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Colorectal cancer (CRC) is ranked the third most common cancer in human worldwide. However, the exact mechanisms of CRC are not well established. Furthermore, there may be differences between mechanisms of CRC in the Asian and in the Western populations. In the present study, we utilized a liquid chromatography-mass spectrometry (LC-MS) metabolomic approach supported by the 16S rRNA next-generation sequencing to investigate the functional and taxonomical differences between paired tumor and unaffected (normal) surgical biopsy tissues from 17 Malaysian patients. Metabolomic differences associated with steroid biosynthesis, terpenoid biosynthesis and bile metabolism could be attributed to microbiome differences between normal and tumor sites. The relative abundances of Anaerotruncus, Intestinimonas and Oscillibacter displayed significant relationships with both steroid biosynthesis and terpenoid and triterpenoid biosynthesis pathways. Metabolites involved in serotonergic synapse/ tryptophan metabolism (Serotonin and 5-Hydroxy-3-indoleacetic acid [5-HIAA]) were only detected in normal tissue samples. On the other hand, S-Adenosyl-L-homocysteine (SAH), a metabolite involves in methionine metabolism and methylation, was frequently increased in tumor relative to normal tissues. In conclusion, this study suggests that local microbiome dysbiosis may contribute to functional changes at the cancer sites. Results from the current study also contributed to the list of metabolites that are found to differ between normal and tumor sites in CRC and supported our quest for understanding the mechanisms of carcinogenesis.
Pubmed ID: 30576312
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Online interface that utilizes an algorithm to predict the most aggregation-prone portions and the corresponding beta-strand inter-molecular pairing for a given input sequence. Users can paste the sequence into the interface and output the appropriate sequence.
View all literature mentionsSoftware platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsDatabase of biological data related to a single initiative, originating from a single organization or from a consortium. A BioProject record provides users a single place to find links to the diverse data types generated for that project. It is a searchable collection of complete and incomplete (in-progress) large-scale sequencing, assembly, annotation, and mapping projects for cellular organisms. Submissions are supported by a web-based Submission Portal. The database facilitates organization and classification of project data submitted to NCBI, EBI and DDBJ databases that captures descriptive information about research projects that result in high volume submissions to archival databases, ties together related data across multiple archives and serves as a central portal by which to inform users of data availability. BioProject records link to corresponding data stored in archival repositories. The BioProject resource is a redesigned, expanded, replacement of the NCBI Genome Project resource. The redesign adds tracking of several data elements including more precise information about a project''''s scope, material, and objectives. Genome Project identifiers are retained in the BioProject as the ID value for a record, and an Accession number has been added. Database content is exchanged with other members of the International Nucleotide Sequence Database Collaboration (INSDC). BioProject is accessible via FTP.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023.Software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data, but also supporting analysis of other types of data. QIMME analyzes and transforms raw sequencing data generated on Illumina or other platforms to publication quality graphics and statistics.
View all literature mentionsMEFIT is a Microarray Experiment Functional Integration Technology. Given any amount of microarray data, it predicts the probability of pairwise functional relationship for any gene pair within individual biological functions. This web site makes the results of this evaluation available for download and provides an online view of the test set predictions based on hierarchical clustering. As a framework, MEFIT uses the results of many microarray experiments in combination with known biological process annotations (drawn from the Gene Ontology, KEGG, MIPS, or a biologist''s own pathways of interest) to predict new gene pair functional relationships within the given biological functions. Or in other words, MEFIT is a system that takes microarray results and known functional annotations as inputs and produces predicted gene pair functional relationships as output. To make these predictions, MEFIT uses a Bayesian network that consumes microarray data as input observations and produces predicted functional relationships through a single unobserved (except during training) node. Furthermore, to make predictions within the context of individual biological functions, a single Bayesian network structure is replicated once per function of interest. These networks with identical structure are then trained using known functional annotations such that each function''s network learns its own set of conditional probabilities. These probabilities encode how predictive each microarray experiment is of a particular function; for example, a sporulation time course might be very predictive of meiosis, but not much help in determining which genes perform ATP synthesis. We''ve evaluated this system using a collection of 40 microarray data sets and 200 biological processes. Select a biological function from the menu to view the results of clustering the S. cerevisiae genome using MEFIT''s test set predictions within that function as a similarity metric. Alternatively, enter a gene name or ORF identifier to list only functions in which that gene is predicted to be active. We are currently able to offer for download: The MEFIT README file, a Windows version and Linux version.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware for handling and analysis of high-throughput microbiome census data.
View all literature mentionsRepository for metabolomics data and metadata which provides analysis tools and access to various resources. NIH grantees may upload data and general users can search metabolomics database. Provides protocols for sample preparation and analysis, information about NIH Metabolomics Program, data sharing guidelines, funding opportunities, services offered by its Regional Comprehensive Metabolomics Resource Cores (RCMRC)s, and training workshops.
View all literature mentionsSource code that infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences. It uses the Jukes-Cantor or generalized time-reversible (GTR) models of nucleotide evolution and the JTT, WAG, or LG models of amino acid evolution.
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