C-SEQer: An Open-Source de Novo Glycan Identification Tool in C+

Area of Science:

• Biochemistry
• Computational Biology
• Analytical Chemistry

Background:

• Glycans are crucial for protein function and cell signaling.
• Mass spectrometry (MS) enables high-throughput glycan analysis but requires robust computational tools.
• Existing open-source de novo glycan MS analysis algorithms are limited.

Purpose of the Study:

• To address the performance limitations of Python-based glycan analysis tools.
• To introduce C-SEQer, a more efficient implementation of the Sweet-SEQer algorithm.
• To provide an open-source, high-performance solution for de novo glycan MS analysis.

Main Methods:

• Re-implementation of the Sweet-SEQer algorithm in C++.
• Comparative performance analysis against the original Python version.
• Validation of output consistency between C-SEQer and Sweet-SEQer.

Main Results:

• C-SEQer achieves comparable analytical results to Sweet-SEQer.
• The C++ implementation demonstrates a ~15-fold reduction in processing time.
• Substantially lower memory consumption was observed with C-SEQer.

Conclusions:

• C-SEQer offers a significant performance improvement for de novo glycan MS analysis.
• This optimized tool addresses computational bottlenecks in glycan data processing.
• The freely available C-SEQer implementation facilitates broader adoption in glycomics research.