ArticleScientific reports2023
Ambiguous genes due to aligners and their impact on RNA-seq data analysis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The promise of long-read RNA-seq: reducing bias in analyses of allele imbalance.NAR genomics and bioinformatics · 2026Article
- Pseudogene Coexpression Networks Reveal a Robust Prognostic Signature for Pediatric B-ALL Survival.Cancer research communications · 2026Article
- RNA-DNA differences in variant calls from cattle tissues result in erroneous eQTLs.BMC genomics · 2024Article
- A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.Nature communications · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.
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Registered trials
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