Tool For Analysis Of Gene Expression Research Articles

Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data.

BackgroundThe advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.ResultsWe present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.ConclusionEvaluation using synthetic data shows that D3E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D3E makes it possible to gain additional biological insights.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users.

Classification of Schizophrenia and Bipolar Disorder by Using Machine Learning Algorithms

Data mining based investigations of disease mediating factors and related potential diagnostic biomarkers using genomic information obtained from gene expression analysis tools become very informative and useful. In the present study, public Gene Expression Omnibus (GEO) genome wide expression dataset (ID: GSE12654) consisting of schizophrenia, bipolar disorders patients besides normal groups were analyzed by using different classification algorithms including kNN, naive bayes and decision tree. A set of most differentially expressed genetic features (p

A Single-Cell Gene-Expression Profile Reveals Inter-Cellular Heterogeneity within Human Monocyte Subsets.

Human monocytes are a heterogeneous cell population classified into three different subsets: Classical CD14++CD16-, intermediate CD14++CD16+, and non-classical CD14+CD16++ monocytes. These subsets are distinguished by their differential expression of CD14 and CD16, and unique gene expression profile. So far, the variation in inter-cellular gene expression within the monocyte subsets is largely unknown. In this study, the cellular variation within each human monocyte subset from a single healthy donor was described by using a novel single-cell PCR gene-expression analysis tool. We investigated 86 different genes mainly encoding cell surface markers, and proteins involved in immune regulation. Within the three human monocyte subsets, our descriptive findings show multimodal expression of key immune response genes, such as CD40, NFⱪB1, RELA, TLR4, TLR8 and TLR9. Furthermore, we discovered one subgroup of cells within the classical monocytes, which showed alterations of 22 genes e.g. IRF8, CD40, CSF1R, NFⱪB1, RELA and TNF. Additionally one subgroup within the intermediate and non-classical monocytes also displayed distinct gene signatures by altered expression of 8 and 6 genes, respectively. Hence the three monocyte subsets can be further subdivided according to activation status and differentiation, independently of the traditional classification based on cell surface markers. Demonstrating the use and the ability to discover cell heterogeneity within defined populations of human monocytes is of great importance, and can be useful in unravelling inter-cellular variation in leukocyte populations, identifying subpopulations involved in disease pathogenesis and help tailor new therapies.

Abstract B1-05: R2: Accessible web-based genomics analysis and visualization platform for biomedical researchers

Abstract In this era of explosive high throughput (HT) genomics data generation, there is a growing need for accessible software solutions that can help unlock biological/clinical characteristics from such data. With the biomedical researcher (with limited or no bioinformatics skills) in mind, we developed a comprehensive web-based system called R2 (http://r2.amc.nl). R2 aids in the analysis and visualization of private/shielded as well as public high throughput data and their associated annotated features. The R2 platform consists of a database storing both publicly accessible as well as shielded datasets with unified gene annotation, supplemented with a large suite of tools that can be used on these data, and their associated annotation. As such the user experiences the same look & feel throughout the mining process. In the public section, R2 hosts over 60,000 HT samples. Next to gene expression (microarray and RNA-seq), the platform is also being employed in the integration, analysis and visualization of other data types, such as aCGH, SNP, ChIP, methylation, miRNA, and NGS whole genome sequencing information. R2 contains an expanding set of interactive analyses which are heavily inter-connected, allowing users to quickly hop from one view (representation of the data) to another. Analyses include, correlation, differential expression, gene sets, gene ontology, transcription factor binding sites, PCA, kmeans, Kaplan Meier scans, signature creation etc. Visualizations include, various gene oriented plots, heatmaps, circos, embedded genome browser, Venn, etc. Many parts of the R2 platform are publicly accessible through the http://r2.amc.nl portal. The R2 gene expression analysis tools have thus far been used in more than 180 peer-reviewed scientific publications including journals as Nature, Nature Genetics and Cell. R2 also supports restricted / shielded use and is used in many (inter)national collaborative efforts involving unpublished datasets. The webservers have been serving over 900.000 pages over the past 12 months (Nov 2014). Citation Format: Jan Koster, Jan J. Molenaar, Rogier Versteeg. R2: Accessible web-based genomics analysis and visualization platform for biomedical researchers. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-05.

