SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip

Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes

BACKGROUND

Since the development of transcriptome analysis systems, many expression evolution studies characterized evolutionary forces acting on gene expression, without explicit discrimination between global expression differences and tissue specific expression differences. However, different types of gene expression alteration should have different effects on an organism, the evolutionary forces that act on them might be different, and different types of genes might show different types of differential expression between species. To confirm this, we studied differentially expressed (DE) genes among closely related groups that have extensive gene expression atlases, and clarified characteristics of different types of DE genes including the identification of regulating loci for differential expression using expression quantitative loci (eQTL) analysis data.

RESULTS

We detected differentially expressed (DE) genes between rice subspecies in five homologous tissues that were verified using japonica and indica transcriptome atlases in public databases. Using the transcriptome atlases, we classified DE genes into two types, global DE genes and changed-tissues DE genes. Global type DE genes were not expressed in any tissues in the atlas of one subspecies, however changed-tissues type DE genes were expressed in both subspecies with different tissue specificity. For the five tissues in the two japonica-indica combinations, 4.6 ± 0.8 and 5.9 ± 1.5 % of highly expressed genes were global and changed-tissues DE genes, respectively. Changed-tissues DE genes varied in number between tissues, increasing linearly with the abundance of tissue specifically expressed genes in the tissue. Molecular evolution of global DE genes was rapid, unlike that of changed-tissues DE genes. Based on gene ontology, global and changed-tissues DE genes were different, having no common GO terms. Expression differences of most global DE genes were regulated by cis-eQTLs. Expression evolution of changed-tissues DE genes was rapid in tissue specifically expressed genes and those rapidly evolved changed-tissues DE genes were regulated not by cis-eQTLs, but by complicated trans-eQTLs.

CONCLUSIONS

Global DE genes and changed-tissues DE genes had contrasting characteristics. The two contrasting types of DE genes provide possible explanations for the previous controversial conclusions about the relationships between molecular evolution and expression evolution of genes in different species, and the relationship between expression breadth and expression conservation in evolution.

DNA barcoding, microarrays and next generation sequencing: recent tools for genetic diversity estimation and authentication of medicinal plants

DNA barcoding, microarray technology and next generation sequencing have emerged as promising tools for the elucidation of plant genetic diversity and its conservation. They are proving to be immensely helpful in authenticating the useful medicinal plants for herbal drug preparations. These newer versions of molecular markers utilize short genetic markers in the genome to characterize the organism to a particular species. This has the potential not only to classify the known and yet unknown species but also has a promising future to link the medicinally important plants according to their properties. The newer trends being followed in DNA chips and barcoding pave the way for a future with many different possibilities. Several of these possibilities might be: characterization of unknown species in a considerably less time than usual, identification of newer medicinal properties possessed by the species and also updating the data of the already existing but unnoticed properties. This can assist us to cure many different diseases and will also generate novel opportunities in medicinal drug delivery and targeting.

'maskBAD'--a package to detect and remove Affymetrix probes with binding affinity differences

BACKGROUND

Hybridization differences caused by target sequence differences can be a confounding factor in analyzing gene expression on microarrays, lead to false positives and reduce power to detect real expression differences. We prepared an R Bioconductor compatible package to detect, characterize and remove such probes in Affymetrix 3'IVT and exon-based arrays on the basis of correlation of signal intensities from probes within probe sets.

RESULTS

Using completely mouse genomes we determined type 1 (false negatives) and type 2 (false positives) errors with high accuracy and we show that our method routinely outperforms previous methods. When detecting 76.2% of known SNP/indels in mouse expression data, we obtain at most 5.5% false positives. At the same level of false positives, best previous method detected 72.6%. We also show that probes with differing binding affinity both hinder differential expression detection and introduce artifacts in cancer-healthy tissue comparison.

CONCLUSIONS

Detection and removal of such probes should be a routine step in Affymetrix data preprocessing. We prepared a user friendly R package, compatible with Bioconductor, that allows the filtering and improving of data from Affymetrix microarrays experiments.

Intraspecific variation in the Populus balsamifera drought transcriptome

Drought is a major limitation to the growth and productivity of trees in the ecologically and economically important genus Populus. The ability of Populus trees to contend with drought is a function of genome responsiveness to this environmental insult, involving reconfiguration of the transcriptome to appropriately remodel growth, development and metabolism. Here we test hypotheses aimed at examining the extent of intraspecific variation in the drought transcriptome using six different Populus balsamifera L. genotypes and Affymetrix GeneChip technology. Within a given genotype there was a positive correlation between the magnitude of water-deficit induced changes in transcript abundance across the transcriptome, and the capacity of that genotype to maintain growth following water deficit. Genotypes that had more similar drought-responsive transcriptomes also had fewer genotypic differences, as determined by microarray-derived single feature polymorphism (SFP) analysis, suggesting that responses may be conserved across individuals that share a greater degree of genotypic similarity. This work highlights the fact that a core species-level response can be defined; however, the underpinning genotype-derived complexities of the drought response in Populus must be taken into consideration when defining both species- and genus-level responses.

A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression arrays

Background

High-density oligonucleotide arrays are effective tools for genotyping numerous loci simultaneously. In small genome species (genome size: < ~300 Mb), whole-genome DNA hybridization to expression arrays has been used for various applications. In large genome species, transcript hybridization to expression arrays has been used for genotyping. Although rice is a fully sequenced model plant of medium genome size (~400 Mb), there are a few examples of the use of rice oligonucleotide array as a genotyping tool.

Results

We compared the single feature polymorphism (SFP) detection performance of whole-genome and transcript hybridizations using the Affymetrix GeneChip^® Rice Genome Array, using the rice cultivars with full genome sequence, japonica cultivar Nipponbare and indica cultivar 93-11. Both genomes were surveyed for all probe target sequences. Only completely matched 25-mer single copy probes of the Nipponbare genome were extracted, and SFPs between them and 93-11 sequences were predicted. We investigated optimum conditions for SFP detection in both whole genome and transcript hybridization using differences between perfect match and mismatch probe intensities of non-polymorphic targets, assuming that these differences are representative of those between mismatch and perfect targets. Several statistical methods of SFP detection by whole-genome hybridization were compared under the optimized conditions. Causes of false positives and negatives in SFP detection in both types of hybridization were investigated.

Conclusions

The optimizations allowed a more than 20% increase in true SFP detection in whole-genome hybridization and a large improvement of SFP detection performance in transcript hybridization. Significance analysis of the microarray for log-transformed raw intensities of PM probes gave the best performance in whole genome hybridization, and 22,936 true SFPs were detected with 23.58% false positives by whole genome hybridization. For transcript hybridization, stable SFP detection was achieved for highly expressed genes, and about 3,500 SFPs were detected at a high sensitivity (> 50%) in both shoot and young panicle transcripts. High SFP detection performances of both genome and transcript hybridizations indicated that microarrays of a complex genome (e.g., of Oryza sativa) can be effectively utilized for whole genome genotyping to conduct mutant mapping and analysis of quantitative traits such as gene expression levels.