Gene-sharing networks reveal organizing principles of transcriptomes in Arabidopsis and other multicellular organisms

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5-25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

Plant Regulomics: a data-driven interface for retrieving upstream regulators from plant multi-omics data

High-throughput technology has become a powerful approach for routine plant research. Interpreting the biological significance of high-throughput data has largely focused on the functional characterization of a large gene list or genomic loci that involves the following two aspects: the functions of the genes or loci and how they are regulated as a whole, i.e. searching for the upstream regulators. Traditional platforms for functional annotation largely help resolving the first issue. Addressing the second issue is essential for a global understanding of the regulatory mechanism, but is more challenging, and requires additional high-throughput experimental evidence and a unified statistical framework for data-mining. The rapid accumulation of 'omics data provides a large amount of experimental data. We here present Plant Regulomics, an interface that integrates 19 925 transcriptomic and epigenomic data sets and diverse sources of functional evidence (58 112 terms and 695 414 protein-protein interactions) from six plant species along with the orthologous genes from 56 whole-genome sequenced plant species. All pair-wise transcriptomic comparisons with biological significance within the same study were performed, and all epigenomic data were processed to genomic loci targeted by various factors. These data were well organized to gene modules and loci lists, which were further implemented into the same statistical framework. For any input gene list or genomic loci, Plant Regulomics retrieves the upstream factors, treatments, and experimental/environmental conditions regulating the input from the integrated 'omics data. Additionally, multiple tools and an interactive visualization are available through a user-friendly web interface. Plant Regulomics is available at http://bioinfo.sibs.ac.cn/plant-regulomics.

Emerging themes in heterotrimeric G-protein signaling in plants

Heterotrimeric G-proteins are key signaling components involved during the regulation of a multitude of growth and developmental pathways in all eukaryotes. Although the core proteins (Gα, Gβ, Gγ subunits) and their basic biochemistries are conserved between plants and non-plant systems, seemingly different inherent properties of specific components, altered wirings of G-protein network architectures, and the presence of novel receptors and effector proteins make plant G-protein signaling mechanisms somewhat distinct from the well-established animal paradigm. G-protein research in plants is getting a lot of attention recently due to the emerging roles of these proteins in controlling many agronomically important traits. New findings on both canonical and novel G-protein components and their conserved and unique signaling mechanisms are expected to improve our understanding of this important module in affecting critical plant growth and development pathways and eventually their utilization to produce plants for the future needs. In this review, we briefly summarize what is currently known in plant G-protein research, describe new findings and how they are changing our perceptions of the field, and discuss important issues that still need to be addressed.

Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS) gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.

Nicotiana attenuata Data Hub (NaDH): an integrative platform for exploring genomic, transcriptomic and metabolomic data in wild tobacco

BACKGROUND

Nicotiana attenuata (coyote tobacco) is an ecological model for studying plant-environment interactions and plant gene function under real-world conditions. During the last decade, large amounts of genomic, transcriptomic and metabolomic data have been generated with this plant which has provided new insights into how native plants interact with herbivores, pollinators and microbes. However, an integrative and open access platform that allows for the efficient mining of these -omics data remained unavailable until now.

DESCRIPTION

We present the Nicotiana attenuata Data Hub (NaDH) as a centralized platform for integrating and visualizing genomic, phylogenomic, transcriptomic and metabolomic data in N. attenuata. The NaDH currently hosts collections of predicted protein coding sequences of 11 plant species, including two recently sequenced Nicotiana species, and their functional annotations, 222 microarray datasets from 10 different experiments, a transcriptomic atlas based on 20 RNA-seq expression profiles and a metabolomic atlas based on 895 metabolite spectra analyzed by mass spectrometry. We implemented several visualization tools, including a modified version of the Electronic Fluorescent Pictograph (eFP) browser, co-expression networks and the Interactive Tree Of Life (iTOL) for studying gene expression divergence among duplicated homologous. In addition, the NaDH allows researchers to query phylogenetic trees of 16,305 gene families and provides tools for analyzing their evolutionary history. Furthermore, we also implemented tools to identify co-expressed genes and metabolites, which can be used for predicting the functions of genes. Using the transcription factor NaMYB8 as an example, we illustrate that the tools and data in NaDH can facilitate identification of candidate genes involved in the biosynthesis of specialized metabolites.

