memes
1.0.0
Meme Suite工具家族的R接口，該界面為DNA，RNA和蛋白質序列提供了多個實用程序。模因通過檢測模因套件的本地安裝，運行命令，然後將結果直接導入R。
Memes目前可在Bioconductor devel分支上找到：
if ( ! requireNamespace( " BiocManager " , quietly = TRUE ))
install.packages( " BiocManager " )
# The following initializes usage of Bioc devel
BiocManager :: install( version = ' devel ' )
BiocManager :: install( " memes " )您可以從GitHub安裝Memes的開發版本：
if ( ! requireNamespace( " remotes " , quietly = TRUE ))
install.packages( " remotes " )
remotes :: install_github( " snystrom/memes " )
# To temporarily bypass the R version 4.1 requirement, you can pull from the following branch:
remotes :: install_github( " snystrom/memes " , ref = " no-r-4 " ) # Get development version from dockerhub
docker pull snystrom/memes_docker:devel
# the -v flag is used to mount an analysis directory,
# it can be excluded for demo purposes
docker run -e PASSWORD= < password > -p 8787:8787 -v < path > / < to > / < project > :/mnt/ < project > snystrom/memes_docker:devel模因依靠模因套件的本地安裝。有關Meme Suite的安裝說明，請參見Meme Suite安裝指南。
模因需要知道您本地機器上meme/bin/目錄的位置。您可以通過4種方式告訴MEME MEME SUITE安裝的位置。如果模因是有效的路徑，則始終更喜歡更具體的定義。在這裡，它們的排名是最不具體的：
meme_path參數options(meme_bin = "/path/to/meme/bin/")設置路徑MEME_BIN=/path/to/meme/bin/在您的.Renviron文件中~/meme/bin/如果模因未能在指定位置檢測您的安裝，則將落回下一個選項。
要驗證模因可以檢測您的模因安裝，請使用check_meme_install()使用上面的搜索herirarchy查找有效的模因安裝。它將報告是否缺少任何工具，並打印出看到的模因的路徑。這對於解決安裝問題的故障排除可能很有用。
library( memes )
# Verify that memes detects your meme install
# (returns all green checks if so)
check_meme_install()
# > checking main install
# > ✓ /opt/meme/bin
# > checking util installs
# > ✓ /opt/meme/bin/dreme
# > ✓ /opt/meme/bin/ame
# > ✓ /opt/meme/bin/fimo
# > ✓ /opt/meme/bin/tomtom
# > ✓ /opt/meme/bin/meme
# > x /opt/meme/bin/streme # You can manually input a path to meme_path
# If no meme/bin is detected, will return a red X
check_meme_install( meme_path = ' bad/path ' )
# > checking main install
# > x bad/path | 功能名稱 | 使用 | 序列輸入 | 主題輸入 | 輸出 |
|---|---|---|---|---|
runStreme() | 圖案發現（簡短主題） | 是的 | 不 | universalmotif_df |
runDreme() | 圖案發現（簡短主題） | 是的 | 不 | universalmotif_df |
runAme() | 圖案富集 | 是的 | 是的 | data.frame（可選： sequences列） |
runFimo() | 主題掃描 | 是的 | 是的 | 主題位置的granges |
runTomTom() | 主題比較 | 不 | 是的 | universalmotif_df w/ best_match_motif和tomtom列* |
runMeme() | 圖案發現（長圖案） | 是的 | 不 | universalmotif_df |
*注意：如果使用universalmotif_df運行runTomTom()則結果將與universalmotif_df結果作為額外的列連接。這可以輕鬆地比較De-Novo發現的主題與它們的比賽。
序列輸入可以是：
Biostrings::XStringSet （可以使用get_sequence()輔助功能從granges生成）Biostrings::XStringSet對象（由get_sequence()生成）主題輸入可以是：
universalmotif對像或universalmotif對象列表runDreme()結果對象（這允許runDreme()的結果直接傳遞到runTomTom() ）list() （例如list("path/to/database.meme", "dreme_results" = dreme_res) ）輸出類型：
runDreme() ， runStreme() ， runMeme()和runTomTom()返回帶有特殊列的data.frames universalmotif_df對象。 motif列包含一個universalmotif對象，每行1個條目。其餘列描述了每個返回基序的屬性。以下列名是特殊的，因為在運行update_motifs()和to_list()以更改存儲在motif列中的圖案的屬性時，使用它們的值。在調用update_motifs()或to_list()時，請謹慎更改這些值，因為這些更改將傳播到motif列。
