TF-Cluster: a pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM)

Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data

The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.

Regulation of regeneration in Arabidopsis thaliana

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs-WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1-were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00121-9.

Current status and trends in forest genomics

Forests are not only the most predominant of the Earth's terrestrial ecosystems, but are also the core supply for essential products for human use. However, global climate change and ongoing population explosion severely threatens the health of the forest ecosystem and aggravtes the deforestation and forest degradation. Forest genomics has great potential of increasing forest productivity and adaptation to the changing climate. In the last two decades, the field of forest genomics has advanced quickly owing to the advent of multiple high-throughput sequencing technologies, single cell RNA-seq, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated genome editing, and spatial transcriptomes, as well as bioinformatics analysis technologies, which have led to the generation of multidimensional, multilayered, and spatiotemporal gene expression data. These technologies, together with basic technologies routinely used in plant biotechnology, enable us to tackle many important or unique issues in forest biology, and provide a panoramic view and an integrative elucidation of molecular regulatory mechanisms underlying phenotypic changes and variations. In this review, we recapitulated the advancement and current status of 12 research branches of forest genomics, and then provided future research directions and focuses for each area. Evidently, a shift from simple biotechnology-based research to advanced and integrative genomics research, and a setup for investigation and interpretation of many spatiotemporal development and differentiation issues in forest genomics have just begun to emerge.

Network and epigenetic characterization of subsets of genes specifically expressed in maize bundle sheath cells

Bundle sheath (BS) cells exhibit dramatically structural differences and functional variations at physiological, biochemical and epigenetic levels as compared to mesophyll (M) cells in maize. The regulatory mechanisms controlling functional divergences between M and BS have been extensively investigated. However, BS cell-related regulatory networks are still not completely characterized. To address this, we herein conducted WGCNA-related co-expression assays using bulk M and BS cell RNA-seq data sets and identified a module containing 384 genes highly expressed in BS cells (including 20 hub TFs) instead of M cells. According to the hub TF centered regulatory network, we found that Dof22 and Dof30 might act as key regulators in the regulation of expression of BS-specific genes, and several MYB TFs exhibited a high collaboration with Dof TFs. By comparing the enrichment levels of histone modifications, we found that genes in the aforementioned module were more enriched with histone acetylation as compared to other BS-enriched DEGs with similar expression levels. Moreover, we found that a subset of genes functioning in photosynthesis, protein auto processing and enzymatic activities were significantly enriched with broad H3K4me3. Thus, our study provides evidence showing that regulatory network and histone modifications may play vital roles in the regulation of a subset of genes with important functions in BS cells.

A study of RNA-editing in Populus trichocarpa nuclei revealed acquisition of RNA-editing on the endosymbiont-derived genes, and a preference for intracellular remodeling genes in adaptation to endosymbiosis

RNA-editing is a post-transcriptional modification that can diversify genome-encoded information by modifying individual RNA bases. In contrast to the well-studied RNA-editing in organelles, little is known about nuclear RNA-editing in higher plants. We performed a genome-wide study of RNA-editing in Populus trichocarpa nuclei using the RNA-seq data generated from the sequenced poplar genotype, 'Nisqually-1'. A total of 24,653 nuclear RNA-editing sites present in 8,603 transcripts were identified. Notably, RNA-editing in P. trichocarpa nuclei tended to occur on endosymbiont-derived genes. We then scrutinized RNA-editing in a cyanobacterial strain closely related to chloroplast. No RNA-editing sites were identified therein, implying that RNA-editing of these endosymbiont-derived genes was acquired after endosymbiosis. Gene ontology enrichment analysis of all the edited genes in P. trichocarpa nuclei demonstrated that nuclear RNA-editing was primarily focused on genes involved in intracellular remodeling processes, which suggests that RNA-editing plays contributing roles in organellar establishment during endosymbiosis. We built a coexpression network using all C-to-U edited genes and then decomposed it to obtain 18 clusters, six of which contained a conserved core motif, A/G-C-A/G. Such a short core motif not only attracted the RNA-editing machinery but also enabled large numbers of sites to be targeted though further study is necessary to verify this finding.

