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

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr
 .

/pdf/enrichr-interactive-and-collaborative-html5-gene-list-1dn7g68rn1.pdf

Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool

Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis’, ‘Mechanistic Networks’, ‘Causal Network Analysis’ and ‘Downstream Effects Analysis’ are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.

/pdf/causal-analysis-approaches-in-ingenuity-pathway-analysis-5fgp4yj97u.pdf

Causal analysis approaches in Ingenuity Pathway Analysis

Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.

/pdf/galaxy-a-comprehensive-approach-for-supporting-accessible-aaovq586zu.pdf

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-kappaBeta pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types.

/pdf/the-landscape-of-somatic-copy-number-alteration-across-human-2stjg261iq.pdf

The landscape of somatic copy-number alteration across human cancers

To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

http://cinjweb.umdnj.edu/sites/molmdweb/documents/connectivitymap.pdf

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Stephen J Chapman1,2, Chiea C Khor1, Fredrik O Vannberg1, Nicholas A Maskell2, Christopher WH Davies3, Emma L Hedley2, Shelley Segal4, Catrin E Moore4, Kyle Knox5, Nicholas P Day6, Stephen H Gillespie7, Derrick W Crook5, Robert JO Davies2 & Adrian VS Hill1 1The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 2Oxford Centre for Respiratory Medicine, Churchill Hospital Site, Oxford Radcliffe Hospital, Oxford OX3 7LJ, UK. 3Department of Respiratory Medicine, Royal Berkshire Hospital, Reading RG1 5AN, UK. 4Department of Paediatrics, John Radcliffe Hospital, Oxford OX3 9DU, UK. 5Department of Microbiology, John Radcliffe Hospital, Oxford OX3 9DU, UK. 6Centre for Clinical Vaccinology and Tropical Medicine, Oxford OX3 9DU, UK. 7Centre for Medical Microbiology, Department of Infection, University College London, London NW1 2BU, UK. e-mail: adrian.hill@well.ox.ac.uk

Jim Lerner

Papers

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

GenePattern 2.0.