Abstract A2-45: R2: Accessible web-based genomics analysis and visualization platform for biomedical researchers

Abstract In this era of explosive high throughput (HT) genomics data generation, there is a growing need for accessible software solutions that can help unlock biological/clinical characteristics from such data. With the biomedical researcher (with limited or no bioinformatics skills) in mind, we developed a comprehensive web-based system called R2 (http://r2.amc.nl). R2 aids in the analysis and visualization of private/shielded as well as public high throughput data and their associated annotated features. The R2 platform consists of a database storing both publicly accessible as well as shielded datasets with unified gene annotation, supplemented with a large suite of tools that can be used on these data, and their associated annotation. As such the user experiences the same look & feel throughout the mining process. In the public section, R2 hosts over 60,000 HT samples. Next to gene expression (microarray and RNA-seq), the platform is also being employed in the integration, analysis and visualization of other data types, such as aCGH, SNP, ChIP, methylation, miRNA, and NGS whole genome sequencing information. R2 contains an expanding set of interactive analyses which are heavily inter-connected, allowing users to quickly hop from one view (representation of the data) to another. Analyses include, correlation, differential expression, gene sets, gene ontology, transcription factor binding sites, PCA, kmeans, Kaplan Meier scans, signature creation etc. Visualizations include, various gene oriented plots, heatmaps, circos, embedded genome browser, Venn, etc. Many parts of the R2 platform are publicly accessible through the http://r2.amc.nl portal. The R2 gene expression analysis tools have thus far been used in more than 180 peer-reviewed scientific publications including journals as Nature, Nature Genetics and Cell. R2 also supports restricted / shielded use and is used in many (inter)national collaborative efforts involving unpublished datasets. The webservers have been serving over 900.000 pages over the past 12 months (Nov 2014). Citation Format: Jan Koster, Jan J. Molenaar, Rogier Versteeg. R2: Accessible web-based genomics analysis and visualization platform for biomedical researchers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-45.

A Methodology for the Development of RESTful Semantic Web Services for Gene Expression Analysis.

Gene expression studies are generally performed through multi-step analysis processes, which require the integrated use of a number of analysis tools. In order to facilitate tool/data integration, an increasing number of analysis tools have been developed as or adapted to semantic web services. In recent years, some approaches have been defined for the development and semantic annotation of web services created from legacy software tools, but these approaches still present many limitations. In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain. Therefore, this paper aims at defining an integrated methodology for the implementation of RESTful semantic web services created from gene expression analysis tools and the semantic annotation of such services. We have applied our methodology to the development of a number of services to support the analysis of different types of gene expression data, including microarray and RNASeq. All developed services are publicly available in the Gene Expression Analysis Services (GEAS) Repository at http://dcm.ffclrp.usp.br/lssb/geas. Additionally, we have used a number of the developed services to create different integrated analysis scenarios to reproduce parts of two gene expression studies documented in the literature. The first study involves the analysis of one-color microarray data obtained from multiple sclerosis patients and healthy donors. The second study comprises the analysis of RNA-Seq data obtained from melanoma cells to investigate the role of the remodeller BRG1 in the proliferation and morphology of these cells. Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services. Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis.

Pseudogene-free amplification of HPRT1 in quantitative reverse transcriptase polymerase chain reaction

Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) provides a powerful tool for precise gene expression analysis. The accuracy of the results highly depends on careful selection of a reference gene for data normalization. HPRT1 (hypoxanthine phosphoribosyl transferase 1) is a frequently used housekeeping gene for normalizing relative expression values. However, the existence of processed pseudogenes for HPRT1 might interfere with reliable results obtained in qRT-PCR due to amplification of unintended products. Here, we designed a primer pair for pseudogene-free amplification of HPRT1 in qRT-PCR. We demonstrate that this primer pair specifically amplified HPRT1 messenger RNA (mRNA) sequence while avoiding coamplification of the pseudogenes.

Selection of reference genes for transcriptional analysis of edible tubers of potato (Solanum tuberosum L.).

