The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

A benchmark of gene expression tissue-specificity metrics

<p>Supplementary figures to the paper "A benchmark of gene expression tissue-specificity metrics".</p

Mouse in the Correlation Net

<p>We study how anatomy influences the evolution of protein-coding genes. The picture shows correlations between different protein parameters in mouse. Yellow lines show positive correlations and blue negative correlations, calculated with partial Pearson coefficient. The picture was done using Circos (Krzywinski et al. 2009). The parameters shown are gene expression level in 22 different organs, intron length and intron number, protein length, gene GC content and evolutionary rate (omega). Expression data were taken from ENCODE (The ENCODE Project Consortium 2007) and all other data are from Ensembl (Flicek et al. 2011).</p

A benchmark of gene expression tissue-specificity metrics

One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g. evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study, we compare nine measures of tissue specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyse their capacity to distinguish gene categories, their robustness to the choice and number of tissues used and their capture of evolutionary conservation signal

Spearman partial correlation with expression values for each tissue separately for a) mouse and b) human.

<p>The width of the lines shows the strength of correlations. Red lines show positive correlations, blue shows negative correlations. Only significant correlations (p<0.0005) are shown. Color of the tissue bands represents different groups of tissues (gastrointestinal system (yellow), central nervous system (red), reproductive system (beige) and misc (orange)).</p

Values of partial Spearman correlations between parameters, over all tissues.

<p>Top right of table: values for mouse (corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131673#pone.0131673.g001" target="_blank">Fig 1A</a>); bottom left of table: values for human (corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131673#pone.0131673.g001" target="_blank">Fig 1B</a>).</p><p>All values are significant (Benjamini-Hochberg FDR 1%), except those in italics. Values in bold are strongly significant (p-value≤0.0005) and are represented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131673#pone.0131673.g001" target="_blank">Fig 1</a>.</p><p>Values of partial Spearman correlations between parameters, over all tissues.</p

Spearman partial correlations in a) mouse and b) human.

<p>The width of the lines shows the strength of correlations. Red lines show positive correlations, blue lines show negative correlations. Only significant correlations (p<0.0005) are shown.</p