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用PC3000修硬盘好吗
用PC3000修硬盘好吗。成功率是多少
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50~80%下载地址：http://downloads.downfeel.com/pc3000.rar&http://bbs.9256.com/printpage.asp?BoardID=18&ID=164&或&http://www.tdr120.com/filepage/soft/dzbook_/PC3000%20chn.htm&下载&PC-3000中文说明&目录&一.PC-3000&综合工具&1.1&用途&1.2&PC-3000软件部分,&版本10.10&1.3&PC-3000综合工具套件,&版本10.10&1.4&质量保证&1.5&用户注册&1.6&PC-3000注册用户支持&1.7&PC-3000综合工具的安装&二.用于诊断和维修任何类型的硬盘驱动器的通用测试工具&(PC-3000AT)&2.1&用途&2.2&准备工作&2.3&PC-3000AT工作时的输出信息&2.4&为待测硬盘驱动器输入参数&2.5&PC-3000AT的工作模式&2.5.1&工作模式选择&2.5.2&查看硬盘驱动器的S.M.A.R.T参数&2.5.3&驱动器测试&2.5.4&控制器测试&2.5.5&综合测试&2.5.5.1&综合测试的组成&2.5.6&缺陷重设&2.5.6.1&自动重设&2.5.6.2&手动重设&2.5.6.3&撤消重设&2.5.7&格式化&三.PC-3000&SHELL&外壳程序&四.PC-DEFECTOSCOPE&缺陷探测器&Ver.2.10&4.1&用途&4.2&准备工作&4.3&使用PC-DEFECTOSCOPE工作&4.4&进行测试&4.5&将PC-DEFECTOSCOPE用于硬盘维修&4.6&输出的缺陷列表文件的结构&一.&PC-3000综合工具&1.1&用途&软硬件综合工具PC-3000版本10.10被设计用于诊断和维修任何IDE接口及其改进(ATA,ATA-2,ATA-3,ATA-4,Ultra&ATA,E-IDE,UDMA66)型的硬盘驱动器,&并可利用特别的专用工具模块(参看工具模块列表)在先进的工厂模式下修理和恢复各种被广泛使用的硬盘驱动器:Conner,&Daeyoung,&Fujitsu(富士通),&Hitachi(日立),&IBM,&Kalok,Maxtor(迈拓),&NEC,&Quantum(昆腾),&Samsung(三星),&Seagate(西捷),&Teac,&Western&Digital(西部数据),&Xebec.&1.2&PC-3000&软件部分,版本10.10&Universal&software&通用软件&PC-3000&SHELL&PC-3000&外壳程序是PC-3000综合工具中用于快捷方便的启动各个工具的图形化界面.&PC-3000AT&Ver.&4.05&用于诊断和修理任何类型的硬盘驱动器的通用测试工具&PC-DEFECTOSCOPE&缺陷探测器&Ver&2.10&用于搜索和隐藏不稳定坏扇区的通用程序&PC-ACIDENT&西部数据(WD)AC系列硬盘驱动器的识别工具&Technological&utilities&各专用工具模块(列表)&(略)&1.3&PC-3000综合工具套件,&版本10.10&(略)&1.4&质量保证&(略)&1.5&用户注册&(略)&1.6&PC-3000注册用户支持&(略)&1.7&PC-3000&综合工具的安装&注意!&要运行PC-3000综合工具,&你需要一台386/486/Pentium级别的pc机,&支持EGA/VGA/SVGA显示模式的显示器,&以及5.0&或更高版的MS-DOS.&要运行PC-3000综合工具中的软件,&剩余可用内存应不低于600KB,(可以在DOS下打入mem&/f命令检查可用内存总数).&也可以启动win95/98系统但不进入win95/98的图形化界面的方式运行pc-3000.(启动95/98时按F8键进command&prompt&mode&--译者注).&pc-3000的卡使用的硬件中断号(IRQ)为12,输入/输出(I/O)端口地址范围是100H&到&10FH.&注意!&这个工具中的有些程序使用了覆盖技术,&建议在DOS或WINDOWS中加载磁盘缓冲程序SMARTDRV.EXE.(可以加快覆盖模块从磁盘上调入内存的速度&--译者注).&1.将PC-3000卡插入电脑中的任一空闲ISA插槽.&2.将加密狗插入打印口LPT1.&3.