <p>This work demonstrates how open-access bioinformatics tools can be applied to perform in-depth comparative genomics without the need for laboratory experiments. The findings may support further evolutionary, phylogenetic, and functional studies of photosynthetic genes in cereals and other plants.</p> |
| title | A Pilot Study on the In Silico Characterization of the rbcL Gene in Rice (Oryza sativa) and Maize (Zea mays) Using BioEdit and EMBOSS. |
| topic | bioinformatic Bioedit emboss Zea mays Oryza sativa rbcl |
| url | https://doi.org/10.5281/zenodo.17037041 |