- Open Access
AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae
© Zheng et al.; licensee BioMed Central Ltd. 2014
- Received: 4 December 2013
- Accepted: 26 February 2014
- Published: 14 March 2014
Algae are important non-vascular plants that have many research applications, including high species diversity, biofuel sources, and adsorption of heavy metals and, following processing, are used as ingredients in health supplements. The increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes has made the development of an integrated resource for retrieving gene expression data and metabolic pathway essential for functional analysis and systems biology. In a currently available resource, gene expression profiles and biological pathways are displayed separately, making it impossible to easily search current databases to identify the cellular response mechanisms. Therefore, in this work the novel AlgaePath database was developed to retrieve transcript abundance profiles efficiently under various conditions in numerous metabolic pathways.
AlgaePath is a web-based database that integrates gene information, biological pathways, and NGS datasets for the green algae Chlamydomonas reinhardtii and Neodesmus sp. UTEX 2219–4. Users can search this database to identify transcript abundance profiles and pathway information using five query pages (Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-expression Analysis). The transcript abundance data of 45 and four samples from C. reinhardtii and Neodesmus sp. UTEX 2219–4, respectively, can be obtained directly on pathway maps. Genes that are differentially expressed between two conditions can be identified using Folds Search. The Gene Group Analysis page includes a pathway enrichment analysis, and can be used to easily compare the transcript abundance profiles of functionally related genes on a map. Finally, the Co-expression Analysis page can be used to search for co-expressed transcripts of a target gene. The results of the searches will provide a valuable reference for designing further experiments and for elucidating critical mechanisms from high-throughput data.
AlgaePath is an effective interface that can be used to clarify the transcript response mechanisms in different metabolic pathways under various conditions. Importantly, AlgaePath can be mined to identify critical mechanisms based on high-throughput sequencing. To our knowledge, AlgaePath is the most comprehensive resource for integrating numerous databases and analysis tools in algae. The system can be accessed freely online at http://algaepath.itps.ncku.edu.tw.
- Next-generation sequencing
- Systems biology
- Gene expression
The global energy crisis poses a major threat to human survival. Gradual depletion of non-renewable energy resources has increased energy costs and pollution. To remedy this situation, renewable energy sources such as biomass have been developed. Biomass is gathered from various plants (including crops or peas) and used to generate bio-alcohol or bio-fuel [1, 2]. However, using crops as a biomass source may crowd out food, especially in poor regions of the world . Thus, an alternative species with less land requirement and high oil production needs to be identified. Algae are highly efficient in oil production, and are a promising biofuel source . In addition to their possible use for biofuel, algae have many research applications . For instance, because of its high species diversity, algae is an excellent model for evolution studies. The diversity of algae has been attributed not only to the evolution force, but also to horizontal gene transfer . Allochthonous genes can increase species diversity and organism competition . Further, because algae are capable of absorbing heavy metals, they can be used to remove pollution in waste water [7–11]. Currently, algae are the most important non-vascular model plants that are used for research.c Because of their diverse applications, numerous studies have investigated the functional genomics of algae. Therefore, with the increasing availability of algae genome and transcriptome data from next-generation sequencing, an integrated resource for the retrieval of gene expression data with metabolic pathways is essential for functional analysis in algae. Further, understanding cellular response mechanisms under different environments is a high priority in systems biology.
Chlamydomonas reinhardtii is a single-celled green algae, which is distributed worldwide in soil and water. C. reinhardtii is used mainly as a model organism when addressing fundamental issues such as photosynthesis, cellular movement, abiotic response mechanisms, and regulation of flagellar mechanism. This model organism is also an important model for non-vascular plant research and information on the genomics and transcript abundance of C. reinhardtii is available in public resources. Neodesmus sp. UTEX 2219–4 is a species of green microalgae, which was isolated from collection number UTEX 2219 from the University of Texas at Austin (UTEX) and identified subsequently as genus Neodesmus. Wang et al.  indicated that oil bodies were accumulated significantly under nitrogen starvation and osmotic stress in Neodesmus sp. UTEX 2219–4. Therefore, this strain was identified as having a high potential for biofuel applications. This study analyzed the transcriptome deep sequencing data that was obtained from four samples under various conditions.
