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Read Mapping from scRNA-Seq Data to a Reference Sequence with Optical Implementation Capability

Keshavarzzadeh, Sara | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58698 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Koohi, Somayyeh
  7. Abstract:
  8. Today, analyzing single-cell RNA sequencing data allows researchers to examine the gene profiles of individual cells and discover cellular differences. However, the rapid growth in data volume has created serious challenges for existing read mapping tools. Current tools either lack the necessary speed or require too much memory. Moreover, most of these tools were designed for bulk RNA data and don't fully consider the unique characteristics of single-cell data, such as high sparsity, dropout events, and cellular heterogeneity.This research presents a tool called FCGMap for mapping single-cell RNA sequencing reads, built on a two-stage architecture. The first stage uses Frequency Chaos Game Representation for pre-screening and reducing the search space. In the second stage, precise mapping is performed only on the identified candidate regions. Additionally, the proposed architecture can be implemented on optical processing systems, which could significantly increase processing speed.The FCGMap tool was evaluated on over 600 simulated datasets including 63 reference genomes and 6 real single-cell sequencing datasets. Results showed that FCGMap operates up to twice as fast as existing tools like STAR and HISAT2 on large datasets. It also uses 54% less memory than STAR. In terms of accuracy, FCGMap achieves 94.65% sensitivity, delivering performance close to the gold standard STAR. Theoretical analyses indicate that optical implementation of FCGMap could reduce processing time by up to 75%.This research provides an efficient method for fast mapping of single-cell RNA sequencing reads, achieving an optimal balance between speed, accuracy, and resource consumption while opening new possibilities for using optical processing technologies in genomic analysis
  9. Keywords:
  10. Single Cell RNA Sequencing (scRNA-seq) ; Optical Computing ; Bioinformatics ; Frequency Chaos Game Representation ; Read Mapping

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