ArticlePLoS computational biology2020
DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.
Article in PLoS computational biology, 2020. 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.
- Impact of common variants on brain gene expression from RNA to protein to schizophrenia risk.Nature communications · 2025Article
- A pipeline for sample tagging of whole genome bisulfite sequencing data using genotypes of whole genome sequencing.BMC genomics · 2023Article
- SMAP is a pipeline for sample matching in proteogenomics.Nature communications · 2022Article
- Review of multi-omics data resources and integrative analysis for human brain disorders.Briefings in functional genomics · 2021Review
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Authors and funding
14 authors.
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Abstract
Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.
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Registered trials
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