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Wood-decay fungi contain the cellular mechanisms to decompose such plant cell wall components as cellulose, hemicellulose, and lignin. A multi-omics approach to the comparative analysis of wood-decay fungi gives not only new insights into their strategies for decomposing recalcitrant plant biomass, but also an understanding of how to exploit these mechanisms for biotechnological applications. We have developed an analytical workflow, Applied Biomass Conversion Design for Efficient Fungal Green Technology (ABCDEFGT), to simplify the analysis and interpretation of transcriptomic and secretomic data. ABCDEFGT utilizes self-organizing maps for grouping genes with similar transcription patterns, and an overlay with secreted proteins. The key feature of ABCDEFGT is simple graphic outputs of genome-wide transcriptomic and secretomic topographies, which enables visual inspection without a priori of the omics data and facilitates discoveries of co-regulated genes and proteins. Genome-wide omics landscapes were built with the newly sequenced fungal species Pycnoporus coccineus, Pycnoporus sanguineus, and Pycnoporus cinnabarinus grown on various carbon sources. Integration of the post-genomic data revealed a global overlap, confirming the pertinence of the genome-wide approach. ABCDEFGT was evaluated by comparison with the latest clustering method for ease of output interpretation, and ABCDEFGT gave a better biological representation of fungal behaviors. The genome-wide multi-omics strategy allowed us to determine the potential synergy of particular enzymes decomposing cellulose, hemicellulose, and lignin such as Lytic Polysaccharide Monooxygenases, modular enzymes associated with a cellulose binding module1, and Class II Peroxidase isoforms co-regulated with oxido-reductases. Overall, ABCDEFGT was capable of visualizing genome-wide transcriptional and secretomic profiles for intuitive interpretations and is suitable for exploration of newly-sequenced organisms.
Pubmed ID: 27605927 RIS Download
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Database that describes the families of structurally-related catalytic and carbohydrate-binding modules (or functional domains) of enzymes that degrade, modify, or create glycosidic bonds. This specialist database is dedicated to the display and analysis of genomic, structural and biochemical information on Carbohydrate-Active Enzymes (CAZymes). CAZy data are accessible either by browsing sequence-based families or by browsing the content of genomes in carbohydrate-active enzymes. New genomes are added regularly shortly after they appear in the daily releases of GenBank. New families are created based on published evidence for the activity of at least one member of the family and all families are regularly updated, both in content and in description. An original aspect of the CAZy database is its attempt to cover all carbohydrate-active enzymes across organisms and across subfields of glycosciences. One can search for CAZY Family pages using the Protein Accession (Genpept Accession, Uniprot Accession or PDB ID), Cazy family name or EC number. In addition, genomes can be searched using the NCBI TaxID. This search can be complemented by Google-based searches on the CAZy site.
View all literature mentionsA statistical framework for genomic data fusion is a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. Matlab code to center a kernel matrix and Matlab code for normalization are available.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software Python package that provides infrastructure to process data from high-throughput sequencing assays. While the main purpose of HTSeq is to allow you to write your own analysis scripts, customized to your needs, there are also a couple of stand-alone scripts for common tasks that can be used without any Python knowledge.
View all literature mentionsSoftware tool for fast and high throughput alignment of shotgun cDNA sequencing reads generated by transcriptomics technologies. Fast splice junction mapper for RNA-Seq reads. Aligns RNA-Seq reads to mammalian-sized genomes using ultra high-throughput short read aligner Bowtie, and then analyzes mapping results to identify splice junctions between exons.TopHat2 is accurate alignment of transcriptomes in presence of insertions, deletions and gene fusions.
View all literature mentionsSoftware package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
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