在硬盘上为PC-3000创建一个目录,将PC-3000光盘上&PC-3000.XXX&目录下的所有文件拷贝进这个目录.这里&XXX&是你的加密狗的号码.&4.把拷贝好的文件的只读和归档属性去掉.&5.把待修硬盘接上电脑电源盒上空余的驱动器电源插头,并用硬盘数据线电缆将硬盘与PC-3000卡连接起来.&注意!&接上或脱开待修硬盘电源插头,连接或拔下硬盘与PC-3000卡间的数据线电缆应在电脑关机时进行.&建议使用一个单独的电源盒给待修硬盘供电,&这样无须关机就可以开关待修硬盘的电源,拔下数据线电缆了.&6.调入shell.exe程序.现在PC-3000可以开始工作了.&注意!&1.如果没有在电脑ISA槽插入PC-3000的卡,&或者PC-3000测试工具没有工&作,&调入SHELL.EXE程序和工具会使电脑失去响应,此时只能按复位键重启电脑.&2.如果没插加密狗或加密狗没工作,&PC-3000不可能工作.&3.在Pentium级或更高档的电脑的第一IDE口上接入40M&到240M的小硬盘运&行PC-3000可能会碰到一些问题.&那么可以在386/486级的电脑上使用这种小硬盘来运行PC-3000.&7.把PC-3000附带的其它卡与待修硬盘连接起来的操作步骤会在对应(要使用这些卡)的专用工具模块的用户手册中说明.&PC-3000综合工具使用方法&现在你拥有了一个顶级的硬盘维修工具&PC-3000综合工具.&如果你以前经常维修硬盘驱动器,&那你大可以跳过这一章,但如果你的经验尚不够老道,作者建议你在开始前先阅读这一章--&IDE(ATA)硬盘驱动器技术说明,IDE(ATA)硬盘驱动器维修基础&.&本章是一篇简要的关于如何使用PC-3000综合工具的指南.&当你维修一台硬盘驱动器时.&不要急于马上开始使用专用工具模块.&首先,&任何硬盘驱动器都应该先用包含在本综合工具中的PC-3000AT通用测试工具进行检查.&这样可以有助于缩小故障范围,从而决定下一步的维修操作.&为了做到这一点,&把硬盘驱动器连到PC-3000卡上,&打开(接上)硬盘驱动器的电源,&启动PC-3000AT.EXE程序.&在打开硬盘驱动器电源后,&硬盘驱动器应该启动主轴电机,&进行磁头重校准(磁头定位到零磁道).&在此过程当中,&会听到一声很明显的磁头定位的声音.&在硬盘驱动器初始化完毕后,&硬盘驱动器就会送出就绪信号.&如果不是这样(主轴电机停转或者根本没有启动或者你听到一声磁头敲击的声音)那么你应该使用在附录2.2部分中说明的方法.&在检查硬盘驱动器已经就绪后(DRDY=DSC=1,BUSY=0&)&(DRDY--&DRIVE&READY&驱动器就绪标志位,&DSC--DISK&SEEK&COMPLETE&磁盘寻道完成标志位,&BUSY--驱动器忙状态位&--译者注),&PC-3000AT程序会试图从硬盘驱动器的描述说明信息域读取数据(可能使用标准ATA命令&Identify&DRV&识别硬盘驱动器&--译者注).&但如果硬盘驱动器就绪信号未被程序检测到,&PC-3000AT会在屏幕上显示一条相应的消息.&在此种情况下应使用在&&IDE(ATA)硬盘驱动器技术说明,硬盘驱动器维修基础&这一章2.2.1节的方法.&再如果,&尽管程序接收到了就绪信号,&但硬盘驱动器的描述说明数据不能被读取(程序提示&Drive&parameters&are&not&determined&硬盘驱动器参数未被测出&)或读取的数据不正确,&这说明可能硬盘驱动器的读写通道部分有缺陷,&或者硬盘驱动器的(盘片上的)firmware(固件)损坏.(固件数据可以利用相应的专用工具模块来恢复).&另外,&还可能这台硬盘驱动器根本没有硬盘驱动器描述说明信息区域,(这主要是指老KALOK/XEBEC型号的硬盘驱动器)或者硬盘描述说明信息区域有数据,&但不能为硬盘驱动器工作所使用.&在这种情况下,&硬盘驱动器的参数应当被操作者手工输入或从PC-3000AT的硬盘驱动器数据库中选择输入以便进行后续的测试过程.&在使用PC-30000AT对硬盘驱动器进行测试并做出了关于它的缺陷的初步判断后(方法在&IDE(ATA)硬盘驱动器技术说明,&IDE&(ATA)硬盘驱动器维修基础&这一章的2.1部分描述),&你就可以启动一个专用工具模块进行更高级的诊断或修复硬盘驱动器的固件.&以厂家所用的方法(factory&mode&工厂模式)进行硬盘驱动器修复的方法的详细描述放在专用工具模块的说明部分.&PC-3000综合工具开发团队衷心祝您成功!&二&用于诊断和维修任何型号硬盘驱动器的通用测试工具(PC-3000AT)&.1&作用&PC-3000AT&测试软件是PC-3000综合工具中用于IDE(ATA接口)硬盘驱动器维修和恢复的基本程序，被设计用于：&（1）.以较一般的方式诊断缺陷,&修理IDE硬盘驱动器.