jMorp: Japanese Multi-Omics Reference Panel update report 2023.

Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a public database for plasma metabolome and proteome analyses and has been continuously updated. The current update will significantly expand the scale of the data (metabolome, genome, transcriptome, and metagenome). In addition, the user interface and backend server implementations were rewritten to improve the connectivity between the items stored in jMorp. This paper provides an overview of the new version of the jMorp.

Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.

Widely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use haplotype information in a reference-free manner by handling it as model parameters, while maintaining comparable imputation accuracy to methods based on the Li and Stephens model. Here, we provide a brief introduction to DL-based reference-free genotype imputation methods, including RNN-IMP, developed by our research group. We then evaluate the performance of RNN-IMP against widely-used Li and Stephens model-based imputation methods in terms of accuracy (R2), using the 1000 Genomes Project Phase 3 dataset and corresponding simulated Omni2.5 SNP genotype data. Although RNN-IMP is sensitive to missing values in input genotypes, we propose a two-stage imputation strategy: missing genotypes are first imputed using denoising autoencoders; RNN-IMP then processes these imputed genotypes. This approach restores the imputation accuracy that is degraded by missing values, enhancing the practical use of RNN-IMP.

Functional Characterization of 12 Dihydropyrimidinase Allelic Variants in Japanese Individuals for the Prediction of 5-Fluorouracil Treatment-Related Toxicity.

The drug 5-fluorouracil (5-FU) is the first-choice chemotherapeutic agent against advanced-stage cancers. However, 10% to 30% of treated patients experience grade 3 to 4 toxicity. The deficiency of dihydropyrimidinase (DHPase), which catalyzes the second step of the 5-FU degradation pathway, is correlated with the risk of developing toxicity. Thus, genetic polymorphisms within DPYS, the DHPase-encoding gene, could potentially serve as predictors of severe 5-FU-related toxicity. We identified 12 novel DPYS variants in 3554 Japanese individuals, but the effects of these mutations on function remain unknown. In the current study, we performed in vitro enzymatic analyses of the 12 newly identified DHPase variants. Dihydrouracil or dihydro-5-FU hydrolytic ring-opening kinetic parameters, Km and Vmax , and intrinsic clearance (CLint = Vmax /Km ) of the wild-type DHPase and eight variants were measured. Five of these variants (R118Q, H295R, T418I, Y448H, and T513A) showed significantly reduced CLint compared with that in the wild-type. The parameters for the remaining four variants (V59F, D81H, T136M, and R490H) could not be determined as dihydrouracil and dihydro-5-FU hydrolytic ring-opening activity was undetectable. We also determined DHPase variant protein stability using cycloheximide and bortezomib. The mechanism underlying the observed changes in the kinetic parameters was clarified using blue-native polyacrylamide gel electrophoresis and three-dimensional structural modeling. The results suggested that the decrease or loss of DHPase enzymatic activity was due to reduced stability and oligomerization of DHPase variant proteins. Our findings support the use of DPYS polymorphisms as novel pharmacogenomic markers for predicting severe 5-FU-related toxicity in the Japanese population. SIGNIFICANCE STATEMENT: DHPase contributes to the degradation of 5-fluorouracil, and genetic polymorphisms that cause decreased activity of DHPase can cause severe toxicity. In this study, we performed functional analysis of 12 DHPase variants in the Japanese population and identified 9 genetic polymorphisms that cause reduced DHPase function. In addition, we found that the ability to oligomerize and the conformation of the active site are important for the enzymatic activity of DHPase.

Functional Characterization of 29 Cytochrome P450 4F2 Variants Identified in a Population of 8380 Japanese Subjects and Assessment of Arachidonic Acid ω-Hydroxylation.

Cytochrome P450 4F2 (CYP4F2) is an enzyme that is involved in the metabolism of arachidonic acid (AA), vitamin E and K, and xenobiotics including drugs. CYP4F2*3 polymorphism (rs2108622; c.1297G>A; p.Val433Met) has been associated with hypertension, ischemic stroke, and variation in the effectiveness of the anticoagulant drug warfarin. In this study, we characterized wild-type CYP4F2 and 28 CYP4F2 variants, including a Val433Met substitution, detected in 8380 Japanese subjects. The CYP4F2 variants were heterologously expressed in 293FT cells to measure the concentrations of CYP4F2 variant holoenzymes using carbon monoxide-reduced difference spectroscopy, where the wild type and 18 holoenzyme variants showed a peak at 450 nm. Kinetic parameters [Vmax , substrate concentration producing half of Vmax (S50 ), and intrinsic clearance (CL int ) as Vmax /S50 ] of AA ω-hydroxylation were determined for the wild type and 21 variants with enzyme activity. Compared with the wild type, two variants showed significantly decreased CL int values for AA ω-hydroxylation. The values for seven variants could not be determined because no enzymatic activity was detected at the highest substrate concentration used. Three-dimensional structural modeling was performed to determine the reason for reduced enzymatic activity of the CYP4F2 variants. Our findings contribute to a better understanding of CYP4F2 variant-associated diseases and possible future therapeutic strategies. SIGNIFICANCE STATEMENT: CYP4F2 is involved in the metabolism of arachidonic acid and vitamin K, and CYP4F2*3 polymorphisms have been associated with hypertension and variation in the effectiveness of the anticoagulant drug warfarin. This study presents a functional analysis of 28 CYP4F2 variants identified in Japanese subjects, demonstrating that seven gene polymorphisms cause loss of CYP4F2 function, and proposes structural changes that lead to altered function.