CONCLUSION

The NaDH provides interactive visualization and data analysis tools that integrate the expression and evolutionary history of genes in Nicotiana, which can facilitate rapid gene discovery and comparative genomic analysis. Because N. attenuata shares many genome-wide features with other Nicotiana species including cultivated tobacco, and hence NaDH can be a resource for exploring the function and evolution of genes in Nicotiana species in general. The NaDH can be accessed at: http://nadh.ice.mpg.de/ .

A high resolution map of the Arabidopsis thaliana developmental transcriptome based on RNA-seq profiling

Arabidopsis thaliana is a long established model species for plant molecular biology, genetics and genomics, and studies of A. thaliana gene function provide the basis for formulating hypotheses and designing experiments involving other plants, including economically important species. A comprehensive understanding of the A. thaliana genome and a detailed and accurate understanding of the expression of its associated genes is therefore of great importance for both fundamental research and practical applications. Such goal is reliant on the development of new genetic and genomic resources, involving new methods of data acquisition and analysis. We present here the genome-wide analysis of A. thaliana gene expression profiles across different organs and developmental stages using high-throughput transcriptome sequencing. The expression of 25 706 protein-coding genes, as well as their stability and their spatiotemporal specificity, was assessed in 79 organs and developmental stages. A search for alternative splicing events identified 37 873 previously unreported splice junctions, approximately 30% of them occurred in intergenic regions. These potentially represent novel spliced genes that are not included in the TAIR10 database. These data are housed in an open-access web-based database, TraVA (Transcriptome Variation Analysis, http://travadb.org/), which allows visualization and analysis of gene expression profiles and differential gene expression between organs and developmental stages.

Illuminating a plant's tissue-specific metabolic diversity using computational metabolomics and information theory

Secondary metabolite diversity is considered an important fitness determinant for plants' biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue-metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function.

SUMO proteases ULP1c and ULP1d are required for development and osmotic stress responses in Arabidopsis thaliana

Sumoylation is an essential post-translational regulator of plant development and the response to environmental stimuli. SUMO conjugation occurs via an E1-E2-E3 cascade, and can be removed by SUMO proteases (ULPs). ULPs are numerous and likely to function as sources of specificity within the pathway, yet most ULPs remain functionally unresolved. In this report we used loss-of-function reverse genetics and transcriptomics to functionally characterize Arabidopsis thaliana ULP1c and ULP1d SUMO proteases. GUS reporter assays implicated ULP1c/d in various developmental stages, and subsequent defects in growth and germination were uncovered using loss-of-function mutants. Microarray analysis evidenced not only a deregulation of genes involved in development, but also in genes controlled by various drought-associated transcriptional regulators. We demonstrated that ulp1c ulp1d displayed diminished in vitro root growth under low water potential and higher stomatal aperture, yet leaf transpirational water loss and whole drought tolerance were not significantly altered. Generation of a triple siz1 ulp1c ulp1d mutant suggests that ULP1c/d and the SUMO E3 ligase SIZ1 may display separate functions in development yet operate epistatically in response to water deficit. We provide experimental evidence that Arabidopsis ULP1c and ULP1d proteases act redundantly as positive regulators of growth, and operate mainly as isopeptidases downstream of SIZ1 in the control of water deficit responses.

