瀹㈡埛绔�
椋熷搧鏅氫節鐐�
鍥介檯椋熷搧
鏈€鏂版悳绱細
 
 
褰撳墠浣嶇疆: 棣栭〉 » 椋熷搧璧勮 » 椋熷搧绉戞妧 » 妞嶇墿杞綍鍥犲瓙缁撳悎浣嶇偣棰勬祴鐮旂┒鍙栧緱鏂扮獊鐮�

妞嶇墿杞綍鍥犲瓙缁撳悎浣嶇偣棰勬祴鐮旂┒鍙栧緱鏂扮獊鐮�

鏀惧ぇ瀛椾綋  缂╁皬瀛椾綋 鏃堕棿锛�2021-04-25 13:11 鏉ユ簮锛氬崕涓啘涓氬ぇ瀛� 鍘熸枃:
鏍稿績鎻愮ず锛氳繎鏃ワ紝鍗庝腑鍐滀笟澶у淇℃伅瀛﹂櫌鐢熺墿缁熻鍥㈤槦鑳″娴锋暀鎺堣棰樼粍鐮斿彂鍑轰竴娆鹃拡瀵规鐗╄浆褰曞洜瀛愮粨鍚堜綅鐐归娴嬬殑宸ュ叿鍙婂叾docker闀滃儚锛岀浉鍏崇爺绌舵垚鏋滃彂琛ㄥ湪鍥介檯鐢熺墿淇℃伅瀛﹂鍩熷鏈湡鍒夿ioinformatics涓娿€�
銆€銆€杩戞棩锛屽崕涓啘涓氬ぇ瀛︿俊鎭闄�鐢熺墿缁熻鍥㈤槦鑳″娴锋暀鎺堣棰樼粍鐮斿彂鍑轰竴娆鹃拡瀵规鐗╄浆褰曞洜瀛愮粨鍚堜綅鐐归娴嬬殑宸ュ叿鍙婂叾docker闀滃儚锛岀浉鍏崇爺绌舵垚鏋滃彂琛ㄥ湪鍥介檯鐢熺墿淇℃伅瀛﹂鍩熷鏈湡鍒夿ioinformatics涓娿€�
 
銆€銆€杞綍鍥犲瓙缁撳悎浣嶇偣锛圱FBS锛夋槸椤哄紡璋冩帶鍏冧欢鐨勫熀鏈粍鎴愰儴鍒嗭紝鍦ㄥ熀鍥犺〃杈剧殑绮剧‘璋冩帶涓捣閲嶈浣滅敤銆俆FBS鏍稿績鍩哄簭鍐呯殑闈炵紪鐮佸彉寮傚彲鑳戒細鏄捐憲鏀瑰彉鍏剁粨鍚堜翰鍜屽姏锛岃繖鍙兘鏄В閲婇仐浼犲彉寮傚浣曞奖鍝嶅鏉傛€х姸鐨勭敓鐗╁鏈哄埗銆傛鐗╀腑杞綍鍥犲瓙缁撳悎浣嶇偣瀹為獙鏁版嵁鐨勭己涔忥紝浠ュ強妞嶇墿TFs鐨勭嫭绔嬭繘鍖栫壒鎬ч兘浣垮緱閴村畾妞嶇墿TFBS鐨勮绠楁柟娉曡惤鍚庝簬鐩稿叧鐨勪汉绫荤爺绌躲€傛湰鐮旂┒棣栧厛浣跨敤娣卞害鍗风Н绁炵粡缃戠粶锛圖eepCNN锛夊湪鍩轰簬鍙敤鐨勬嫙鍗楄姤Dap-seq鏁版嵁闆嗗缓绔嬩簡265涓嫙鍗楄姤TFBS鐨勯娴嬫ā鍨嬶紝骞朵笖灏嗗叾杩佺Щ鐢ㄤ簬棰勬祴鍏朵粬妞嶇墿鐨勫悓婧怲F涓€�
銆€銆€寤烘ā缁撴灉琛ㄦ槑锛孌eepCNN鍦�265涓嫙鍗楄姤鏁版嵁闆嗕笂閮借幏寰椾簡寰堥珮鐨勯娴嬬簿纭害锛堝钩鍧嘇UC杈�0.96锛夛紝闃愭槑浜嗗叾鍦ㄦ鐗㏕FBS棰勬祴鏂归潰鐨勫彲琛屾€с€傞€氳繃杩涗竴姝ユ繁鍏ュ垎鏋怐eepCNN涓嵎绉牳鐨勬€ц川锛屼綔鑰呮彁渚涗簡妯″瀷鐨勭敓鐗╁鍙В閲婃€э細DeepCNN涓嶄粎鑳藉涔犲埌褰撳墠杞綍鍥犲瓙鍦ㄥ簭鍒楀綋涓殑鍏抽敭缁撳悎motif锛岃€屼笖鑳藉瀛︿範鍒颁笌璇ヨ浆褰曞洜瀛愬叡鍚屽崗浣滅殑杞綍鍥犲瓙鐨勭粨鍚坢otif銆�
 