MADS-box transcription factors MADS11 and DAL1 interact to mediate the vegetative-to-reproductive transition in pine

The reproductive transition is an important event that is crucial for plant survival and reproduction. Relative to the thorough understanding of the vegetative phase transition in angiosperms, a little is known about this process in perennial conifers. To gain insight into the molecular basis of the regulatory mechanism in conifers, we used temporal dynamic transcriptome analysis with samples from seven different ages of Pinus tabuliformis to identify a gene module substantially associated with aging. The results first demonstrated that the phase change in P. tabuliformis occurred as an unexpectedly rapid transition rather than a slow, gradual progression. The age-related gene module contains 33 transcription factors and was enriched in genes that belong to the MADS (MCMl, AGAMOUS, DEFICIENS, SRF)-box family, including six SOC1-like genes and DAL1 and DAL10. Expression analysis in P. tabuliformis and a late-cone-setting P. bungeana mutant showed a tight association between PtMADS11 and reproductive competence. We then confirmed that MADS11 and DAL1 coordinate the aging pathway through physical interaction. Overexpression of PtMADS11 and PtDAL1 partially rescued the flowering of 35S::miR156A and spl1,2,3,4,5,6 mutants in Arabidopsis (Arabidopsis thaliana), but only PtMADS11 could rescue the flowering of the ft-10 mutant, suggesting PtMADS11 and PtDAL1 play different roles in flowering regulatory networks in Arabidopsis. The PtMADS11 could not alter the flowering phenotype of soc1-1-2, indicating it may function differently from AtSOC1 in Arabidopsis. In this study, we identified the MADS11 gene in pine as a regulatory mediator of the juvenile-to-adult transition with functions differentiated from the angiosperm SOC1.

Transcription factors involved in the regulatory networks governing the Calvin-Benson-Bassham cycle

Transcription factors (TFs) play crucial roles in the regulation of photosynthesis; elucidating these roles will facilitate our understanding of photosynthesis and thus accelerate its improvement for enhancing crop yield. Promoter analysis of 52 nuclear-encoded Populus tomentosa Carr. genes involved in the Calvin-Benson-Bassham (CBB) cycle revealed 706 motifs and 326 potentially interacting TFs. A backward elimination random forest (BWERF) algorithm reduced the number of TFs to 40, involved in a three-layer gene regulatory network (GRN) including 46 photosynthesis genes (bottom layer), 25 TFs (second layer) and 15 TFs (top layer). Phenotype-genotype association identified 248 single-nucleotide polymorphisms (SNPs) within 72 genes associated with 11 photosynthesis traits. Of the regulatory pairs identified by the BWERF (202 pairs), 77 TF-target combinations harbored SNPs associated with the same trait, supporting similar mechanisms of phenotype modulation. We used expression quantitative trait nucleotide (eQTN) analysis to identify causal SNPs affecting gene expression, identifying 1851 eQTN signals for 50 eGenes (genes whose expressions are regulated by eQTNs). Distribution patterns identified 14 eQTNs from seven TFs associated with eight expression levels of their downstream targets (defined in the GRN), whereas seven TF-target pairs were also identified by phenotype-genotype associations. To further validate the roles of TFs at the metabolic level, we selected 6764 SNPs from 55 genes (identified by GRN-association or GRN-eQTN pairs or both) for metabolic association, identifying variants within 10 TFs affecting metabolic processes underlying the CBB cycle. Our study provides new insights into the photosynthesis pathway in poplar and may facilitate understanding of processes underlying photosynthesis improvement.

The transcriptional activity of a temperature-sensitive transcription factor module is associated with pollen shedding time in pine

It has long been known that the pollen shedding time in pine trees is correlated with temperature, but the molecular basis for this has remained largely unknown. To better understand the mechanisms driving temperature response and to identify the hub regulators of pollen shedding time regulation in Pinus tabuliformis Carr., we identified a set of temperature-sensitive genes by carrying out a comparative transcriptome analysis using six early pollen shedding trees (EPs) and six late pollen shedding trees (LPs) during mid-winter and at three consecutive time points in early spring. We carried out a weighted gene co-expression network analysis and constructed a transcription factor (TF) collaborative network, merging the common but differentially expressed TFs of the EPs and LPs into a joint network. We found five hub genes in the core TF module whose expression was rapidly induced by low temperatures. The transcriptional activity of this TF module was strongly associated with pollen shedding time, and likely to produce the fine balance between cold hardiness and growth activity in early spring. We confirmed the key role of temperature in regulating flowering time and identified a transcription factor module associated with pollen shedding time in P. tabuliformis. This suggests that repression of growth activity by repressors is the main mechanism balancing growth and cold hardiness in pine trees in early spring. Our results provide new insights into the molecular mechanisms regulating seasonal flowering time in pines.

