Frequency of selected linear models according to their MSE within 1000 modelling repeats.

<p>Frequency of selected linear models according to their MSE within 1000 modelling repeats.</p

Non-chromosomal information enhances the fraction of phenotypic variance explained.

<p>Three linear models with different complexity are applied to measure the fraction of phenotypic variance. The first model (<i>simple</i>) includes only the gene deletion status (red), the second model (<i>additive</i>) considers the gene deletion status and non-chromosomal elements (orange), and finally the third model (<i>interaction</i>) includes both chromosomal and non-chromosomal elements as well as their interaction (yellow). The fraction of phenotypic variance is thereby approximated by the average coefficient of determination (</p><p></p><p></p><p>Ra2</p><p></p><p></p>) of 1000 randomly sampled sub-sets. Aside from the control <i>MCM22</i> and the gene deletion <i>PHO88(non-killer)</i> experiment, model accuracy increases considerably when non-chromosomal information is included and much more when the interaction is taken into account.<p></p

Complexity of BIC selected statistical models.

<p>Complexity of BIC selected statistical models.</p

Coevolution network of the inter gp120-gp41 coevolving pair Pro238-Glu630.

<p>This pair may affect the gp120-gp41 interaction, although their are not proximal, through their intra-domain coevolving residue partners.</p

Predicted coevolving residue pairs within V3.

<p>TP predicted coevolving pairs are connected with a green dash and the FP ones are shown as red bonds. Amino acid numbering is according the HXB2 reference sequence and the V3 coordinate structure solved by Huang et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143245#pone.0143245.ref011" target="_blank">11</a>] is applied for visualisation. (A) Travers and co-authors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143245#pone.0143245.ref042" target="_blank">42</a>] identified 24 coevolving pairs of which the majority is FP. (B) Coevolution predictions made by GREMLIN identified almost exclusively TP.</p

Predicted coevolving pairs between amino acids located in V3 and other structural regions of HIV-1 Env.

<p>Gp120 is shown in cartoon representation, with V1 coloured in blue, V2 in pink and V3 in orange. (A) All inter V3 coevolving pairs are highlighted with green (TP) or red (FP) coloured dashes. (B) Coevolving amino acid pairs Glu293-Asn295, Glu293-Thr297, His330-Asn332 and His330-Ser334 (shown in sticks representation) are mediated by N-linked glycans (shown as black lines). (C) Predicted contacts between amino acids located in V1V2 and V3. The involved amino acids are highlighted as coloured sticks.</p

Coevolving pairs between amino acids in V1V2 and other structural HIV-1 Env domains.

<p>V1, V2 and V3 are shown in skyblue, pink and orange coloured cartoon illustration. (A) 59 predicted and in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143245#pone.0143245.ref049" target="_blank">49</a>] crystallised coevolving residue pairs; with TP illustrated as green and FP as red dashes. (B) Two long-distance coevolving amino acids are quite likely mediated by a N-linked glycan. The involved amino acids are shown in stick representation. (C) Three long-distance residue pairs (Ile165-Lys192, Gly167-Lys192, and Gly167-Met426) are presumably inter gp120 contacts. The intra- and inter-gp120 distances are shown as coloured (orange,light green and yellow) bonds. The inter-gp120 distances are in all cases smaller than the intra-gp120 ones.</p

HIV cell entry.

<p>Schematic illustration of HIV-1 entry steps attachment and coreceptor binding.</p

MOESM1 of Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

Additional file 1. Details of machine learning methods

MOESM2 of Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

Additional file 2. Gender stratified analysis