{"id":19592915,"url":"https://github.com/soedinglab/bammmotif2","last_synced_at":"2025-04-27T14:34:00.333Z","repository":{"id":74858899,"uuid":"65297876","full_name":"soedinglab/BaMMmotif2","owner":"soedinglab","description":"Bayesian Markov Model motif discovery tool version 2 - An expectation maximization algorithm for the de novo discovery of enriched motifs as modelled by higher-order Markov models. ","archived":false,"fork":false,"pushed_at":"2021-02-01T12:03:16.000Z","size":9674,"stargazers_count":13,"open_issues_count":4,"forks_count":5,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-04-05T00:51:17.761Z","etag":null,"topics":["bioinformatics","chip-seq","motif-analysis","motif-discovery","ngs-analysis"],"latest_commit_sha":null,"homepage":"https://bammmotif.mpibpc.mpg.de/","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/soedinglab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-08-09T13:37:52.000Z","updated_at":"2024-12-18T13:39:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"64d44047-c843-4254-9718-a34f16a17a40","html_url":"https://github.com/soedinglab/BaMMmotif2","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FBaMMmotif2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FBaMMmotif2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FBaMMmotif2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FBaMMmotif2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/soedinglab","download_url":"https://codeload.github.com/soedinglab/BaMMmotif2/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251154855,"owners_count":21544567,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","chip-seq","motif-analysis","motif-discovery","ngs-analysis"],"created_at":"2024-11-11T08:37:24.380Z","updated_at":"2025-04-27T14:33:55.314Z","avatar_url":"https://github.com/soedinglab.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# BaMM!motif - v2\n\n**Ba**yesian **M**arkov **M**odel **motif** discovery software (version 2).\n\n(C) Johannes Soeding, Wanwan Ge, Anja Kiesel, Matthias Siebert\n\n[![Build Status](https://travis-ci.org/soedinglab/BaMMmotif2.svg?branch=master)](https://travis-ci.org/soedinglab/BaMMmotif2)\n\n## Requirements\nTo compile from source, you need:\n\n  * [GCC](https://gcc.gnu.org/) compiler 4.7 or later (we suggest GCC-5.x)\n  * [CMake](http://cmake.org/) 2.8.11 or later\n  \nC++ packages\n  * [Boost](http://www.boost.org/) \n\nTo plot BaMM logos you need R and several R packages \n\n  * [R](https://cran.r-project.org/) 2.14.1 or later\n  * install.packages( \"zoo\" )\n  * install.packages( \"argparse\" )\n  * install.packages( \"fdrtool\" )\n  * install.packages( \"LSD\" )\n  * install.packages( \"grid\" )\n  * install.packages( \"gdata\" )\n\n## Installation\n\n### Clone it from GIT\n\n      git clone https://github.com/soedinglab/BaMMmotif2.git BaMMmotif\n      cd BaMMmotif\n\n### How to compile BaMM!motif?\n\n\n#### Linux\n      mkdir build\n      cd build\n      cmake -DCMAKE_INSTALL_PREFIX=${HOME}/opt/BaMM ..