Low doses of triazine xenobiotics mobilize ABA and cytokinin regulations in a stress- and low-energy-dependent manner

The extent of residual contaminations of pesticides through drift, run-off and leaching is a potential threat to non-target plant communities. Arabidopsis thaliana responds to low doses of the herbicide atrazine, and of its degradation products, desethylatrazine and hydroxyatrazine, not only in the long term, but also under conditions of short-term exposure. In order to investigate underlying molecular mechanisms of low-dose responses and to decipher commonalities and specificities between different chemical treatments, parallel transcriptomic studies of the early effects of the atrazine-desethylatrazine-hydroxyatrazine chemical series were undertaken using whole-genome microarrays. All of the triazines under study produced coordinated and specific changes in gene expression. Hydroxyatrazine-responsive genes were mainly linked to root development, whereas atrazine and desethylatrazine mostly affected molecular signaling networks implicated in stress and hormone responses. Analysis of signaling-related genes, promoter sites and shared-function interaction networks highlighted the involvement of energy-, stress-, abscisic acid- and cytokinin-regulated processes, and emphasized the importance of cold-, heat- and drought-related signaling in the perception of low doses of triazines. These links between low-dose xenobiotic impacts and stress-hormone crosstalk pathways give novel insights into plant-pesticide interactions and plant-pollution interactions that are essential for toxicity evaluation in the context of environmental risk assessment.

MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters

In this work, we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. The source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.

SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks

The establishment of a collaborative network of transcription factors (TFs) followed by decomposition and then construction of subnetworks is an effective way to obtain sets of collaborative TFs; each set controls a biological process or a complex trait. We previously developed eight gene association methods for genome-wide coexpression analysis between each TF and all other genomic genes and then constructing collaborative networks of TFs but only one algorithm, called Triple-Link Algorithm, for building collaborative subnetworks. In this study, we developed two more algorithms, Single Seed-Growing Algorithm (SSGA) and Multi-Seed Growing Algorithm (MSGA), for building collaborative subnetworks of TFs by identifying the fully-linked triple-node seeds from a decomposed collaborative network and then growing them into subnetworks with two different strategies. The subnetworks built from the three algorithms described above were comparatively appraised in terms of both functional cohesion and intra-subnetwork association strengths versus inter-subnetwork association strengths. We concluded that SSGA and MSGA, which performed more systemic comparisons and analyses of edge weights and network connectivity during subnetwork construction processes, yielded more functional and cohesive subnetworks than Triple-Link Algorithm. Together, these three algorithms provide alternate approaches for acquiring subnetworks of collaborative TFs. We also presented a framework to outline how to use these three algorithms to obtain collaborative TF sets governing biological processes or complex traits.

Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes

BACKGROUND

Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.

RESULTS

A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically.

CONCLUSIONS

We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.

Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response

Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.

Elucidating functional context within microarray data by integrated transcription factor-focused gene-interaction and regulatory network analysis

Microarrays do not yield direct evidence for functional connections between genes. However, transcription factors (TFs) and their binding sites (TFBSs) in promoters are important for inducing and coordinating changes in RNA levels, and thus represent the first layer of functional interaction. Similar to genes, TFs act only in context, which is why a TF/TFBS-based promoter analysis of genes needs to be done in the form of gene(TF)-gene networks, not individual TFs or TFBSs. In addition, integration of the literature and various databases (e.g. GO, MeSH, etc) allows the adding of genes relevant for the functional context of the data even if they were initially missed by the microarray as their RNA levels did not change significantly. Here, we outline a TF-TFBSs network-based strategy to assess the involvement of transcription factors in agonist signaling and demonstrate its utility in deciphering the response of human microvascular endothelial cells (HMEC-1) to leukemia inhibitory factor (LIF). Our strategy identified a central core of eight TFs, of which only STAT3 had previously been definitively linked to LIF in endothelial cells. We also found potential molecular mechanisms of gene regulation in HMEC-1 upon stimulation with LIF that allow for the prediction of changes of genes not used in the analysis. Our approach, which is readily applicable to a wide variety of expression microarray and next generation sequencing RNA-seq results, illustrates the power of a TF-gene networking approach for elucidation of the underlying biology.

