Additional file 1 of Prediction of virus-host protein-protein interactions mediated by short linear motifs

Table S1. Candidate interactions between human and HIV-1 (interactions.zip) available at https://figshare.com/articles/interactions_zip/4648714 . (ZIP 2549.76 kb

Additional file 3 of Prediction of virus-host protein-protein interactions mediated by short linear motifs

Table S3. SLiM sets C,D,R and derived for HIV-1 proteins. (SLiM_sets.zip) available at https://figshare.com/articles/Short_Linear_Motif_Sets_common_to_human_and_HIV-1/4648732 . (ZIP 145 kb

Exploration of Noncoding Sequences in Metagenomes

<div><p>Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 whole metagenomes, including coding and noncoding sequences using the following sequence patterns: (G+C) content, Codon Usage (Cd), Trinucleotide Usage (Tn), and functional assignments for ORF prediction. Herein, we present evidence of a high proportion of noncoding sequences discarded in common similarity-based methods in metagenomics, and the kind of relevant information present in those. We found a high density of trinucleotide repeat sequences (TRS) in noncoding sequences, with a regulatory and adaptive function for metagenome communities. We present associations between trinucleotide values and gene function, where metagenome clustering correlate with microorganism adaptations and kinds of metagenomes. We propose here that noncoding sequences have relevant information to describe metagenomes that could be considered in a whole metagenome analysis in order to improve their organization, classification protocols, and their relation with the environment.</p> </div

Proportion of NCS mapped to complete bacterial genomes.

<p><b>A.</b> Distribution of taxonomical classes mapped in complete genomes with NCS. <b>B.</b> Distribution of taxonomical classes mapped in coding sequences with NCS.</p

DOE JGI metagenomes classification and number of sequences analyzed.

<p>DOE JGI metagenomes classification and number of sequences analyzed.</p

Common functional assignments among metagenomes.

<p>The size of the bars indicates the number per functional category, and the colors indicate the type of category.</p

A metagenomic framework.

<p>At the top are shown, the three metagenomic categories. Averaged values per category for each parameter are shown above the arrows. The parameters (1–8) were calculated from complete metagenomes, parameter 9 was calculated from NCS (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059488#pone.0059488.s002" target="_blank">Table S1</a>) and parameters 10 and 11 are behaviors inferred from literature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059488#pone.0059488-Cuvelier1" target="_blank">[9]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059488#pone.0059488-Weinberg1" target="_blank">[11]</a>.</p

Sequence patterns defined.

<p><b>A.</b> (G+C) content distribution in metagenomes. <b>B.</b> Codon and trinucleotide usage. (Blue: Coding sequences; Red: Entire sequences).</p

Hierarchical clustering trees.

<p>Representation of structural and functional profiles for <i>Env</i>, <i>HAs</i> and <i>Eng</i> metagenomes. The lines correspond to the metagenome category and the shaded sections correspond to conserved clustering organization of metagenomes among the trees. Asterisk indicates not functional associations for the OAEMG1 metagenome.</p

Functional analysis of coding sequences from metagenomes.

<p>Identification of metagenome clustering according to functional assignments based on Pfam models. The color bar indicates frequency of functional category from low (blue) to high (red).</p