Potato (Solanum tuberosum) yield has increased dramatically over the last 50 years and this has been achieved by a combination of improved agronomy and biotechnology efforts. Gene studies are taking place to improve new qualities and develop new cultivars. Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) is a bench-marking analytical tool for gene expression analysis, but its accuracy is highly dependent on a reliable normalization strategy of an invariant reference genes. For this reason, the goal of this work was to select and validate reference genes for transcriptional analysis of edible tubers of potato. To do so, RT-qPCR primers were designed for ten genes with relatively stable expression in potato tubers as observed in RNA-Seq experiments. Primers were designed across exon boundaries to avoid genomic DNA contamination. Differences were observed in the ranking of candidate genes identified by geNorm, NormFinder and BestKeeper algorithms. The ranks determined by geNorm and NormFinder were very similar and for all samples the most stable candidates were C2, exocyst complex component sec3 (SEC3) and ATCUL3/ATCUL3A/CUL3/CUL3A (CUL3A). According to BestKeeper, the importin alpha and ubiquitin-associated/ts-n genes were the most stable. Three genes were selected as reference genes for potato edible tubers in RT-qPCR studies. The first one, called C2, was selected in common by NormFinder and geNorm, the second one is SEC3, selected by NormFinder, and the third one is CUL3A, selected by geNorm. Appropriate reference genes identified in this work will help to improve the accuracy of gene expression quantification analyses by taking into account differences that may be observed in RNA quality or reverse transcription efficiency across the samples.

Gene Expression Study ofArachis HypogaeaL.

Arachis hypogaea L. (The peanut) is an important oilseed crop in tropical and subtropical regions of the world. This species belongs to the subfamily Fabaceae and family Leguminosae. Different parts of the plant such as leaves and seeds are used for many purposes in India. Next-generation sequencing technology (NGS) such as RNA-seq has provided a powerful approach for analyzing the Transcriptome accurately and cheaply. This study is focus on gene expression study of RNA-seq of Arachis hypogaea L. (The peanut); Three SRA files of BioProject ID 243319 downloaded from NCBI database and genome of Arabidopsis thaliana was considered as reference genome for gene expression study. Data analysis carried out with many Bioinformatics tools such as TopHat2, Cufflinks, Cuffmerge, Cuffcompare and Cuffdiff. CummeRbund tool was used to manage, visualize and integrate all of the data produced by a Cuffdiff tool for gene expression analysis. These data reported in the current study will serve as a valuable genetic resource of the Arachis hypogaea L.

The Next Horizon in Proteomics and Genomics Research

The completion of the human genome project a decade ago opened new avenues to discover cures to diverse diseases. The potential to discover new genes raised high hopes for many discoveries that would make it possible to cure or even prevent human diseases. The outcome has not kept up with the excitement. Of the predicted proteins in the human genome, a vast number are of unknown nature and their relevance remains a mystery [1]. These novel proteins together with the noncoding RNAs constitute the ‘dark matter’ of the genome [2-7]. Hence a problem remains about the path that future genomic research should follow: should institutions and researchers continue on their current course of studying the known genes, or should they take the higher-risk route of working to demystifying the dark matter of the human genome? Currently, researchers face fewer challenges and a higher likelihood of bringing their projects to fruition by continuing to study the known genes. Nonetheless, studying the novel uncharacterized proteins offers numerous advantages. Only through such study can a broad understanding of the complexity of the human genome emerge; this should provide a basis for better pharmaceuticals and diagnostic markers for diverse diseases [8-10]. Further, the mystery surrounding the genome can disappear as more research goes into the dark matter area of the genome. Powerful bioinformatics and proteomics tools, which offer ways to harness the vast amount of genomic information, are increasingly available [6,7]. Genome-wide association databases such as the Genetic Association Database (GAD) [11], the Phenotype-Genotype Integrator (PheGenI) [12], the database of Clinical Variations (ClinVar) [13] and the International HapMap Project (HapMap) [14] allow a user to rapidly establish phenotype-associated traits for a large number of genes in a single experiment. High-throughput gene expression analysis tools for both mRNAs and proteins can provide an initial hint on diverse tissue expression of the genes to be analyzed. Going from the genome to phenome to function and finally to clinical relevance has never before been easier. A key issue resides in convincing researchers of the value of following the unknown genes. Funding agencies and industry have tended to favor projects that offer the highest probability of success; consequently, not much research has delved into the dark matter of the genome. Recently the US National Cancer Institute (NCI) announced a new initiative to address the paucity of attention that has been given to this basic research: “Illuminating the Dark Matter of the Genome for Druggability” [15]. This is a welcome step in the right direction. More funding by so influential an institution as NCI will undoubtedly lead other institutions around the world to foster study of the unknown elements in the human genome.