&（2）.使用ATA标准命令50H(格式化磁道命令&--译者注)进行低级格式化来正确修复硬盘驱动器.&（3）.在支持缺陷重设机制(defect&reassign)的硬盘驱动器上(用缺陷重设机制)隐藏坏扇区.&（4）.以用户输入参数,软件输出信息的操控形式进行自动化驱动器测试(指综合测试模式).&PC-3000AT&测试软件必须与&PC-3000AT&卡配合工作.注意!&PC-3000AT&4.0&及更高版本既支持使用CHS扇区定址模式也支持使用LBA扇区定址模式进行驱动器测试.测试软件的CHS/LBA扇区定址模式切换是用键盘右部的数字小键盘上的一个预设按键来进行.(请看第三章&&测试软件工作时的输出信息).&CHS扇区定址模式是PC-3000AT默认使用的测试运作模式.&它和LBA扇区定址模式的差别在于有&LBA&mode&字样标记在屏幕上的工作模式描述区和软件输出的信息中.&2.2&准备工作&1.将PC-3000AT卡用硬盘数据线电缆与要被测试的硬盘驱动器IDE口相连.&2.接上硬盘驱动器电源.&3.调入PC-3000综合工具的外壳程序&PC-SHELL，并从中启动PC-3000AT程序.&2.3&PC-3000AT&工作时的输出信息&为便于理解PC-3000AT测试软件以&仪表面板&形式显示在电脑屏幕上的输出信息，这个&仪表面板&由以下几部分组成：&&display显示器&-&显示关于测试过程的信息.&在显示器的上部你可以看见&MODEL(型号)&这一栏,&它包含要被测试的硬盘驱动器的类型和参数信息:&柱面数(CYL),&磁头数(HEAD),&扇区数(SEC).&在LBA扇区定址模式下，&MODEL(型号)&栏将显示总共可用LBA扇区数而不是柱面数(CYL),磁头数(HEAD),扇区数(SEC).&在显示器的下部你可以看到&STATE(状态)&这一栏,&它包含硬盘驱动器的状态信息:&就绪/忙(READY/BUSY),&在进行需时较长的测试的当前进度百分比&%&(DONE)&,&当前柱面(CYL),&磁头(HEAD),&扇区(SEC),&在测试中检测到的驱动器出错的次数(ERRS).&在LBA扇区定址模式下,只会显示当前扇区的LBA扇区号而不是当前扇区的柱面号(CYL),&磁头号(HEAD),&扇区号(SEC)参数.&两行&LED(发光二极管)指示灯&&-&显示被测试的硬盘驱动器的状态寄存器和错误寄存器的信息,&可用于监视硬盘驱动器在测试中的状态,&及判断驱动器的故障原因.&不活动的LED指示灯为蓝色,&活动时为黄色或红色,&红色表示发生了错误.&状态寄存器显示了IDE(ATA)硬盘驱动器的当前状态.&状态寄存器的值在每一个命令执行后都会更新.&错误寄存器会在命令执行后当状态寄存器的错误指示位(ERROR)被设置时显示硬盘驱动器的(具体何种错误类型)状态.&&keyboard键盘&&-&对应于电脑键盘右部的数字小键盘.这些按键的作用随测试软件当前所处的状态变化.&以下这些按键的作用是是固定的:&[Enter]&-&输入参数，开始各种测试.&[Cancel]&-&用于取消当前的测试或设置的参数(可以与ESC互换使用).&[Exit]&-&用于取消当前的测试或设置的参数,&然后退回到模式选择(&MODE&SELECTION&)菜单.&2.4.为待测的硬盘驱动器输入参数&PC-3000AT开始运行时，首先它将测定待测硬盘驱动器的类型和CHS(柱面数,磁头数,扇区数)参数,&测定过程结束会在屏幕上&MODEL(型号)&栏显示硬盘驱动器的类型和参数.&然后PC-3000AT会进入&MODE&SELECTION(工作模式选择)&菜单.&如果要用LBA扇区定址模式测试硬盘驱动器请按键盘区预设的[LBA]键.&但如果待测硬盘驱动器不支持LBA扇区定址模式,&[LBA]键不会显示在屏幕上的键盘区上.&注意!&一些老型号的硬盘驱动器(例如&CP&3000)不能使用物理参数工作,&对于这一类硬盘驱动器,请从PC-3000AT提供的硬盘驱动器数据库中输入它的合适参数.&如果由于待测硬盘驱动器有故障,&它的参数不能被PC-3000AT测出,&PC-3000AT会在屏幕上显示如下消息:&Drive&parameters&are&not&determined&(硬盘驱动器参数未被测出)&此时按任意键会使PC-3000AT改变当前工作模式进入&DRIVE&TYPE&SELECTION(硬盘驱动器类型选择)&工作模式,&这个工作模式会显示如下菜单:&DRIVE&TYPE&SELECTION&(硬盘驱动器类型选择)&Identify&DRV&(识别硬盘驱动器)&User&Type&(用户自定义类型)&Coner&(Coner&类型硬盘驱动器)&Fujitsu&(Fujitsu&富士通类型硬盘驱动器)&Maxtor&(Maxtor&迈拓类型硬盘驱动器)&Quantum&(Quantum&昆腾类型硬盘驱动器)&你可以使用上([Up])&下([Down])光标键及回车键([Enter])进行如下操作:&--选择&Idenjtify&DRV&(识别硬盘驱动器)&菜单,&PC-3000AT会尝试再次测定硬盘驱动器的类型与参数&--选择&User&Type&(用户自定义类型)&,&PC-3000AT会要求你手工输入硬盘驱动器的参数(CHS)&--从PC-3000AT提供的硬盘驱动器数据库选择合适类型(如迈拓,富士通,昆腾,&coner)&注意!