Abstract
Background
Reconstruction of amino acid sequences from assembled transcriptome is of interest in personalized medicine, for example, to predict drug-target (or protein-protein) interaction considering individual’s genomic variations. Most of the existing transcriptome assemblers, however, seems not well suited for this purpose.
Methods
In this work, we present StringFix, an annotation guided transcriptome assembly and protein sequence reconstruction software tool that takes genome-aligned reads and the annotations associated to the reference genome as input. The tool ‘fixes’ the pre-annotated transcript sequence by taking small variations into account, finally to produce possible amino acid sequences that are likely to exist in the test tissue.
Results
The results show that, using outputs from existing reference-based assemblers as the input GTF-guide, StringFix could reconstruct amino acid sequences more precisely with higher sensitivity than direct generation using the recovered transcripts from all the assemblers we tested.
Conclusion
By using StringFix with the existing reference-based assemblers, one can recover not only a novel transcripts and isoforms but also the possible amino acid sequence stemming from them.
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Data and Software availability
The python code was deposited to PyPI and Github, respectively. Installation instruction, usage and example codes can be found at https://github.com/combio-dku/ (Project name: StringFix, license: GPL 3.0, Operating system(s): Platform independent, Programming language: python 3, other requirements: None). The datasets used in this work can be freely downloaded from the gene expression omnibus (GEO) at https://www.ncbi.nlm.nih.gov/geo/ using their accession number.
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Acknowledgements
The authors gratefully acknowledge the Center for Bio-Medical Engineering Core Facility at Dankook University.
Funding
This work was supported by the research fund of Dankook university in 2022.
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Lee, J., Kim, M., Han, K. et al. StringFix: an annotation-guided transcriptome assembler improves the recovery of amino acid sequences from RNA-Seq reads. Genes Genom 45, 1599–1609 (2023). https://doi.org/10.1007/s13258-023-01458-7
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DOI: https://doi.org/10.1007/s13258-023-01458-7