FASTQ Quality Score Reference — Both Encodings Decoded (.csv)
Every twelfth base of the six reads with its Phred score, the ASCII character it takes under both offsets, and the error probability that score implies. It converts a quality-encoding argument into a lookup you can diff.
read_id,position,base,phred_score,char_phred33,char_phred64,error_probability
NX_READ_001,1,G,37,F,e,1.995e-04
NX_READ_001,13,G,37,F,e,1.995e-04
NX_READ_001,25,A,34,C,b,3.981e-04
NX_READ_001,37,A,35,D,c,3.162e-04
NX_READ_001,49,G,34,C,b,3.981e-04
NX_READ_002,1,A,38,G,f,1.585e-04
NX_READ_002,13,T,35,D,c,3.162e-04
NX_READ_002,25,A,37,F,e,1.995e-04
NX_READ_002,37,C,36,E,d,2.512e-04
NX_READ_002,49,G,33,B,a,5.012e-04
NX_READ_003,1,A,40,I,h,1.000e-04
NX_READ_003,13,A,38,G,f,1.585e-04
NX_READ_003,25,C,38,G,f,1.585e-04
NX_READ_003,37,A,33,B,a,5.012e-04
NX_READ_003,49,G,34,C,b,3.981e-04
NX_READ_004,1,C,39,H,g,1.259e-04
NX_READ_004,13,C,39,H,g,1.259e-04
NX_READ_004,25,A,36,E,d,2.512e-04
NX_READ_004,37,C,36,E,d,2.512e-04
NX_READ_004,49,T,33,B,a,5.012e-04
NX_READ_005,1,A,40,I,h,1.000e-04
NX_READ_005,13,A,38,G,f,1.585e-04
NX_READ_005,25,G,35,D,c,3.162e-04
NX_READ_005,37,A,35,D,c,3.162e-04
NX_READ_005,49,C,33,B,a,5.012e-04
NX_READ_006,1,A,38,G,f,1.585e-04
NX_READ_006,13,G,37,F,e,1.995e-04
NX_READ_006,25,A,38,G,f,1.585e-04
NX_READ_006,37,C,34,C,b,3.981e-04
NX_READ_006,49,T,31,@,_,7.943e-04
Specifications
- Rows
- 30
- Columns
- 7
- Sampled Every
- 12
- Shows Both Offsets
- true
- Error Probability Rule
- P = 10^(-Q/10)
- Describes
- both FASTQ twins
Testing contract
Reference control- Scenario
- Decode the sampled positions from both FASTQ twins and compare the scores and error probabilities with this table.
- Expected result
- Every sampled position matches on score and on both ASCII characters, and the error_probability column equals ten to the power of minus the score over ten.
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“FASTQ Quality Score Reference — Both Encodings Decoded (.csv)” is a deterministic Novus Examples fixture for Scientific data, Editor testing, Conversion testing. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.
Documented properties for this file: 30 rows · 7 columns. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Scientific fixtures are small, valid, and fully synthetic — no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.
Code examples
import pandas as pd
df = pd.read_csv("quality-score-reference.csv")
print(df.head())
print(df.dtypes)Related files
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