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population.

In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

Functional Characterization of 40 CYP3A4 Variants by Assessing Midazolam 1'-Hydroxylation and Testosterone 6ß-Hydroxylation.

CYP3A4 is among the most abundant liver and intestinal drug-metabolizing cytochrome P450 enzymes, contributing to the metabolism of more than 30% of clinically used drugs. Therefore, interindividual variability in CYP3A4 activity is a frequent cause of reduced drug efficacy and adverse effects. In this study, we characterized wild-type CYP3A4 and 40 CYP3A4 variants, including 11 new variants, detected among 4773 Japanese individuals by assessing CYP3A4 enzymatic activities for two representative substrates (midazolam and testosterone). The reduced carbon monoxide-difference spectra of wild-type CYP3A4 and 31 CYP3A4 variants produced with our established mammalian cell expression system were determined by measuring the increase in maximum absorption at 450 nm after carbon monoxide treatment. The kinetic parameters of midazolam and testosterone hydroxylation by wild-type CYP3A4 and 29 CYP3A4 variants (K m , k cat , and catalytic efficiency) were determined, and the causes of their kinetic differences were evaluated by three-dimensional structural modeling. Our findings offer insight into the mechanism underlying interindividual differences in CYP3A4-dependent drug metabolism. Moreover, our results provide guidance for improving drug administration protocols by considering the information on CYP3A4 genetic polymorphisms. SIGNIFICANCE STATEMENT: CYP3A4 metabolizes more than 30% of clinically used drugs. Interindividual differences in drug efficacy and adverse-effect rates have been linked to ethnicity-specific differences in CYP3A4 gene variants in Asian populations, including Japanese individuals, indicating the presence of CYP3A4 polymorphisms resulting in the increased expression of loss-of-function variants. This study detected alterations in CYP3A4 activity due to amino acid substitutions by assessing the enzymatic activities of coding variants for two representative CYP3A4 substrates.

A genotype imputation method for de-identified haplotype reference information by using recurrent neural network.

Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals' privacy.

COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference.

The advent of RNA-sequencing and microarray technologies has led to rapid growth of transcriptome data generated for a wide range of organisms, under various cellular, organ and individual conditions. Since the number of possible combinations of intercellular and extracellular conditions is almost unlimited, cataloging all transcriptome conditions would be an immeasurable challenge. Gene coexpression refers to the similarity of gene expression patterns under various conditions, such as disease states, tissue types, and developmental stages. Since the quality of gene coexpression data depends on the quality and quantity of transcriptome data, timely usage of the growing data is key to promoting individual research in molecular biology. COXPRESdb (http://coxpresdb.jp) is a database providing coexpression information for 11 animal species. One characteristic feature of COXPRESdb is its ability to compare multiple coexpression data derived from different transcriptomics technologies and different species, which strongly reduces false positive relationships in individual gene coexpression data. Here, we summarized the current version of this database, including 23 coexpression platforms with the highest-level quality till date. Using various functionalities in COXPRESdb, the new coexpression data would support a broader area of research from molecular biology to medical sciences.

jMorp: Japanese Multi Omics Reference Panel.

We developed jMorp, a new database containing metabolome and proteome data for plasma obtained from >5000 healthy Japanese volunteers from the Tohoku Medical Megabank Cohort Study, which is available at https://jmorp.megabank.tohoku.ac.jp. Metabolome data were measured by proton nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC-MS), while proteome data were obtained by nanoLC-MS. We released the concentration distributions of 37 metabolites identified by NMR, distributions of peak intensities of 257 characterized metabolites by LC-MS, and observed frequencies of 256 abundant proteins. Additionally, correlation networks for the metabolites can be observed using an interactive network viewer. Compared with some existing databases, jMorp has some unique features: (i) Metabolome data were obtained using a single protocol in a single institute, ensuring that measurement biases were significantly minimized; (ii) The database contains large-scale data for healthy volunteers with various health records and genome data and (iii) Correlations between metabolites can be easily observed using the graphical viewer. Metabolites data are becoming important intermediate markers for evaluating the health states of humans, and thus jMorp is an outstanding resource for a wide range of researchers, particularly those in the fields of medical science, applied molecular biology, and biochemistry.