\n      make\n      make install\n      \nAdjust `${HOME}/opt/BaMM` if you want to change the directory for installation\n\n#### OS X\nOS X ships clang instead of gcc. We recommend using [Homebrew](http://brew.sh/) to install gcc.\n\nHaving installed Homebrew, all required dependencies can be installed using the `brew` command\n\n      brew tap homebrew/versions\n      brew tap homebrew/science\n      brew install gcc5 cmake R\n\n#### Compilation\n\n      export CXX=g++-5\n      export CC=gcc-5\n      export LDFLAGS=\"-static-libgcc -static-libstdc++\"\n\n      mkdir build\n      cd build\n      cmake -DCMAKE_INSTALL_PREFIX=${HOME}/opt/BaMM ..\n      make \n      make install\n      \n#### Environment setup\nAdd this line to your $HOME/.bashrc (or .zshrc...) to add BaMMmotif to your PATH:\n\n    export PATH=${PATH}:${HOME}/opt/BaMM/bin\n    \nUpdate your environment:    \n\n    source $HOME/.bashrc\n\n## How to use BaMM!motif from the command line?\n\n### SYNOPSIS\n\n      BaMMmotif DIRPATH FILEPATH [OPTIONS]\n\n### DESCRIPTION\n\n      Bayesian Markov Model motif discovery software.\n\n      DIRPATH\n          Output directory for the results.\n\n      FILEPATH\n          FASTA file with positive sequences of equal length.\n\n### OPTIONS\n\nSequence options\n\n      --alphabet \u003cSTRING\u003e\n          STANDARD.         For alphabet type ACGT, default setting;\n          METHYLC.          For alphabet type ACGTM;\n          HYDROXYMETHYLC.   For alphabet type ACGTH;\n          EXTENDED.         For alphabet type ACGTMH.\n      \n      --ss\n          Search motif only on single strand strands (positive sequences).\n          This option is not recommended for analyzing ChIP-seq data.\n          By default, BaMM searches motifs on both strands.\n          \n      --negSeqSet \u003cFILEPATH\u003e\n          FASTA file with negative/background sequences used to learn the\n          (homogeneous) background BaMM. If not specified, the background BaMM\n          is learned from the positive sequences.\n\n  Options to initialize BaMM(s) from file\n\n      --bindingSiteFile \u003cFILEPATH\u003e\n          File with binding sites of equal length (one per line).\n      \n      --PWMFile \u003cSTRING\u003e\n          File that contains position weight matrices (PWMs).\n      \n      --BaMMFile \u003cSTRING\u003e\n          File that contains a model in bamm file format.\n\n      --maxPWM \u003cINTEGER\u003e\n          Number of models to be learned by BaMM!motif, specific for PWMs.\n\n  Options for the (inhomogeneous) motif BaMMs\n\n      -k|--order \u003cINTEGER\u003e\n          Model order. The default is 2.\n\n      -a|--alpha \u003cFLOAT\u003e [\u003cFLOAT\u003e...]\n          Order-specific prior strength. The default is 1.0 (for k = 0) and\n          beta x gamma^k (for k \u003e 0). The options -b and -g are ignored.\n\n      -b|--beta \u003cFLOAT\u003e\n          Calculate order-specific alphas according to beta x gamma^k (for\n          k \u003e 0). The default is 7.0.