模因圍繞UniversalMotif軟件包構建，該軟件包為R. universalmotif_df對像中的操縱圖案提供了一個框架，可以分別使用to_df()和to_list（）和to_list()函數在data.frame和universalmotif列表格式之間進行互連。這允許使用所有其他Bioconductor圖案軟件包使用memes結果，因為universalmotif對象可以使用convert_motifs()轉換為任何其他主題類型。
runTomTom()返回一個特殊列： tomtom ，這是每個輸入基線的所有匹配數據的data.frame 。可以使用tidyr::unnest(tomtom_results, "tomtom")擴展這一點，並用nest_tomtom()重新定位。由runTomTom()返回的best_match_前綴列指示圖案的值，這是與輸入圖案的最佳匹配。
suppressPackageStartupMessages(library( magrittr ))
suppressPackageStartupMessages(library( GenomicRanges ))
# Example transcription factor peaks as GRanges
data( " example_peaks " , package = " memes " )
# Genome object
dm.genome <- BSgenome.Dmelanogaster.UCSC.dm6 :: BSgenome.Dmelanogaster.UCSC.dm6 get_sequence函數將GRanges或GRangesList作為輸入，並分別返回序列作為BioStrings::XStringSet或XStringSet對象列表。 get_sequence將以每個序列來自的基因組坐標來命名每個FASTA條目。
# Generate sequences from 200bp about the center of my peaks of interest
sequences <- example_peaks % > %
resize( 200 , " center " ) % > %
get_sequence( dm.genome ) runDreme()接受Xstringset或FastA文件作為輸入的路徑。您可以將其他序列或洗牌輸入序列作為控制數據集。
# runDreme accepts all arguments that the commandline version of dreme accepts
# here I set e = 50 to detect motifs in the limited example peak list
# In a real analysis, e should typically be < 1
dreme_results <- runDreme( sequences , control = " shuffle " , e = 50 )模因圍繞環球軟件包構建。結果以universalmotif_df格式返回，該格式是R data.Frame，可以使用to_list()在data.frame和universalmotif格式之間無縫互連以將其轉換為universalmotif列表格式，而to_df()則可以轉換回data .frame格式。使用to_list()允許使用所有universalmotif函數使用memes結果：
library( universalmotif )
dreme_results % > %
to_list() % > %
view_motifs()
可以使用runTomTom()將發現的主題與已知的TF主題匹配，後者可以作為輸入的輸入路徑，通往.meme格式化文件， universalmotif列表或runDreme()的結果。
TomTom使用已知基礎的數據庫，可以將其傳遞到database參數作為.meme格式文件或universalmotif對象的路徑。
可選地，您可以將Environment變量MEME_DB設置為.Renviron中的renviron中的文件，或將options中的meme_db值設置為有效的.meme格式文件和memes將使用該文件作為數據庫。模因始終更喜歡用戶輸入而不是全局變量設置。
options( meme_db = system.file( " extdata/flyFactorSurvey_cleaned.meme " , package = " memes " ))
m <- create_motif( " CMATTACN " , altname = " testMotif " )
tomtom_results <- runTomTom( m ) tomtom_results
# > motif name altname consensus alphabet strand icscore type
# > 1 <mot:motif> motif testMotif CMATTACN DNA +- 13 PPM
# > bkg best_match_name best_match_altname
# > 1 0.25, 0.25, 0.25, 0.25 prd_FlyReg prd
# > best_db_name best_match_offset best_match_pval best_match_eval
# > 1 flyFactorSurvey_cleaned 0 9.36e-05 0.052
# > best_match_qval best_match_strand
# > 1 0.0353 +
# > best_match_motif
# > 1 <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>
#> tomtom
