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

Characteristics derived from mutation and other mechanisms that are advantageous for survival are often preserved during evolution by natural selection. Some genes are conserved in many organisms because they are responsible for fundamental biological function, others are conserved for their unique functional characteristics. Therefore one would expect the rate of molecular evolution for individual genes to be dependent on their biological function. Whether this expectation holds for genes duplicated by whole genome duplication is not known. We empirically demonstrate here, using duplicated genes generated from the Arabidopsis thaliana α-duplication event, that the rate of molecular evolution of genes duplicated in this event depend on biological function. Using functional clustering based on gene ontology annotation of gene pairs, we show that some duplicated genes, such as defense response genes, are under weaker purifying selection or under stronger diversifying selection than other duplicated genes, such as protein translation genes, as measured by the ratio of nonsynonymous to synonymous divergence (dN/dS). These results provide empirical evidence indicating that molecular evolution rate for genes duplicated in whole genome duplication, as measured by dN/dS, may depend on biological function, which we characterize using gene ontology annotation. Furthermore, the general approach used here provides a framework for comparative analysis of molecular evolution rate for genes based on their biological function.

/pdf/functional-bias-in-molecular-evolution-rate-of-arabidopsis-1iyb6zy8tn.pdf

Functional bias in molecular evolution rate of Arabidopsis thaliana

Background
DNA methylation is an important epigenetic mark relevant to normal development and disease genesis. A common approach to characterizing genome-wide DNA methylation is using Next Generation Sequencing technology to sequence bisulfite treated DNA. The short sequence reads are mapped to the reference genome to determine the methylation statuses of Cs. However, despite intense effort, a much smaller proportion of the reads derived from bisulfite treated DNA (usually about 40-80%) can be mapped than regular short reads mapping (> 90%), and it is unclear what factors lead to this low mapping efficiency.

/pdf/investigating-bisulfite-short-read-mapping-failure-with-5b24gjw5vr.pdf

Investigating bisulfite short-read mapping failure with hairpin bisulfite sequencing data

Background
With the development of sequencing technologies, more and more sequence variants are available for investigation. Different classes of variants in the human genome have been identified, including single nucleotide substitutions, insertion and deletion, and large structural variations such as duplications and deletions. Insertion and deletion (indel) variants comprise a major proportion of human genetic variation. However, little is known about their effects on humans. The absence of understanding is largely due to the lack of both biological data and computational resources.

https://link.springer.com/content/pdf/10.1186%2F1471-2105-15-5.pdf

Quantitative prediction of the effect of genetic variation using hidden Markov models.

CRISPR/CAS9 (epi)genome editing revolutionized the field of gene and cell therapy Our previous study demonstrated that a rapid and robust reactivation of the HIV latent reservoir by a catalytically-deficient Cas9 (dCas9)-synergistic activation mediator (SAM) via HIV long terminal repeat (LTR)-specific MS2-mediated single guide RNAs (msgRNAs) directly induces cellular suicide without additional immunotherapy However, potential off-target effect remains a concern for any clinical application of Cas9 genome editing and dCas9 epigenome editing After dCas9 treatment, potential off-target responses have been analyzed through different strategies such as mRNA sequence analysis, and functional screening In this study, a comprehensive analysis of the host transcriptome including mRNA, lncRNA, and alternative splicing was performed using human cell lines expressing dCas9-SAM and HIV-targeting msgRNAs The control scrambled msgRNA (LTR_Zero), and two LTR-specific msgRNAs (LTR_L and LTR_O) groups show very similar expression profiles of the whole transcriptome Among 839 identified lncRNAs, none exhibited significantly different expression in LTR_L vs LTR_Zero group In LTR_O group, only TERC and scaRNA2 lncRNAs were significantly decreased Among 142,791 mRNAs, four genes were differentially expressed in LTR_L vs LTR_Zero group There were 21 genes significantly downregulated in LTR_O vs either LTR_Zero or LTR_L group and one third of them are histone related The distributions of different types of alternative splicing were very similar either within or between groups There were no apparent changes in all the lncRNA and mRNA transcripts between the LTR_L and LTR_Zero groups This is an extremely comprehensive study demonstrating the rare off-target effects of the HIV-specific dCas9-SAM system in human cells This finding is encouraging for the safe application of dCas9-SAM technology to induce target-specific reactivation of latent HIV for an effective “shock-and-kill” strategy

/pdf/comprehensive-off-target-analysis-of-dcas9-sam-mediated-hiv-2wsj4uia23.pdf

Comprehensive off-target analysis of dCas9-SAM-mediated HIV reactivation via long noncoding RNA and mRNA profiling

The molecular mechanisms underlying the development and progression of colorectal cancer (CRC) have not been clarified. The purpose of the present study was to identify key genes that may serve as novel therapeutic targets or prognostic predictors in patients with CRC using bioinformatics analysis. Four gene expression datasets were downloaded from the Gene Expression Omnibus database, which revealed 19 upregulated and 34 downregulated differentially expressed genes (DEGs). The downregulated DEGs were significantly enriched in eight pathways according to Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction network was constructed with 52 DEGs and 458 edges. Ten key genes were identified according to the degree value, betweenness centrality and closeness centrality. Survival analysis revealed that low expression of four of the ten genes, carcinoembryonic antigen related cell adhesion molecule 7 (CEACAM7), solute carrier family 4 member 4 (SLC4A4), glucagon (GCG) and chloride channel accessory 1 (CLCA1) genes, were associated with unfavorable prognosis in CRC. Furthermore, gene set enrichment analysis revealed that two pathways were significantly enriched in the CEACAM7 low-expression group. Thus, CEACAM7, SLC4A4, GCG and CLCA1 may be prognostic markers or therapeutic targets of CRC. Low CEACAM7 expression may be associated with the activation of glycosaminoglycan biosynthesis-chondroitin sulfate and extracellular matrix receptor interaction pathways and may affect the prognosis of CRC.

/pdf/four-targeted-genes-for-predicting-the-prognosis-of-48xmjhodhb.pdf

Liqing Zhang

Papers

Functional bias in molecular evolution rate of Arabidopsis thaliana

Investigating bisulfite short-read mapping failure with hairpin bisulfite sequencing data

Quantitative prediction of the effect of genetic variation using hidden Markov models.

Comprehensive off-target analysis of dCas9-SAM-mediated HIV reactivation via long noncoding RNA and mRNA profiling

Four targeted genes for predicting the prognosis of colorectal cancer: A bioinformatics analysis case