Conceptual examination of emotion-related terms in Japanese: In the psychological studies of expressive behavior

DNA-seq library statistics. Total number of reads, percentage of mapped reads and percentage of properly paired reads from DNA-seq of 16 individual Ae. albopictus mosquitoes (Foshan strain). (DOCX 20 kb

Additional file 1: of BigQ: a NoSQL based framework to handle genomic variants in i2b2

This file contains supplementary tables, figures and BigCouch tuning parameters.(DOCX 43 kb

Additional file 8: Figure S1. of Comparative genomics shows that viral integrations are abundant and express piRNAs in the arboviral vectors Aedes aegypti and Aedes albopictus

qRT-PCR based expression of AlbFlavi34 in different developmental stages of Ae. albopictus. (PDF 102 kb

Additional file 2: Table S2. of Comparative genomics shows that viral integrations are abundant and express piRNAs in the arboviral vectors Aedes aegypti and Aedes albopictus

List of viral genomes used for in silico screening of Dipteran genomes. The only DNA arbovirus is in yellow. (XLSX 38 kb

Additional file 1: Table S1. of Comparative genomics shows that viral integrations are abundant and express piRNAs in the arboviral vectors Aedes aegypti and Aedes albopictus

List of Dipteran species analysed. (XLSX 34 kb

Genes associated with the phenotype.

<p>Genes associated with the phenotype.</p

Classification performance for each data set.

<p>Classification performance for each data set.</p

Examples of variants influencing more than one gene.

<p>Protein encoded by gene <i>A</i> interacts with proteins encoded by gene <i>B</i> and gene <i>C</i>. (a) Variant on gene <i>A</i>, <i>V</i>_<i>A</i> contributes to the score both for <i>A</i> and <i>B</i> due to their interaction. In the same way, variants on gene <i>B</i>, <i>V</i>_<i>B</i>, and on gene <i>C</i>, (<i>V</i>_<i>C</i>) both contribute to the scores of genes <i>A</i> <i>B</i> and <i>C</i>. (b) Resulting variant contributions on final PPIs scores.</p

Data fusion framework.

<p>Sequencing data are collapsed to calculate their mutational loads using four ROIs, namely genes, pathways, domains and PPIs. This allows studying ROI-phenotype associations along the four correspondent axes. Each element tested for association then becomes a feature for a prediction model. Single ROI types are combined to create data sets. Each data set is split into a training and test set. The training set is used to tune the learning parameters of a RF model and then select the best set of features, while the test set is used to measure the prediction performances.</p

Interconnected, statistically significant ROIs.

<p>Graphical representation of statistically significant ROIs and their overlapping. The direction of the arrow means that an element is included into another. Gene ROIs (light blue) can be part of pathway (green) or PPI (grey) ROIs, while domain ROIs (purple) can be part of gene ROIs.</p