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.

Exact pattern matching aims to locate all occurrences of a pattern in a text. Many algorithms have been proposed, but two algorithms, the Knuth-Morris-Pratt (KMP) and the Boyer-Moore (BM), are most widespread. It is the basis of some approximate string matching algorithms like BLAST, and in many cases it is desirable to locate an exact rather than approximate matches. Although several studies included measures with small alphabets, none of them specifically designed an algorithm to target nucleotide sequences. Since there are also no application programming interfaces available for pattern matching in nucleotide sequences, these two issues were aimed to be resolved. A portion of the Chlamydomonas reinhardtii genome (30 mega bases) was searched with queries ranging from 10 to 2000 nucleotides and an alternating number of matches between one and 25000. The results indicate that the use of two of the algorithms developed in this study is sufficient to efficiently cover the complete search space as presented in the experiment conducted here. Thus the aim of implementing an algorithm specifically targeting pattern matching in nucleotide sequences and making it available to the general public as an advanced programming interface was achieved. All algorithms are freely available at: http://bioinformatics.iyte.edu.tr/supplements/peerj/. PeerJ PrePrints | https://doi.org/10.7287/peerj.preprints.1758v1 | CC-BY 4.0 Open Access | rec: 19 Feb 2016, publ: 19 Feb 2016

Exact pattern matching: Adapting the Boyer-Moore algorithm for DNA searches

Proteins define phenotypes and their dysregulation leads to diseases. Post-translational regulation of protein abundance can be achieved by microRNAs (miRNAs). Therefore studying this method of gene regulation is of high importance. MicroRNAs interact with their target messenger RNA via hybridization within a specialized molecular framework. Many miRNAs and their targets have been identified and they are listed in various databases like miRTarBase. The experimental identification of functional miRNA-mRNA pairs is difficult and, therefore, they are detected computationally which is complicated due to missing negative data. Machine learning has been used for miRNA and target detection and many features have been described for miRNAs and miRNA:mRNA target duplexes generally on a per species basis. However, many claims of cross-kingdom regulation via miRNAs have been made and, therefore, we were interested whether it is possible to differentiate among species based on the target sequence in the mRNA alone. Thus, we investigated whether miRNA targets sites within the 3’UTR can be differentiated between species based on k-mer features only. Target information of one species was used as positive examples and the others as negative ones to establish machine learning models. It was observed that few features were sufficient for successful categorization of mircoRNA targets to species. For example mouse versus Caenorhabditis elegans reached up to 97% average accuracy over 100 fold cross validation. The simplicity of the approach, based on just k-mers, is promising for automatic categorization systems. In the future, this approach will help scrutinize alleged cross-kingdom regulation via miRNAs in respect to miRNA from one species targeting mRNAs in another.

Species categorization via MicroRNAs based on 3’UTR target sites using sequence features

Boron is an essential plant micronutrient; but is toxic at high concentrations. Boron toxicity can severely affect crop productivity in arid and semi-arid environments. Puccinellia distans (Jacq.) Parl., common alkali grass, is found throughout the world and can survive under boron concentrations that are lethal for other plant species. In addition, P. distans can accumulate very high levels of this element. Despite these interesting features, very little research has been performed to elucidate the boron tolerance mechanism in this species. In this study, P. distans samples were analyzed by RNA sequencing to identify genes related to boron tolerance and hyperaccumulation. Abundance levels of selected differentially expressed transcripts were validated by real-time PCR. The results indicated that the hyperaccumulation mechanism of P. distans involves many transcriptomic changes including those that lead to: alterations in the malate pathway, changes in cell wall components that allow sequestration of excess boron without toxic effects, and increased expression of at least one putative boron transporter and two putative aquaporins. Elucidation of the boron accumulation mechanism is important to develop approaches for bioremediation of boron contaminated soils.

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Boron Hyperaccumulation Mechanisms in Puccinellia distans as Revealed by Transcriptomic Analysis

MicroRNAs (miRNAs) are small non-coding RNA sequences that have been implicated in many physiological processes. Furthermore, miRNAs have been shown to be important biomarkers for diseases and their mimics are tested as drug candidates. The experimental discovery of miRNAs is complicated because both miRNAs and their targets need to be expressed for the confirmation of functional interaction. This is difficult since miRNA expression is under spatiotemporal control. This has motivated the development of computational methods for miRNA detection. Such computational methods typically involve the characterization of candidate sequences with features designed by domain experts and the application of statistical or machine learning algorithms. While such features can successfully encode domain knowledge, feature engineering is a difficult and time consuming task. Additionally, some engineered features pose excessive computational complexity that can hinder the large scale detection of miRNA. In contrast, advances of representation learning methods such as deep learning provide for automatic development of effective features directly from data. In this work, we propose a method that uses domain knowledge to create an efficient image representation of miRNA molecules encoding sequence, structure, and implicitly some thermodynamic information. We then use this low-level feature representation of the molecules to develop a hierarchical deep representation using a convolutional neural network model, which directly detects precursor miRNAs. With this method we achieve state-of-the-art performance on all previously used datasets. Additionally, detection is achieved in real time thereby overcoming the high computational cost for current pre-miRNA feature calculations such as p-value based ones. Finally, the encoding and modeling process opens possibilities for interpretability of the models9 behavior, which may lead to novel biological interpretations of miRNA genesis and targeting.

https://www.biorxiv.org/content/biorxiv/early/2020/04/25/840579.full.pdf

Detection of pre-microRNA with Convolutional Neural Networks

Big data and complex analysis workflows (pipelines) are common issues in data driven science such as bioinformatics. Large amounts of computational tools are available for data analysis. Additionally, many workflow management systems to piece together such tools into data analysis pipelines have been developed. For example, more than 50 computational tools for read mapping are available representing a large amount of duplicated effort. Furthermore, it is unclear whether these tools are correct and only a few have a user base large enough to have encountered and reported most of the potential problems. Bringing together many largely untested tools in a computational pipeline must lead to unpredictable results. Yet, this is the current state. While presently data analysis is performed on personal computers/workstations/clusters, the future will see development and analysis shift to the cloud. None of the workflow management systems is ready for this transition. This presents the opportunity to build a new system, which will overcome current duplications of effort, introduce proper testing, allow for development and analysis in public and private clouds, and include reporting features leading to interactive documents.

/pdf/towards-an-internet-of-science-u1mqo6uw4z.pdf

Jens Allmer

Papers

Exact pattern matching: Adapting the Boyer-Moore algorithm for DNA searches

Species categorization via MicroRNAs based on 3’UTR target sites using sequence features

Boron Hyperaccumulation Mechanisms in Puccinellia distans as Revealed by Transcriptomic Analysis

Detection of pre-microRNA with Convolutional Neural Networks

Towards an Internet of Science.