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

The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.

https://bioinformatics.cs.vt.edu/~easychair/HusonBryant_MBE_2006.pdf

Application of Phylogenetic Networks in Evolutionary Studies

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

/pdf/dbpedia-a-nucleus-for-a-web-of-open-data-64wla3p0zr.pdf

DBpedia: a nucleus for a web of open data

This collaboratively edited knowledgebase provides a common source of data for Wikipedia, and everyone else.

https://dl.acm.org/doi/pdf/10.1145/2629489

Wikidata: a free collaborative knowledgebase

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

/pdf/data-integration-a-theoretical-perspective-58aesjvhuk.pdf

Data integration: a theoretical perspective

With the proliferation of database views and curated databases, the issue of data provenance - where a piece of data came from and the process by which it arrived in the database - is becoming increasingly important, especially in scientific databases where understanding provenance is crucial to the accuracy and currency of data. In this paper we describe an approach to computing provenance when the data of interest has been created by a database query. We adopt a syntactic approach and present results for a general data model that applies to relational databases as well as to hierarchical data such as XML. A novel aspect of our work is a distinction between "why" provenance (refers to the source data that had some influence on the existence of the data) and "where" provenance (refers to the location(s) in the source databases from which the data was extracted).

/pdf/why-and-where-a-characterization-of-data-provenance-1hkicechm8.pdf

Why and Where: A Characterization of Data Provenance

1 Introduction 2 A Syntax for Data 3 XML 4 Query Languages 5 Query Languages for XML 6 Interpretation and advanced features 7 Typing semistructured data 8 Query Processing 9 The Lore system 10 Strudel 11 Database products supporting XML

https://homepages.dcc.ufmg.br/~laender/material/Data-on-the-Web-Skeleton.pdf

Data on the Web: From Relations to Semistructured Data and XML

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.

/pdf/the-iuphar-bps-guide-to-pharmacology-in-2016-towards-curated-3aw085xw9l.pdf

The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

The International Union of Basic and Clinical Pharmacology/British Pharmacological Society (IUPHAR/BPS) Guide to PHARMACOLOGY (http://www.guidetopharmacology.org) is a new open access resource providing pharmacological, chemical, genetic, functional and pathophysiological data on the targets of approved and experimental drugs. Created under the auspices of the IUPHAR and the BPS, the portal provides concise, peer-reviewed overviews of the key properties of a wide range of established and potential drug targets, with in-depth information for a subset of important targets. The resource is the result of curation and integration of data from the IUPHAR Database (IUPHAR-DB) and the published BPS 'Guide to Receptors and Channels' (GRAC) compendium. The data are derived from a global network of expert contributors, and the information is extensively linked to relevant databases, including ChEMBL, DrugBank, Ensembl, PubChem, UniProt and PubMed. Each of the ∼6000 small molecule and peptide ligands is annotated with manually curated 2D chemical structures or amino acid sequences, nomenclature and database links. Future expansion of the resource will complete the coverage of all the targets of currently approved drugs and future candidate targets, alongside educational resources to guide scientists and students in pharmacological principles and techniques.

/pdf/the-iuphar-bps-guide-to-pharmacology-an-expert-driven-9396sxlm2x.pdf

The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands

In semistructured data, the information that is normally associated with a schema is contained within the data, which is sometimes called “self-describing”. In some forms of semistructured data there is no separate schema, in others it exists but only places loose constraints on the data. Semistructured data has recently emerged as an important topic of study for a variety of reasons. First, there are data sources such as the Web, which we would like to treat as databases but which cannot be constrained by a schema. Second, it may be desirable to have an extremely flexible format for data exchange between disparate databases. Third, even when dealing with structured data, it may be helpful to view it. as semistructured for the purposes of browsing. This tutorial will cover a number of issues surrounding such data: finding a concise formulation, building a sufficiently expressive language for querying and transformation, and optimizat,ion problems.

Peter Buneman

Papers

Why and Where: A Characterization of Data Provenance

Data on the Web: From Relations to Semistructured Data and XML

The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands

Semistructured data