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In this study we performed such analysis by applying two of the more advanced motif detection methods on both synthetic and real datasets with different properties.
We performed bioinformatic analysis of the 141 SNO sites (from the 82 protein-SNOs) by several sequence motif detection methods, including secondary structure and solvent accessibility predictions and hydrophobicity analysis (as described in Materials and Methods).
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Analysis of transcription factors binding sites (TFBSs) was performed using the de novo sequence motif detection method XXmotifs [ 39].
Suppose we are given a set of M motifs ℳ m, m=1,…, M, which may either come from a database or from a de novo motif detection method.
To identify motifs in our DC signaling network, we used a fully connected directed graph that was reconstructed from our DC map, as described in detail in Additional file 1, followed by a motif search using several detection methods [ 78- 80].
To this end we have developed DEMON, a novel method for motif detection.
DISCOVER is a discriminative method for motif detection in higher eukaryotic genomes that enjoys the dual advantage of modeling CRM architecture of sequences and features of individual motifs.
Motifs in the promoter regions of the differentially expressed RefSeq genes were detected using the MEME motif detection program [ 32].
De novo motif detection using these regions by peak-motif detected ETS⇔CRE motif as the best-enriched motif.
We did not detect a UBE2QL1 E3 ligase binding partner through conventional detection methods and so undertook an in silico search (http://elm.eu.org/) for potential binding motifs in the full length UBE2QL1 protein sequence.
We used the network motif detection tool FANMOD FAst Network MOtiff Detection) 20 for detecting motifs in all networks in Table 1.
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