An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing

Philipp Röchner; Clarissa Krämer; Johannes U Mayer; Franz Rothlauf; Steffen Albrecht; Maximilian Sprang

q-bio.GNApr 4, 2026

An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing

Authors: Philipp Röchner, Clarissa Krämer, Johannes U Mayer, Franz Rothlauf, Steffen Albrecht, Maximilian Sprang

Organizations: Johannes Gutenberg University Mainz · University of Southern Denmark · Department of Dermatology, University Medical Center of the Johannes Gutenberg University · Institute of Quantitative and Computational Biology, Johannes Gutenberg University Mainz ·

Abstract

Next-generation sequencing (NGS) is a key technique for studying the DNA and RNA of organisms. However, identifying quality problems in NGS data across different experimental settings remains challenging. To develop automated quality-control tools, researchers require datasets with features that capture the characteristics of quality problems. Existing NGS repositories, however, offer only a limited number of quality-related features. To address this gap, we propose a dataset derived from 37,491 NGS samples with two types of quality-related feature representations. The first type consists of 34 features derived from quality control tools (QC-34 features). The second type has a variable number of features ranging from eight to 1,183. These features were derived from read counts in problematic genomic regions identified by the ENCODE blocklist (BL features). All features describe the same human and mouse samples from five genomic assays, allowing direct comparison of feature representations. The proposed dataset includes a binary quality label, derived from automated quality control and domain experts. Among all samples, $3.2\%$ are of low quality. Supervised machine learning algorithms accurately predicted quality labels from the features, confirming the relevance of the provided feature representations. The proposed feature representations enable researchers to study how different feature types (QC-34 vs. BL features) and granularities (varying number of BL features) affect the detection of quality problems.

Figures & tables

Figure 1: Feature generation (a) Researchers upload their experimental data as FASTQ files to the ENCODE database. (b) The experimental data is automatically reviewed based on quality metrics. If these metrics indicate insufficient quality, ENCODE quality experts manually review the samples and label the reported data as released or revoked . (c) We downloaded $37,491$ FASTQ files and their associated metadata from ENCODE, then mapped the reads to the reference genomes using Bowtie 2. (d) The $34$ QC-34 features are generated using quality control and bioinformatics tools. (e) For the BL features, we first integrate the quality-related and species-specific blocklisted regions to create a combined human-mouse blocklist. The integration can be done with varying degrees of restriction, based on the alignment ratio r , yielding $n$ blocklisted regions. The feature values for the BL- $n$ features are the number of reads in each of the $n$ blocklisted regions.

Assay Type	Released		Revoked		Human		Mouse		Overall
Assay Type	#	%	#	%	#	%	#	%	#	%
ChIP-Seq	25,491	70.24	962	80.17	23,479	76.18	2,974	44.57	26,453	70.56
RNA-Seq	4,401	12.13	70	5.83	2,268	7.36	2,203	33.02	4,471	11.93
DNase-Seq	5,596	15.42	151	12.58	4,252	13.80	1,495	22.41	5,747	15.33
eCLIP	803	2.21	17	1.42	820	2.66	0	0.00	820	2.19
Overall	36,291	96.80	1,200	3.20	30,819	82.20	6,672	17.80	37,491	100.00

Table 1: Absolute (#) and relative (%) distribution of assay types in the samples. The table shows the overall distribution and the distribution for released and revoked samples, as well as for human and mouse samples.

Figure 2: Distribution of the QC-34 feature subsets (a) ordinal RAW features (b) numeric MAP, TSS, and LOC features on a logarithmic scale

Figure 3: Number of BL features depending on the alignment ratio As the alignment ratio decreases, more genomic regions with lower sequence similarity between species are included in the cross-species blocklist, resulting in more BL features.

Feature	Missing Rate	Explanation
Accession	$0.0\%$	ID assigned to each experiment or sample; refers to the dataset within the ENCODE database
Project	$0.0\%$	Name or ID of the ENCODE project to which the dataset belongs; helps categorize datasets by research focus or initiative
Dataset	$0.0\%$	ID referring to the corresponding ENCODE Dataset or Experiment the sample belongs to, which contains more detailed information, see Table 3
Date created	$0.0\%$	Date when the dataset was created or submitted to ENCODE; helps to track the timeline of data submissions and updates
Status	$0.0\%$	Status of the dataset, such as released and revoked
Biosample name	$0.0\%$	Name or ID of the biological sample used in the experiment; provides information about the tissue, cell line, or species from which the data were derived

Table 2: Metadata features of the ENCODE samples

Feature	Missing Rate	Explanation
ID	$0.0\%$	Experiment accession, identical with the accessions in the Dataset and Experiment column of the metadata file
Project	$0.0\%$	Project accession; each project can contain multiple experiments and datasets
Status	$0.0\%$	Experiment status, such as released , revoked , or archived ; depends on the files of the experiment
Biosample summary	$0.02\%$	Description of the specimen biology, e.g. Homo sapiens K562
Biosample accession	$0.02\%$	ID or accession for the biosample(s) prepared for a single or multiple batches used in this experiment
Organism	$0.02\%$	Organism investigated in the experiment

Table 3: Metadata features of the ENCODE experiments

Figure 4: Feature validation Performance of supervised classifiers for detecting revoked samples on the test set depending on the number of BL features ( $\circ$ ) and QC-34 ( $\square$ ) features across different genomic assays: (a) ChIP-Seq samples, (b) RNA-Seq samples, (c) DNase-Seq samples, and (d) eCLIP samples.

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Alan F. Karr, Regina Ruane

Fraunhofer USA Center Mid-Atlantic, Riverdale MD 20737 · Computational Social Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104

Training Data Curation Cross-Dataset Generalization

Open paper AskPDF arXiv

We describe extensive numerical experiments assessing and quantifying how classifier performance depends on the quality of the training data, a frequently neglected component of the analysis of classifiers. More specifically, in the scientific context of metagenomic assembly of short DNA reads into "contigs," we examine the effects of degrading the quality of the training data by multiple mechanisms, and for four classifiers -- Bayes classifiers, neural nets, partition models and random forests. We investigate both individual behavior and congruence among the classifiers. We find breakdown-like behavior that holds for all four classifiers, as degradation increases and they move from being mostly correct to only coincidentally correct, because they are wrong in the same way. In the process, a picture of spatial heterogeneity emerges: as the training data move farther from analysis data, classifier decisions degenerate, the boundary becomes less dense, and congruence increases.

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