The BaMM webserver offers four workflows for analyzing regulatory motifs. In the following the worflows are described in detail.
De-novo motif discovery¶
For our higher-order BaMM models, de-novo motif discovery takes place in two stages: seeding and motif refinement. In the seeding stage, we use PEnG-motif, a very fast algorithm for finding enriched IUPAC base patterns which we optimize to seed PWMs. In the refinement stage, the PWM seeds are then optimized to higher-order BaMMs.
The default de-novo workflow hides the seeding phase from the user. The best-performing seeds are automatically selected for higher-order refinement. We also offer a second, slightly more complex workflow, that reports possible seed PWMs and lets the user choose seeds for refinement. You can access it by clicking following button on the bottom of the de-novo motif discovery workflow:
In its simplest form, the de-novo motif discovery workflow requires a fasta file with sequences and reports up to four higher-order models.
By clicking on the
Advanced 0ptions, the user can choose a wide variety of additional settings and parameters organized into four subgroups: general settings, seeding stage, refinement stage and settings for further analyses.
- Search on both strands
- if unchecked, motifs can not lie on the reverse complemented strand. Searching on the PLUS strand only can be useful for stranded data, such as RNA.
- Background Sequences
- by default the background model is learnt as a homogeneous Markov model on the input sequences. If you have a separate negative set, you can upload it as a fasta file here.
- Background Model Order
- sets the order of the background model. The higher the background order, the more realistic the background model. We recommend order 2 for ChIP-seq data. For very short motifs (e.g.) RNA binding motifs, order of 1 or 0 may be necessary to detect the motif.
- Pattern Length
- The length W of patterns on the sequences to be searched.
- Z-Score Threshold
- Only W-mers which surpass this z-score threshold will be considered for seed optimization.
- Count Threshold
- Only W-mers that surpass this count threshold will be considered for seed optimization.
- IUPAC Optimization Score
Scoring function that is optimized in IUPAC pattern generation. Currently there are three options:
- LOGPVAL: optimize to IUPAC pattern with the lowest p-value
- MUTUAL_INFO: optimize to IUPAC pattern that has the highest mutual information between presence of a motif and being a positive sequence
- ENRICHMENT: optimize to IUPAC pattern with the highest enrichment over negative sequences
- Skip EM
- When unchecked, the seeds are not optimized with the Expectation-Maximization (EM) algorithm.
- Number of optimized seeds
- Up to this amount of seeds are refined to higher-order models.
- Model Order
- order of the Markov model. Models with high orders are more time consuming to train.
- Flank extension
- extend the core seed by extra positions to the left and the right. Can be used to learn weakly informative flanking regions.
Settings for further analyses¶
- Run motif scanning
- uncheck to skip scanning the input sequences for motif occurrences.
- Motif scanning p-value cutoff
- p-value cut-off for calling a position a binding site.
- Run motif evaluation
- uncheck to skip motif performance evaluation.
- Run motif-motif compare
- uncheck to skip motif annotation with models from one of our databases
- MMcompare e-value cutoff
- e-value cutoff for reporting motif-motif matches with our motif database
Motif scan takes a motif and a set of sequences and predicts binding positions of the motif. The uploaded motif can be either in MEME-format (>= version 4) or in BaMM format.
When scanning with a BaMM motif two files are required: A BaMM model (extension
*.ihbcp) and its corresponding background frequencies (extension
By default the performance of the motifs on the input set is evaluated. Optionally the motifs can also be annotated with one of our motif databases.
Please refer to Usage for a detailed description of the advanced parameter settings.
The BaMMs fall into following sub-collections:
- 613 motif models for
- 354 motif models for
- 19 motif models for
- 16 motif models for
- 34 motif models for
- 360 motif models for
Drosophila melanogaster(fly) based on ModERN [KVG+17]
Please be aware that the BaMM databases are automatically generated. While comparison against manually curated databases showed that they are generally of high quality, sometimes we also learn co-factors or a combination of protein-of-interest and cofactors.
The motif-motif comparison tool allows to search with a motif in MEME or BaMM format against a motif subcollection of our database. The e-value is the only configurable parameter.