&如果待测硬盘驱动器的CHS参数输入不正确,&PC-3000AT将不能正确测试和诊断这个硬盘驱动器.&在手工输入待测硬盘驱动器参数或从数据库中为待测硬盘驱动器选择合适参数时,待测硬盘驱动器在LBA扇区定址模式下的可用扇区总数会被计算,&这个数值等于柱面数(CYL),磁头数(HEAD),扇区数(SEC)三者的乘积.&当待测硬盘驱动器的CHS参数被定好之后,&PC-3000AT就会进入MODE&SELECTION(工作模式选择)菜单.&2.5&PC-3000AT&的工作模式&2.5.1&MODE&SELECTION&工作模式选择&在工作模式选择菜单中可以用上([Up])下([Down])光标键及回车([Enter])键选择工作模式,&用[Cancel]和[Exit]键退出所选择的工作模式.&工作模式选择菜单的主菜单:&MODE&SELECTION(工作模式选择)&Drive&type&selection&(硬盘驱动器类型选择)&Drive&test&(驱动器测试)&Controller&test&(控制器测试)&Complex&test&(综合测试)&Defects&relocation&(缺陷重设)&Formatting&(格式化)&Exit&(退出)&Drive&type&selection(硬盘驱动器类型选择)--此工作模式用于由PC-3000AT软件测定待测硬盘驱动器的类型及参数(可能使用标准ATA命令ECH&,&Identify&Drv识别硬盘驱动器)或由用户手工输入硬盘驱动器参数.&Drive&test(驱动器测试)&--&此工作模式用于测试和修理:&--read/write&channel&读写通道&(硬盘电路系统中对磁头读取的微弱信号进行放大,滤波,数据/时钟分离;&对数据进行编码,磁头写电流驱动,写预补偿的电路部分&--译者注).&--positioning&system&磁头定位系统.&(硬盘机械系统中,&用于移动磁头到指定磁道的零部件总成,&目前普遍使用音圈电机(VCM)带动磁头臂旋转的磁头定位方式&--译者注)&--spindle&motor&and&its&controller&chip&主轴电机及其控制芯片&(主轴电机用于旋转盘片,&目前常用直流无刷电机;&电机控制芯片负责驱动直流无刷电机旋转及稳速控制&--译者注.)&Controler&test(控制器测试)&--&此工作模式用于测试和修理:&--interface&controller&接口控制器&(硬盘电路系统中负责主机接口,&缓存接口,&驱动器接口的部件&--译者注)&--MPU微处理器&(硬盘驱动器的控制中心,&在固件[firmware&-&固化在ROM芯片中及存放在盘片上的使用该种微处理器指令系统编写的专用软件]控制下负责完成寻道,&纠错&,&自动化缺陷重设等工作.&--译者注)&--Read/write&channel&读写通道&(硬盘电路系统中对磁头读取的微弱信号进行放大,滤波,数据/时钟分离;&对数据进行编码,&磁头写电流驱动的电路部分&--译者注).&--buffer&RAM&缓冲存储器&(硬盘驱动器电路系统中负责存储从读写通道读取的扇区数据,&并将其通过接口传送给PC;&存储从接口由PC传送来的数据,&送入读写通道的电路部分&--译者注)&Complex&test(综合测试)&由用户输入参数,PC-3000AT输出信息操控形式的工作模式.&Defects&relocation(缺陷重设)&--选这个工作模式,如待测硬盘驱动器支持缺陷重设机制,&PC-3000AT会利用缺陷重设机制进行缺陷重设.&Formatting(格式化)&--在此工作模式下,&PC-3000AT会对支持ATA标准命令50H(格式化磁道命令&--译者注)的硬盘驱动器执行正确的格式化修复过程.&Exit(退出)&--从PC-3000AT程序中退回到PC-3000&SHELL&外壳程序界面或者退到DOS下.&按键[SMART]及[Passp]用于查看硬盘驱动器的S.M.A.R.T参数以及硬盘驱动器的描述说明区域内存放的信息.