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

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

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

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)

Evolution of Protein Molecules

Effect of antibiotic use and composting on antibiotic resistance gene abundance and resistome risks of soils receiving manure-derived amendments.

Knowledge of the distribution of N(6)-methyladenosine (m(6)A) is invaluable for understanding RNA biological functions. However, limitation in experimental methods impedes the progress towards the identification of m(6)A site. As a complement of experimental methods, a support vector machine based-method is proposed to identify m(6)A sites in Saccharomyces cerevisiae genome. In this model, RNA sequences are encoded by their nucleotide chemical property and accumulated nucleotide frequency information. It is observed in the jackknife test that the accuracy achieved by the proposed model in identifying the m(6)A site was 78.15%. For the convenience of experimental scientists, a web-server for the proposed model is provided at http://lin.uestc.edu.cn/server/m6Apred.php.

/pdf/identification-and-analysis-of-the-n-6-methyladenosine-in-2x5k1xedkn.pdf

Identification and analysis of the N(6)-methyladenosine in the Saccharomyces cerevisiae transcriptome.

Tandemly arrayed genes (TAGs) are an important genomic component. However, most previous studies have focused on individual TAG families, and a broader characterization of their genomic distribution is not yet available. In this study, we examined the distribution of TAGs in the Arabidopsis thaliana genome and examined TAG density with relation to recombination rates. Recombination rates along A. thaliana chromosomes were estimated by comparing a genetic map with the genome sequence. Average recombination rates in A. thaliana are high, and rates vary more than threefold among chromosomal regions. Comparisons between TAG density and recombination indicate a positive correlation on chromosomes 1, 2, and 3. Moreover, there is a consistent centromeric effect. Relative to single-copy genes, TAGs are proportionally less frequent in centromeres than on chromosomal arms. We also examined several factors that have been proposed to affect the sequence evolution of TAG members. Sequence divergence is related to the number of members in the TAG, but genomic location has no obvious effect on TAG sequence divergence, nor does the presence of unrelated genes within a TAG. Overall, the distribution of TAGs in the genome is not consistent with theoretical models predicting the accumulation of repeats in regions of low recombination but may be consistent with stabilizing selection models of TAG evolution.

/pdf/does-recombination-shape-the-distribution-and-evolution-of-323k0n9awf.pdf

Does recombination shape the distribution and evolution of tandemly arrayed genes (TAGs) in the Arabidopsis thaliana genome

Insertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. With the advance of next-generation sequencing technology, many indel calling tools have been developed; however, evaluation and comparison of these tools using large-scale real data are still scant. Here we evaluated seven popular and publicly available indel calling tools, GATK Unified Genotyper, VarScan, Pindel, SAMtools, Dindel, GTAK HaplotypeCaller, and Platypus, using 78 human genome low-coverage data from the 1000 Genomes project. Comparing indels called by these tools with a known set of indels, we found that Platypus outperforms other tools. In addition, a high percentage of known indels still remain undetected and the number of common indels called by all seven tools is very low. All these findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results.

https://link.springer.com/content/pdf/10.1186%2Fs40246-015-0042-2.pdf

Performance evaluation of indel calling tools using real short-read data

We compared the single-nucleotide polymorphisms (SNPs) in humans in 182 housekeeping and 148 tissue-specific genes. SNPs were divided into rare and common polymorphisms based on their frequencies. We found that housekeeping genes tend to be less polymorphic than tissue-specific genes for both rare and common SNPs. Using mouse as a second species for computing sequence divergences, we found no evidence of positive selection: for both housekeeping and tissue-specific genes, the ratio of nonsynonymous to synonymous common SNPs per site showed no significant difference from that of divergence. Similarly, we observed no evidence of positive selection for the 289 and 149 genes that have orthologs available for divergence calculation between humans and chimpanzees and between humans and Old World monkeys, respectively. A comparison with previous SNP studies suggests that approximately 20% of the nonsynonymous SNPs in the human population are nearly neutral and that positive selection in the human genome might not be as frequent as previously thought.

/pdf/human-snps-reveal-no-evidence-of-frequent-positive-selection-27lnq9qv3j.pdf

Liqing Zhang

Papers

Effect of antibiotic use and composting on antibiotic resistance gene abundance and resistome risks of soils receiving manure-derived amendments.

Identification and analysis of the N(6)-methyladenosine in the Saccharomyces cerevisiae transcriptome.

Does recombination shape the distribution and evolution of tandemly arrayed genes (TAGs) in the Arabidopsis thaliana genome

Performance evaluation of indel calling tools using real short-read data

Human SNPs Reveal No Evidence of Frequent Positive Selection