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Table 1 The 29 genes (with two isoforms) used to compare the performance of RPiso and Ribomap

From: A tool for analyzing and visualizing ribo-seq data at the isoform level

Gene name

isoform A versus isoform B

Ribo-seq reads uniquely mapped to the unique exon of each isoform (by Bowtie): A versus B

TL(A) versus TL(B) calculated by RPiso

TL(A) versus TL(B) calculated by by Ribomap

ALG3

NM_005787 versus NM_001006941

91 versus 2 (A > B)

0.9 versus 0.01 (A > B)

110 versus 274 (A < B)

CNRIP1

NM_015463 versus NM_001111101

1 versus 0 (A > B)

0.01 versus 0 (A > B)

1 versus 2 (A < B)

COL5A1

NM_000093 versus NM_001278074

11 versus 0 (A > B)

0.69 versus 0 (A > B)

92 versus 1104 (A < B)

COQ6

NM_182476 versus NM_182480

9 versus 4 (A > B)

0.3 versus 0.11 (A > B)

12 versus 162 (A < B)

CRY2

NM_021117 versus NM_001127457

4 versus 0 (A > B)

0.06 versus 0 (A > B)

4 versus 30 (A < B)

EEF1E1

NM_001135650 versus NM_004280

124 versus 0 (A > B)

1.76 versus 0.6 (A > B)

135 versus 232 (A < B)

EGLN3

NM_022073 versus NM_001308103

30 versus 0 (A > B)

0.55 versus 0.02 (A > B)

22 versus 98 (A < B)

GALNT2

NM_004481 versus NM_001291866

181 versus 0 (A > B)

4.21 versus 0 (A > B)

234 versus 1926 (A < B)

KLF10

NM_005655 versus NM_001032282

53 versus 0 (A > B)

1.7 versus 0 (A > B)

89 versus 681 (A < B)

LCORL

NM_001166139 versus NM_153686

22 versus 13 (A > B)

0.17 versus 0.15 (A > B)

31 versus 98 (A < B)

LIG3

NM_013975 versus NM_002311

57 versus 0 (A > B)

0.4 versus 0.01 (A > B)

65 versus 300 (A < B)

LRIG3

NM_153377 versus NM_001136051

9 versus 0 (A > B)

0.09 versus 0.04 (A > B)

17 versus 128 (A < B)

LRP5

NM_002335 versus NM_001291902

3 versus 0 (A > B)

0.46 versus 0 (A > B)

321 versus 338 (A < B)

LYSMD1

NM_001136543 versus NM_212551

7 versus 0 (A > B)

0.18 versus 0 (A > B)

7 versus 28 (A < B)

MAP1S

NM_018174 versus NM_001308363

5 versus 0 (A > B)

0.15 versus 0 (A > B)

10 versus 135 (A < B)

MAT2B

NM_013283 versus NM_182796

23 versus 0 (A > B)

3.61 versus 0 (A > B)

58 versus 1068 (A < B)

NAF1

NM_138386 versus NM_001128931

127 versus 0 (A > B)

1.2 versus 0 (A > B)

133 versus 406 (A < B)

NDUFA11

NM_175614 versus NM_001193375

28 versus 0 (A > B)

1.92 versus 0 (A > B)

29 versus 355 (A < B)

NSMAF

NM_001144772 versus NM_003580

17 versus 13 (A > B)

0.11 versus 0.09 (A > B)

25 versus 167 (A < B)

NT5DC2

NM_001134231 versus NM_022908

62 versus 0 (A > B)

0.67 versus 0 (A > B)

62 versus 296 (A < B)

PPIL3

NM_130906 versus NM_032472

18 versus 0 (A > B)

0.59 versus 0 (A > B)

61 versus 75 (A < B)

PRKG1

NM_006258 versus NM_001098512

42 versus 0 (A > B)

0.2 versus 0 (A > B)

44 versus 77 (A < B)

RASA1

NM_002890 versus NM_022650

176 versus 0 (A > B)

0.45 versus 0 (A > B)

192 versus 321 (A < B)

RFC3

NM_002915 versus NM_181558

240 versus 0 (A > B)

2.01 versus 1.43 (A > B)

279 versus 808 (A < B)

RPS15

NM_001018 versus NM_001308226

22 versus 0 (A > B)

18.06 versus 0 (A > B)

24 versus 2482 (A < B)

CPD

NM_001304 versus NM_001199775

9 versus 0 (A > B)

0.11 versus 0.52 (A < B)

640 versus 33 (A > B)

PPP2R2A

NM_002717 versus NM_001177591

12 versus 0 (A > B)

0.44 versus 1.77 (A < B)

859 versus 39 (A > B)

RPL14

NM_001034996 versus NM_003973

3 versus 0 (A > B)

0.2 versus 65.57 (A < B)

12,815 versus 83 (A > B)

SUCLG2

NM_003848 versus NM_001177599

54 versus 0 (A > B)

0.41 versus 1.98 (A < B)

911 versus 16 (A > B)

  1. Take the gene ALG3 as an example. ALG3 has two isoforms (NM_005787 and NM_001006941). Each isoform has its unique exon. The unique exon of NM_005787 has much more Ribo-seq footprints (91 vs. 2 uniquely mapped reads) than the unique exon of NM_001006941 has. Therefore, the translational level (TL) of NM_005787 should be higher than that of NM_001006941. Our RPiso supports this assertion (0.9 vs. 0.01) while Ribomap contradicts this assertion (110 vs. 274). Therefore, our RPiso outperformed Ribomap in this case. In total, our RPiso outperformed Ribomap in 86% (25/29) of the cases (bold-faced names), suggesting that our RPiso estimates isoform abundance more accurately than Ribomap does