Increasing amounts of next generation sequencing (NGS) data have become available in recent years and algal biochemistry and biology have attracted growing interest for their potential in developing renewable biofuel. In addition, RNA-seq technology has been used widely to identify algal cellular physiology and metabolism under various types of abiotic stress and/or nutrient deficiency [13–18]. For example, a previous study investigated cellular response mechanisms by characterizing the C. reinhardtii transcriptome under nutrient-replete and sulfur-depleted conditions . Based on Roche 454 and Illumina sequencing, Miller et al. investigated diversion of metabolism and transcript abundance during nitrogen repletion and deprivation in C. reinhardtii. Consequently, the increasing use of NGS has made the integration of high-throughput data from different experiments a priority concern. Genevestigator  is a high performance platform that integrates many public microarray experiments and visualizes gene expression data in different biological contexts. However, although nine important higher plants have been collected in the Genevestigator database, none of them are non-vascular plants. The Bio-Analytic Resource for Plant Biology (BAR)  also integrates numerous microarray data and provides expression profile similarity rankings of homologous genes in plant species; however, BAR cannot access algae-related information. BioCyc  is a collection of databases that provides the metabolic pathways of sequenced organisms including C. reinhardtii, but no gene expression data have been integrated into the collection. To our knowledge, the Algal Functional Annotation Tool (AFAT) is the first database in which algae gene expression data and metabolic pathways have been collected . AFAT provides an integrated data-mining environment for algal genomics by integrating multiple annotation databases into a centralized system. Unfortunately, the metabolic pathways and gene expression profiles are displayed in separate windows in AFAT, making it difficult for users to understand the variations in cellular responses under different conditions. Additionally, the complex IDs and datasets in AFAT can often confuse users who want to search for interesting genes and pathways. For gene expression analysis, the AFAT website provides only an expression similarity search and the result page outputs only a Gene ID. As a result, it is relatively difficult to compare the variations of gene expression under different conditions using AFAT tools. Surprisingly, there are currently no other databases or tools that can be used to combine gene expression data with metabolic pathways in algae for systems biology, thereby necessitating the development of additional databases and related tools.
In this work, the novel AlgaePath database, which allows the efficient retrieval of cellular response data from different conditions, was developed. AlgaePath integrates various high-throughput datasets of C. reinhardtii and the stress-induced oil accumulation dataset of Neodesmus sp. UTEX 2219–4. AlgaePath can be used to identify transcript abundance profiles and to compare variations among different conditions in a pathway map through five query functions (Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-expression Analysis). The novel database can be accessed freely online at http://AlgaePath.itps.ncku.edu.tw.
Transcriptome data of Neodesmus sp.UTEX 2219–4
The Neodesmus sp. UTEX 2219–4 transcriptome was sequenced on a Roche 454 system. The transcriptome data were obtained under four conditions (normal, nitrogen starvation, sorbitol stress, and salt stress) and have been integrated into the AlgaePath database. The sequence data processing and analysis have not been published (unpublished data in Dr. Ching-Nen Nathan Chen’s lab). The assembled sequences were annotated based on their similarity to sequences in the NCBI non-redundant (nr) database and the UniProt UniRef50 protein database. Currently, there are 20,698 Neodesmus sp. UTEX 2219–4 transcripts (including isotigs, contigs, and singletons) in the AlgaePath database.
Transcriptome data of C. reinhardtii
The C. reinhardtii reference sequences and their annotation were downloaded from Phytozome (http://www.phytozome.net/, v5.3.1 of Chlamydomonas annotations) . A total of 19,529 transcripts with annotations from Pfam (10,122) , PANTHER (8,905) , NCBI eukaryotic orthologous groups (KOG) (5,623) , KEGG (2,724 KEGG ortholog), and Arabidopsis homologous genes (9,106) were obtained. The RNA-seq transcriptome data were accessed from the Gene Expression Omnibus (GEO). In this study, the GSE17970, GSE24367, GSE25622, GSE33927, GSE33548, GSE34826, and GSE35305 datasets were used [13–18]. The conditions used for RNA-seq transcripts are sulfur depletion, nitrogen deprivation, mineral nutrient treatment, various concentration of carbon dioxide (CO2), oxidative stress, and Fe deprivation, including 45 samples. Adaptors were trimmed from all the sequences in the different samples and then the reads were mapped back to the reference sequences. The Bowtie2  and BLAT  programs were used with their default parameters to map the Illumina sequence reads (Solexa) and the 454 pyro-sequence reads separately to the reference sequences. Finally, the expression level of each of the transcripts was calculated using reads per kilobase per million reads (RPKM).
