PlantGDB: a resource for comparative plant genomics

Duvick, Jon; Fu, Ann; Muppirala, Usha; Sabharwal, Mukul; Wilkerson, Matthew D.; Lawrence, Carolyn J.; Lushbough, Carol; Brendel, Volker

doi:10.1093/nar/gkm1041

Cited by 251 publications

(207 citation statements)

References 23 publications

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“…The PUTs sets were retrieved by assembling 531,159 ESTs and mRNA sequences (version 169a) downloaded from PlantGDB (http://www.plantgdb.org/) [31]. In brief, ESTs were analyzed for the contaminating vectors and then homo polymer tails were removed.…”

Section: Sequence Datamentioning

confidence: 99%

“…The genome of H. vulgare was recently sequenced and a total of 26,159 genes were predicted as a part of genome analysis pipeline [30]. Altogether, the availability of genomic sequence of H. vulgare and the availability of EST database (http://www.plantgdb.org/) [31] provide an opportunity to perform genome wide computational based analysis of AS.…”

Section: Introductionmentioning

confidence: 99%

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Genome‐wide analysis of alternative splicing events in Hordeum vulgare: Highlighting retention of intron‐based splicing and its possible function through network analysis

et al. 2015

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a b s t r a c tIn this study, using homology mapping of assembled expressed sequence tags against the genomic data, we identified alternative splicing events in barley. Results demonstrated that intron retention is frequently associated with specific abiotic stresses. Network analysis resulted in discovery of some specific sub-networks between miRNAs and transcription factors in genes with high number of alternative splicing, such as cross talk between SPL2, SPL10 and SPL11 regulated by miR156 and miR157 families. To confirm the alternative splicing events, elongation factor protein (MLOC_3412) was selected followed by experimental verification of the predicted splice variants by Semi quantitative Reverse Transcription PCR (qRT-PCR). Our novel integrative approach opens a new avenue for functional annotation of alternative splicing through regulatory-based network discovery.

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Section: Sequence Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genome‐wide analysis of alternative splicing events in Hordeum vulgare: Highlighting retention of intron‐based splicing and its possible function through network analysis

et al. 2015

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“…As discussed earlier, elucidation of biological pathways, in particular lipid metabolic pathways is useful for genetic engineering efforts directed towards augmenting lipid accumulation in microalgae (Georgianna and Mayfield, 2012;Khozin-Goldberg and Cohen, 2011;Yu et al, 2011). While several such databases specific for lipid pathway analyses have been constructed for plants (Beisson et al, 2003;Caspi et al, 2008;Child et al, 2012;Duvick et al, 2007;Mao et al, 2009;Mekhedov et al, 2000;Sucaet and Deva, 2011), very few to date are available for the study of microalgae (Lopez et al, 2011;May et al, 2009). It is likely that the dearth of algal genomic data as compared to higher plants has contributed to the present status of limited pathway databases and web resources.…”

Section: Databases and Other Bioinformatics Resources For Studying LImentioning

confidence: 99%

“…It was developed as a part of the GoFORSYS (German Systems Biology research initiative) (http://www.goforsys.de) project and currently provides curated experimental information on a total of 253 pathways, 2851 enzymes, 1419 enzymatic reactions, 1416 compounds, and 928 literature citations. The web-portal also houses the known and predicted biochemical pathways from other widely recognized pathway databases such as PlantGDB (Duvick et al, 2007), PlnTFDB (Riano-Pachon et al, 2007), Quantprime (Arvidsson et al, 2008), ProMEX (Hummel et al, 2007), and MapMan (Usadel et al, 2005). To facilitate phylogenomic analyses, Inparanoid (Remm et al, 2001) and OrthoMCL-DB (Chen et al, 2006) databases are also integrated into the tool.…”

Section: Databases and Other Bioinformatics Resources For Studying LImentioning

confidence: 99%

Agrigenomics for Microalgal Biofuel Production: An Overview of Various Bioinformatics Resources and Recent Studies to Link OMICS to Bioenergy and Bioeconomy

Misra

Panda²,

Parida³

2013

OMICS: A Journal of Integrative Biology

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Microalgal biofuels offer great promise in contributing to the growing global demand for alternative sources of renewable energy. However, to make algae-based fuels cost competitive with petroleum, lipid production capabilities of microalgae need to improve substantially. Recent progress in algal genomics, in conjunction with other ''omic'' approaches, has accelerated the ability to identify metabolic pathways and genes that are potential targets in the development of genetically engineered microalgal strains with optimum lipid content. In this review, we summarize the current bioeconomic status of global biofuel feedstocks with particular reference to the role of ''omics'' in optimizing sustainable biofuel production. We also provide an overview of the various databases and bioinformatics resources available to gain a more complete understanding of lipid metabolism across algal species, along with the recent contributions of ''omic'' approaches in the metabolic pathway studies for microalgal biofuel production.