銆€銆€鏈€鍚庡綋浣跨敤杩佺Щ瀛︿範鎶€鏈皾璇曚粠璁$畻鐨勯€斿緞瑙e喅鐩墠妞嶇墿TFBS鐮旂┒闂鐨勫洶闅炬椂锛屼綔鑰呭彂鐜板湪涓嶅悓鐨勬鐗╃绫讳腑锛岃縼绉诲涔犵殑琛ㄧ幇鍏锋湁寰堝ぇ鐨勪笉鍚屻€傚湪姘寸ɑ鐨勫崄涓猅F涓殑涓変釜閮藉彇寰椾簡姣旇緝濂界殑棰勬祴鏁堟灉锛孊ZIP23 銆丒RF48鍜孧ADS29鐨� PPV锛圥ositive predictive value锛夊垎鍒负0.752銆�0.951鍜�0.816銆傝€屽綋杩佺Щ鍒扮帀绫冲拰澶ц眴涓椂锛岄娴嬫晥鏋滃潎涓嶇敋鐞嗘兂銆傝繖琛ㄦ槑杩佺Щ瀛︿範鍦ㄦ鐗╃殑璺ㄧ墿绉嶈浆褰曞洜瀛愮粨鍚堜綅鐐归娴嬮棶棰樹笂鍏锋湁涓€瀹氱殑鍙鎬э紝浣嗘槸鏈潵鎴戜滑浠嶉渶璁捐鏇村姞鏈夋晥鐨勮縼绉诲涔犵瓥鐣ャ€�
 
銆€銆€涓轰簡鎻愪緵鏇存柟渚裤€佹洿浼樿川鐨勭敓鐗╀俊鎭鏈嶅姟锛岃棰樼粍涓烘鍏锋湁楂樼簿纭巼杈ㄥ埆杞綍鍥犲瓙缁撳悎浣嶇偣鐨勬繁搴﹀嵎绉缁忕綉缁滄ā鍨嬫惌寤轰簡docker闀滃儚锛岄€氳繃涓嬭浇璇ラ暅鍍忓苟鍦ㄦ湰鍦伴厤缃彲浠ュ疄鐜扮绾块娴嬫鐗╄浆褰曞洜瀛愮粨鍚堜綅鐐圭殑棰勬祴鍔熻兘锛坔ttps://github.com/liulifenyf/TSPTFBS锛夈€�
 
銆€銆€銆愯嫳鏂囨憳瑕併€�
 
銆€銆€Motivation: Both the lack or limitation of experimental data of transcription factor binding sites 锛圱FBS锛� in plants and the independent evolutions of plant TFs make computational approaches for identifying plant TFBSs lagging behind the relevant human researches. Observing that TFs are highly conserved among plant species, here we first employ the deep convolutional neural network 锛圖eepCNN锛� to build 265 Arabidopsis TFBS prediction models based on available DAP-seq 锛圖NA affinity purification sequencing锛� datasets, and then transfer them into homologous TFs in other plants.
 
銆€銆€Results: DeepCNN not only achieves greater successes on Arabidopsis TFBS predictions when compared with gkm-SVM and MEME, but also has learned its known motif for most Arabidopsis TFs as well as cooperative TF motifs with PPI 锛坧rotein-protein-interaction锛� evidences as its biological interpretability. Under the idea of transfer learning, trans-species prediction performances on ten TFs of other three plants of Oryza sativa, Zea mays and Glycine max demonstrate the feasibility of current strategy.
 
銆€銆€Availability and implementation: The trained 265 Arabidopsis TFBS prediction models were packaged in a Docker image named TSPTFBS, which is freely available on DockerHub at https://hub.docker.com/r/vanadiummm/tsptfbs. Source code and documentation are available on GitHub at: https://github.com/liulifenyf/TSPTFBS.
 
銆€銆€Contact: huxuehai@mail.hzau.edu.cn
 
銆€銆€鍘熸枃閾炬帴锛歨ttps://academic.oup.com/bioinformatics/article/37/2/260/6069568
鏃ユ湡锛�2021-04-25
 
 鏍囩锛� 鐢熺墿
 绉戞櫘锛� 鐢熺墿

声明:

①凡本网所有原始/编译文章及图片、图表的版权均属食品伙伴网所有,未经授权,禁止转载,如需转载,请联系取得授权后转载。
② 凡本网注明“信息来源:XXX(非食品伙伴网)”的信息,均来源于网络,转载的目的在于传递更多的信息,仅供网友学习参考使用,并不代表本网赞同其观点和对其真实性负责,著作权及版权归原作者所有,转载无意侵犯版权,如有侵权,请速来函告知,我们将尽快处理。
※ 联系方式 邮箱:news@foodmate.net qq:1530909346 电话:0535-2122172

 
[ 椋熷搧璧勮鎼滅储 ]  [ 鍔犲叆鏀惰棌 ]  [ 鍛婅瘔濂藉弸 ]  [ 鎵撳嵃鏈枃 ]  [ 鍏抽棴绐楀彛 ]

 

 
 
浼氬睍鍔ㄦ€�MORE +
 
鎺ㄨ崘鍥炬枃
鎸夊瓧姣嶆绱� A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
椋熷搧浼欎即缃戣祫璁儴  鐢佃瘽锛�0535-2122172  浼犵湡锛�0535-2129828   閭锛歯ews@www.sqrdapp.com   QQ:鐐瑰嚮杩欓噷缁欐垜鍙戞秷鎭�
椴佸叕缃戝畨澶� 37060202000128鍙�
Baidu
map