Normalization of expression data of each transcripts and identification differentially expressed genes
Because different transcriptome datasets were used, Deseq R package was applied to normalize various algae raw data and identify differentially expressed genes (DEGs) depending on negative binomial distribution. DEGs based on 17 comparison groups (see Additional file 1: Table S1) were pre-run and saved in AlgaePath database. However, there is the other option in AlgaePath web interface. Various samples could be selected to compare gene expression level, and identify DEGs based on expression fold change. Furthermore, Giorgi et al. indicated that Variance-Stabilizing Transformated (VST) RNA-seq data brings RNA-seq samples hierarchically closer to microarrays than RPKM normalization or raw counts when reconstructing co-expression networks . Therefore, Variance-Stabilizing Transformation (VST) method was used to transform DEseq pre-normalized data. The VST was also performed by Deseq R package . The VST normalized data were than used to analyze co-expression genes.
Analysis of co-expressed genes
and denote the average expression values of X and Y genes under all conditions in each category, respectively n represents the total number of samples in each category.
Additionally, The Spearman’s rank-correlation is less sensitive than the Pearson correlation to strong outliers. AlgaePath also provide the co-expression results using Spearman’s correlation. Users can identify co-expression genes based on different statistics methods.
Pathways identification of each transcript and gene group analysis.
where, i denotes the number of transcripts in the gene group involved in the j pathway; n is the total number of transcripts in the gene group annotated in KEGG; M is the number of all C. reinhardtii transcripts involved in the j pathway; and N is the total number of all Ch. reinhardtii transcripts annotated in KEGG.
Web interface of AlgaePath
Comparative analysis of gene expression profiles under various conditions
Comparative analysis of gene expression under various conditions can help biologists identify critical genes involved in changes of interest. For example, the feasibility of using algae in biofuel production has been studied  and scientists have attempted to overexpress acetyl-CoA carboxylase (a substrate for fatty acid production) in microalgae to improve oil production . Unfortunately, until now, the performance of algae in oil production has been insignificant . Rismani-Yazdi et al. recently identified some important metabolic pathways related to microalgae-based biofuel feedstock by comparing transcriptome sequencing data under four different conditions. Numerous NGS transcriptome datasets are currently available in public resources and they need to be integrated so that important checkpoints can be identified in biological mechanisms. Importantly, in the AlgaePath database, users can easily investigate variations in transcript abundance under various conditions in a specific pathway based on user selection. All output result pages are displayed in a figure and in a downloadable table, as shown in Figure 3. The user-friendly interface can help users determine efficiently whether their genes of interest undergo significant changes in expression under different conditions. In this sense, AlgaePath is similar to the other resources that have been developed for higher plants (e.g. Genevestigator and the BAR database). Importantly, the biological pathways in AlgaePath are thoroughly combined with the transcript abundance data. Many important phenotypic changes that cause serious biological reactions are affected by more than one gene; therefore, researchers need to determine which genes have expression patterns that are similar to their target gene. Such information can help biologists clarify the mechanisms that they are interested in. Because co-expression analysis is very important in systems biology research, the ability to study the co-expression of genes in the six categories available in AlgaePath will be highly effective. The six categories were designed to provide users with a variety of options for data mining because many genes are co-expressed only under specific conditions. The AlgaePath system will provide users a multifunctional analysis platform for algae-related research.