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“…These databases include EST datasets from all available species, including plants, animals, and microorganisms. PlantGDB (Duvick et al 2008) contains plant ESTs, selected by plant sciences experts from such databases. Plant researchers can thus perform homology searches for query plant sequences using the selected datasets that include only plant ESTs, leading to a more stable (or irrespective of the difference of performance in each analyzing PC) search performance.…”

mentioning

confidence: 99%

Plant expressed sequence tags databases: practical uses and the improvement of their searches using network module analysis

Ogata

Suzuki

2011

Plant Biotechnology

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Sequencing technology has been rapidly advancing. Giga-sequencers, which produce several gigabases of fragmented sequences per run, are attractive for decoding genomes and expressed sequence tags (ESTs). A variety of plant genomes and ESTs have been sequenced since the decoding of the genome of Arabidopsis thaliana, the model plant. ESTs are useful for functional analyses of genes and proteins and as biomarkers, which are used to identify particular tissues and conditions due to the specificity of their expression. Sequenced plant genomes and ESTs have been entered into public databases, where they are freely downloadable. Sequences representative of particular functions or structures have been collected from public databases to curate smaller databases useful for studying protein function. Here, we discuss the uses of the currently available plant EST datasets. We also demonstrate the use of network module analysis to perform more stable (or irrespective of the difference of performance in each analyzing PC) homology searches and to provide more information on molecular functions of plant ESTs and proteins.Key words: Database, expressed sequence tag (EST), homology search, network module analysis.Plant Biotechnology 28, 351-360 (2011) DOI: 10.5511/plantbiotechnology.11.0818a Abbreviations: BLAST, Basic Local Alignment Search Tool; EST, expressed sequence tags; GBFF, GenBank flatfile; GFF, General Feature Format; GPFF, GenPept flatfime. This article can be found at http://www.jspcmb.jp/ Published online September 25, 2011 perform more stable homology searches and to provide more information on molecular functions of ESTs, downsizing while maintaining the precision of homology search and also constructing local modules, in which sequences are highly homologous and thus belong to a group with a common feature, are useful. The PSI-BLAST algorithm and its derivatives (Altschul et al. 1997;Lee et al. 2009;Li et al. 2011) focus on sequenceto-sequence hits between multiple sequences. To evaluate relationships between multiple elements (e.g., gene or metabolite), network module analysis is a useful approach (Saito et al. 2008). Network module analyses have been applied to plant gene co-expression, in which a plant gene is related to other genes based on similar expression profiles (Aoki et al. 2007;Ficklin and Feltus 2011;Huber et al. 2007;Marino-Ramirez et al. 2009;Ogata et al. 2010;Winden et al. 2011). This approach allows a co-expression module, which includes coexpressed gene to be assigned to a particular biological process. By identifying homologies between sequences, network module analysis can be used to create, a homology network in which a sequence (node) is connected to other sequences on the basis of high homology. To perform such analysis for a homology network, we used our algorithm (Ogata et al. 2009) according to the following processes: 1) performing BLAST for any pairs of sequences, 2) calculating association indices between pairs as described in "Userfriendly tools for using plant EST a...

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PlantGDB: a resource for comparative plant genomics

Cited by 251 publications

References 23 publications

Genome‐wide analysis of alternative splicing events in Hordeum vulgare: Highlighting retention of intron‐based splicing and its possible function through network analysis

Genome‐wide analysis of alternative splicing events in Hordeum vulgare: Highlighting retention of intron‐based splicing and its possible function through network analysis

Agrigenomics for Microalgal Biofuel Production: An Overview of Various Bioinformatics Resources and Recent Studies to Link OMICS to Bioenergy and Bioeconomy

Plant expressed sequence tags databases: practical uses and the improvement of their searches using network module analysis

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