&这些信息是由PC-3000AT使用ATA-4标准的Identify&DRV命令从硬盘驱动器读取并解码而来.&2.5.2&查看硬盘驱动器的S.M.A.R.T(Self-Monitor&Analysis&and&Reporting&Technology)参数&(Self-Monitor&Analysis&and&Reporting&Technology&是指自我监测,分析,报告技术,&在ATA-3标准中被引入.采用SMART技术,在硬盘驱动器工作的同时,硬盘驱动器的微控制器会在固件中的SMART程序模块控制下自动持续定期监视驱动器部分,电路部分的工作状态参数,&一旦它们的值超过临界值时,会以某种方式向主机报告该硬盘驱动器已经不可靠,请将硬盘上的数据备份&--译者注)&按下[SMART]键会在屏幕上看到以下这些S.M.A.R.T参数:&ID&-监控参数编号&对于西部数据(Western&Digital)硬盘驱动器,&编号与驱动器的SMART参数对应关系列表如下：&ID(编号)&监控参数&1&读取出错比率&4&驱动器启动/停止次数&5&因出错而被重设的扇区总数&10&主轴电机启动失败重试次数&11&驱动器校准(回零磁道)失败重试次数&199&ULTRA&DMA&CRC&错误率&(ULTRA&DMA&由ATA-4标准引入,&这种数据传输模式增加了对传输的数据的CRC循环冗余码校验过程&--译者注）&200&出错区域比率&(现今IDE硬盘驱动器皆使用ZBR[分区域记录]技术,&盘片表面划分为数个区域,&不同区域的磁道扇区数目不同,&同一区域内各磁道扇区数相同,&盘片外圈区域磁道长扇区数目较多,&盘片内圈区域磁道短扇区数目较少.&--译者注)&对于富士通(Fujitsu)硬盘驱动器,&编号与驱动器的SMART参数对应关系列表如下：&ID(编号)&监控参数&1&读取出错比率&2&数据传输速率(带宽)指标&3&主轴启动时间&(从驱动器加电至正常工作电压,&至主轴电机启动达到正常转速,&使驱动&器进入就绪状态所经历的时间&--译者注)&4&主轴电机被激活的次数&(主轴电机可以被电源管理模式命令停转,&在唤醒时被激活启动&--译者注)&5&因缺陷而被重设替换的扇区总数&7&寻道错误比率&8&寻道时间指标&9&加电工作时间&10&启动主轴电机重试次数&12&硬盘驱动器加电/断电次数&199&Ultra&ATA&CRC&错误比率&200&写入出错比率&注意!&同一编号在不同厂商的硬盘驱动器中对应不同的监控参数.&Attribute&value&属性(指监控参数)的当前值&--属性值的范围为1&到&253.&最初属性值是最大值.&伴随着硬盘驱动器的运行老化将快要失灵,&属性值会跟着降低.&因此属性值较高说明硬盘驱动器出故障的可能性很小,&而属性值较低则说明硬盘驱动器的可靠性已经很低,出故障的可能性很高.&代表硬盘驱动器可靠性的各项属性值的上界通常设为100(如IBM,Quantum,Fujitsu的硬盘驱动器)&或&253(Samsung&三星硬盘驱动器).&不过,&也有例外,如由西部数据制造的WDAD34000,&WDAC33100,&及WDAC31600这几款型号的硬盘驱动器可靠性属性值初值被设为200,&而下界是100.&Threshold&value&属性的极限值(临界值)&--&由硬盘驱动器开发商决定每项属性的极限值.&即使仅有一项属性值低于它的极限值,&也意味着存放在这个硬盘驱动器上的数据已经很危险了.可靠性属性的组成项目及初值,极限值由硬盘驱动器生产商根据每种硬盘驱动器类型分别确定.&&Pre-Failure/advisory&(即将失灵/通知)&&位&-&这是一个由所有属性值综合确定的参数.&用于指示快要失灵的硬盘驱动器的状态.&这一&位可以用来指示硬盘驱动器的三种状态:&&Pre-Failure/advisory&(即将失灵/通知)&&位&-&值为&0,&同时各项可靠性属性值高于极限值,&说明硬盘驱动器目前处于可靠性很高的状态.&&Pre-Failure/advisory&(即将失灵/通知)&&位&-&值为&0,&同时各项可靠性属性值快要小于极限值,&说明硬盘驱动器目前处于可靠性较低的状态.&&Pre-Failse/advisory&(即将失灵/通知)&&位&-&值为&1,&同时各项可靠性属性值小于极限值&,&说明硬盘驱动器即将失灵.&&Result&&结果&--&由所有监控参数综合决定的一般化结果.有以下几种结果:&OK&--&当前各项属性值远高于对应的各项极限值.&!&--&当前有属性值低于对应的极限值,&&即将失灵/通知&&位值为&0;&!!!