![]() ![]() For interpreting these high-throughput experimental data, biologists often study the functional relationships among these genes or gene products. In recent years, high-throughput experimental techniques such as microarray, mass spectrometry, and large-scale mutagenesis identified hundreds of interesting genes and gene products. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up-to-date pathway analysis results for users (ii) SubpathwayMiner supports multiple species (∼100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner facilitates sub-pathway identification of metabolic pathways by using pathway structure information. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. ![]()
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