Analysis Description -------------------- MEME (Mixed Effects Model of Evolution) estimates a site-wise synonymous (α) and a two-category mixture of non-synonymous (β-, with proportion p-, and β+ with proportion [1-p-]) rates, and uses a likelihood ratio test to determine if β+ > α at a site. The estimates aggregate information over a proportion of branches at a site, so the signal is derived from episodic diversification, which is a combination of strength of selection [effect size] and the proportion of the tree affected. A subset of branches can be selected for testing as well, in which case an additional (nuisance) parameter will be inferred -- the non-synonymous rate on branches NOT selected for testing. Multiple partitions within a NEXUS file are also supported for recombination - aware analysis. Version 3.0 adds a different format for ancestral state reconstruction, branch-site posterior storage, and site-level heterogeneity testing. Version 4 adds support for multiple hits and more than 2 rate classes on omega, as well as site-level imputation option - __Requirements__: in-frame codon alignment and a phylogenetic tree - __Citation__: Detecting Individual Sites Subject to Episodic Diversifying Selection. _PLoS Genet_ 8(7): e1002764. - __Written by__: Sergei L. Kosakovsky Pond, Steven Weaver - __Contact Information__: spond@temple.edu - __Analysis Version__: 4.0 >code –> Universal /home/datamonkey/datamonkey-js-server/production/app/meme/output/656497ed1fdac30a835a1cd3.tre /home/datamonkey/datamonkey-js-server/production/app/meme/output/656497ed1fdac30a835a1cd3.tre >Loaded a multiple sequence alignment with **11** sequences, **1177** codons, and **1** partitions from `/home/datamonkey/datamonkey-js-server/production/app/meme/output/656497ed1fdac30a835a1cd3` >branches –> All >Select the p-value threshold to use when testing for selection (permissible range = [0,1], default value = 0.1): >pvalue –> 0.1 >[Advanced setting, will result in MUCH SLOWER run time] Perform parametric bootstrap resampling to derive site-level null LRT distributions up to this many replicates per site. Recommended use for small to medium (<30 sequences) datasets (permissible range = [0,1000], default value = 50, integer): >resample –> 0 The number omega rate classes to include in the model [2 is the strongly recommended default] (permissible range = [2,4], default value = 2, integer): >rates –> 2 >multiple-hits –> None >impute-states –> No >precision –> standard ### Branches to include in the MEME analysis Selected 19 branches to include in the MEME analysis: `HOMOSAPIENS, GORILLAGORILLAGORILLA, Node7, RHINOPITHECUSROXELLANA, Node6, CERCOCEBUSATYS, Node5, PONGOABELII, Node4, CHLOROCEBUSSABAEUS, Node3, MACACAFASCICULARIS, Node2, PANTROGLODYTES, Node1, PANPANISCUS, NOMASCUSLEUCOGENYS, AOTUSNANCYMAAE, Node17` ### Obtaining branch lengths and nucleotide substitution biases under the nucleotide GTR model >kill-zero-lengths –> Yes ### Deleted 3 zero-length internal branches: `Node1, Node3, Node5` * Log(L) = -27757.54, AIC-c = 55569.13 (27 estimated parameters) * 1 partition. Total tree length by partition (subs/site) 205.989 ### Obtaining the global omega estimate based on relative GTR branch lengths and nucleotide substitution biases * Log(L) = -26741.99, AIC-c = 53546.13 (31 estimated parameters) * 1 partition. Total tree length by partition (subs/site) 228.124 * non-synonymous/synonymous rate ratio for *test* = 0.9613 >full-model –> Yes Only analyze sites whose 1-based indices match the following list (null to skip) : >limit-to-sites –> null For sites whose 1-based indices match the following list, write out likelihood function snapshots (null string to skip) : >save-lf-for-sites –> null ### Improving branch lengths, nucleotide substitution biases, and global dN/dS ratios under a full codon model * Log(L) = -26727.13 * Log(L) = -26727.13, AIC-c = 53516.41 (31 estimated parameters) * 1 partition. Total tree length by partition (subs/site) 4433.742 * non-synonymous/synonymous rate ratio for *test* = 0.9996 ### For partition 1 these sites are significant at p <=0.1 | Codon | Partition | alpha |non-syn rate (beta) distribution, rates : weights| LRT |Episodic selection detected?| # branches | List of most common codon substitutions at this site | |:----------:|:----------:|:----------:|:-----------------------------------------------:|:----------:|:--------------------------:|:----------:|:---------------------------------------------------------------------:| | 14 | 1 | 0.006 | 0.00/13.42 : 0.00/1.00 | 5.344 | Yes, p = 0.0316 | 2 | [1]AGC>GCG,Agc>Ggc,GAA>CCG,gCG>gAA | | 20 | 1 | 0.000 | 0.00/11.44 : 0.00/1.00 | 3.706 | Yes, p = 0.0738 | 2 | [1]GAT>ACC,GAT>CCG | | 4 | 1 | 0.000 | 0.00/3.97 : 0.00/1.00 | 3.574 | Yes, p = 0.0790 | 1 | [1]cCg>cAg,CCG>TTT | | 23 | 1 | 0.000 | 0.00/170.23 : 0.00/1.00 | 9.666 | Yes, p = 0.0035 | 3 | [2]AGC>GCG|[1]GCg>CTg,gCG>gGC | | 36 | 1 | 0.413 | 0.21/65.44 : 0.00/1.00 | 3.766 | Yes, p = 0.0715 | 1 | [1]CcG>AcC,cCG>cGC,cGC>cCG,Cgc>Ggc | | 17 | 1 | 0.000 | 0.00/2386.96 : 0.00/1.00 | 3.493 | Yes, p = 0.0824 | 4 | [1]CtG>TtT,GGC>CTG,gGC>gAA,gGC>gCG | | 74 | 1 | 0.000 | 0.00/1.10 : 0.00/1.00 | 5.369 | Yes, p = 0.0312 | 1 | [1]cCg>cTg,CCg>GTg,cTg>cCg | | 100 | 1 | 0.000 | 0.00/0.47 : 0.00/1.00 | 4.619 | Yes, p = 0.0459 | 2 | [1]cTg>cCg,CTG>GGC,Ggc>Agc | | 106 | 1 | 0.000 | 0.00/0.43 : 0.00/1.00 | 3.429 | Yes, p = 0.0852 | 2 | [1]aGc>aCc,CCG>AGC,cCg>cTg | | 80 | 1 | 0.000 | 0.00/10.56 : 0.00/1.00 | 3.563 | Yes, p = 0.0794 | 2 | [1]CCG>GGC,CCG>TAT | | 73 | 1 | 0.004 | 0.00/10.59 : 0.00/1.00 | 5.008 | Yes, p = 0.0376 | 2 | [1]CTG>AGC,Gtg>Ctg,GtG>TtT,TTT>GCG | | 99 | 1 | 0.000 | 0.00/1.34 : 0.00/1.00 | 8.804 | Yes, p = 0.0054 | 1 | [1]cCG>cAT,cCg>cTg | | 72 | 1 | 0.107 | 0.00/145.56 : 0.00/1.00 | 5.883 | Yes, p = 0.0239 | 1 | [1]AGC>CCG,AGC>GAT,gAT>gGC,GGC>CCG | | 126 | 1 | 0.000 | 0.00/10.94 : 0.00/1.00 | 4.422 | Yes, p = 0.0508 | 2 | [1]Ccg>Gcg,CCg>GTg,GCG>TAT,GTg>CAg | | 129 | 1 | 0.000 | 0.00/3.41 : 0.00/1.00 | 4.234 | Yes, p = 0.0560 | 2 | [1]Ccg>Gcg,CCg>GTg,Gcg>Ccg,GTG>AGC | | 105 | 1 | 0.002 | 0.00/7.31 : 0.00/1.00 | 3.222 | Yes, p = 0.0950 | 1 | [1]CCG>GGC,cTg>cCg,GGC>CCG | | 150 | 1 | 0.000 | 0.00/1.71 : 0.00/1.00 | 4.115 | Yes, p = 0.0596 | 3 | [2]cTg>cCg|[1]CCG>AAC | | 124 | 1 | 0.000 | 0.00/7.11 : 0.00/1.00 | 3.210 | Yes, p = 0.0956 | 2 | [1]GGC>CCG,GGC>CTG | | 157 | 1 | 0.000 | 0.00/1.05 : 0.00/1.00 | 3.232 | Yes, p = 0.0945 | 3 | [2]CCg>GTg|[1]ACc>GGc,GTG>ACC | | 115 | 1 | 0.007 | 0.01/2154.76 : 0.00/1.00 | 19.028 | Yes, p = 0.0000 | 1 | [1]CCG>GAT,GAT>CTG,gAT>gCG | | 144 | 1 | 0.000 | 0.00/856.81 : 0.00/1.00 | 8.655 | Yes, p = 0.0058 | 2 | [2]cCg>cTg|[1]cCg>cAg,CTG>ACC | | 170 | 1 | 0.000 | 0.00/2.66 : 0.00/1.00 | 3.623 | Yes, p = 0.0770 | 3 | [1]CCG>AGC,cCg>cTg,CCg>GTg,cTg>cCg | | 143 | 1 | 0.003 | 0.00/15.58 : 0.00/1.00 | 6.213 | Yes, p = 0.0202 | 1 | [1]CAg>GTg,CTG>AGC,Gtg>Atg,Gtg>Ctg | | 158 | 1 | 0.000 | 0.00/6.55 : 0.00/1.00 | 3.243 | Yes, p = 0.0940 | 2 | [1]ACC>CTG,CcG>AcC | | 167 | 1 | 0.000 | 0.00/2.73 : 0.00/1.00 | 5.833 | Yes, p = 0.0246 | 2 | [1]AGC>CAG,AGC>CCG,cCg>cTg | | 180 | 1 | 0.000 | 0.00/0.55 : 0.00/1.00 | 4.046 | Yes, p = 0.0618 | 1 | [1]cCg>cTg,Ccg>Gcg,cTg>cCg | | 190 | 1 | 0.000 | 0.00/6.48 : 0.00/1.00 | 5.311 | Yes, p = 0.0321 | 3 | [1]AAC>GTG,aCc>aAc,AcC>GcG,CCg>GTg,Gcg>Ccg | | 192 | 1 | 0.000 | 0.00/2.06 : 0.00/1.00 | 6.047 | Yes, p = 0.0220 | 2 | [1]CAG>AGC,CAg>GTg,gTg>gCg | | 199 | 1 | 0.000 | 0.00/1.58 : 0.00/1.00 | 5.527 | Yes, p = 0.0287 | 2 | [1]CCg>GTg,cTg>cCg,Ctg>Gtg | | 200 | 1 | 0.111 | 0.11/12.38 : 0.00/1.00 | 3.535 | Yes, p = 0.0806 | 0 | [1]aGc>aCc,CTG>AGC,CTG>GGC,GGC>CTG | | 172 | 1 | 0.015 | 0.00/205.23 : 0.00/1.00 | 8.383 | Yes, p = 0.0067 | 1 | [1]cAG>cGC,cCG>cAT,CCG>TTT,Cgc>Ggc,GGC>CCG | | 246 | 1 | 0.000 | 0.00/2.62 : 0.00/1.00 | 3.431 | Yes, p = 0.0851 | 2 | [2]AGC>GTG|[1]gTg>gCg | | 235 | 1 | 0.014 | 0.00/62.69 : 0.00/1.00 | 6.503 | Yes, p = 0.0174 | 1 | [1]ACC>GTG,ATT>CGC,CGC>GCG,GtG>AtT,gTg>gCg | | 