\n\n      -g|--gamma \u003cFLOAT\u003e\n          Calculate order-specific alphas according to beta x gamma^k (for\n          k \u003e 0). The default is 3.0.\n\n      --extend \u003cINTEGER\u003e{1,2}\n          Extend BaMMs by adding uniformly initialized positions to the left\n          and/or right of initial BaMMs. Invoking e.g. with --extend 0 2 adds\n          two positions to the right of initial BaMMs. Invoking with --extend 2\n          adds two positions to both sides of initial BaMMs. By default, BaMMs\n          are not being extended.\n      \n      -q \u003cFLOAT\u003e\n          Prior probability for a positive sequence to contain a motif. The\n          default is 0.9.\n          \n      -s, --sOrder \u003cINTERGER\u003e\n          The order of k-mer for sampling pseudo/negative set. The default is 2.\n\n  Options for the (homogeneous) background BaMM\n\n      -K \u003cINTEGER\u003e\n          Order. The default is 2.\n\n      -A|--Alpha \u003cFLOAT\u003e\n          Prior strength. The default is 10.0.\n      \n      --bgModelFile \u003cSTRING\u003e\n          Read in background model from a bamm-formatted file. \n\n  EM options\n\n      --EM\n          Triggers Expectation Maximization (EM) algorithm.\n          \n  Gibbs sampling options\n\n      --CGS\n          Triggers Collapsed Gibbs Sampling (CGS) algorithm.\n      \n      --maxCGSIterations \u003cINTEGER\u003e \n          Limit the number of CGS iterations.\n          It should be larger than 5 and defaults to 100.\n\n  Options for model evaluation\n      \n      --FDR\n          Triggers False-Discovery-Rate (FDR) estimation.\n        \n      -m|--mFold \u003cINTEGER\u003e\n          Number of negative sequences as multiple of positive sequences.\n          The default is 10.\n      \n      -n, --cvFold \u003cINTEGER\u003e\n          Fold number for cross-validation. \n          The default is 5, which means the training set is 4-fold of the test set.\n          \n  Output options\n\n      --saveBaMMs\n          Write optimized BaMM(s) to disk.\n\n      --saveInitBaMMs\n          Write initialized BaMM(s) to disk.\n          \n      --verbose\n          Verbose terminal printouts.\n\n      -h, --help\n          Printout this help.\n\n## Downstream analysis\n\n### Evaluate the performance of BaMMs\n\nFor evaluating the optimized BaMM models, a file with extension `.stats` is required. It can be generated either by running `BaMMmotif` with `--FDR` flag, or by running `FDR` program independently.\n\nEither\n\n    ${HOME}/opt/BaMM/bin/BaMMmotif [OUTPUT_FIR] [FASTAFILE] [MOTIF_FILE] [options] --FDR\n\nor\n\n    ${HOME}/opt/BaMM/bin/FDR [OUTPUT_FIR] [FASTAFILE] [MOTIF_FILE]\n\nR script `evaluateBaMM.R` is provided in the installation directory `${HOME}/opt/BaMM/bin` to calculate the performance score AUSFC and optionally plot precision-recall curve, partial ROC, and sensitivity-FDR curve. You can run it like:\n\n    ${HOME}/opt/BaMM/bin/evaluateBaMM.R [INPUT_DIR] [PREFIX_OF_STATS_FILE] [options]\n    \nThe options are:\n\n`--SFC 1` for plotting the sensitivity-false discovery rate curve.