Cross-species gene expression analysis of species specific differences in the preclinical assessment of pharmaceutical compounds.

Animals are frequently used as model systems for determination of safety and efficacy in pharmaceutical research and development. However, significant quantitative and qualitative differences exist between humans and the animal models used in research. This is as a result of genetic variation between human and the laboratory animal. Therefore the development of a system that would allow the assessment of all molecular differences between species after drug exposure would have a significant impact on drug evaluation for toxicity and efficacy. Here we describe a cross-species microarray methodology that identifies and selects orthologous probes after cross-species sequence comparison to develop an orthologous cross-species gene expression analysis tool. The assumptions made by the use of this orthologous gene expression strategy for cross-species extrapolation is that; conserved changes in gene expression equate to conserved pharmacodynamic endpoints. This assumption is supported by the fact that evolution and selection have maintained the structure and function of many biochemical pathways over time, resulting in the conservation of many important processes. We demonstrate this cross-species methodology by investigating species specific differences of the peroxisome proliferator-activator receptor (PPAR) α response in rat and human.

Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated With Acute Renal Allograft Rejection

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated with Acute Renal Allograft Rejection

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

IFPA Meeting 2013 Workshop Report II: Use of ‘omics’ in understanding placental development, bioinformatics tools for gene expression analysis, planning and coordination of a placenta research network, placental imaging, evolutionary approaches to understanding pre-eclampsia

Workshops are an important part of the IFPA annual meeting as they allow for discussion of specialized topics. At the IFPA meeting 2013 twelve themed workshops were presented, five of which are summarized in this report. These workshops related to various aspects of placental biology but collectively covered areas of new technologies for placenta research: 1) use of ‘omics’ in understanding placental development and pathologies; 2) bioinformatics and use of omics technologies; 3) planning and coordination of a placenta research network; 4) clinical imaging and pathological outcomes; 5) placental evolution.

Genes Responsive to Low-Intensity Pulsed Ultrasound in MC3T3-E1 Preosteoblast Cells

Although low-intensity pulsed ultrasound (LIPUS) has been shown to enhance bone fracture healing, the underlying mechanism of LIPUS remains to be fully elucidated. Here, to better understand the molecular mechanism underlying cellular responses to LIPUS, we investigated gene expression profiles in mouse MC3T3-E1 preosteoblast cells exposed to LIPUS using high-density oligonucleotide microarrays and computational gene expression analysis tools. Although treatment of the cells with a single 20-min LIPUS (1.5 MHz, 30 mW/cm2) did not affect the cell growth or alkaline phosphatase activity, the treatment significantly increased the mRNA level of Bglap. Microarray analysis demonstrated that 38 genes were upregulated and 37 genes were downregulated by 1.5-fold or more in the cells at 24-h post-treatment. Ingenuity pathway analysis demonstrated that the gene network U (up) contained many upregulated genes that were mainly associated with bone morphology in the category of biological functions of skeletal and muscular system development and function. Moreover, the biological function of the gene network D (down), which contained downregulated genes, was associated with gene expression, the cell cycle and connective tissue development and function. These results should help to further clarify the molecular basis of the mechanisms of the LIPUS response in osteoblast cells.

Direct confocal acquisition of fluorescence from X-gal staining on thick tissue sections

X-gal staining is a common procedure used in the histochemical monitoring of gene expression by light microscopy. However, this procedure does not permit the direct confocal acquisition of images, thus preventing the identification of labelled cells on the depth (Z) axis of tissue sections and leading sometimes to erroneous conclusions in co-localization and gene expression studies. Here we report a technique, based on X-gal fluorescence emission and mathematically-based optical correction, to obtain high quality fluorescence confocal images. This method, combined with immunofluorescence, makes it possible to unequivocally identify X-gal-labelled cells in tissue sections, emerging as a valuable tool in gene expression and cell tracing analysis.