Seed Production Affects Maternal Growth and Senescence in Arabidopsis

Correlative control (influence of one organ over another organ) of seeds over maternal growth is one of the most obvious phenotypic expressions of the trade-off between growth and reproduction. However, the underlying molecular mechanisms are largely unknown. Here, we characterize the physiological and molecular effects of correlative inhibition by seeds on Arabidopsis (Arabidopsis thaliana) inflorescences, i.e. global proliferative arrest (GPA) during which all maternal growth ceases upon the production of a given number of seeds. We observed transcriptional responses to growth- and branching-inhibitory hormones, and low mitotic activity in meristems upon GPA, but found that meristems retain their identity and proliferative potential. In shoot tissues, we detected the induction of stress- and senescence-related gene expression upon fruit production and GPA, and a drop in chlorophyll levels, suggestive of altered source-sink relationships between vegetative shoot and reproductive tissues. Levels of shoot reactive oxygen species, however, strongly decreased upon GPA, a phenomenon that is associated with bud dormancy in some perennials. Indeed, gene expression changes in arrested apical inflorescences after fruit removal resembled changes observed in axillary buds following release from apical dominance. This suggests that GPA represents a form of bud dormancy, and that dominance is gradually transferred from growing inflorescences to maturing seeds, allowing offspring control over maternal resources, simultaneously restricting offspring number. This would provide a mechanistic explanation for the constraint between offspring quality and quantity.

A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes

As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

Plant single-cell and single-cell-type metabolomics

In conjunction with genomics, transcriptomics, and proteomics, plant metabolomics is providing large data sets that are paving the way towards a comprehensive and holistic understanding of plant growth, development, defense, and productivity. However, dilution effects from organ- and tissue-based sampling of metabolomes have limited our understanding of the intricate regulation of metabolic pathways and networks at the cellular level. Recent advances in metabolomics methodologies, along with the post-genomic expansion of bioinformatics knowledge and functional genomics tools, have allowed the gathering of enriched information on individual cells and single cell types. Here we review progress, current status, opportunities, and challenges presented by single cell-based metabolomics research in plants.

Interaction specificity and coexpression of rice NPR1 homologs 1 and 3 (NH1 and NH3), TGA transcription factors and Negative Regulator of Resistance (NRR) proteins

Background

The nonexpressor of pathogenesis-related genes 1, NPR1 (also known as NIM1 and SAI1), is a key regulator of SA-mediated systemic acquired resistance (SAR) in Arabidopsis. In rice, the NPR1 homolog 1 (NH1) interacts with TGA transcriptional regulators and the Negative Regulator of Resistance (NRR) protein to modulate the SAR response. Though five NPR1 homologs (NHs) have been identified in rice, only NH1 and NH3 enhance immunity when overexpressed. To understand why NH1 and NH3, but not NH2, NH4, or NH5, contribute to the rice immune response, we screened TGA transcription factors and NRR-like proteins for interactions specific to NH1 and NH3. We also examined their co-expression patterns using publicly available microarray data.

Results

We tested five NHs, four NRR homologs (RHs), and 13 rice TGA proteins for pair-wise protein interactions using yeast two-hybrid (Y2H) and split YFP assays. A survey of 331 inter-family interactions revealed a broad, complex protein interaction network. To investigate preferred interaction partners when all three families of proteins were present, we performed a bridged split YFP assay employing YFPN-fused TGA, YFPC-fused RH, and NH proteins without YFP fusions. We found 64 tertiary interactions mediated by NH family members among the 120 sets we examined. In the yeast two-hybrid assay, each NH protein was capable of interacting with most TGA and RH proteins. In the split YFP assay, NH1 was the most prevalent interactor of TGA and RH proteins, NH3 ranked the second, and NH4 ranked the third. Based on their interaction with TGA proteins, NH proteins can be divided into two subfamilies: NH1, NH2, and NH3 in one family and NH4 and NH5 in the other.

In addition to evidence of overlap in interaction partners, co-expression analyses of microarray data suggest a correlation between NH1 and NH3 expression patterns, supporting their common role in rice immunity. However, NH3 is very tightly co-expressed with RH1 and RH2, while NH1 is strongly, inversely co-expressed with RH proteins, representing a difference between NH1 and NH3 expression patterns.

Conclusions

Our genome-wide surveys reveal that each rice NH protein can partner with many rice TGA and RH proteins and that each NH protein prefers specific interaction partners. NH1 and NH3 are capable of interacting strongly with most rice TGA and RH proteins, whereas NH2, NH4, and NH5 have weaker, limited interaction with TGA and RH proteins in rice cells. We have identified rTGA2.1, rTGA2.2, rTGA2.3, rLG2, TGAL2 and TGAL4 proteins as the preferred partners of NH1 and NH3, but not NH2, NH4, or NH5. These TGA proteins may play an important role in NH1- and NH3-mediated immune responses. In contrast, NH4 and NH5 preferentially interact with TGAL5, TGAL7, TGAL8 and TGAL9, which are predicted to be involved in plant development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-461) contains supplementary material, which is available to authorized users.

