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 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.

/pdf/spades-a-new-genome-assembly-algorithm-and-its-applications-35zec7ea02.pdf

SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Methods for analyzing polynucleotide sequences

BACKGROUND & AIMS: Primary sclerosing cholangitis (PSC) is a rare chronic cholestatic liver disease characterized by multi-focal bile duct strictures. To date, underlying molecular mechanisms of PSC remain unclear and therapeutic options for PSC patients are limited. We performed cell-free messenger RNA (cf-mRNA) next generation sequencing (RNA-Seq) to characterize the circulating transcriptome of PSC and non-invasively investigate potentially bioactive signals that are associated with PSC. METHODS: Serum cf-mRNA profiles were compared among 50 individuals with PSC, 20 healthy controls, and 235 individuals with non-alcoholic fatty liver disease (NAFLD). Tissue and cell type-of-origin genes that are dysregulated in subjects with PSC were evaluated. Subsequently, diagnostic classifiers were built using PSC dysregulated cf-mRNA genes. RESULTS: Differential expression analysis of the cf-mRNA transcriptomes of PSC and healthy controls resulted in identification of 1407 dysregulated genes. Furthermore, differentially expressed genes between the liver diseases (PSC, NAFL, Non-Alcohol Steatohepatitis (NASH)) and healthy controls shared common genes known to be involved in liver pathophysiology. In particular, genes from liver- and specific cell type-origin, including hepatocyte, hepatic stellate cells and Kupffer cells, were highly abundant in cf-mRNA of subjects with PSC. Gene cluster analysis revealed that liver-specific genes dysregulated in PSC form a distinct cluster which corresponded to a subset of the PSC subject population. Finally, we developed a cf-mRNA classifier using liver-specific genes which discriminated PSC from healthy control subjects using gene transcripts of liver origin. CONCLUSIONS: Blood-based whole transcriptome cf-mRNA profiling revealed high abundance of liver-specific genes in PSC subject sera which may be used to diagnose PSC patients. We identified several unique cf-mRNA profiles of subjects with PSC. These findings may also have utility for non-invasive molecular stratification of subjects with PSC for pharmacotherapy safety and response studies.

https://doi.org/10.1097/hc9.0000000000000140

Circulating cell-free messenger RNA secretome characterization of primary sclerosing cholangitis

L'invention porte sur un procede de determination precise du nombre de molecules modeles dans une bibliotheque d'acides nucleiques (par exemple ADN) devant etre sequences. Le procede ne necessite pas de grandes quantites de l'echantillon d'ADN, et ne necessite pas non plus la preparation d'une courbe d'etalonnage. Le procede est notamment applicable a des methodologies de « sequencage par synthese », ou la quantification de la bibliotheque de depart est importante. Le procede utilise une PCR en temps reel quantitative, notamment une PCR numerique, qui mesure le nombre de molecules individuelles dans un echantillon. Le present procede peut en particulier utiliser un dispositif microfluidique pour executer de grands nombres de reactions de PCR. Chaque reaction de PCR est surveillee en temps reel par une combinaison amorce/sonde. L'amorce sens est adaptee pour contenir une sequence non sur l'adaptateur mais qui correspond a une sequence de sonde. Une courte sonde qui genere une fluorescence durant le procede de PCR est utilisee.

Étalonnage par pcr numérique pour un séquençage à rendement élevé

Vorrichtung und verfahren zur durchführung von assays und screening mit hohem durchsatz

L'invention porte sur des compositions et des procedes pour determiner simultanement la presence ou l'absence d'une aneuploidie fœtale et la quantite relative d'acides nucleiques fœtaux dans un echantillon obtenu d'une femme enceinte. Le procede englobe l'utilisation de technologies de sequencage et exploite l'apparition de polymorphismes pour fournir un procede non invasif simplifie, pouvant s'appliquer a la pratique des diagnostics prenataux.

Stephen R. Quake

Papers

Methods for analyzing polynucleotide sequences

Circulating cell-free messenger RNA secretome characterization of primary sclerosing cholangitis

Étalonnage par pcr numérique pour un séquençage à rendement élevé

Vorrichtung und verfahren zur durchführung von assays und screening mit hohem durchsatz

Détermination simultanée de l'aneuploïdie et de la fraction fœtale