Find the shortcut. Learn the biology.
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 test.csv against train set train.csv
Data Leakage:!WarningHow a flag is decided →
gb-qc v1.0.0 · run 2026-08-26 23:58:57

Basic Descriptive Statistics

Filename test.csv train.csv
Number of sequences 1000 7274
Minimum length 111 105
Mean length 553.34 532.90
Maximum length 6000 5964

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 test.csv train.csv
Data Leakage (percentage) 0.80% 0.12%
Data Leakage (count) 8 9
Similarity Histogram
11 high-similarity alignments
Query (Q) Target (T) Q Cov. T Cov. % Identity % Similarity E-value Alignment
seq_222_test seq_1834_train 1.00 1.00 98.4 98.40 3.42e-164
seq_471_test seq_6214_train 1.00 1.00 97.2 97.20 3.41e-146
seq_446_test seq_6214_train 1.00 1.00 97.2 97.20 3.41e-146
seq_442_test seq_2212_train 1.00 1.00 97.1 97.10 3.02e-159
seq_222_test seq_5366_train 1.00 1.00 97.1 97.10 1.50e-157
seq_471_test seq_1613_train 1.00 1.00 96.9 96.90 1.53e-144
seq_446_test seq_1613_train 1.00 1.00 96.9 96.90 1.53e-144
seq_875_test seq_5994_train 1.00 0.98 96.1 94.56 0.00e+00
seq_637_test seq_6090_train 1.00 1.00 93.9 93.90 1.27e-95
seq_480_test seq_1046_train 1.00 1.00 91.3 91.30 6.01e-138
seq_826_test seq_237_train 1.00 0.98 92.5 90.56 9.59e-138