This prefix is pre-pended to all files generated by the current run. If left empty, a system-generated number will be assigned as prefix.
Number of clusters:
You may provide a specific number of clusters (e.g. 3), or an interval of partitions (e.g. 1-8). In the second case, the method will suggest the optimal number of cluster it found in the data, given the parameter configuration of the job. Maximum number of clusters: 15.
The algorithm will attempt to align all sequences to common windows of N amino acids, and construct its PSSMs on these alignments. Specify with this option the length of the alignment window. Minimum motif length: 2
Make clustering moves at each iteration:
By default, simple shift moves are performed at each iteration, indel moves every 10 iterations, single peptide moves every 20 iterations, phase shift moves every 100 iterations.
You can alter this behavior by ticking this option; simple shift and phase shift moves become disabled, and single peptide moves are made at each iteration. This set-up is recommended for "nearly-aligned" data, where clustering and indels should be sampled more regularly than extensions at the termini. That is the case, for example, of sets of MHC class I ligands of different length, which would in most cases require central indels to model peptide bulging of long ligand.
Max deletion length:
The maximum length of consecutive deletions in a peptide sequence.
Max insertion length:
The maximum length of consecutive insertions in a peptide sequence.
Number of seeds for initial conditions:
Gibbs sampling is a heuristic rather than a rigorous optimization procedure. Therefore, it cannot guarantee that the most optimal solution is always reached from any starting configuration. A common procedure to boost performance is to repeat the sampling from a number of initial random configurations and select the solution that appears to be optimal in terms of the fitness function that governs the system. Specify with this parameter the number of initial configurations used to initialize the system.
Penalty factor for inter-cluster similarity (λ):
This parameter modulates how similar the clusters are allowed to be. If you believe your data contains multiple specificities with well-defined motifs, λ can be relatively high; on the other hand, if your aim is to detect subtle differences in mostly homogenous data, the parameter λ should be set to a lower value.
Weigth on small clusters (σ):
This parameter can be used to specify how small clusters are allowed to be. With low values of σ the method will tend to produce small specialized clusters, while larger σ will return larger and more general clusters.
Use trash cluster to remove outliers:
The trash cluster is used to collect the peptides that appear not to match any of the motifs being identified. The behaviour of the trash-cluster is identical to any of the other clusters, with the difference that the sequences in the trash cluster do not contribute to the overall score of the system.
Threshold for discarding to trash:
This parameter specifies a baseline on the peptide scores, below which peptides are tossed into the trash cluster. If you believe your data contains some degree of noise, you may experiment with increasing this value and observe how many sequences become filtered out by the trash cluster.
Number of iterations per sequence per temperature step:
This parameter ("I") specificies how long your clustering schedule should be. Note that total number of iterations is the results of "I" multiplied by the number of sequences times the number of temperature steps, and it will increase linearly the execution time.
Initial Monte Carlo temperature:
The temperature is a scalar, lowered by discreet steps as the iterations progress. The temperature influences the probability of accepting or rejecting the moves of the algorith. In the initial iterations (high temperature) the program is free to explore the landscape of solutions, and as the system cools off only moves that increase the energy will be accepted.
Number of temperature steps:
The number of steps in the cooling schedule (starting from the initial temperature specified above).
Interval between Indel moves:
Specifies how often to attempt introducing insertions and deletions (see glossary).
Interval between Single peptide moves:
Specifies how often to attempt moving a sequence between clusters (see glossary).
Interval between Phase shift moves:
Specifies how often to attempt shifting the alignment window of a single cluster.
Background amino acid frequencies:
Construction of PSSMs relies on calculating the frequency of a given residue at a given position, compared to the expected background frequency of that amino acid. You may use a flat background model identical for all amino acids (Flat), a pre-calculated distribution reflecting the relative frequency of each residue in naturally occurring proteins (Pre-calculated Uniprot), or determine the background model directly from the dataset you submitted (From data).
Preference for hydrophobic AAs at P1:
In the special case of MHC class II data, we have previously found helpful to guide the alignment by expressing a preference for hydrophobic residues at the P1 of the alignment.
Sequence weighting type:
Data redundancy may affect the quality of the clustering. You may use an explicit clustering of the sequences in a given group (Clustering), or use a faster heuristic that calculates the degree of variability at each column in the alignment (Heuristic, recommended); you may also disable sequence weighting for downweighting of redundant sequences (None).