GlyGen: Computational and informatics resources and tools for glycosciences research

Although ongoing technical advances are accelerating the pace and sophistication of data acquisition in glycoscience, the transformation of these data to glycobiology knowledge, insight, and understanding is slowed by the limited number of tools that facilitate their integration with biological knowledge. Thus, to fill in the critical gaps, there is a need for a broadly relevant and sustainable glycoinformatics resource that can provide tools and data to address specific glycoscience questions. GlyGen is an integrated, extendable and cross-disciplinary glycoinformatics resource that will facilitate knowledge discovery in basic and translational glycobiology by integrating multidisciplinary data and knowledge from diverse resources. It will address glycobiology questions that can currently be answered only by extensive literature-based research and manual collection of data from disparate resources. The aims of the GlyGen project includes integrating and exchange of up-to-date glycobiology-related information and data with partnering data sources such as EMBL-EBI, NCBI, UniProt, UniCarbKB, and others; creating an intuitive web portal to search and browse for glycoscience knowledge that will also include off-line data analysis, data exploration, and mining. Furthermore, the GlyGen project includes the development of essential new information resources, namely the Glycan Microarray Database that will provide key information about the interactions of glycans with other biomolecules and a Glycan Naming Ontology (GNOme) that facilitates interpretation of incomplete structural information in the context of biological functions. GlyGen\u27s comprehensive data integration framework and valuable user\u27s feedback will provide unprecedented support for complex queries spanning diverse data types relevant to glycobiology, extending its scope beyond the mapping of glycan data to genes and proteins. The resource would be publicly available and will facilitate the sharing and dissemination of glycobiology knowledge. It will provide new opportunities for a systems-level understanding of glycobiology in disease and development, even for scientists who do not specialize in glycobiology

Comparative Transcriptional Profiling of Two Wheat Genotypes, with Contrasting Levels of Minerals in Grains, Shows Expression Differences during Grain Filling

<div><p>Wheat is one of the most important cereal crops in the world. To identify the candidate genes for mineral accumulation, it is important to examine differential transcriptome between wheat genotypes, with contrasting levels of minerals in grains. A transcriptional comparison of developing grains was carried out between two wheat genotypes- <i>Triticum aestivum</i> Cv. WL711 (low grain mineral), and <i>T. aestivum</i> L. IITR26 (high grain mineral), using Affymetrix GeneChip Wheat Genome Array. The study identified a total of 580 probe sets as differentially expressed (with <i>log2</i> fold change of ≥2 at p≤0.01) between the two genotypes, during grain filling. Transcripts with significant differences in induction or repression between the two genotypes included genes related to metal homeostasis, metal tolerance, lignin and flavonoid biosynthesis, amino acid and protein transport, vacuolar-sorting receptor, aquaporins, and stress responses. Meta-analysis revealed spatial and temporal signatures of a majority of the differentially regulated transcripts.</p></div

Similarity search meta-analysis and mineral concentration analysis.

<p>(a) The similarity search in Genevestigator, using the differentially expressed metal related transcripts in our data, revealed the top most perturbation comparing transcriptome between the developing grains of LOK-1 and WH291. (b) Concentration of micronutrients (Fe, Zn and Mn) in mature grains of LOK-1 and WH291.</p

Enrichment of GO terms in 466 genes up-regulated (≥2 <i>log2</i> fold) in developing grains of WL711 during 14 and/or 28 DAA.

<p>Contingency and key as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111718#pone-0111718-t001" target="_blank">Table 1</a>.</p><p>Enrichment of GO terms in 466 genes up-regulated (≥2 <i>log2</i> fold) in developing grains of WL711 during 14 and/or 28 DAA.</p

Differentially expressed transcripts at 14 and 28 DAA.

<p>(a) Correlation plot represents the pairwise correlation between biological replicates of the samples (b) Volcano plots represents the differentially expressed transcripts, satisfying the criteria of p≤0.01.</p

Differentially expressed transcripts, satisfying the criteria of p≤0.01 (IITR26 <i>vs.</i> WL711) at 14 DAA, identified in ‘cellular response overview’ using MapMan software version 3.6.0RC1.

<p>The <i>log2</i> fold change in the transcript levels were used for the analysis.</p

Quantitative RT-PCR analyses of a few candidate genes: Metallothionein, NAM-1, LEA-12, and Sec-E. Each bar indicates standard error in three biological replicates.

<p>Quantitative RT-PCR analyses of a few candidate genes: Metallothionein, NAM-1, LEA-12, and Sec-E. Each bar indicates standard error in three biological replicates.</p

Differentially expressed transcripts with ≥2 <i>log2</i> fold change expression difference at p≤0.01, between IITR26 <i>vs.</i> WL711.

<p>(a) Venn diagram shows the total number of the differentially expressed transcripts and overlap at 14 and 28 DAA (b) Differentially regulated transcripts in biological and functional MapMan BINs.</p

Distribution of differentially up-regulated (IITR26 <i>vs.</i> WL711; ≥2 <i>log2</i> fold change; p≤0.01) transcripts related to metal accumulation and stress response in IITR26 (probe sets in black) and WL711 (probe sets in blue) at 14 DAA.

<p>Data is extracted from Blast2GO results of combined graphs (Biological Processes). The annotation of the probe sets is given in tables S3.</p