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OPENSEQ.org
D3_GNAI2
Genes: A B A+B
Length: 400 355 729
Sequences: 324 2153 61
Seq/Len: 0.81 6.06 0.08
MirrorTree (Pazo et al. 2001) 0.06
Download Alignment
We filter this alignment to remove positions that have > 75% gaps before running GREMLIN.

[Show Jackhmmer results] - Maybe useful in deciding what regions to trim.
Δgene Ratio of paralogs Joined
A B Seq/Len
1 0.08 0.01 0.00
2 0.08 0.01 0.00
5 0.10 0.01 0.00
10 0.11 0.02 0.00
20 0.12 0.02 0.01
100 0.12 0.02 0.02
∞ 0.16 0.02 0.08
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
193_S 70_R 1.28 0.12 0.00
239_P 94_D 1.20 0.11 0.00
343_L 143_R 1.06 0.09 0.00
44_V 152_S 1.02 0.08 0.00
205_V 64_E 1.01 0.08 0.00
295_S 163_I 0.97 0.07 0.00
66_Y 157_L 0.97 0.07 0.00
343_L 70_R 0.93 0.07 0.00
337_A 73_V 0.93 0.07 0.00
116_A 128_I 0.92 0.07 0.00
338_F 290_E 0.92 0.07 0.00
68_V 337_F 0.92 0.07 0.00
381_V 218_G 0.91 0.07 0.00
71_L 91_L 0.91 0.07 0.00
128_R 157_L 0.90 0.06 0.00
50_V 247_S 0.89 0.06 0.00
75_D 91_L 0.88 0.06 0.00
67_L 267_L 0.87 0.06 0.00
336_G 9_D 0.87 0.06 0.00
279_E 133_A 0.86 0.06 0.00
47_N 91_L 0.86 0.06 0.00
71_L 354_L 0.85 0.06 0.00
216_K 81_I 0.85 0.06 0.00
391_R 61_Y 0.85 0.06 0.00
49_L 332_N 0.84 0.06 0.00
381_V 175_V 0.84 0.06 0.00
61_Q 147_Y 0.84 0.06 0.00
247_P 74_Y 0.84 0.06 0.00
47_N 157_L 0.83 0.06 0.00
272_R 74_Y 0.83 0.06 0.00
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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