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

Leaf image based cucumber disease recognition using sparse representation classification

Growing concerns regarding increasing rates of antibiotic resistance call for global monitoring efforts. Monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is of particular interest as these media can serve as sources of potential novel antibiotic resistance genes (ARGs), as hot spots for ARG exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequence-based monitoring has recently enabled direct access and profiling of the total metagenomic DNA pool, where ARGs are identified or predicted based on the best hits of homology searches against existing databases. Unfortunately, this approach tends to produce high rates of false negatives. To address such limitations, we propose here a deep leaning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two models, deepARG-SS and deepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Performance evaluation of the deep learning models over 30 classes of antibiotics demonstrates that the deepARG models can predict ARGs with both high precision (>0.97) and recall (>0.90) for most of the antibiotic resistance categories. The models show advantage over the traditional best hit approach by having consistently much lower false negative rates and thus higher overall recall (>0.9). As more data become available for under-represented antibiotic resistance categories, the deepARG models performance can be expected to be further enhanced due to the nature of the underlying neural networks. The deepARG models are available both in command line version and via a Web server at http://bench.cs.vt.edu/deeparg. Our newly developed ARG database, deepARG-DB, containing predicted ARGs with high confidence and high degree of manual curation, greatly expands the current ARG repository. DeepARG-DB can be downloaded freely to benefit community research and future development of antibiotic resistance-related resources.

/pdf/deeparg-a-deep-learning-approach-for-predicting-antibiotic-297o7uu4ur.pdf

DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

We characterized rates and patterns of synonymous and nonsynonymous substitution in 242 duplicated gene pairs on chromosomes 2 and 4 of Arabidopsis thaliana. Based on their collinear order along the two chromosomes, the gene pairs were likely duplicated contemporaneously, and therefore comparison of genetic distances among gene pairs provides insights into the distribution of nucleotide substitution rates among plant nuclear genes. Rates of synonymous substitution varied 13.8-fold among the duplicated gene pairs, but 90% of gene pairs differed by less than 2.6-fold. Average nonsynonymous rates were approximately fivefold lower than average synonymous rates; this rate difference is lower than that of previously studied nonplant lineages. The coefficient of variation of rates among genes was 0.65 for nonsynonymous rates and 0.44 for synonymous rates, indicating that synonymous and nonsynonymous rates vary among genes to roughly the same extent. The causes underlying rate variation were explored. Our analyses tentatively suggest an effect of physical location on synonymous substitution rates but no similar effect on nonsynonymous rates. Nonsynonymous substitution rates were negatively correlated with GC content at synonymous third codon positions, and synonymous substitution rates were negatively correlated with codon bias, as observed in other systems. Finally, the 242 gene pairs permitted investigation of the processes underlying divergence between paralogs. We found no evidence of positive selection, little evidence that paralogs evolve at different rates, and no evidence of differential codon usage or third position GC content.

Patterns of Nucleotide Substitution Among Simultaneously Duplicated Gene Pairs in Arabidopsis thaliana

We analyzed the completed human genome for recent segmental duplications (size > or = 1 kb and sequence similarity > or = 90%) We found that approximately 4% of the genome is covered by duplications and that the extent of segmental duplication varies from 1% to 14% among the 24 chromosomes Intrachromosomal duplication is more frequent than interchromosomal duplication in 15 chromosomes The duplication frequencies in pericentromeric and subtelomeric regions are greater than the genome average by approximately threefold and fourfold We examined factors that may affect the frequency of duplication in a region Within individual chromosomes, the duplication frequency shows little correlation with local gene density, repeat density, recombination rate, and GC content, except chromosomes 7 and Y For the entire genome, the duplication frequency is correlated with each of the above factors Based on known genes and Ensembl genes, the proportion of duplications containing complete genes is 34% and 107%, respectively The proportion of duplications containing genes is higher in intrachromosomal than in interchromosomal duplications, and duplications containing genes have a higher sequence similarity and tend to be longer than duplications containing no genes Our simulation suggests that many duplications containing genes have been selectively maintained in the genome

https://ir.nctu.edu.tw:443/bitstream/11536/25344/1/000225730100014.pdf

Patterns of Segmental Duplication in the Human Genome

Ortholog assignment is a critical and fundamental problem in comparative genomics, since orthologs are considered to be functional counterparts in different species and can be used to infer molecular functions of one species from those of other species MSOAR is a recently developed high-throughput system for assigning one-to-one orthologs between closely related species on a genome scale It attempts to reconstruct the evolutionary history of input genomes in terms of genome rearrangement and gene duplication events It assumes that a gene duplication event inserts a duplicated gene into the genome of interest at a random location (ie, the random duplication model) However, in practice, biologists believe that genes are often duplicated by tandem duplications, where a duplicated gene is located next to the original copy (ie, the tandem duplication model) In this paper, we develop MSOAR 20, an improved system for one-to-one ortholog assignment For a pair of input genomes, the system first focuses on the tandemly duplicated genes of each genome and tries to identify among them those that were duplicated after the speciation (ie, the so-called inparalogs), using a simple phylogenetic tree reconciliation method For each such set of tandemly duplicated inparalogs, all but one gene will be deleted from the concerned genome (because they cannot possibly appear in any one-to-one ortholog pairs), and MSOAR is invoked Using both simulated and real data experiments, we show that MSOAR 20 is able to achieve a better sensitivity and specificity than MSOAR In comparison with the well-known genome-scale ortholog assignment tool InParanoid, Ensembl ortholog database, and the orthology information extracted from the well-known whole-genome multiple alignment program MultiZ, MSOAR 20 shows the highest sensitivity Although the specificity of MSOAR 20 is slightly worse than that of InParanoid in the real data experiments, it is actually better than that of InParanoid in the simulation tests Our preliminary experimental results demonstrate that MSOAR 20 is a highly accurate tool for one-to-one ortholog assignment between closely related genomes The software is available to the public for free and included as online supplementary material

https://www.cs.ucr.edu/~jiang/nsf/shi-csb.pdf

Liqing Zhang

Papers

Leaf image based cucumber disease recognition using sparse representation classification

DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

Patterns of Nucleotide Substitution Among Simultaneously Duplicated Gene Pairs in Arabidopsis thaliana

Patterns of Segmental Duplication in the Human Genome

MSOAR 2.0: Incorporating tandem duplications into ortholog assignment based on genome rearrangement