Case study: identification of critical genes during nitrogen starvation and sulfur depletion
Evaluation of AlgaePath and future perspectives
The comparison between algal functional annotation tool (AFAT) [] and AlgaePath
Neodesmus sp. UTEX 2219-4
Identifiers ID and keyword
Gene symbol, Gene ID from various database, keyword, DNA/protein sequences
Pathways information of each transcript
Yes, only pathway map (out link to KEGG)
Yes, combine pathway map with transcript abundance profiles under various conditions
Gene group analysis (Pathway enrichment)
Yes, only mark genes in a pathway map (out link to KEGG)
Yes, not only mark genes in a pathway map but with transcript abundance profiles under various conditions
Differentially expression genes
Yes, easily identify differentially expressed genes between two samples
Expression similar search
Yes, only provide identifier ID in the expression map
Yes, provide detail information of co-expression genes
Pathway map with gene expression profiles
A major advantage of AlgaePath over AFAT lies in its ability to integrate transcript abundance and pathway maps; however, although the genome sequence of C. reinhardtii was completed in 2007, the functions of many of the genes are still unknown . In addition, some of the algal gene information in AlgaePath is limited; therefore, as more high-throughput experimental datasets become available it will be essential to continuously update the high-throughput data in AlgaePath. Although single datasets can be used to find interesting phenomena and to solve some related problems, the integration of many datasets can help increase the ability of researchers to identity variations in cellular responses under various conditions. Therefore, an improved normalization method needs to be developed to normalize many datasets from various platforms, including microarray, and Roche 454 or Illumina.
The emergence of whole transcriptome and genome research has made an integrative database for displaying cellular response essential. Algae are important non-model plants that have many research applications, including higher species diversity, sources of biofuel, adsorption of heavy metals and, following processing, health supplements. AlgaePath is a web-based database that comprehensively integrates C. reinhardtii and Neodesmus sp. UTEX 2219–4 gene information, biological pathways, and transcript abundance profiles from various databases. AlgaePath provides an effective interface for users interested in obtaining further insights into the transcript response mechanisms in different metabolic pathways under various conditions. Moreover, the Gene Group Analysis and Co-expression Analysis tools in AlgaePath can be used to detect similar expression transcripts of a target gene and enrichment pathways (functions) in a gene group. The results obtained using AlgaePath will provide a valuable reference for future efforts to elucidate critical mechanisms by mining high-throughput data. Importantly, the AlgaePath database is a significant contribution to algae research.
The AlgaePath database is publicly available at http://AlgaePath.itps.ncku.edu.tw.
This research was supported by a grant from National Science Council of the Republic of China for financially supporting this research under Contract NSC 99-2628-B-006 -016 -MY3and NSC 102-2313-B-006 -004. Ted Knoy is appreciated for his editorial assistance.
- McKendry P: Energy production from biomass (part 1): overview of biomass. Bioresour Technol. 2002, 83 (1): 37-46. 10.1016/S0960-8524(01)00118-3.PubMedView ArticleGoogle Scholar
- McKendry P: Energy production from biomass (part 2): conversion technologies. Bioresour Technol. 2002, 83 (1): 47-54. 10.1016/S0960-8524(01)00119-5.PubMedView ArticleGoogle Scholar
- Martin MA: First generation biofuels compete. N Biotechnol. 2010, 27 (5): 596-608. 10.1016/j.nbt.2010.06.010.PubMedView ArticleGoogle Scholar
- Fan J, Yan C, Andre C, Shanklin J, Schwender J, Xu C: Oil accumulation is controlled by carbon precursor supply for fatty acid synthesis in Chlamydomonas reinhardtii. Plant Cell Physiol. 2012, 53 (8): 1380-1390. 10.1093/pcp/pcs082.PubMedView ArticleGoogle Scholar