#> 1 prd_FlyReg, tup_SOLEXA_10, CG13424_Cell, CG11085_Cell, BH2_Cell, CG13424_SOLEXA_2, Tup_Cell, Tup_SOLEXA, Bsh_Cell, Exex_SOLEXA, Odsh_SOLEXA, Unc4_Cell, Ubx_FlyReg, Unc4_SOLEXA, E5_Cell, inv_SOLEXA_5, BH2_SOLEXA, Zen_SOLEXA, CG33980_SOLEXA_2_10, BH1_SOLEXA, CG33980_SOLEXA_2_0, Hgtx_Cell, NK7.1_Cell, Slou_Cell, CG13424_SOLEXA, Zen2_Cell, AbdA_SOLEXA, Antp_SOLEXA, Btn_Cell, Dfd_SOLEXA, Eve_SOLEXA, Ftz_Cell, Hmx_SOLEXA, Hmx_Cell, CG34031_Cell, zen2_SOLEXA_2, En_Cell, Pb_SOLEXA, Slou_SOLEXA, Unpg_Cell, inv_SOLEXA_2, ovo_FlyReg, lim_SOLEXA_2, C15_SOLEXA, Ems_Cell, Btn_SOLEXA, Unpg_SOLEXA, Pb_Cell, Bsh_SOLEXA, Scr_SOLEXA, Zen2_SOLEXA, CG34031_SOLEXA, Eve_Cell, Pph13_Cell, BH1_Cell, CG11085_SOLEXA, CG32532_Cell, en_FlyReg, Dll_SOLEXA, Dfd_Cell, Dr_SOLEXA, Ap_Cell, Ro_Cell, CG4136_SOLEXA, CG33980_SOLEXA, Hbn_SOLEXA, Lbl_Cell, Otp_Cell, Rx_Cell, CG32532_SOLEXA, NK7.1_SOLEXA, Dr_Cell, Odsh_Cell, Al_SOLEXA, Antp_Cell, Hgtx_SOLEXA, Ftz_SOLEXA, Lab_SOLEXA, Dfd_FlyReg, Ap_SOLEXA, Awh_SOLEXA, CG11294_SOLEXA, CG4136_Cell, E5_SOLEXA, Ro_SOLEXA, PhdP_SOLEXA, CG12361_SOLEXA_2, Ind_Cell, Scr_Cell, CG9876_Cell, CG18599_Cell, CG9876_SOLEXA, Otp_SOLEXA, Lbl_SOLEXA, Ubx_Cell, Ubx_SOLEXA, en_SOLEXA_2, Pph13_SOLEXA, Rx_SOLEXA, CG15696_SOLEXA, CG18599_SOLEXA, Ems_SOLEXA, Repo_Cell, Dll_Cell, C15_Cell, CG12361_SOLEXA, Abd-A_FlyReg, Repo_SOLEXA, Zen_Cell, Inv_Cell, En_SOLEXA, Lim3_Cell, Lim1_SOLEXA, CG15696_Cell, Crc_CG6272_SANGER_5, Lab_Cell, CG32105_SOLEXA, Bap_SOLEXA, CG9437_SANGER_5, AbdA_Cell, pho_FlyReg, CG33980_Cell, Cad_SOLEXA, CG4328_SOLEXA, CG4328_Cell, Gsc_Cell, vri_SANGER_5, AbdB_SOLEXA, Xrp1_CG6272_SANGER_5, Al_Cell, Exex_Cell, br-Z4_FlyReg, CG11294_Cell, Aef1_FlyReg, CG7745_SANGER_5, PhdP_Cell, Awh_Cell, prd, tup, lms, CG11085, B-H2, lms, tup, tup, bsh, exex, OdsH, unc-4, Ubx, unc-4, E5, inv, B-H2, zen, CG33980, B-H1, CG33980, HGTX, NK7.1, slou, lms, zen2, abd-A, Antp, btn, Dfd, eve, ftz, Hmx, Hmx, CG34031, zen2, en, pb, slou, unpg, inv, ovo, Lim1, C15, ems, btn, unpg, pb, bsh, Scr, zen2, CG34031, eve, Pph13, B-H1, CG11085, CG32532, en, Dll, Dfd, Dr, ap, ro, CG4136, CG33980, hbn, lbl, otp, Rx, CG32532, NK7.1, Dr, OdsH, al, Antp, HGTX, ftz, lab, Dfd, ap, Awh, CG11294, CG4136, E5, ro, PHDP, CG12361, ind, Scr, CG9876, CG18599, CG9876, otp, lbl, Ubx, Ubx, en, Pph13, Rx, CG15696, CG18599, ems, repo, Dll, C15, CG12361, abd-A, repo, zen, inv, en, Lim3, Lim1, CG15696, crc, lab, CG32105, bap, CG9437, abd-A, pho, CG33980, cad, CG4328, CG4328, Gsc, vri, Abd-B, Xrp1, al, exex, br, CG11294, Aef1, CG7745, PHDP, Awh, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package 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[package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, <S4 class 'universalmotif' [package "universalmotif"] with 20 slots>, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, flyFactorSurvey_cleaned, 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# >
# > [Hidden empty columns: family, organism, nsites, bkgsites, pval, qval,
# > eval.]runTomTom()將其結果作為列將其結果添加到runDreme()結果data.frame。
full_results <- dreme_results % > %