Semantic integration of gene expression analysis tools and data sources using software connectors.

BackgroundThe study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data.ResultsWe have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data.ConclusionsThe proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Postgwas: Advanced GWAS Interpretation in R

We present a comprehensive toolkit for post-processing, visualization and advanced analysis of GWAS results. In the spirit of comparable tools for gene-expression analysis, we attempt to unify and simplify several procedures that are essential for the interpretation of GWAS results. This includes the generation of advanced Manhattan and regional association plots including rare variant display as well as novel interaction network analysis tools for the investigation of systems-biology aspects. Our package supports virtually all model organisms and represents the first cohesive implementation of such tools for the popular language R. Previous software of that range is dispersed over a wide range of platforms and mostly not adaptable for custom work pipelines. We demonstrate the utility of this package by providing an example workflow on a publicly available dataset.

NGS-based targeted RNA sequencing for expression profiling and relative quantitation of specific gene isoforms and fusions in tumor-specific panels.

11108 Background: Gene expression signatures have become a useful tool for the identification of tumor subtypes and response to specific therapies. Expression of tumor, metastatic and macrophage specific transcripts utilizing alternative promoters and transcriptional start sites can further characterize these tumors . NGS is a powerful tool for gene expression analysis, however larger sample input requirements (>100ng) and excessive sequencing depth requirements (30-40M tags/sample) to detect the expression of rare isoforms or fusions in tumor samples are prohibitive for clinical assay development. We describe the development of a targeted RNA sequencing assay for the relative quantitation of specific gene expression signatures, known splice variants and gene fusions from less than 100 ng of starting material in a single tube universal amplification format. Methods: Primers for 52 genes, isoforms and gene fusion products were designed using the universal amplification strategy. 10 ng of RNA from 5 matched tumor/adjacent normal breast cancer tumor pairs were assayed. Libraries were prepared for sequencing by emPCR and sequenced on Ion Torrent PGM. Data were aligned via TMAP. Relative expression was determined vs. housekeeping genes or wild type transcripts. Results: All gene targets were detected at significant levels in at least one tumor sample. Robust expression profiling (5 log dynamic range) was obtained from FFPE macrodissected tumor and normal samples with as little as 200K reads/sample. Immune specific transcripts demonstrated differential expression (CCL3, AIF, FCGR3A and CSF1) across patients and matched pairs as well as an upregulation of CXCL12, indicative of tumor associated macrophages. Conclusions: Targeted RNAseq demonstrates detection and quantitation of relative expression levels of not only tumor subclass specific gene expression signatures, but immune cell specific transcripts from 10ng of FFPE derived total RNA derived from macrodissected tumor samples. The lower input requirements, quicker turnaround time and incredible sensitivity of targeted RNAseq make this assay a useful tool for clinical assay development.

New insights into Chlamydomonas reinhardtii hydrogen production processes by combined microarray/RNA‐seq transcriptomics

Hydrogen production with Chlamydomonas reinhardtii induced by sulphur starvation is a multiphase process while the cell internal metabolism is completely remodelled. The first cellular response is characterized by induction of genes with regulatory functions, followed by a total remodelling of the metabolism to provide reduction equivalents for cellular processes. We were able to characterize all major processes that provide energy and reduction equivalents during hydrogen production. Furthermore, C.reinhardtii showed a strong transcript increase for gene models responsible for stress response and detoxification of oxygen radicals. Finally, we were able to determine potential bottlenecks and target genes for manipulation to increase hydrogen production or to prolong the hydrogen production phase. The investigation of transcriptomic changes during the time course of hydrogen production in C.reinhardtii with microarrays and RNA-seq revealed new insights into the regulation and remodelling of the cell internal metabolism. Both methods showed a good correlation. The microarray platform can be used as a reliable standard tool for routine gene expression analysis. RNA-seq additionally allowed a detailed time-dependent study of gene expression and determination of new genes involved in the hydrogen production process.