1. Fig. 1. Sample NGS data in FASTQ format (SRA's srr032209), with parts being shortened and numbered: (1) read identifiers; (2) sequence of bases; (3) ‘+’ followed by optional comments; and (4) Q-scores.
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
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2. Fig. 2. Some statistics for each of the three described datasets: distribution of minimal and maximal Q-scores over the population of reads (a and b), and distribution of Q-scores (c) and T-gaps (d).
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
Bioinformatics | © The Author Published by Oxford University Press. All rights reserved. For Permissions, please

3. Fig. 3. Workflow of our investigation
Fig. 3. Workflow of our investigation. Dashed arrows depict a representation of Q-scores alone.
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
Bioinformatics | © The Author Published by Oxford University Press. All rights reserved. For Permissions, please

4. Fig. 4. Relative mapping accuracy of the three lossy transformations, measured for the dataset SRR The 100% baseline employs standard Q-scores, resulting in 12.1 (out of a total of 18.8) million reads being mapped successfully by MAQ. The horizontal dotted line shows where relative mapping accuracy is 99%. The two vertical dotted lines indicate the lowest values for |Σ| at which this accuracy is still attainable (|Σ|=19 for Truncating and UniBinning; |Σ|=5 for LogBinning).
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
Bioinformatics | © The Author Published by Oxford University Press. All rights reserved. For Permissions, please

5. Fig. 5. Compression effectiveness for SRR using various block sizes and different lossless transformations: (a) No transformation; (b) MinShifting; (c) FreqOrdering; (d) GapTranslating. The x-axis represents block size. As reference, the zero-order self-information is indicated as solid black lines.
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
Bioinformatics | © The Author Published by Oxford University Press. All rights reserved. For Permissions, please

6. Fig. 6. Compression effectiveness achieved with LogBinning for SRR032209, with k and |Σ| varying on the horizontal, and lossless transformations varying on the vertical dimensions. Black lines represent the self-information, which is unaffected by block size.
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
Bioinformatics | © The Author Published by Oxford University Press. All rights reserved. For Permissions, please

7. Fig. 7. Compression and decompression (including transformation) times averaged over three trials for SRR Lossless transformation GapTranslating and no lossy transformation for (a) k=1 and (b) k=256; and with no lossless transformation and lossy transformation LogBinning at |Σ|=5 for (c) k=1 and (d) k=256.
From: Transformations for the compression of FASTQ quality scores of next-generation sequencing data
Bioinformatics. 2011;28(5): doi: /bioinformatics/btr689
Bioinformatics | © The Author Published by Oxford University Press. All rights reserved. For Permissions, please