ArticleGenome biology2021
seqQscorer: automated quality control of next-generation sequencing data using machine learning.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Review
- Integration of bulk RNA-seq pipeline metrics for assessing low-quality samples.BMC bioinformatics · 2026Article
- Review
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Assessing and mitigating batch effects in large-scale omics studies.Genome biology · 2024Review
- Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets.Genome biology · 2024Article
- Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis.Saudi journal of biological sciences · 2023Article
- Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.BMC bioinformatics · 2022Article
- A quality control portal for sequencing data deposited at the European genome-phenome archive.Briefings in bioinformatics · 2022Article
- Statistical guidelines for quality control of next-generation sequencing techniques.Life science alliance · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal and external functional genomics datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at https://github.com/salbrec/seqQscorer .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.