Additional file 2: of KIR diversity in three ethnic minority populations in China

Table S1.  KIR diversity in three ethnic minority populations in China with other populations

Additional file 3: of KIR diversity in three ethnic minority populations in China

Table S2. PC1 and PC2 of 22 populations

Additional file 1: of KIR diversity in three ethnic minority populations in China

Figure S1. Map of China showing the city of three study populations. DNA samples of the Kazakh and Uyghur ethnic minority populations were collected from the Xinjiang autonomous region (Urumqi) of Northwest China, the Tibetan ethnic minority populations were collected from the Tibet autonomous region (Lhasa) of Southwest China

MitProNet: A Knowledgebase and Analysis Platform of Proteome, Interactome and Diseases for Mammalian Mitochondria

<div><p>Mitochondrion plays a central role in diverse biological processes in most eukaryotes, and its dysfunctions are critically involved in a large number of diseases and the aging process. A systematic identification of mitochondrial proteomes and characterization of functional linkages among mitochondrial proteins are fundamental in understanding the mechanisms underlying biological functions and human diseases associated with mitochondria. Here we present a database MitProNet which provides a comprehensive knowledgebase for mitochondrial proteome, interactome and human diseases. First an inventory of mammalian mitochondrial proteins was compiled by widely collecting proteomic datasets, and the proteins were classified by machine learning to achieve a high-confidence list of mitochondrial proteins. The current version of MitProNet covers 1124 high-confidence proteins, and the remainders were further classified as middle- or low-confidence. An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene expression profiles, protein-protein interactions, functional similarity and metabolic pathways. The functional-linkage network should be a valuable resource for the study of biological functions of mitochondrial proteins and human mitochondrial diseases. Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms. All proteins, functional linkages and disease candidate genes in MitProNet were annotated according to the information collected from their original sources including GO, GEO, OMIM, KEGG, MIPS, HPRD and so on. MitProNet features a user-friendly graphic visualization interface to present functional analysis of linkage networks. As an up-to-date database and analysis platform, MitProNet should be particularly helpful in comprehensive studies of complicated biological mechanisms underlying mitochondrial functions and human mitochondrial diseases. MitProNet is freely accessible at <a href="http://bio.scu.edu.cn:8085/MitProNet" target="_blank">http://bio.scu.edu.cn:8085/MitProNet</a>.</p></div

Integrated mitochondrial proteomic datasets for an inventory of mammalian mitochondrial proteins.

<p>Integrated mitochondrial proteomic datasets for an inventory of mammalian mitochondrial proteins.</p

Venn diagram of the four datasets: MitoCom (high-confidence), MitoCom (middle-confidence), MitoCarta and MitoPred.

<p>Venn diagram of the four datasets: MitoCom (high-confidence), MitoCom (middle-confidence), MitoCarta and MitoPred.</p

Descriptions and parameters of four networks.

<p>Descriptions and parameters of four networks.</p

System architecture and main contents of MitProNet.

<p>MitProNet is composed of three sections including mitochondrial protein part lists, annotations of mitochondrial protein and disease information.</p

TP/FP ratios vs. LR cutoff, and corresponding sensitivity.

<p>TP: True Positive; FP: False Positive. Sensitivity = TP/(TP+FN).</p

Web pages in MitProNet.

<p>(A) A list page of mitochondrial proteins. The mitochondrial proteins can be listed according to proteomic datasets, confidence levels and organisms, respectively. (B) The outcome page for the query protein NDUFS7, an annotated disease gene for Leigh syndrome. The page provides a brief summary of the query protein, subcellular localization evidences and a FLN among the query protein. Moreover, the query protein is annotated according to the information collected from their original sources including GO, KEGG, MIPS and OMIM. (C) The prioritization results for Leigh syndrome. The result page includes a brief description for this phenotype, disease genes and a FLN among these genes. The disease genes are listed dividedly as the known genes and the candidates that are ordered by these ranking scores.</p