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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jul 4, 2025

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data.

Dottie Yu¹, Ram Ayyala¹, Sarah Hany Sadek^2,3

¹Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, USA.

Biorxiv : the Preprint Server for Biology

|January 31, 2024

Summary

This summary is machine-generated.

This study benchmarks 12 RNA-seq based human leukocyte antigen (HLA) typing tools across diverse datasets. Results will guide selection of accurate and efficient HLA callers for clinical and research applications.

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Last Updated: Jul 4, 2025

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

Area of Science:

Genomics
Immunogenetics
Bioinformatics

Background:

Accurate human leukocyte antigen (HLA) typing is critical for transplantation and pharmacogenomics.
Numerous computational tools now impute HLA types from RNA-sequencing (RNA-seq) data.
A comprehensive performance comparison of these tools is lacking, hindering informed selection.

Approach:

Benchmarking 12 RNA-seq HLA callers using 682 samples from 8 datasets with gold-standard HLA types.
Evaluating accuracy, parameter optimization, allele/loci-level discrepancies, and computational costs (CPU time, RAM).
Assessing performance across European and African ancestries to identify potential biases.

Key Points:

Performance evaluation of 12 HLA typing tools across 5 loci (HLA-A, -B, -C, -DRB1, -DQB1).
Analysis includes accuracy, parameter tuning, computational resource usage, and read length impact.
Investigating ancestry-related accuracy disparities for HLA callers.

Conclusions:

RNA-seq based HLA callers show promise for high-quality typing.
Current tools may not optimally balance accuracy and computational efficiency across all ancestries.
This study will provide essential guidance for selecting appropriate HLA typing tools.