Bioinformatic applications in blood group molecular typing

Area of Science:

• Genetics
• Transfusion Medicine
• Bioinformatics

Background:

• Molecular laboratory methods for predicting red blood cell antigen expression are established in transfusion practice.
• High-throughput molecular blood typing, utilizing next-generation sequencing (NGS), enables comprehensive, high-resolution donor and patient typing.

Purpose of the Study:

• To review recent literature on genomic blood typing approaches.
• To outline essential IT infrastructure considerations for adopting high-throughput molecular blood typing.
• To discuss available software, analytical algorithms, and the role of artificial intelligence in this field.

Main Methods:

• Review of recent scientific literature on genomic blood typing.
• Analysis of requirements for computational and software infrastructures.
• Exploration of artificial intelligence applications in blood typing data interpretation.

Main Results:

• High-throughput NGS platforms (microarray, short-read, long-read) facilitate widespread, high-resolution blood typing.
• Robust computational and software infrastructures are necessary for processing, storing, and interpreting large datasets.
• Artificial intelligence is emerging as a key technology in this rapidly evolving area.

Conclusions:

• The adoption of advanced genomic blood typing technologies requires significant investment in IT infrastructure and analytical tools.
• Effective data management and interpretation are critical for realizing the full benefits of high-throughput molecular blood typing.
• Future advancements will likely involve greater integration of AI for enhanced efficiency and discovery in transfusion medicine.