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Automated quality control for genomic machine learning datasets: scores the biases, duplicates and data leakage a classifier could exploit before you train on it.
Searching test set enhancer_promoter_interaction_GM12878_val.csv.gz against train set enhancer_promoter_interaction_GM12878_train.csv.gz
Data Leakage:!WarningHow a flag is decided →
gb-qc v1.0.0 · run 2026-09-15 22:31:34

Basic Descriptive Statistics

Filename enhancer_promoter_interaction_GM12878_val.csv.gz enhancer_promoter_interaction_GM12878_train.csv.gz
Number of sequences 2000 10000
Minimum length 5000 5000
Mean length 5000.00 5000.00
Maximum length 5000 5000

Every test sequence searched against the train set with MMseqs2, and scored on its best hit. Similarity is min(query coverage, target coverage) × percent identity: how much of the shorter sequence the alignment spans, times how much of that matches. The histogram is the distribution of those best hits.

The flag is the percentage of test sequences at or above the threshold: Pass at 0%, Warning below 2%, Fail at 2% or more. A test sequence that near-duplicates a training one is one the model has already seen, so the accuracy it earns there is not evidence. What to do about it →

The panel under the histogram lists those alignments, up to the first 100; every one of them, listed or not, is exported to mmseqs/mmseqs2_search_result.tsv beside this report. Its columns are:

Query (Q)
Identifier of the test sequence: seq_<i>_test, where i is its 0-based position in the input test file.
Target (T)
Identifier of the train sequence: seq_<i>_train, where i is its 0-based position in the input train file.
Q Cov.
Fraction of the query sequence covered by the alignment (0–1).
T Cov.
Fraction of the target sequence covered by the alignment (0–1).
% Identity
Percent identical aligned positions in the aligned region.
% Similarity
Similarity score used for leakage detection, calculated as min(Q Cov., T Cov.) × % Identity.
E-value
MMseqs2 E-value (lower is more "significant").
Alignment
Click “Show” to expand the alignment visualisation for a row.
Filename enhancer_promoter_interaction_GM12878_val.csv.gz enhancer_promoter_interaction_GM12878_train.csv.gz
Data Leakage (percentage) 1.40% 0.29%
Data Leakage (count) 28 29
Similarity Histogram
29 high-similarity alignments
Query (Q) Target (T) Q Cov. T Cov. % Identity % Similarity E-value Alignment
seq_1219_test seq_6977_train 0.99 0.99 99.5 98.50 0.00e+00
seq_255_test seq_365_train 0.99 0.99 99.5 98.50 0.00e+00
seq_1878_test seq_3532_train 0.99 0.99 99.5 98.50 0.00e+00
seq_1420_test seq_6912_train 0.99 0.99 99.4 98.41 0.00e+00
seq_1728_test seq_5547_train 0.99 0.99 99.4 98.41 0.00e+00
seq_723_test seq_5313_train 0.99 0.99 99.4 98.41 0.00e+00
seq_1108_test seq_5005_train 0.99 0.99 99.4 98.41 0.00e+00
seq_807_test seq_6139_train 0.99 0.99 99.4 98.41 0.00e+00
seq_983_test seq_4199_train 0.99 0.99 99.4 98.41 0.00e+00
seq_1785_test seq_6559_train 0.99 0.99 99.4 98.41 0.00e+00
seq_1090_test seq_5105_train 0.99 0.99 99.4 98.41 0.00e+00
seq_82_test seq_7947_train 0.99 0.99 99.4 98.41 0.00e+00
seq_1702_test seq_1575_train 0.99 0.99 99.4 98.41 0.00e+00
seq_1119_test seq_7979_train 0.99 0.99 99.4 98.41 0.00e+00
seq_357_test seq_7111_train 0.99 0.99 99.3 98.31 0.00e+00
seq_1091_test seq_9089_train 0.99 0.99 99.3 98.31 0.00e+00
seq_815_test seq_8443_train 0.99 0.99 99.3 98.31 0.00e+00
seq_1278_test seq_3128_train 0.99 0.99 99.3 98.31 0.00e+00
seq_1639_test seq_4947_train 0.99 0.99 99.3 98.31 0.00e+00
seq_981_test seq_7821_train 0.99 0.99 99.3 98.31 0.00e+00
seq_761_test seq_8945_train 0.99 0.99 99.3 98.31 0.00e+00
seq_244_test seq_1591_train 0.99 0.99 99.3 98.31 0.00e+00
seq_445_test seq_8785_train 0.99 0.99 99.3 98.31 0.00e+00
seq_236_test seq_3248_train 0.99 0.99 99.2 98.21 0.00e+00
seq_1331_test seq_2048_train 0.99 0.99 99.2 98.21 0.00e+00
seq_1091_test seq_5895_train 0.99 1.00 98.9 97.91 0.00e+00
seq_54_test seq_8170_train 0.99 1.00 98.9 97.91 0.00e+00
seq_633_test seq_3467_train 0.98 0.98 98.7 96.73 0.00e+00
seq_802_test seq_9456_train 0.95 0.95 97.9 93.00 0.00e+00