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OPENSEQ.org
L5-vs-L33
Genes: A B A+B
Length: 179 55 232
Sequences: 2042 1895 997
Seq/Len: 11.41 34.45 4.3
MirrorTree (Pazo et al. 2001) 0.40
Download Alignment
We filter this alignment to remove positions that have > 75% gaps before running GREMLIN.

[Show HHblits results] - Maybe useful in deciding what regions to trim.
Δgene Ratio of paralogs Joined
A B Seq/Len
1 0.09 0.08 0.00
2 0.09 0.08 0.00
5 0.09 0.08 0.00
10 0.09 0.08 0.02
20 0.09 0.08 0.12
100 0.09 0.10 1.82
∞ 0.09 0.13 4.26
Paired alignment generation
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Sequence A E-value:
Iterations:
Sequence B E-value:
Iterations:
Δgene MSA
Figure 1: The effect of Δgene on ratio of paralogs in the genome for each gene, and the number of sequences in the resulting joined alignment. Figure 2: Distribution of Δgene across all genomes. Figure 3: Current parameters. You can use the form to adjust the parameters and resubmit the job!
GREMLIN results (Scaled_score > 1):
Figure 4: The darker and larger the blue dots, the higher strength in coevolution. 		Inter Residue pairs sorted by strength in coevolution signal:
i j Scaled Score Prob I_Prob
112_R 6_R 1.20 0.90 0.22
87_C 27_K 0.91 0.65 0.09
24_S 46_H 0.81 0.52 0.06
105_T 39_F 0.79 0.49 0.06
144_D 9_I 0.79 0.49 0.06
5_H 48_I 0.76 0.45 0.05
97_W 11_L 0.76 0.45 0.05
115_R 46_H 0.74 0.42 0.05
12_V 41_P 0.71 0.39 0.04
71_R 45_Q 0.70 0.37 0.04
23_N 29_T 0.70 0.36 0.04
74_V 24_T 0.68 0.34 0.04
45_A 39_F 0.68 0.34 0.03
152_L 44_R 0.68 0.34 0.03
98_E 44_R 0.67 0.33 0.03
136_I 33_K 0.65 0.31 0.03
87_C 33_K 0.65 0.31 0.03
117_L 4_G 0.64 0.29 0.03
125_R 5_I 0.63 0.28 0.03
112_R 33_K 0.63 0.28 0.03
54_A 34_L 0.62 0.28 0.03
166_G 37_K 0.62 0.27 0.03
132_V 39_F 0.61 0.27 0.03
142_D 39_F 0.61 0.26 0.03
111_I 10_K 0.60 0.26 0.03
112_R 10_K 0.60 0.26 0.03
14_K 34_L 0.60 0.25 0.03
106_I 8_K 0.60 0.25 0.02
36_L 8_K 0.58 0.24 0.02
134_E 33_K 0.58 0.24 0.02
Figure 5: The i (protein A) and j (protein B) are positions as given in the query sequences.

Scaled Score = raw_score/average(raw_scores)
Prob = P(contact | scaled_score, seq/len)
I_Prob = P(contact | scaled_score, seq/len, top_inter_score)
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