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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R...

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Updated: Jun 12, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

Harmonizing immune cell sequences for computational analysis with large language models.

Areej Alsaafin¹, Hamid R Tizhoosh¹

¹Department of Artificial Intelligence & Informatics, KIMIA Lab, Mayo Clinic, Rochester, MN, 55905, United States.

Biology Methods & Protocols

|September 18, 2024

Summary

This summary is machine-generated.

Seqwash harmonizes immune cell sequences for large language models (LLMs), improving data quality. This standardization enhances downstream task performance in sequencing data analysis.

Keywords:

Immune cells large language models sequencing

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Area of Science:

Bioinformatics
Computational Biology
Immunoinformatics

Background:

Immune cell sequencing generates complex data.
Standardizing sequencing profiles is crucial for accurate analysis.
Large language models (LLMs) offer potential for pattern recognition in biological data.

Purpose of the Study:

To introduce Seqwash (SEQuence Weighted Alignment for Sorting and Harmonization), an algorithm for processing sequencing profiles.
To harmonize immune cell sequences into a unified representation for LLM analysis.
To improve feature quality and downstream task performance using standardized sequencing data.

Main Methods:

Development of the Seqwash algorithm.
Application of Seqwash to immune cell sequencing data.
Utilizing LLMs for pattern embedding and information filtering.

Main Results:

Seqwash effectively harmonizes immune cell sequencing profiles.
Standardized profiles exhibit improved feature quality.
Enhanced performance observed in both supervised and unsupervised downstream sequencing data tasks.

Conclusions:

Seqwash is an effective tool for standardizing immune cell sequencing data.
The algorithm empowers LLMs to identify meaningful patterns in biological sequences.
Seqwash facilitates improved downstream analysis of sequencing data.