\n\n`--ROC5 1` for plotting the partial ROC with the first 5% of TPR.\n\n`--PRC 1` for plotting the precision-recall curve.\n\nYou will get the following plots:\n\n![image](example/images/JunD_motif_1_SFC.jpeg)\n\n![image](example/images/JunD_motif_1_pROC.jpeg)\n\n![image](example/images/JunD_motif_1_PRC.jpeg)\n\nThe performance scores such as AUSFC, pAUC amd AUPRC are written in the `.bmscore` file.\n    \n### How to plot BaMM logos?\n\nR script `platBaMMLogo.R` is provided in the installation directory `${HOME}/opt/BaMM/bin` to plot the BaMM logo from a BaMM flat file. \n\nIt requires output files with extension `.ihbcp`, `.ihbp`, `.hbcp` or `.hbp` from BaMMmotif as input.\n\nThe logo order is an integer between 0 to 2. \n\n    plotBaMMLogo.R [INPUT_DIR] [PREFIX_OF_OCCURRENCE_FILE] [LOGO_ORDER]\n\nYou will get the following plots:\n\n![image](example/images/JunD_motif_1-logo-order-0.png)\n\n![image](example/images/JunD_motif_1-logo-order-1.png)\n\n![image](example/images/JunD_motif_1-logo-order-2.png)\n\n### Motif distribution analysis\n\nFor visualizing the distribution of motifs in the sequence set, you need to generate either a `.occurrence` file by executing `BaMMmotif` with a `--scoreSeqset` flag or by executing `BaMMScan`.\n\nEither\n\n    ${HOME}/opt/BaMM/bin/BaMMmotif [OUTPUT_FIR] [FASTAFILE] [MOTIF_FILE] [options] --scoreSeqset\n\nor\n    \n    ${HOME}/opt/BaMM/bin/BaMMScan [OUTPUT_FIR] [FASTAFILE] [MOTIF_FILE]\n    \nAfter obtaining a `.occurrence` file, you can run R script `plotMotifDistribution.R` provided in the installation directory `${HOME}/opt/BaMM/bin` to visualise the motif distribution:\n\n    ${HOME}/opt/BaMM/bin/plotMotifDistribution.R [INPUT_DIR] [PREFIX_OF_OCCURRENCE_FILE] [option]\n\nThe option is:\n\n`--ss 1` for only plotting the distribution of motif on single strand. Otherwise, it will visualize motif distribution on both strands.\n\nYou will get one of the following plots:\n\n![image](example/images/JunD_motif_1_ds_distribution.jpeg)\n\n![image](example/images/JunD_motif_1_ss_distribution.jpeg)\n\nNote that, this analysis currently only work for sequences set with sequences of the same length.\n\n## BaMM flat file format\n\nBaMM!motif generates two files for each inhomogeneous BaMM: \n\n1. file with extension `.ihbp` contains probabilities of BaMM model;\n\n2. file with extension `.ihbcp` contains conditional probabilities of BaMM model.\n\nThe format is the same for these two files. While blank lines separate BaMM positions, lines 1 to *k*+1 of each BaMM position contain the (conditional) probabilities for order 0 to order *k*. For instance, the format for a BaMM of order 2 and length *W* is as follows:\n\nFilename extension: `.ihbp`\n\nP\u003csub\u003e1\u003c/sub\u003e(A) P\u003csub\u003e1\u003c/sub\u003e(C) P\u003csub\u003e1\u003c/sub\u003e(G) P\u003csub\u003e1\u003c/sub\u003e(T)\u003cbr\u003e\nP\u003csub\u003e1\u003c/sub\u003e(AA) P\u003csub\u003e1\u003c/sub\u003e(AC) P\u003csub\u003e1\u003c/sub\u003e(AG) P\u003csub\u003e1\u003c/sub\u003e(AT) P\u003csub\u003e1\u003c/sub\u003e(CA) P\u003csub\u003e1\u003c/sub\u003e(CC) P\u003csub\u003e1\u003c/sub\u003e(CG) ... P\u003csub\u003e1\u003c/sub\u003e(TT)\u003cbr\u003e\nP\u003csub\u003e1\u003c/sub\u003e(AAA) P\u003csub\u003e1\u003c/sub\u003e(AAC) P\u003csub\u003e1\u003c/sub\u003e(AAG) P\u003csub\u003e1\u003c/sub\u003e(AAT) P\u003csub\u003e1\u003c/sub\u003e(ACA) P\u003csub\u003e1\u003c/sub\u003e(ACC) P\u003csub\u003e1\u003c/sub\u003e(ACG) ... P\u003csub\u003e1\u003c/sub\u003e(TTT)\u003cbr\u003e\n\nP\u003csub\u003e2\u003c/sub\u003e(A) P\u003csub\u003e2\u003c/sub\u003e(C) P\u003csub\u003e2\u003c/sub\u003e(G) P\u003csub\u003e2\u003c/sub\u003e(T)\u003cbr\u003e\nP\u003csub\u003e2\u003c/sub\u003e(AA) P\u003csub\u003e2\u003c/sub\u003e(AC) P\u003csub\u003e2\u003c/sub\u003e(AG) P\u003csub\u003e2\u003c/sub\u003e(AT) P\u003csub\u003e2\u003c/sub\u003e(CA) P\u003csub\u003e2\u003c/sub\u003e(CC) P\u003csub\u003e2\u003c/sub\u003eCG) ... P\u003csub\u003e2\u003c/sub\u003e(TT)\u003cbr\u003e\nP\u003csub\u003e2\u003c/sub\u003e(AAA) P\u003csub\u003e2\u003c/sub\u003e(AAC) P\u003csub\u003e2\u003c/sub\u003e(AAG) P\u003csub\u003e2\u003c/sub\u003e(AAT) P\u003csub\u003e2\u003c/sub\u003e(ACA) P\u003csub\u003e2\u003c/sub\u003e(ACC) P\u003csub\u003e2\u003c/sub\u003e(ACG) ... P\u003csub\u003e2\u003c/sub\u003e(TTT)\u003cbr\u003e\n...\n\nP\u003csub\u003eW\u003c/sub\u003e(A) P\u003csub\u003eW\u003c/sub\u003e(C) P\u003csub\u003eW\u003c/sub\u003e(G) P\u003csub\u003eW\u003c/sub\u003e(T)\u003cbr\u003e\nP\u003csub\u003eW\u003c/sub\u003e(AA) P\u003csub\u003eW\u003c/sub\u003e(AC) P\u003csub\u003eW\u003c/sub\u003e(AG) P\u003csub\u003eW\u003c/sub\u003e(AT) P\u003csub\u003eW\u003c/sub\u003e(CA) P\u003csub\u003eW\u003c/sub\u003e(CC) P\u003csub\u003eW\u003c/sub\u003eCG) ... P\u003csub\u003eW\u003c/sub\u003e(TT)\u003cbr\u003e\nP\u003csub\u003eW\u003c/sub\u003e(AAA) P\u003csub\u003eW\u003c/sub\u003e(AAC) P\u003csub\u003eW\u003c/sub\u003e(AAG) P\u003csub\u003eW\u003c/sub\u003e(AAT) P\u003csub\u003eW\u003c/sub\u003e(ACA) P\u003csub\u003eW\u003c/sub\u003e(ACC) P\u003csub\u003eW\u003c/sub\u003e(ACG) ... P\u003csub\u003eW\u003c/sub\u003e(TTT)\u003cbr\u003e\n\nFilename extension: `.ihbcp`\n\nP\u003csub\u003e1\u003c/sub\u003e(A) P\u003csub\u003e1\u003c/sub\u003e(C) P\u003csub\u003e1\u003c/sub\u003e(G) P\u003csub\u003e1\u003c/sub\u003e(T)\u003cbr\u003e\nP\u003csub\u003e1\u003c/sub\u003e(A|A) P\u003csub\u003e1\u003c/sub\u003e(C|A) P\u003csub\u003e1\u003c/sub\u003e(G|A) P\u003csub\u003e1\u003c/sub\u003e(T|A) P\u003csub\u003e1\u003c/sub\u003e(A|C) P\u003csub\u003e1\u003c/sub\u003e(C|C) P\u003csub\u003e1\u003c/sub\u003e(G|C) ... P\u003csub\u003e1\u003c/sub\u003e(T|T)\u003cbr\u003e\nP\u003csub\u003e1\u003c/sub\u003e(A|AA) P\u003csub\u003e1\u003c/sub\u003e(C|AA) P\u003csub\u003e1\u003c/sub\u003e(G|AA) P\u003csub\u003e1\u003c/sub\u003e(T|AA) P\u003csub\u003e1\u003c/sub\u003e(A|AC) P\u003csub\u003e1\u003c/sub\u003e(C|AC) P\u003csub\u003e1\u003c/sub\u003e(G|AC) ... P\u003csub\u003e1\u003c/sub\u003e(T