BackgroundResults of phylogenetic analysis are often visualized as phylogenetic trees. Such a tree can typically only include up to a few hundred sequences. When more than a few thousand sequences are to be included, analyzing the phylogenetic relationships among them becomes a challenging task. The recent frequent outbreaks of influenza A viruses have resulted in the rapid accumulation of corresponding genome sequences. Currently, there are more than 7500 influenza A virus genomes in the database. There are no efficient ways of representing this huge data set as a whole, thus preventing a further understanding of the diversity of the influenza A virus genome.ResultsHere we present a new algorithm, "PhyloMap", which combines ordination, vector quantization, and phylogenetic tree construction to give an elegant representation of a large sequence data set. The use of PhyloMap on influenza A virus genome sequences reveals the phylogenetic relationships of the internal genes that cannot be seen when only a subset of sequences are analyzed.ConclusionsThe application of PhyloMap to influenza A virus genome data shows that it is a robust algorithm for analyzing large sequence data sets. It utilizes the entire data set, minimizes bias, and provides intuitive visualization. PhyloMap is implemented in JAVA, and the source code is freely available at http://www.biochem.uni-luebeck.de/public/software/phylomap.html

BackgroundRecent progress in high-throughput technologies has greatly contributed to the development of DNA methylation profiling. Although there are several reports that describe methylome detection of whole genome bisulfite sequencing, the high cost and heavy demand on bioinformatics analysis prevents its extensive application. Thus, current strategies for the study of mammalian DNA methylomes is still based primarily on genome-wide methylated DNA enrichment combined with DNA microarray detection or sequencing. Methylated DNA enrichment is a key step in a microarray based genome-wide methylation profiling study, and even for future high-throughput sequencing based methylome analysis.ResultsIn order to evaluate the sensitivity and accuracy of methylated DNA enrichment, we investigated and optimized a number of important parameters to improve the performance of several enrichment assays, including differential methylation hybridization (DMH), microarray-based methylation assessment of single samples (MMASS), and methylated DNA immunoprecipitation (MeDIP). With advantages and disadvantages unique to each approach, we found that assays based on methylation-sensitive enzyme digestion and those based on immunoprecipitation detected different methylated DNA fragments, indicating that they are complementary in their relative ability to detect methylation differences.ConclusionsOur study provides the first comprehensive evaluation for widely used methodologies for methylated DNA enrichment, and could be helpful for developing a cost effective approach for DNA methylation profiling.

BackgroundThe leaf is an important plant organ, and how it will respond to future global warming is a question that remains unanswered. The effects of experimental warming on leaf photosynthesis and respiration acclimation has been well studied so far, but relatively little information exists on the structural and biochemical responses to warming. However, such information is very important to better understand the plant responses to global warming. Therefore, we grew Arabidopsis thaliana at the three day/night temperatures of 23/18°C (ambient temperature), 25.5/20.5°C (elevated by 2.5°C) and 28/23°C (elevated by 5°C) to simulate the middle and the upper projected warming expected within the 21st century for this purpose.ResultsThe 28/23°C treatment significantly reduced the life span, total biomass and total weight of seeds compared with the other two temperatures. Among the three temperature regimes, the concentrations of starch, chlorophyll, and proline were the lowest at 28/23°C, whereas the total weight of seeds, concentrations of chlorophyll and proline, stomatal density (SD), stomatal conductance (gs), net CO2 assimilation rate (A) and transpiration rate (E) were the highest at 25.5/20.5°C. Furthermore, the number of chloroplasts per cell and mitochondrial size were highest at 25.5/20.5°C and lowest at 28/23°C.ConclusionsThe conditions whereby the temperature was increased by 2.5°C were advantageous for Arabidopsis. However, a rise of 5°C produced negative effects, suggesting that lower levels of warming may benefit plants, especially those which belong to the same functional group as Arabidopsis, whereas higher levels of warming may produce negative affects. In addition, the increase in A under moderately warm conditions may be attributed to the increase in SD, chlorophyll content, and number of chloroplasts. Furthermore, starch accumulation in chloroplasts may be the main factor influencing chloroplast ultrastructure, and elevated temperature regulates plant respiration by probably affecting mitochondrial size. Finally, high SOD and CAT activities may enable plants grown at elevated temperatures to exhibit relatively high tolerance to temperature stress, thus alleviating the harmful effects of superoxide anion radicals and hydrogen peroxide.

BackgroundResults of phylogenetic analysis are often visualized as phylogenetic trees. Such a tree can typically only include up to a few hundred sequences. When more than a few thousand sequences are to be included, analyzing the phylogenetic relationships among them becomes a challenging task. The recent frequent outbreaks of influenza A viruses have resulted in the rapid accumulation of corresponding genome sequences. Currently, there are more than 7500 influenza A virus genomes in the database. There are no efficient ways of representing this huge data set as a whole, thus preventing a further understanding of the diversity of the influenza A virus genome.ResultsHere we present a new algorithm, "PhyloMap", which combines ordination, vector quantization, and phylogenetic tree construction to give an elegant representation of a large sequence data set. The use of PhyloMap on influenza A virus genome sequences reveals the phylogenetic relationships of the internal genes that cannot be seen when only a subset of sequences are analyzed.ConclusionsThe application of PhyloMap to influenza A virus genome data shows that it is a robust algorithm for analyzing large sequence data sets. It utilizes the entire data set, minimizes bias, and provides intuitive visualization. PhyloMap is implemented in JAVA, and the source code is freely available at http://www.biochem.uni-luebeck.de/public/software/phylomap.html

BackgroundRecent progress in high-throughput technologies has greatly contributed to the development of DNA methylation profiling. Although there are several reports that describe methylome detection of whole genome bisulfite sequencing, the high cost and heavy demand on bioinformatics analysis prevents its extensive application. Thus, current strategies for the study of mammalian DNA methylomes is still based primarily on genome-wide methylated DNA enrichment combined with DNA microarray detection or sequencing. Methylated DNA enrichment is a key step in a microarray based genome-wide methylation profiling study, and even for future high-throughput sequencing based methylome analysis.ResultsIn order to evaluate the sensitivity and accuracy of methylated DNA enrichment, we investigated and optimized a number of important parameters to improve the performance of several enrichment assays, including differential methylation hybridization (DMH), microarray-based methylation assessment of single samples (MMASS), and methylated DNA immunoprecipitation (MeDIP). With advantages and disadvantages unique to each approach, we found that assays based on methylation-sensitive enzyme digestion and those based on immunoprecipitation detected different methylated DNA fragments, indicating that they are complementary in their relative ability to detect methylation differences.ConclusionsOur study provides the first comprehensive evaluation for widely used methodologies for methylated DNA enrichment, and could be helpful for developing a cost effective approach for DNA methylation profiling.