- Singh J, Gu S: Commercialization potential of microalgae for biofuels production. Renew Sust Energ Rev. 2010, 14 (9): 2596-2610. 10.1016/j.rser.2010.06.014.View ArticleGoogle Scholar
- Lommer M, Specht M, Roy AS, Kraemer L, Andreson R, Gutowska MA, Wolf J, Bergner SV, Schilhabel MB, Klostermeier UC, Beiko RG, Rosenstiel P, Hippler M, Laroche J: Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation. Genome Biol. 2012, 13 (7): R66-10.1186/gb-2012-13-7-r66.PubMed CentralPubMedView ArticleGoogle Scholar
- Çetinkaya Dönmez G, Aksu Z, Öztürk A, Kutsal T: A comparative study on heavy metal biosorption characteristics of some algae. Process Biochem. 1999, 34 (9): 885-892. 10.1016/S0032-9592(99)00005-9.View ArticleGoogle Scholar
- Roy D, Greenlaw PN, Shane BS: Adsorption of heavy metals by green algae and ground rice hulls. J Environ Sci Health Part A. 1993, 28 (1): 37-50.Google Scholar
- Gupta V, Shrivastava A, Jain N: Biosorption of Chromium (VI) from Aqueous solutions by green algae spirogyra species. Water Res. 2001, 35 (17): 4079-4085. 10.1016/S0043-1354(01)00138-5.PubMedView ArticleGoogle Scholar
- Katz ME, Wright JD, Miller KG, Cramer BS, Fennel K, Falkowski PG: Biological overprint of the geological carbon cycle. Mar Geol. 2005, 217 (3): 323-338.View ArticleGoogle Scholar
- Longhurst AR: Role of the marine biosphere in the global carbon cycle. Limnol Oceanogr. 1991, 36 (8): 1507-1526. 10.4319/lo.1918.104.22.1687.View ArticleGoogle Scholar
- Wang ST, Pan YY, Liu CC, Chuang LT, Chen CNN: Characterization of a green microalga UTEX 2219–4: effects of photosynthesis and osmotic stress on oil body formation. Bot Stud. 2011, 52 (3): 305-312.Google Scholar
- Boyle NR, Page MD, Liu B, Blaby IK, Casero D, Kropat J, Cokus SJ, Hong-Hermesdorf A, Shaw J, Karpowicz SJ, Gallaher SD, Johnson S, Benning C, Pellegrini M, Grossman A, Merchant SS: Three acyltransferases and nitrogen-responsive regulator are implicated in nitrogen starvation-induced triacylglycerol accumulation in Chlamydomonas. J Biol Chem. 2012, 287 (19): 15811-15825. 10.1074/jbc.M111.334052.PubMed CentralPubMedView ArticleGoogle Scholar
- Fang W, Si Y, Douglass S, Casero D, Merchant SS, Pellegrini M, Ladunga I, Liu P, Spalding MH: Transcriptome-wide changes in Chlamydomonas reinhardtii gene expression regulated by carbon dioxide and the CO2-concentrating mechanism regulator CIA5/CCM1. Plant Cell. 2012, 24 (5): 1876-1893. 10.1105/tpc.112.097949.PubMed CentralPubMedView ArticleGoogle Scholar
- Fischer BB, Ledford HK, Wakao S, Huang SG, Casero D, Pellegrini M, Merchant SS, Koller A, Eggen RI, Niyogi KK: Singlet oxygen resistant 1 links reactive electrophile signaling to singlet oxygen acclimation in Chlamydomonas reinhardtii. Proc Natl Acad Sci U S A. 2012, 109 (20): E1302-E1311. 10.1073/pnas.1116843109.PubMed CentralPubMedView ArticleGoogle Scholar
- Gonzalez-Ballester D, Casero D, Cokus S, Pellegrini M, Merchant SS, Grossman AR: RNA-seq analysis of sulfur-deprived Chlamydomonas cells reveals aspects of acclimation critical for cell survival. Plant Cell. 2010, 22 (6): 2058-2084. 10.1105/tpc.109.071167.PubMed CentralPubMedView ArticleGoogle Scholar
- Miller R, Wu G, Deshpande RR, Vieler A, Gartner K, Li X, Moellering ER, Zauner S, Cornish AJ, Liu B, Bullard B, Sears BB, Kuo MH, Hegg EL, Shachar-Hill Y, Shiu SH, Benning C: Changes in transcript abundance in Chlamydomonas reinhardtii following nitrogen deprivation predict diversion of metabolism. Plant Physiol. 2010, 154 (4): 1737-1752. 10.1104/pp.110.165159.PubMed CentralPubMedView ArticleGoogle Scholar
- Urzica EI, Adler LN, Page MD, Linster CL, Arbing MA, Casero D, Pellegrini M, Merchant SS, Clarke SG: Impact of oxidative stress on ascorbate biosynthesis in Chlamydomonas via regulation of the VTC2 gene encoding a GDP-L-galactose phosphorylase. J Biol Chem. 2012, 287 (17): 14234-14245. 10.1074/jbc.M112.341982.PubMed CentralPubMedView ArticleGoogle Scholar