|TT)\u003cbr\u003e\n\nP\u003csub\u003e2\u003c/sub\u003e(A) P\u003csub\u003e2\u003c/sub\u003e(C) P\u003csub\u003e2\u003c/sub\u003e(G) P\u003csub\u003e2\u003c/sub\u003e(T)\u003cbr\u003e\nP\u003csub\u003e2\u003c/sub\u003e(A|A) P\u003csub\u003e2\u003c/sub\u003e(C|A) P\u003csub\u003e2\u003c/sub\u003e(G|A) P\u003csub\u003e2\u003c/sub\u003e(T|A) P\u003csub\u003e2\u003c/sub\u003e(A|C) P\u003csub\u003e2\u003c/sub\u003e(C|C) P\u003csub\u003e2\u003c/sub\u003e(G|C) ... P\u003csub\u003e2\u003c/sub\u003e(T|T)\u003cbr\u003e\nP\u003csub\u003e2\u003c/sub\u003e(A|AA) P\u003csub\u003e2\u003c/sub\u003e(C|AA) P\u003csub\u003e2\u003c/sub\u003e(G|AA) P\u003csub\u003e2\u003c/sub\u003e(T|AA) P\u003csub\u003e2\u003c/sub\u003e(A|AC) P\u003csub\u003e2\u003c/sub\u003e(C|AC) P\u003csub\u003e2\u003c/sub\u003e(G|AC) ... P\u003csub\u003e2\u003c/sub\u003e(T|TT)\u003cbr\u003e\n...\n\nP\u003csub\u003eW\u003c/sub\u003e(A) P\u003csub\u003eW\u003c/sub\u003e(C) P\u003csub\u003eW\u003c/sub\u003e(G) P\u003csub\u003eW\u003c/sub\u003e(T)\u003cbr\u003e\nP\u003csub\u003eW\u003c/sub\u003e(A|A) P\u003csub\u003eW\u003c/sub\u003e(C|A) P\u003csub\u003eW\u003c/sub\u003e(G|A) P\u003csub\u003eW\u003c/sub\u003e(T|A) P\u003csub\u003eW\u003c/sub\u003e(A|C) P\u003csub\u003eW\u003c/sub\u003e(C|C) P\u003csub\u003eW\u003c/sub\u003e(G|C) ... P\u003csub\u003eW\u003c/sub\u003e(T|T)\u003cbr\u003e\nP\u003csub\u003eW\u003c/sub\u003e(A|AA) P\u003csub\u003eW\u003c/sub\u003e(C|AA) P\u003csub\u003eW\u003c/sub\u003e(G|AA) P\u003csub\u003eW\u003c/sub\u003e(T|AA) P\u003csub\u003eW\u003c/sub\u003e(A|AC) P\u003csub\u003eW\u003c/sub\u003e(C|AC) P\u003csub\u003eW\u003c/sub\u003e(G|AC) ... P\u003csub\u003eW\u003c/sub\u003e(T|TT)\u003cbr\u003e\n\n\nIn addition, BaMM!motif generates two files for the homogeneous background BaMM:\n1. file with extension `.ihbp` contains probabilities of background model;\n\n2. file with extension `.ihbcp` contains conditional probabilities of background model.\n\nFor instance, the format for a background BaMM of order 2 is as follows:\n\nFilename extension: `.hbp`\n\nP(A) P(C) P(G) P(T)\u003cbr\u003e\nP(AA) P(AC) P(AG) P(AT) P(CA) P(CC) P(CG) ... P(TT)\u003cbr\u003e\nP(AAA) P(AAC) P(AAG) P(AAT) P(ACA) P(ACC) P(ACG) ... P(TTT)\u003cbr\u003e\n\nFilename extension: `.hbcp`\n\nP(A) P(C) P(G) P(T)\u003cbr\u003e\nP(A|A) P(C|A) P(G|A) P(T|A) P(A|C) P(C|C) P(G|C) ... P(T|T)\u003cbr\u003e\nP(A|AA) P(C|AA) P(G|AA) P(T|AA) P(A|AC) P(C|AC) P(G|AC) ... P(T|TT)\u003cbr\u003e\n\n## License\n\nBaMM!motif is released under the GNU General Public License v3 or later. See LICENSE for more details.\n\n## Notes\n\nWe are welcoming bug reports! Please contact us at soeding@mpibpc.mpg.de .\n\nFor the seeding phase, we recommend to use our de novo motif discovery tool [PEnG-motif](https://github.com/soedinglab/PEnG-motif).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoedinglab%2Fbammmotif2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoedinglab%2Fbammmotif2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoedinglab%2Fbammmotif2/lists"}