Integrating the interactome and the transcriptome of Drosophila

Background

Networks of interacting genes and gene products mediate most cellular and developmental processes. High throughput screening methods combined with literature curation are identifying many of the protein-protein interactions (PPI) and protein-DNA interactions (PDI) that constitute these networks. Most of the detection methods, however, fail to identify the in vivo spatial or temporal context of the interactions. Thus, the interaction data are a composite of the individual networks that may operate in specific tissues or developmental stages. Genome-wide expression data may be useful for filtering interaction data to identify the subnetworks that operate in specific spatial or temporal contexts. Here we take advantage of the extensive interaction and expression data available for Drosophila to analyze how interaction networks may be unique to specific tissues and developmental stages.

Results

We ranked genes on a scale from ubiquitously expressed to tissue or stage specific and examined their interaction patterns. Interestingly, ubiquitously expressed genes have many more interactions among themselves than do non-ubiquitously expressed genes both in PPI and PDI networks. While the PDI network is enriched for interactions between tissue-specific transcription factors and their tissue-specific targets, a preponderance of the PDI interactions are between ubiquitous and non-ubiquitously expressed genes and proteins. In contrast to PDI, PPI networks are depleted for interactions among tissue- or stage- specific proteins, which instead interact primarily with widely expressed proteins. In light of these findings, we present an approach to filter interaction data based on gene expression levels normalized across tissues or developmental stages. We show that this filter (the percent maximum or pmax filter) can be used to identify subnetworks that function within individual tissues or developmental stages.

Conclusions

These observations suggest that protein networks are frequently organized into hubs of widely expressed proteins to which are attached various tissue- or stage-specific proteins. This is consistent with earlier analyses of human PPI data and suggests a similar organization of interaction networks across species. This organization implies that tissue or stage specific networks can be best identified from interactome data by using filters designed to include both ubiquitously expressed and specifically expressed genes and proteins.

Network types and their application in natural variation studies in plants

We are in the age of data-driven biology. Not even a decade after the invention of high-throughput sequencing technologies, there are methods that accurately monitor DNA polymorphisms, transcription profiles, methylation states, transcription factor binding sites, chromatin compactness, nucleosome positions, dynamic histone marks, and so on. We are starting to generate comparable amounts of protein or metabolite data. A key issue is how are we going to make sense of all this information. Network analysis is the most promising method to integrate, query and display large amounts of data for human interpretation. This review shortly summarizes the basic types of networks, their properties and limitations. In addition, I introduce the application of networks to the study of the molecular mechanisms behind natural phenotypic variation.

Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

Protocol: a fast and simple in situ PCR method for localising gene expression in plant tissue

BACKGROUND

An important step in characterising the function of a gene is identifying the cells in which it is expressed. Traditional methods to determine this include in situ hybridisation, gene promoter-reporter fusions or cell isolation/purification techniques followed by quantitative PCR. These methods, although frequently used, can have limitations including their time-consuming nature, limited specificity, reliance upon well-annotated promoters, high cost, and the need for specialized equipment. In situ PCR is a relatively simple and rapid method that involves the amplification of specific mRNA directly within plant tissue whilst incorporating labelled nucleotides that are subsequently detected by immunohistochemistry. Another notable advantage of this technique is that it can be used on plants that are not easily genetically transformed.

RESULTS

An optimised workflow for in-tube and on-slide in situ PCR is presented that has been evaluated using multiple plant species and tissue types. The protocol includes optimised methods for: (i) fixing, embedding, and sectioning of plant tissue; (ii) DNase treatment; (iii) in situ RT-PCR with the incorporation of DIG-labelled nucleotides; (iv) signal detection using colourimetric alkaline phosphatase substrates; and (v) mounting and microscopy. We also provide advice on troubleshooting and the limitations of using fluorescence as an alternative detection method. Using our protocol, reliable results can be obtained within two days from harvesting plant material. This method requires limited specialized equipment and can be adopted by any laboratory with a vibratome (vibrating blade microtome), a standard thermocycler, and a microscope. We show that the technique can be used to localise gene expression with cell-specific resolution.

