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 number of sequenced plant genomes and associated genomic resources is growing rapidly with the advent of both an increased focus on plant genomics from funding agencies, and the application of inexpensive next generation sequencing. To interact with this increasing body of data, we have developed Phytozome (http://www.phytozome.net), a comparative hub for plant genome and gene family data and analysis. Phytozome provides a view of the evolutionary history of every plant gene at the level of sequence, gene structure, gene family and genome organization, while at the same time providing access to the sequences and functional annotations of a growing number (currently 25) of complete plant genomes, including all the land plants and selected algae sequenced at the Joint Genome Institute, as well as selected species sequenced elsewhere. Through a comprehensive plant genome database and web portal, these data and analyses are available to the broader plant science research community, providing powerful comparative genomics tools that help to link model systems with other plants of economic and ecological importance.

/pdf/phytozome-a-comparative-platform-for-green-plant-genomics-1ezqiu8w9i.pdf

Phytozome: a comparative platform for green plant genomics

Microalgae represent an exceptionally diverse but highly specialized group of micro-organisms adapted to various ecological habitats. Many microalgae have the ability to produce substantial amounts (e.g. 20-50% dry cell weight) of triacylglycerols (TAG) as a storage lipid under photo-oxidative stress or other adverse environmental conditions. Fatty acids, the building blocks for TAGs and all other cellular lipids, are synthesized in the chloroplast using a single set of enzymes, of which acetyl CoA carboxylase (ACCase) is key in regulating fatty acid synthesis rates. However, the expression of genes involved in fatty acid synthesis is poorly understood in microalgae. Synthesis and sequestration of TAG into cytosolic lipid bodies appear to be a protective mechanism by which algal cells cope with stress conditions, but little is known about regulation of TAG formation at the molecular and cellular level. While the concept of using microalgae as an alternative and renewable source of lipid-rich biomass feedstock for biofuels has been explored over the past few decades, a scalable, commercially viable system has yet to emerge. Today, the production of algal oil is primarily confined to high-value specialty oils with nutritional value, rather than commodity oils for biofuel. This review provides a brief summary of the current knowledge on oleaginous algae and their fatty acid and TAG biosynthesis, algal model systems and genomic approaches to a better understanding of TAG production, and a historical perspective and path forward for microalgae-based biofuel research and commercialization.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1365-313X.2008.03492.x

Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances

The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.

https://www.springer.com/cda/content/document/cda_downloaddocument/BioEnergyResearch_Second+Generation+Biofuels+High-Efficiency+Microalgae+for+Biodiesel+Production.pdf?SGWID=0-0-45-1347846-0

Second generation biofuels: high-efficiency microalgae for biodiesel production

Microalgae are considered one of the most promising feedstocks for biofuels. The productivity of these photosynthetic microorganisms in converting carbon dioxide into carbon-rich lipids, only a step or two away from biodiesel, greatly exceeds that of agricultural oleaginous crops, without competing for arable land. Worldwide, research and demonstration programs are being carried out to develop the technology needed to expand algal lipid production from a craft to a major industrial process. Although microalgae are not yet produced at large scale for bulk applications, recent advances—particularly in the methods of systems biology, genetic engineering, and biorefining—present opportunities to develop this process in a sustainable and economical way within the next 10 to 15 years.

https://science.sciencemag.org/content/sci/329/5993/796.full.pdf

An outlook on microalgal biofuels.

MicroRNAs (miRNAs) have attracted heightened attention for their role as post-transcriptional regulators ofgene expression. It has become clear that miRNAs can both up- and downregulate protein expression. According to currentestimates, most human genes are harboring miRNAs and/or are regulated by them. Thus miRNAs form a complexnetwork of expression regulation which tightly interacts with known gene regulatory networks. Similar to some transcriptionfactors, some miRNAs can have hundreds of target transcripts whose expression they modulate. Thus miRNAs canform complex regulatory networks by themselves, but because their expression is often tightly coordinated with gene expression,they form an intertwined regulatory network with many possible interactions among gene and miRNA regulatorypathways. In this review we first consider gene regulatory networks. Then we discuss microRNAs and their implicationin cancer and how they may form regulatory networks. Finally, we give our perspective and provide an outlook includingthe aspect of personalized medicine.

