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

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

https://www.zora.uzh.ch/id/eprint/165500/1/30476243.pdf

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

/pdf/structure-function-and-diversity-of-the-healthy-human-4mecpxyl8r.pdf

Structure, function and diversity of the healthy human microbiome

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

/pdf/string-v10-protein-protein-interaction-networks-integrated-2gg77rxec0.pdf

STRING v10: protein–protein interaction networks, integrated over the tree of life

AUTHOR'S CORRECTION The Amino Terminus of Pseudomonas aeruginosa Outer Membrane Protein OprF Forms Channels in Lipid Bilayer Membranes: Correlation with a Three-Dimensional Model

This chapter addresses several different computational methods for the identification of periplasmic proteins from sequence information alone. The benefits, pitfalls, and performance of the methods are discussed, and an approach for the optimal computational identification of periplasmic proteins from a sequenced genome is presented. Recognizing that PSORT I could be significantly improved, the authors' group set out to develop a new method, PSORTb, for the prediction of protein subcellular localization in bacteria. Indeed, many of the other methods described in the chapter use ePSORTdb as a source of training and testing data. By parsing the remaining records into an easy-to-manipulate format such as tab-delimited text format, researchers can then identify periplasmic proteins by either manually reviewing each annotated localization site or extracting any records with an instance of the word “periplasm”. The former approach is slow, but has the advantage of allowing the researchers to incorporate their own expert knowledge into the review process. Of all the methods developed for signal peptide prediction, the suite of tools developed at the Technical University of Denmark has consistently been ranked as the best by several independent evaluations. These programs include SignalP, LipoP, and TatP, which are discussed individually. The chapter has presented an overview of a selection of methods for the computational identification of periplasmic proteins. While the analytical pipeline described in the chapter will identify a large proportion of periplasmic proteins with a moderate to high degree of confidence, the need still exists for improved localization prediction methods.

Methods for the Computational Prediction of Periplasmic Proteins

There are numerous past and current examples of ontology-driven projects that provide auto-generated user interfaces for managing entities and relations, each presenting its own varied and complex data model. Our Datum Proof Sheet application aims to simplify the application development landscape by building community consensus about the way basic categorical, textual and numeric datum fields should be described within the OBOFoundry community of ontologies. The proof sheet shows selected datums (grouped under the context of an OBI “data representational model” item) as form inputs on an HTML page, enabling an application ontology’s contents to be presented to end users (ranging in our case from epidemiologists to software developers) for review without necessarily having a working application to showcase them in. The basic relations and cases necessary for presenting datums in a user interface are mostly satisfied by OBI’s design, but we introduce a few extra elements to bring more clarity to datum specifications, and to provide user interface term labels and definitions that may differ from those that ontologists prefer in the “backend”. Keywords—ontology; user interface; datum; categorical; measurable; scalar variable

/pdf/an-obi-ontology-datum-proof-sheet-25oaiyy7to.pdf

An OBI Ontology Datum Proof Sheet.

Pathogen-associated gene discovery workflows for novel antivirulence therapeutic development

https://www.molecularconnections.com/files/nmeth.1931.pdf

Fiona S. L. Brinkman

Papers

AUTHOR'S CORRECTION The Amino Terminus of Pseudomonas aeruginosa Outer Membrane Protein OprF Forms Channels in Lipid Bilayer Membranes: Correlation with a Three-Dimensional Model

Methods for the Computational Prediction of Periplasmic Proteins

An OBI Ontology Datum Proof Sheet.

Pathogen-associated gene discovery workflows for novel antivirulence therapeutic development

p rotein interaction data curation: the i nternational m olecular exchange (ime x) consortium