&--&属性值低于对应的极限值,&&即将失灵/通知&&位值为&1;&如果待测硬盘驱动器不支持S.M.A.R.T诊断,&按下[SMART]键会使硬盘驱动器指示ABRT(abort放弃)错误,并且PC-3000AT会在屏幕上显示如下消息:&This&drive&does&not&support&S.M.A.R.T&(这台硬盘驱动器不支持S.M.A.R.T.)&2.5.3&驱动器测试&Drive&test(驱动器测试)&--&此工作模式用于测试和修理:&--读写通道&(硬盘电路系统中对磁头读取的微弱信号进行放大,滤波,数据/时钟分离,&对数据进行编码,&磁头写电流驱动,写预补偿的电路部分&--译者注).&--磁头定位系统.&(硬盘机械系统中,&用于移动磁头到指定磁道的零部件总成,&目前普遍使用音圈电机(VCM)带动磁头臂旋转的磁头定位方式&--译者注)&--主轴电机及其控制芯片&(主轴电机用于旋转盘片,&目前常用直流无刷电机;&电机控制芯片负责驱动直流无刷电机旋转及稳速.&--译者注)&测试信息会显示在屏幕的STATE(状态)栏上:&Ready/Busy&(就绪/忙)&--根据硬盘驱动器的状态寄存器中的BSY位的值指示硬盘驱动器的当前状态是已经就绪在等待主机命令还是尚在&忙着&执行上一条主机命令而未就绪.&CYL&(柱面号)&当前(活动)的柱面号&HEAD&(磁头号)&当前(活动)的磁头号&ERRS&(错误数)&已经检测到的错误数量&各个按键作用:&按PC-3000AT测试工具的键盘(电脑键盘右部的数字小键盘区上预设)上?/td&&
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Microfluidics for exosome isolation and analysis: enabling liquid biopsy for personalized medicine
Corresponding authors
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, USA
Department of Biomedical Engineering, Texas A&M University, College Station, USA
Exosomes, the smallest sized extracellular vesicles (∽30–150 nm) packaged with lipids, proteins, functional messenger RNAs and microRNAs, and double-stranded DNA from their cells of origin, have emerged as key players in intercellular communication. Their presence in bodily fluids, where they protect their cargo from degradation, makes them attractive candidates for clinical application as innovative diagnostic and therapeutic tools. But routine isolation and analysis of high purity exosomes in clinical settings is challenging, with conventional methods facing a number of drawbacks including low yield and/or purity, long processing times, high cost, and difficulties in standardization. Here we review a promising solution, microfluidic-based technologies that have incorporated a host of separation and sensing capabilities for exosome isolation, detection, and analysis, with emphasis on point-of-care and clinical applications. These new capabilities promise to advance fundamental research while paving the way toward routine exosome-based liquid biopsy for personalized medicine.
