抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Data Mining Practical Machine Learning Tools and Techniques

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

/pdf/analysis-of-protein-coding-genetic-variation-in-60-706-43wjw7d23z.pdf

Analysis of protein-coding genetic variation in 60,706 humans

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Prediction of protein-peptide interaction with nearest neighbor algorithm

The regulation of transcriptome expression level is a complex process involving multiple-level interactions among molecules such as protein coding RNA (mRNA), long noncoding RNA (lncRNA), and microRNA (miRNA), which are essential for the transcriptome stability and maintenance and regulation of body homeostasis. The availability of multilevel expression data enables a comprehensive view of the regulatory network. In this study, we analyzed the coding and noncoding gene expression profiles of 301 patients with uterine corpus endometrial carcinoma (UCEC). A new method was proposed to construct a genome-wide integrative network based on variance inflation factor (VIF) regression method. The cross-regulation relations of mRNA, lncRNA, and miRNA were then selected based on clique-searching algorithm from the network, when any two molecules of the three were shown as interacting according to the integrative network. Such relation, which we call the mRNA-lncRNA-miRNA triplet, demonstrated the complexity in transcriptome regulation process. Finally, six UCEC-related triplets were selected in which the mRNA participates in endometrial carcinoma pathway, such as CDH1 and TP53. The multi-type RNAs are proved to be cross-regulated as to each of the six triplets according to literature. All the triplets demonstrated the association with the initiation and progression of UCEC. Our method provides a comprehensive strategy for the investigation of transcriptome regulation mechanism.

/pdf/cancer-related-triplets-of-mrna-lncrna-mirna-revealed-by-1x3xczd5hs.pdf

Cancer-Related Triplets of mRNA-lncRNA-miRNA Revealed by Integrative Network in Uterine Corpus Endometrial Carcinoma.

In systems biology, regulatory pathway is one of the most important research areas. However, regulatory pathwayis so complicated that we still poorly understand this system. On the other hand, with rapid accumulated informationon different organisms, it becomes more and more possible to in-depth investigate regulatory pathway. To understandregulatory pathway well, figuring out the components of each pathway is the most important step. In this study, a network-based method was proposed to classify human genes into corresponding pathways. The information of proteinproteininteractions retrieved from STRING was used to construct a network and jackknife test was employed to evaluatethe method. As a result, the first order prediction accuracy was 87.91%, indicating that interactive proteins always havesimilar biological regulatory functions. By comparing the predicted results obtained from other methods based on blastand amino acid composition, respectively, it implies that our prediction method is quite promising that may provide anopportunity to understand this complicated pathway system well.

Prediction of human genes' regulatory functions based on proteinprotein interaction network.

Identification of retinoblastoma related genes with shortest path in a protein–protein interaction network

In systems biology, it is a great challenge for researchers to identify whether the given set of organic compounds can combine together and form a meaningful pathway. Fortunately, it becomes more and more feasible to address and solve such a problem with the rapidly accumulated information on various organisms. Based on the attainable information, a novel computational approach is proposed to investigate this problem by adopting the metabolic pathway of yeast as the subject of the study. And we produced a benchmark dataset with 13,736 pathways consisting of both valid and invalid pathways and identified the valid pathways among them. Each of these pathways was encoded into a numeric vector, consisting of three parts: graph property, chemical functional group, and chemical structural set. Methods of Minimum Redundancy Maximum Relevance and Incremental Feature Selection were utilized to select an optimal feature set, and Nearest Neighbor Algorithm was adopted as the classification model, while Jackknife Test was used to evaluate the model. As a result, an optimal feature set consisting of 16 features, which were able to identify the valid pathways most successfully, was obtained.

Tao Huang

Papers

Prediction of protein-peptide interaction with nearest neighbor algorithm

Cancer-Related Triplets of mRNA-lncRNA-miRNA Revealed by Integrative Network in Uterine Corpus Endometrial Carcinoma.

Prediction of human genes' regulatory functions based on proteinprotein interaction network.

Identification of retinoblastoma related genes with shortest path in a protein–protein interaction network

Prediction of Metabolic Pathway Using Graph Property, Chemical Functional Group and Chemical Structural Set