Summary

Basic Descriptive Statistics
✖ Data Leakage
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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 transcription_factor_prediction_human_3_val.csv.gz against train set transcription_factor_prediction_human_3_train.csv.gz
Data Leakage:✖FailHow a flag is decided →
gb-qc v1.0.0 · run 2026-09-15 22:30:51

Basic Descriptive Statistics

Filename transcription_factor_prediction_human_3_val.csv.gz transcription_factor_prediction_human_3_train.csv.gz
Number of sequences 1000 27294
Minimum length 101 101
Mean length 101.00 101.00
Maximum length 101 101
✖

Data Leakage

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 transcription_factor_prediction_human_3_val.csv.gz transcription_factor_prediction_human_3_train.csv.gz
Data Leakage (percentage) 2.70% 0.14%
Data Leakage (count) 27 39
Similarity Histogram
41 high-similarity alignments
Query (Q) Target (T) Q Cov. T Cov. % Identity % Similarity E-value Alignment
seq_578_test seq_19651_train 1.00 1.00 100.0 100.00 3.52e-52
seq_429_test seq_21365_train 1.00 1.00 100.0 100.00 3.52e-52
seq_937_test seq_310_train 0.99 0.99 100.0 99.00 1.71e-51
seq_700_test seq_6101_train 0.99 0.99 100.0 99.00 1.71e-51
seq_560_test seq_22128_train 0.99 0.99 100.0 99.00 1.71e-51
seq_907_test seq_2126_train 0.99 0.99 100.0 99.00 1.71e-51
seq_887_test seq_22757_train 0.97 0.97 100.0 97.00 3.97e-50
seq_887_test seq_24827_train 0.97 0.97 100.0 97.00 3.97e-50
seq_680_test seq_15743_train 0.96 0.96 100.0 96.00 1.90e-49
seq_532_test seq_8651_train 1.00 1.00 96.0 96.00 2.08e-45
seq_81_test seq_195_train 0.96 0.96 100.0 96.00 1.90e-49
seq_948_test seq_3871_train 0.96 0.96 100.0 96.00 1.90e-49
seq_50_test seq_7102_train 0.96 0.96 100.0 96.00 1.90e-49
seq_680_test seq_7126_train 0.95 0.95 100.0 95.00 9.05e-49
seq_948_test seq_13569_train 0.95 0.95 100.0 95.00 9.05e-49
seq_575_test seq_23553_train 0.94 0.94 100.0 94.10 4.30e-48
seq_948_test seq_2126_train 0.94 0.94 100.0 94.10 4.30e-48
seq_662_test seq_16398_train 0.94 0.94 100.0 94.10 4.30e-48
seq_958_test seq_2062_train 0.98 0.98 95.9 93.98 4.46e-44
seq_747_test seq_18090_train 0.93 0.93 100.0 93.10 2.03e-47
seq_179_test seq_16473_train 0.93 0.93 100.0 93.10 2.03e-47
seq_760_test seq_14929_train 0.98 0.98 94.9 93.00 1.99e-42
seq_872_test seq_5752_train 0.92 0.92 100.0 92.10 9.55e-47
seq_429_test seq_21807_train 0.92 0.92 100.0 92.10 9.55e-47
seq_493_test seq_10230_train 0.92 0.92 100.0 92.10 9.55e-47
seq_179_test seq_195_train 0.92 0.92 100.0 92.10 9.55e-47
seq_915_test seq_2643_train 0.96 0.97 95.9 92.06 4.24e-42
seq_740_test seq_19543_train 0.98 0.98 93.9 92.02 8.60e-41
seq_984_test seq_3564_train 0.98 0.98 93.9 92.02 8.60e-41
seq_575_test seq_20196_train 0.91 0.91 100.0 91.10 4.47e-46
seq_493_test seq_26446_train 0.91 0.91 100.0 91.10 4.47e-46
seq_847_test seq_11471_train 0.91 0.91 100.0 91.10 4.47e-46
seq_747_test seq_18374_train 0.91 0.91 100.0 91.10 4.47e-46
seq_958_test seq_1586_train 0.99 0.99 92.0 91.08 3.30e-38
seq_958_test seq_13868_train 0.96 0.96 94.8 91.01 4.06e-41
seq_740_test seq_5735_train 0.98 0.99 92.0 90.16 6.22e-37
seq_984_test seq_20689_train 0.98 0.98 91.9 90.06 1.44e-37
seq_833_test seq_9130_train 0.96 0.96 93.8 90.05 1.70e-39
seq_230_test seq_18831_train 0.97 0.97 92.8 90.02 1.57e-38
seq_131_test seq_10843_train 1.00 1.00 90.0 90.00 1.14e-35
seq_958_test seq_21772_train 1.00 1.00 90.0 90.00 1.14e-35