This article is part of the themed collections:
The article was
received on 05 Jun 2017,
accepted on 07 Aug 2017
first published on 10 Aug 2017
Article type:
Critical Review
Lab Chip, 2017,17,
ReferenceManager
Open access:
J. C. Contreras-Naranjo, H. Wu and V. M. Ugaz,
Lab Chip, 2017,&17, 3558
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Ting-Wen&Chen+, , Hsin-Pai&Li+, , , Chi-Ching&Lee, , Ruei-Chi&Gan, , , Po-Jung&Huang, , Timothy&H&Wu, Cheng-Yang&Lee, , Yi-Feng&Chang,
and Petrus&Tang, , +Contributed equallyReceived: 19&March&2014Accepted: 20&June&2014
Chromatin is a dynamic but highly regulated structure. DNA-binding proteins such as transcription factors, epigenetic and chromatin modifiers are responsible for regulating specific gene expression pattern and may result in different phenotypes. To reveal the identity of the proteins associated with the specific region on DNA, chromatin immunoprecipitation (ChIP) is the most widely used technique. ChIP assay followed by next generation sequencing (ChIP-seq) or microarray (ChIP-chip) is often used to study patterns of protein-binding profiles in different cell types and in cancer samples on a genome-wide scale. However, only a limited number of bioinformatics tools are available for ChIP datasets analysis.
We present ChIPseek, a web-based tool for ChIP data analysis providing summary statistics in graphs and offering several commonly demanded analyses. ChIPseek can provide statistical summary of the dataset including histogram of peak length distribution, histogram of distances to the nearest transcription start site (TSS), and pie chart (or bar chart) of genomic locations for users to have a comprehensive view on the dataset for further analysis. For examining the potential functions of peaks, ChIPseek provides peak annotation, visualization of peak genomic location, motif identification, sequence extraction, and comparison between datasets. Beyond that, ChIPseek also offers users the flexibility to filter peaks and re-analyze the filtered subset of peaks. ChIPseek supports 20 different genome assemblies for 12 model organisms including human, mouse, rat, worm, fly, frog, zebrafish, chicken, yeast, fission yeast, Arabidopsis, and rice. We use demo datasets to demonstrate the usage and intuitive user interface of ChIPseek.
ChIPseek provides a user-friendly interface for biologists to analyze large-scale ChIP data without requiring any programing skills. All the results and figures produced by ChIPseek can be downloaded for further analysis. The analysis tools built into ChIPseek, especially the ones for selecting and examine a subset of peaks from ChIP data, provides invaluable helps for exploring the high through-put data from either ChIP-seq or ChIP-chip. ChIPseek is freely available at .
ChIP-seqChIP-chipAnalysis toolWeb-servicesPeak annotationMotif identificationFilter toolsComparison
The chromatin immunoprecipitation (ChIP) assay is a powerful technique to examine the specific interaction between proteins and DNA within living cells [–]. The DNA-binding proteins play important roles in regulating many cellular processes including gene expression, replication, recombination, repair, methylation and chromatin remodeling. The ChIP assay provides comprehensive analysis to reveal specific DNA and protein interaction according to: modifications on DNA-binding proteins (phosphorylation, acetylation, methylation, ribosylation and ubiquitination); DNA spatial and temporal regulatio different cell types and physiological conditions. Application of ChIP assay is therefore versatile, specific applications are listed as follows: identifying the transcription factor binding sites [], locating the sites of histone modifications [], mapping the hypermethylated DNA region in stem cells [], identifying the changes in epigenetic modifications according to different developmental stages [], and identifying the epigenetic changes in different cell types [] and in cancers [, ].To perform ChIP assay, proteins and DNA are in vivo-crosslinked by formaldehyde, chromatin is sonicated into small fragments, and an antibody is used to immunoprecipitate the protein-DNA complexes []. The DNA fragments are further purified and subjected to various analyses, for instance, PCR, cloning, hybridization and sequencing. The ChIP assay not only allows quantitative detection of a given protein on a particular DNA site but also permits the mapping of genome-wide DNA-protein interaction. With the advancement of sequencing technology, ChIP assay has been adapted to use high throughput sequencing (ChIP-seq) for mapping the DNA region or even defining the consensus sequences that are involved in DNA-protein interaction [–]. Several peak calling tools have been proposed for ChIP-seq data analysis, such as MACS, PeakSeq and HPeak [–]. However, a major challenge comes after the ChIP-seq—processing, analyzing and presenting the ChIP-seq data. Several tools have been developed for ChIP-data analysis. For instance, CisGenome provides analysis and visualization of ChIP-data []; Hypergeometric Optimization of Motif EnRichment (HOMER) can find motifs from peak files and annotated peaks []; Positioning database and analysis tool (Podbat) provides analysis and visualization of peaks []; Cistrome contains many ChIP data analysis packages []; EpiExplorer offers an interactive web interface for visualization of peak distribution []; Genomic Association Test (GAT) can test whether multiple sets of peaks significantly overlap with each other []; PscanChIP can identify the enriched transcription factor binding sites []; and PAVIS focuses on annotation and visualization of peaks []. A summary of these tools is shown in Table&.Table 1