CONCLUSIONS

The in situ PCR method presented here is highly sensitive and specific. It reliably identifies the cellular expression pattern of even highly homologous and low abundance transcripts within target tissues, and can be completed within two days of harvesting tissue. As such, it has considerable advantages over other methods, especially in terms of time and cost. We recommend its adoption as the standard laboratory technique of choice for demonstrating the cellular expression pattern of a gene of interest.

Cell-specific expression profiling of rare cell types as exemplified by its impact on our understanding of female gametophyte development

Expression profiling of single cells can yield insights into cell specification, cellular differentiation processes, and cell type-specific responses to environmental stimuli. Recent work has established excellent tools to perform genome-wide expression studies of individual cell types, even if the cells of interest occur at low frequency within an organ. We review the advances and impact of gene expression studies of rare cell types, as exemplified by recently gained insights into the development and function of the angiosperm female gametophyte. The detailed transcriptional characterization of different stages during female gametophyte development has significantly helped to improve our understanding of cellular specification or cell-cell communication processes. Next-generation sequencing approaches--used increasingly for expression profiling--will now allow for comparative approaches that focus on agriculturally, ecologically or evolutionarily relevant aspects of plant reproduction.

Microgenomics: genome-scale, cell-specific monitoring of multiple gene regulation tiers

The expression of nuclear protein-coding genes is controlled by dynamic mechanisms ranging from DNA methylation, chromatin modification, and gene transcription to mRNA maturation, turnover, and translation and the posttranslational control of protein function. A genome-scale assessment of the spatiotemporal regulation of gene expression is essential for a comprehensive understanding of gene regulatory networks. However, there are major obstacles to the precise evaluation of gene regulation in multicellular plant organs; these include the monitoring of regulatory processes at levels other than steady-state transcript abundance, resolution of gene regulation in individual cells or cell types, and effective assessment of transient gene activity manifested during development or in response to external cues. This review surveys the advantages and applications of microgenomics technologies that enable panoramic quantitation of cell-type-specific expression in plants, focusing on the importance of querying gene activity at multiple steps in the continuum, from histone modification to selective translation.

Correlation networks visualization

New, in silico ways of generating hypotheses based on large data sets have emerged in the past decade. These data sets have been used to investigate different aspects of plant biology, especially at the level of transcriptome, from tissue-specific expression patterns to patterns in as little as a few cells. Such publicly available data are a boon to researchers for hypothesis generation by providing a guide for experimental work such as phenotyping or genetic analysis. More advanced computational methods can leverage these data via gene coexpression analysis, the results of which can be visualized and refined using network analysis. Other kinds of networks of, e.g., protein-protein interactions, can also be used to inform biology. These networks can be visualized and analyzed with additional information on gene expression levels, subcellular localization, etc., or with other emerging kinds information. Finally, cross-level correlation is an area that will become increasingly important. Visualizing these cross-level correlations will require new data visualization tools.

Exploring the shallow end; estimating information content in transcriptomics studies

Transcriptomics is a major platform to study organismal biology. The advent of new parallel sequencing technologies has opened up a new avenue of transcriptomics with ever deeper and deeper sequencing to identify and quantify each and every transcript in a sample. However, this may not be the best usage of the parallel sequencing technology for all transcriptomics experiments. I utilized the Shannon Entropy approach to estimate the information contained within a transcriptomics experiment and tested the ability of shallow RNAseq to capture the majority of this information. This analysis showed that it was possible to capture nearly all of the network or genomic information present in a variety of transcriptomics experiments using a subset of the most abundant 5000 transcripts or less within any given sample. Thus, it appears that it should be possible and affordable to conduct large scale factorial analysis with a high degree of replication using parallel sequencing technologies.