- Hruz T, Laule O, Szabo G, Wessendorp F, Bleuler S, Oertle L, Widmayer P, Gruissem W, Zimmermann P: Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes. Adv. Bioinform. 2008, 2008: 420747-View ArticleGoogle Scholar
- Patel RV, Nahal HK, Breit R, Provart NJ: BAR expressolog identification: expression profile similarity ranking of homologous genes in plant species. Plant J cCell Mole Biol. 2012, 71 (6): 1038-1050. 10.1111/j.1365-313X.2012.05055.x.View ArticleGoogle Scholar
- Latendresse M, Paley S, Karp PD: Browsing metabolic and regulatory networks with BioCyc. Methods Mol Biol. 2012, 804: 197-216. 10.1007/978-1-61779-361-5_11.PubMed CentralPubMedView ArticleGoogle Scholar
- Lopez D, Casero D, Cokus SJ, Merchant SS, Pellegrini M: Algal functional annotation tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data. BMC Bioinform. 2011, 12: 282-10.1186/1471-2105-12-282.View ArticleGoogle Scholar
- Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, Mitros T, Dirks W, Hellsten U, Putnam N, Rokhsar DS: Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012, 40 (Database issue): D1178-D1186.PubMed CentralPubMedView ArticleGoogle Scholar
- Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer EL, Eddy SR, Bateman A, Finn RD: The Pfam protein families database. Nucleic Acids Res. 2012, 40 (Database issue): D290-D301.PubMed CentralPubMedView ArticleGoogle Scholar
- Mi H, Muruganujan A, Thomas PD: PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 2013, 41 (Database issue): D377-D386.PubMed CentralPubMedView ArticleGoogle Scholar
- Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN: The COG database: an updated version includes eukaryotes. BMC Bioinform. 2003, 4 (1): 41-10.1186/1471-2105-4-41.View ArticleGoogle Scholar
- Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012, 9 (4): 357-359. 10.1038/nmeth.1923.PubMed CentralPubMedView ArticleGoogle Scholar
- Kent WJ: BLAT---the BLAST-like alignment tool. Genome Res. 2002, 12 (4): 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMed CentralPubMedView ArticleGoogle Scholar
- Hu Q, Sommerfeld M, Jarvis E, Ghirardi M, Posewitz M, Seibert M, Darzins A: Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances. Plant J Cell Mole Biol. 2008, 54 (4): 621-639. 10.1111/j.1365-313X.2008.03492.x.View ArticleGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.PubMed CentralPubMedView ArticleGoogle Scholar
- Huttenhower C, Haley EM, Hibbs MA, Dumeaux V, Barrett DR, Coller HA, Troyanskaya OG: Exploring the human genome with functional maps. Genome Res. 2009, 19 (6): 1093-1106. 10.1101/gr.082214.108.PubMed CentralPubMedView ArticleGoogle Scholar
- Rosenberg JN, Oyler GA, Wilkinson L, Betenbaugh MJ: A green light for engineered algae: redirecting metabolism to fuel a biotechnology revolution. Curr Opin Biol. 2008, 19 (5): 430-436. 10.1016/j.copbio.2008.07.008.View ArticleGoogle Scholar
- Rismani-Yazdi H, Haznedaroglu BZ, Bibby K, Peccia J: Transcriptome sequencing and annotation of the microalgae Dunaliella tertiolecta: pathway description and gene discovery for production of next-generation biofuels. BMC Genomics. 2011, 12: 148-10.1186/1471-2164-12-148.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang ZT, Ullrich N, Joo S, Waffenschmidt S, Goodenough U: Algal lipid bodies: stress induction, purification, and biochemical characterization in wild-type and starchless Chlamydomonas reinhardtii. Eukaryot Cell. 2009, 8 (12): 1856-1868. 10.1128/EC.00272-09.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang Z, Shrager J, Jain M, Chang CW, Vallon O, Grossman AR: Insights into the survival of Chlamydomonas reinhardtii during sulfur starvation based on microarray analysis of gene expression. Eukaryot Cell. 2004, 3 (5): 1331-1348. 10.1128/EC.3.5.1331-1348.2004.PubMed CentralPubMedView ArticleGoogle Scholar
- Merchant SS, Prochnik SE, Vallon O, Harris EH, Karpowicz SJ, Witman GB, Terry A, Salamov A, Fritz-Laylin LK, Marechal-Drouard L, et al: The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science. 2007, 318 (5848): 245-250. 10.1126/science.1143609.PubMed CentralPubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.