I tried to run this using a set of compound IDs, and I got an error. I also get the same error using your example code. , but if I run enrichKEGG using gene ID, it works fine, suggesting that the KEGG website is okay, but the cpd IDs don't map. ...
Because the MetaCyc reactions are based on regrouping the predicted EC numbers the resulting pathway predictions also partially correspond to the original read counts. To understand this, it's important understand how the EC gene family abundances are calculated per ASV: by default, the read depth...
Among the paths toward cell death, anoikis (homelessness) is the predominant pathway when cells lose integrin-mediated cell-to-extracellular matrix interactions [2, 4]. These anchorage-independent cells can be either together or individuals without cadherin-mediated cell-to-cell interactions [5,6,7...
Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30 ArticleCASPubMedPubMed CentralGoogle Scholar Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Keseler IM, Krummenacker M, Midford PE, Ong Q, Ong WK, Paley...
the C-terminus and the N-terminus. Analysis of the sequences of the four chains reveals that approximately 110 amino acids near the N-terminus exhibit significant variability, known as the variable region. In contrast, other regions of the sequence remain relatively consistent, referred to as the...
Functional enrichment analyses were conducted by querying DEGs against the KEGG database (based on KOBAS 3.0, with default parameters). 2.4. Untargeted metabolomics The liver, heart, brain, forelimb, and tail (n = 6 per tissue) of acclimated larvae (on the 80th day after acclimation) were ...
DEG functions were explored through GO and KEGG pathway analysis and the terms which q-value ≤0.05 were defined as significant enriched. This was performed to identify significantly enriched metabolic pathways. 2.4. Proteome with ITRAQ and Data Analysis The samples used in RNA-seq were used to ...
(map_l_1_name)forpathwayinmap_l_1['children']:try:forgenesinpathway['children']:pathway_name=pathway['name'][0:5]+'\t'+pathway['name'][6:]# print(genes['name'])k_num=genes['name'].split(sep=' ')[0]gene_name=genes['name'].split(sep=' ')[1].split(sep=';')[0]anno=...