We address biological questions by combining molecular biology and computational methods. Our focus is on the genetic basis of plant specialized metabolism and the evolution of biosynthetic capabilities. To elucidate biosynthetic networks, we combine data from our own sequencing with public datasets. Bioinformatics tools are developed to answer specific questions through the analysis of large datasets, often employing methods of artificial intelligence. We investigate the regulation of biosynthetic networks through transcriptomics. Phylogenetic analyses assist in selecting candidate genes for specific molecular functions. Identified genes and biosynthetic pathways serve as a basis for experiments using synthetic biology methods. The results from the various approaches culminate in biotechnological applications.
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Plain Text - 4.8 KB - MD5: 778e89fab0d7b78a39aae5a6d8b5ce6d
Gzip Archive - 18.3 MB - MD5: 11508dbc56970e062e5a216431999f25
structural annotation
Gzip Archive - 147.6 KB - MD5: f392895ed32ca2e884d2383c0bbd3ca7
functional annotation
Gzip Archive - 30.5 MB - MD5: 45cc9fa3a93143f4bcc9dd41da1194cf
Gzip Archive - 784.2 MB - MD5: 4901f8a030ed515b3ac3172e5db3aeff
genome sequence
Gzip Archive - 19.9 MB - MD5: 3c45bf8a726c3838464d680b0db7047b
Jun 24, 2026
Meckoni, Samuel Nestor; Vieira Salgado de Oliveira, Julie Anne; Pucker, Boas, 2026, "Utricularia gibba genome sequence and annotation", https://doi.org/10.60507/FK2/JJ5QZX, bonndata, V1
The genome of an Utricularia gibba plant was sequenced with nanopore long reads. The genome sequence was assembled with hifiasm and the gene models were predicted by Helixer, BRAKER3 and GeMoMa. The functional annotation was predicted based on sequence similarity to well characterized Arabidopsis thaliana sequences.
Gzip Archive - 30.6 MB - MD5: ec4a8127235682f928cd9eec74ab0640
Utricularia gibba genome sequence as FASTA file
Gzip Archive - 666.3 KB - MD5: 50493cb4e09dfaec5e422bb95e94e0d6
Gzip Archive - 8.4 MB - MD5: 81ff27c41633b009564593b709eee512
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