/pdf/intersection-of-microrna-and-gene-regulatory-networks-and-283rwlywyj.pdf

Intersection of MicroRNA and Gene Regulatory Networks and their Implication in Cancer

Traditionally, machine learning algorithms build classification models from positive and negative examples. Recently, one-class classification (OCC) receives increasing attention in machine learning for problems where the negative class cannot be defined unambiguously. This is specifically problematic in bioinformatics since for some important biological problems the target class (positive class) is easy to obtain while the negative one cannot be measured. Artificially generating the negative class data can be based on unreliable assumptions. Several studies have applied two-class machine learning to predict microRNAs (miRNAs) and their target. Different approaches for the generation of an artificial negative class have been applied, but may lead to a biased performance estimate. Feature selection has been well studied for the twoâclass classification problem, while fewer methods are available for feature selection in respect to OCC. In this study, we present a feature selection approach for applying one-class classification to the prediction of miRNA targets. A comparison between one-class and two-class approaches is presented to highlight that their performance are similar while one-class classification is not based on questionable artificial data for training and performance evaluation. We further show that the feature selection method we tried works to a degree, but needs improvement in the future. Perhaps it could be combined with other approaches.

/pdf/feature-selection-for-microrna-target-prediction-1dbj0kke8b.pdf

Feature Selection for MicroRNA Target Prediction

The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed. In order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application. We present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.

https://link.springer.com/content/pdf/10.1186%2F1471-2105-9-302.pdf

2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments.

MicroRNAs (miRNAs) are small RNA molecules which are known to take part in post-transcriptional regulation of gene expression. Here, VANESA, an existing platform for reconstructing, visualizing, and analysis of large biological networks, has been further expanded to include all experimentally validated human miRNAs available within miRBase, TarBase and miRTarBase. This is done by integrating a custom hybrid miRNA database to DAWIS-M.D., VANESA's main data source, enabling the visualization and analysis of miRNAs within large biological pathways such as those found within the Kyoto Encyclopedia of Genes and Genomes (KEGG). Interestingly, 99.15 % of human KEGG pathways either contain genes which are targeted by miRNAs or harbor them. This is mainly due to the high number of interaction partners that each miRNA could have (e.g.: hsa-miR-335-5p targets 2544 genes and 71 miRNAs target NUFIP2). We demonstrate the usability of our system by analyzing the measles virus KEGG pathway as a proof-of-principle model and further highlight the importance of integrating miRNAs (both experimentally validated and predicted) into biological networks for the elucidation of novel miRNA-mRNA interactions of biological importance.

https://www.degruyter.com/downloadpdf/j/jib.2017.14.issue-1/jib-2016-0004/jib-2016-0004.xml

Visualization and Analysis of MicroRNAs within KEGG Pathways using VANESA.

European hazelnut (Corylus avellana) is a diploid tree species and is widely used in confections. Hazelnuts are, to a large part, produced in Turkey with the cultivar “Tombul” widely grown in the Black Sea region. In this work, the “Tombul” genome was partially sequenced by next-generation sequencing technology yielding 29.2% (111.85 Mb) of the ~ 385 Mb (1C). This sequence information was used to develop genetic markers in order to enable differentiation of material before the long maturation process and to facilitate future breeding strategies. A total of 90,142 simple sequence repeats (SSRs) were identified in the contigs giving a frequency of 1 SSR per 1240 nt in the assembly. Mononucleotides were the most abundant SSR marker type (60.9%) followed by di- and trinucleotides. Primer pairs were designed for 75,139 (83.3%) of the SSRs. Fifty SSR primers were applied to 47 hazelnut accessions from nine countries to test their effectiveness and polymorphism. The markers amplified an average of 3.2 fragments. The highest polymorphism information content value was for cavSSR11062 (0.97) and the lowest (0.04) was for cavSSR13386. Two markers were monomorphic: cavSSR12855 and cavSSR13267. Single-copy SSR primers were also assessed for their ability to discriminate 19 Turkish cultivars, and it was found that seven primer pairs (Cav4217, Cav14875, Cav14418, Cav2704, Cav12862, Cav3909, Cav1361) were sufficient for this task. Thus, this study developed new SSR markers for use in hazelnut breeding and genetic studies and also provide a method to distinguish and identify true-type Turkish cultivars.

Development of Simple Sequence Repeat Markers in Hazelnut (Corylus avellana L.) by Next-Generation Sequencing and Discrimination of Turkish Hazelnut Cultivars

Jens Allmer

Papers

Intersection of MicroRNA and Gene Regulatory Networks and their Implication in Cancer

Feature Selection for MicroRNA Target Prediction

2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments.

Visualization and Analysis of MicroRNAs within KEGG Pathways using VANESA.

Development of Simple Sequence Repeat Markers in Hazelnut (Corylus avellana L.) by Next-Generation Sequencing and Discrimination of Turkish Hazelnut Cultivars