DeGNServer: deciphering genome-scale gene networks through high performance reverse engineering analysis

Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.

GPLEXUS: enabling genome-scale gene association network reconstruction and analysis for very large-scale expression data

The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level. Most current state-of-the-art computational methods for genome-wide GAN reconstruction require high-performance computational resources. However, even high-performance computing cannot fully address the complexity involved with constructing GANs from very large-scale expression profile datasets, especially for the organisms with medium to large size of genomes, such as those of most plant species. Here, we present a new approach, GPLEXUS (http://plantgrn.noble.org/GPLEXUS/), which integrates a series of novel algorithms in a parallel-computing environment to construct and analyze genome-wide GANs. GPLEXUS adopts an ultra-fast estimation for pairwise mutual information computing that is similar in accuracy and sensitivity to the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) method and runs ∼1000 times faster. GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional subnetworks. Furthermore, GPLEXUS includes a novel ‘condition-removing’ method to identify the major experimental conditions in which each subnetwork operates from very large-scale gene expression datasets across several experimental conditions, which allows users to annotate the various subnetworks with experiment-specific conditions. We demonstrate GPLEXUS’s capabilities by construing global GANs and analyzing subnetworks related to defense against biotic and abiotic stress, cell cycle growth and division in Arabidopsis thaliana.

Nitrogen deprivation promotes Populus root growth through global transcriptome reprogramming and activation of hierarchical genetic networks

We show a distinct and previously poorly characterized response of poplar (Populus tremula × Populus alba) roots to low nitrogen (LN), which involves activation of root growth and significant transcriptome reprogramming. Analysis of the temporal patterns of enriched ontologies among the differentially expressed genes revealed an ordered assembly of functionally cohesive biological events that aligned well with growth and morphological responses. A core set of 28 biological processes was significantly enriched across the whole studied period and 21 of these were also enriched in the roots of Arabidopsis thaliana during the LN response. More than half (15) of the 28 processes belong to gene ontology (GO) terms associated with signaling and signal transduction pathways, suggesting the presence of conserved signaling mechanisms triggered by LN. A reconstruction of genetic regulatory network analysis revealed a sub-network centered on a PtaNAC1 (P. tremula × alba NAM, ATAF, CUC 1) transcription factor. PtaNAC1 root-specific up-regulation increased root biomass and significantly changed the expression of the connected hub genes specifically under LN. Our results provide evidence that the root response to LN involves hierarchically structured genetic networks centered on key regulatory factors. Targeting these factors via genetic engineering or breeding approaches can allow dynamic adjustment of root architecture in response to variable nitrogen availabilities in the soil.

Evaluation of gene association methods for coexpression network construction and biological knowledge discovery

BACKGROUND

Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical.

METHODS AND RESULTS

In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods.

CONCLUSIONS

We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

Transcriptomic analysis of phenotypic changes in birch (Betula platyphylla) autotetraploids

Plant breeders have focused much attention on polyploid trees because of their importance to forestry. To evaluate the impact of intraspecies genome duplication on the transcriptome, a series of Betula platyphylla autotetraploids and diploids were generated from four full-sib families. The phenotypes and transcriptomes of these autotetraploid individuals were compared with those of diploid trees. Autotetraploids were generally superior in breast-height diameter, volume, leaf, fruit and stoma and were generally inferior in height compared to diploids. Transcriptome data revealed numerous changes in gene expression attributable to autotetraploidization, which resulted in the upregulation of 7052 unigenes and the downregulation of 3658 unigenes. Pathway analysis revealed that the biosynthesis and signal transduction of indoleacetate (IAA) and ethylene were altered after genome duplication, which may have contributed to phenotypic changes. These results shed light on variations in birch autotetraploidization and help identify important genes for the genetic engineering of birch trees.