SAM modelfromalign - use an existing multiple alignment to create an initial model R. Hughey, A. Krogh modelfromalign modelfromalign seqlab modelfromalign perl "modelfromalign" 0 run Run name perl " $value" test 1 alignfile Aligned sequences (-alignfile) perl " -alignfile $value" 2 8 sam_model_file $run.mod sam_model perl 1 input Input options 2 alignment_weights Sequence weights for alignments used to form initial models (-alignment_weights) perl ($value)? " -alignment_weights $value" : "" 2 control Control options 2 align_fim Add FIMs to the ends of the initial model (-align_fim) perl ($value)? " -align_fim 1":"" 0 2 regul Regularizers and mixtures parameters 2 regularizerfile Regularizer (-regularizerfile) perl ($value)? " -regularizerfile /local/gensoft/lib/sam/$value":"" 2 long_match.regularizer trained.regularizer weak-gap.regularizer sam1.3.regularizer trained.regularizer: Regularizer optimized for unweighted transition counts on some set of re-estimated HSSP alignments cheap_gap.regularizer: Makes gap opening and closing very cheap allowing exploration of many different alignments, but giving too high a cost to long matches long_match.regularizer: Assigns somewhat reasonable gap costs for unweighted data, useful for sweeping away 'chatter' into big matches and big gaps, by making gap opening expensive but gap extension fairly cheap. reglength Length of the regularizer (-reglength) perl ($value)? " -reglength $value" : "" 2 priorlibrary Dirichlet mixture prior (-priorlibrary) perl ($value && $value ne $vdef)? " -priorlibrary /local/gensoft/lib/sam/$value":"" recode1.20comp 2 mall-opt.9comp opt-weight1.9comp uprior.9comp null.1comp recode1.20comp uprior9.plib: The 9-component library discussed in the aforementioned paper. Optimized for unweighted blocks data. mall-opt.9comp: Library re-optimized for unweighted data from an HSSP subset. opt-weight1.9comp: Library reoptimized for weighted version of same HSSP subset. recode1.20comp: A 20-component Dirichlet mixture trained on (realigned) HSSP alignments that have a diverse set of sequences. Intended for use in recoding inputs to neural net, but also useful as a standard regularizer. null.1comp: A one-component regularizer with tiny alpha, to get effectively no regularization. prior_weight Weight of the prior library (-prior_weight) perl ($value && $value != $vdef)? " -prior_weight $value" : "" 1.0 2 perl $priorlibrary del_jump_conf Confidence in the regularizer for transitions leaving a delete state. The regularizer's transition values are multiplied by this number (-del_jump_conf) perl ($value && $value != $vdef)? " -del_jump_conf $value" : "" 1.0 2 ins_jump_conf Confidence in the regularizer for transitions leaving an insert state (-ins_jump_conf) perl ($value && $value != $vdef)? " -ins_jump_conf $value" : "" 1.0 2 insconf Confidence in the regularizer for character probabilities in an insert state (-insconf) perl ($value && $value != $vdef)? " -insconf $value" : "" 10000 2 The high default means that the regularizer will overpower the actual counts determined by aligning sequences to the model. The regularizer's character insert values are multiplied by this number. match_jump_conf Confidence in the regularizer for transitions leaving a match state (-match_jump_conf) perl ($value && $value != $vdef)? " -match_jump_conf $value" : "" 1.0 2 matchconf Confidence in the regularizer for character probabilities in a match state (-matchconf) perl ($value && $value != $vdef)? " -matchconf $value" : "" 1.0 2 mainline_cutoff Confidence in the regularizer for transitions leaving a match state (-mainline_cutoff) perl ($value && $value != $vdef)? " -mainline_cutoff $value" : "" 0.5 2 output Output options 2 binary_output Write models in binary format (-binary_output) perl ($value)? " -binary_output 1":"" 0 2