Estimating carrier frequencies of newborn screening disorders using a whole-genome reference panel of 3552 Japanese individuals.

Incidence rates of Mendelian diseases vary among ethnic groups, and frequencies of variant types of causative genes also vary among human populations. In this study, we examined to what extent we can predict population frequencies of recessive disorders from genomic data, and explored better strategies for variant interpretation and classification. We used a whole-genome reference panel from 3552 general Japanese individuals constructed by the Tohoku Medical Megabank Organization (ToMMo). Focusing on 32 genes for 17 congenital metabolic disorders included in newborn screening (NBS) in Japan, we identified reported and predicted pathogenic variants through variant annotation, interpretation, and multiple ways of classifications. The estimated carrier frequencies were compared with those from the Japanese NBS data based on 1,949,987 newborns from a previous study. The estimated carrier frequency based on genomic data with a recent guideline of variant interpretation for the PAH gene, in which defects cause hyperphenylalaninemia (HPA) and phenylketonuria (PKU), provided a closer estimate to that by the observed incidence than the other methods. In contrast, the estimated carrier frequencies for SLC25A13, which causes citrin deficiency, were much higher compared with the incidence rate. The results varied greatly among the 11 NBS diseases with single responsible genes; the possible reasons for departures from the carrier frequencies by reported incidence rates were discussed. Of note, (1) the number of pathogenic variants increases by including additional lines of evidence, (2) common variants with mild effects also contribute to the actual frequency of patients, and (3) penetrance of each variant remains unclear.

Pathogenic mutations identified by a multimodality approach in 117 Japanese Fanconi anemia patients.

Fanconi anemia is a rare recessive disease characterized by multiple congenital abnormalities, progressive bone marrow failure, and a predisposition to malignancies. It results from mutations in one of the 22 known FANC genes. The number of Japanese Fanconi anemia patients with a defined genetic diagnosis was relatively limited. In this study, we reveal the genetic subtyping and the characteristics of mutated FANC genes in Japan and clarify the genotype-phenotype correlations. We studied 117 Japanese patients and successfully subtyped 97% of the cases. FANCA and FANCG pathogenic variants accounted for the disease in 58% and 25% of Fanconi anemia patients, respectively. We identified one FANCA and two FANCG hot spot mutations, which are found at low percentages (0.04-0.1%) in the whole-genome reference panel of 3,554 Japanese individuals (Tohoku Medical Megabank). FANCB was the third most common complementation group and only one FANCC case was identified in our series. Based on the data from the Tohoku Medical Megabank, we estimate that approximately 2.6% of Japanese are carriers of disease-causing FANC gene variants, excluding missense mutations. This is the largest series of subtyped Japanese Fanconi anemia patients to date and the results will be useful for future clinical management.

Regional genetic differences among Japanese populations and performance of genotype imputation using whole-genome reference panel of the Tohoku Medical Megabank Project.

BACKGROUND: Genotype imputation from single-nucleotide polymorphism (SNP) genotype data using a haplotype reference panel consisting of thousands of unrelated individuals from populations of interest can help to identify strongly associated variants in genome-wide association studies. The Tohoku Medical Megabank (TMM) project was established to support the development of precision medicine, together with the whole-genome sequencing of 1070 human genomes from individuals in the Miyagi region (Northeast Japan) and the construction of the 1070 Japanese genome reference panel (1KJPN). Here, we investigated the performance of 1KJPN for genotype imputation of Japanese samples not included in the TMM project and compared it with other population reference panels. RESULTS: We found that the 1KJPN population was more similar to other Japanese populations, Nagahama (south-central Japan) and Aki (Shikoku Island), than to East Asian populations in the 1000 Genomes Project other than JPT, suggesting that the large-scale collection (more than 1000) of Japanese genomes from the Miyagi region covered many of the genetic variations of Japanese in mainland Japan. Moreover, 1KJPN outperformed the phase 3 reference panel of the 1000 Genomes Project (1KGPp3) for Japanese samples, and IKJPN showed similar imputation rates for the TMM and other Japanese samples for SNPs with minor allele frequencies (MAFs) higher than 1%. CONCLUSIONS: 1KJPN covered most of the variants found in the samples from areas of the Japanese mainland outside the Miyagi region, implying 1KJPN is representative of the Japanese population's genomes. 1KJPN and successive reference panels are useful genome reference panels for the mainland Japanese population. Importantly, the addition of whole genome sequences not included in the 1KJPN panel improved imputation efficiencies for SNPs with MAFs under 1% for samples from most regions of the Japanese archipelago.

ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index.

ATTED-II (http://atted.jp) is a coexpression database for plant species to aid in the discovery of relationships of unknown genes within a species. As an advanced coexpression analysis method, multispecies comparisons have the potential to detect alterations in gene relationships within an evolutionary context. However, determining the validity of comparative coexpression studies is difficult without quantitative assessments of the quality of coexpression data. ATTED-II (version 9) provides 16 coexpression platforms for nine plant species, including seven species supported by both microarray- and RNA sequencing (RNAseq)-based coexpression data. Two independent sources of coexpression data enable the assessment of the reproducibility of coexpression. The latest coexpression data for Arabidopsis (Ath-m.c7-1 and Ath-r.c3-0) showed the highest reproducibility (Jaccard coefficient = 0.13) among previous coexpression data in ATTED-II. We also investigated the statistical basis of the mutual rank (MR) index as a coexpression measure by bootstrap sampling of experimental units. We found that the error distribution of the logit-transformed MR index showed normality with equal variances for each coexpression platform. Because the MR error was strongly correlated with the number of samples for the coexpression data, typical confidence intervals for the MR index can be estimated for any coexpression platform. These new, high-quality coexpression data can be analyzed with any tool in ATTED-II and combined with external resources to obtain insight into plant biology.

NCMine: Core-peripheral based functional module detection using near-clique mining.

MOTIVATION: The identification of functional modules from protein-protein interaction (PPI) networks is an important step toward understanding the biological features of PPI networks. The detection of functional modules in PPI networks is often performed by identifying internally densely connected subnetworks, and often produces modules with "core" and "peripheral" proteins. The core proteins are the ones having dense connections to each other in a module. The difference between core and peripheral proteins is important to understand the functional roles of proteins in modules, but there are few methods to explicitly elucidate the internal structure of functional modules at gene level. RESULTS: We propose NCMine, which is a novel network clustering method and visualization tool for the core-peripheral structure of functional modules. It extracts near-complete subgraphs from networks based on a node-weighting scheme using degree centrality, and reports subgroups as functional modules. We implemented this method as a plugin of Cytoscape, which is widely used to visualize and analyze biological networks. The plugin allows users to extract functional modules from PPI networks and interactively filter modules of interest. We applied the method to human PPI networks, and found several examples with the core-peripheral structure of modules that may be related to cancer development. AVAILABILITY AND IMPLEMENTATION: The Cytoscape plugin and tutorial are available at Cytoscape AppStore. (http://apps.cytoscape.org/apps/ncmine). CONTACT: kengo@ecei.tohoku.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems.

The COXPRESdb (http://coxpresdb.jp) provides gene coexpression relationships for animal species. Here, we report the updates of the database, mainly focusing on the following two points. For the first point, we added RNAseq-based gene coexpression data for three species (human, mouse and fly), and largely increased the number of microarray experiments to nine species. The increase of the number of expression data with multiple platforms could enhance the reliability of coexpression data. For the second point, we refined the data assessment procedures, for each coexpressed gene list and for the total performance of a platform. The assessment of coexpressed gene list now uses more reasonable P-values derived from platform-specific null distribution. These developments greatly reduced pseudo-predictions for directly associated genes, thus expanding the reliability of coexpression data to design new experiments and to discuss experimental results.

ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression.

ATTED-II (http://atted.jp) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.

COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals.

Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.

ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants.

ATTED-II (http://atted.jp) is a database of coexpressed genes that was originally developed to identify functionally related genes in Arabidopsis and rice. Herein, we describe an updated version of ATTED-II, which expands this resource to include additional agriculturally important plants. To improve the quality of the coexpression data for Arabidopsis and rice, we included more gene expression data from microarray and RNA sequencing studies. The RNA sequencing-based coexpression data now cover 94% of the Arabidopsis protein-encoding genes, representing a substantial increase from previously available microarray-based coexpression data (76% coverage). We also generated coexpression data for four dicots (soybean, poplar, grape and alfalfa) and one monocot (maize). As both the quantity and quality of expression data for the non-model species are generally poorer than for the model species, we verified coexpression data associated with these new species using multiple methods. First, the overall performance of the coexpression data was evaluated using gene ontology annotations and the coincidence of a genomic feature. Secondly, the reliability of each guide gene was determined by comparing coexpressed gene lists between platforms. With the expanded and newly evaluated coexpression data, ATTED-II represents an important resource for identifying functionally related genes in agriculturally important plants.