EASTR: Identifying and eliminating systematic alignment errors in multi-exon genes

Ida Shinder (), Richard Hu, Hyun Joo Ji, Kuan-Hao Chao and Mihaela Pertea ()
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Ida Shinder: Johns Hopkins School of Medicine
Richard Hu: Johns Hopkins University
Hyun Joo Ji: Johns Hopkins University
Kuan-Hao Chao: Johns Hopkins University
Mihaela Pertea: Johns Hopkins University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Accurate alignment of transcribed RNA to reference genomes is a critical step in the analysis of gene expression, which in turn has broad applications in biomedical research and in the basic sciences. We reveal that widely used splice-aware aligners, such as STAR and HISAT2, can introduce erroneous spliced alignments between repeated sequences, leading to the inclusion of falsely spliced transcripts in RNA-seq experiments. In some cases, the ‘phantom’ introns resulting from these errors make their way into widely-used genome annotation databases. To address this issue, we present EASTR (Emending Alignments of Spliced Transcript Reads), a software tool that detects and removes falsely spliced alignments or transcripts from alignment and annotation files. EASTR improves the accuracy of spliced alignments across diverse species, including human, maize, and Arabidopsis thaliana, by detecting sequence similarity between intron-flanking regions. We demonstrate that applying EASTR before transcript assembly substantially reduces false positive introns, exons, and transcripts, improving the overall accuracy of assembled transcripts. Additionally, we show that EASTR’s application to reference annotation databases can detect and correct likely cases of mis-annotated transcripts.

Date: 2023
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DOI: 10.1038/s41467-023-43017-4

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