runTomTom()AME用於測試目標序列中已知基序的富集。 runAme()將使用.Renviron或options(meme_db = "path/to/database.meme")中的MEME_DB條目作為主題數據庫。或者，它將接受類似於runTomTom()類似的所有有效輸入。
# here I set the evalue_report_threshold = 30 to detect motifs in the limited example sequences
# In a real analysis, evalue_report_threshold should be carefully selected
ame_results <- runAme( sequences , control = " shuffle " , evalue_report_threshold = 30 )
ame_results
# > # A tibble: 2 x 17
# > rank motif_db motif_id motif_alt_id consensus pvalue adj.pvalue evalue tests
# > <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <int>
# > 1 1 /usr/lo… Eip93F_… Eip93F ACWSCCRA… 5.14e-4 0.0339 18.8 67
# > 2 2 /usr/lo… Cf2-PB_… Cf2 CSSHNKDT… 1.57e-3 0.0415 23.1 27
# > # … with 8 more variables: fasta_max <dbl>, pos <int>, neg <int>,
# > # pwm_min <dbl>, tp <int>, tp_percent <dbl>, fp <int>, fp_percent <dbl> view_tomtom_hits允許將輸入圖案與TomTom的最高點擊進行比較。對這些匹配的手動檢查很重要，因為有時最重要的比賽並不總是正確的分配。更改top_n可以使您以排名的降序顯示其他匹配項。
full_results % > %
view_tomtom_hits( top_n = 1 )
# > $m01_AGAGC 
將runAme()的結果視為熱圖可能很有用。 plot_ame_heatmap()可以創建複雜的可視化，以分析不同區域類型之間的富集（有關詳細信息，請參見小插圖）。這是一個簡單的示例熱圖。
ame_results % > %
plot_ame_heatmap()
FIMO工具用於識別與已知主題的匹配。 runFimo將作為包含基匹配基因組坐標的GRanges對象返回這些命中。
# Query MotifDb for a motif
e93_motif <- MotifDb :: query( MotifDb :: MotifDb , " Eip93F " ) % > %
universalmotif :: convert_motifs()
# > See system.file("LICENSE", package="MotifDb") for use restrictions.
# Scan for the E93 motif within given sequences
fimo_results <- runFimo( sequences , e93_motif , thresh = 1e-3 )
# Visualize the sequences matching the E93 motif
plot_sequence_heatmap( fimo_results $ matched_sequence ) 
模因還支持使用R外部的Meme Suite生成的導入結果（例如，在meme-suite.org上運行作業或在命令行上運行）。這使得使用先前存在的模因套件結果具有下游模因函數。
| 模因工具 | 功能名稱 | 文件類型 |
|---|---|---|
| streme | importStremeXML() | streme.xml |
| Dreme | importDremeXML() | dreme.xml |
| Tomtom | importTomTomXML() | tomtom.xml |
| AME | importAme() | ame.tsv* |
| fimo | importFimo() | fimo.tsv |
| 模因 | importMeme() | meme.txt |
* importAME()還可以在AME使用method = "fisher"時使用“ sequences.tsv”輸出，這是可選的。
該模因套件當前不支持Windows，儘管可以在Cygwin或Windows Linux Subsytem（WSL）下安裝。請注意，如果將模因安裝在Cygwin或WSL上，則還必須在Cygwin或WSL內運行R才能使用模因。
另一種解決方案是使用Docker安裝模因套件來運行虛擬環境。我們提供一個模因碼頭容器
與模因套房，R Studio和所有memes依賴關係預裝在一起。
Memes是來自Meme Suite的一些選擇工具的包裝，該工具是由另一組開發的。除了引用模因之外，請引用與您使用的工具相對應的模因套件工具。
如果您在分析中使用runDreme() ，請引用：
蒂莫西·貝利（Timothy L.
如果您在分析中使用runTomTom() ，請引用：
Shobhit Gupta，Ja Stamatoyannopolous，Timothy Bailey和William Stafford Noble，“量化圖案之間的相似性”，Genome Biology，8（2）：R24，2007。全文。
如果您在分析中使用runAme() ，請引用：
Robert McLeay和Timothy L. Bailey，“主題富集分析：統一框架和方法評估”，BMC BioInformatics，11：165，2010，doi：10.1186/1471-2105-11-165。全文
如果您在分析中使用runFimo() ，請引用：
查爾斯·E·格蘭特（Charles E.
Meme Suite是免費的，可用於非營利性使用，但營利性用戶應購買許可證。有關詳細信息，請參見Meme Suite版權頁面。