242 | 1 | 0.139 | 0.10/192.57 : 0.00/1.00 | 5.335 | Yes, p = 0.0317 | 1 | [1]AGC>GCG,cCG>cGC,cCg>cTg,Cgc>Agc,cGC>cAG | | 298 | 1 | 0.000 | 0.00/2.07 : 0.00/1.00 | 7.445 | Yes, p = 0.0108 | 1 | [1]AGC>GAT,ATt>CAt,GAt>ATt | | 304 | 1 | 0.005 | 0.00/4.43 : 0.00/1.00 | 3.148 | Yes, p = 0.0988 | 0 | [1]ACC>CAG,aTT>aCC,cAG>cGC,Cgc>Agc | | 326 | 1 | 0.008 | 0.00/20.69 : 0.00/1.00 | 3.609 | Yes, p = 0.0776 | 1 | [1]aGC>aAA,CTG>AGC,GCG>AAA,GCg>CTg | | 345 | 1 | 3.458 | 1.94/445.99 : 0.00/1.00 | 7.380 | Yes, p = 0.0111 | 1 | [1]CAG>AGC,CaG>GaA,CTG>AAC,cTg>cAg,gAA>gTG,GTG>CAT | Error: Master node received an error:HyPhy killed by signal 15 Function call stack 1 : [namespace = mpi.aux.qQbzCFdb] Call((queue[from])[utility.getGlobalValue("terms.mpi.callback")],from,Eval(result),(queue[from])[utility.getGlobalValue("terms.mpi.arguments")]); ------- 2 : [namespace = mpi.ctNtIifD] node=aux._handle_receieve(queue); ------- 3 : mpi.QueueJob(meme.queue,"meme.handle_a_site",{"0":"meme.site_likelihood","1":"meme.site_likelihood_bsrel","2":alignments.serialize_site_filter((meme.filter_specification[meme.partition_index])[utility.getGlobalValue("terms.data.name")],(_pattern_info_[utility.getGlobalValue("terms.data.sites")])[0]),"3":meme.partition_index,"4":_pattern_info_,"5":meme.site_model_mapping,"6":meme.scaler_mapping,"7":0},"meme.store_results"); ------- 4 : Call("utility.ForEachPair.CB",utility.ForEachPair.key,object[utility.ForEachPair.key]); ------- 5 : utility.ForEachPair(meme.site_patterns,"_pattern_","_pattern_info_",' meme.pattern_count_this += 1; io.ReportProgressBar("", "Working on site pattern " + (meme.pattern_count_this) + "/" + meme.pattern_count_all + " in partition " + (1+meme.partition_index)); meme.run_site = selection.io.sitelist_matches_pattern (_pattern_info_[terms.data.sites], meme.site_filter["site-filter"], FALSE); if (_pattern_info_[utility.getGlobalValue("terms.data.is_constant")] || (!meme.run_site)) { meme.store_results (-1,None,{"0" : "meme.site_likelihood", "1" : "meme.site_likelihood_bsrel", "2" : None, "3" : meme.partition_index, "4" : _pattern_info_, "5" : meme.site_model_mapping, "6" : meme.scaler_mapping, "7" : 0 }); } else { mpi.QueueJob (meme.queue, "meme.handle_a_site", {"0" : "meme.site_likelihood", "1" : "meme.site_likelihood_bsrel", "2" : alignments.serialize_site_filter ((meme.filter_specification[meme.partition_index])[utility.getGlobalValue("terms.data.name")], (_pattern_info_[utility.getGlobalValue("terms.data.sites")])[0]), "3" : meme.partition_index, "4" : _pattern_info_, "5" : meme.site_model_mapping, "6" : meme.scaler_mapping, "7" : 0 }, "meme.store_results"); } pattern_count_all '); -------