Summary of available tools
Web-based analysis tool&&&???&???Graphic user interface?&&???&???Annotation?&????&???Plot of position relative to TSS?&&???&?&?Plot of peak length distribution&&&&&?&&&?Filter (by peak length)&&&&&?&&&?Filter (by position relative to TSS)&&&&&&&&&?Post-filter analysis&&&&&&&&&?Motif identification?&?&?&&?&?Comparison??&???+?&&?Link to UCSC genome browser??&&??&?&?Plot of genomic location?&&??&&&??Sequence extraction&?&&&&&&&?Built-in peak databases?&&&&?&&&?Enrichment analysis&&&??&???&As shown in Table&, most of the existing tools do not have built-in genomic location features for comparison. There are already many large-scale projects that focus on ChIP-seq of transcription factors and histone modifications, such as the ENCODE and modENCODE projects [, ]. The ENCODE consortia have already completed ChIP-seq for 161 transcription factors in more than 100 cell types using the same guidelines and quality-controls []. These databases are highly valuable and very informative, especially in terms of their breadth and quality. Yet most of these tools do not provide the functionality to filter based on peak length or on position relative to a transcription start site (TSS). However, after obtaining a list of peaks from either ChIP-seq or ChIP-chip, researchers want to obtain an overview of all peaks and then pick out the probable or significant peaks for further validation and investigation. The common procedure to obtain the candidate peaks is to filter and then re-analyze the subset of data. To exploit the well-established databases and provide the functionality to filter peaks, we present a web-based analysis tool, ChIPseek, which can (1) (2) filter datasets based
(3) filter datasets based on the distance to the nearest TSS; (4) identify enriched motifs based on uploaded datasets
(5) compa (6) extract peak sequences and
(7) compare an uploaded dataset or filtered dataset with built- (8) plot peaks on chromosome ideograms, and (9) select peaks based on their closest genes corresponding to the gene ontology (GO) or signaling pathways (KEGG database) [, ].ChIPseek takes BED, GFF and text files as input and provides annotation for the peaks. In addition to peak annotation, ChIPseek provides pie charts, bar charts and histograms for peak location distribution, which offer an intuitive way to understand the distribution of all peaks. ChIPseek also generates histograms indicating the frequency distribution of the peak length and the distance to the nearest TSS. All these figures or tables generated by ChIPseek can be downloaded for further analysis. Currently, ChIPseek supports data from Homo sapiens, Mus musculus, Rattus norvegicus, Caenorhabditis elegans, Drosophila melanogaster, Xenopus tropicalis, Danio rerio, Gallus gallus, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana and Oryza sativa. Overall, ChIPseek provides both systematic visualization and filter tools for ChIP data analysis through a user-friendly interface. We believe that ChIPseek is a useful and valuable tool for further ChIP and epigenetic studies.
ChIPseek is mainly constructed using python and the website is written in mod-python, Google Charts, jQuery, JavaScript and PHP [–]. ChIPseek also integrates several well-developed and widely used tools, such as HOMER and BEDTools [, ], to provide better peak annotation and analysis.
The first result generated by ChIPseek is an annotation table. After uploading a file(s), ChIPseek first checks for file format. If the uploaded file(s) contains the proper location information, then ChIPseek can further utilize annotatePeaks.pl from HOMER [] to annotate the peaks according to their genomic locations.
In the annotation table, ChIPseek provides a link to the UCSC genome browser to allow interactive access to the genomic sequence for each peak. ChIPseek also adds custom tracks of uploaded files on the UCSC website. To add custom tracks, ChIPseek incorporates BEDTools [] and two tools downloaded from UCSC ftp [, ]. ChIPseek first uses fetchChromSizes to produce a file of recorded sizes of chromosomes belonging to the organism of interest. ChIPseek then uses mergeBed and sortBed to ensure that the input peak file is compatible with further conversion. Finally, ChIPseek employs bedGraphToBigWig to convert the peak file, together with chromosome size information, into bigWig file. The input for bedGraphToBigWig must be non-redundant and sorted by position. ChIPseek utilizes shell scripts to prepare a proper input BED file for bedGraphToBigWig. The bigWig file can then be uploaded to UCSC to visualize the peak information. If users upload multiple peak files, then ChIPseek can generate multiple tracks and display them simultaneously in the UCSC genome browser.
ChIPseek generates several distribution figures based on the annotation results. These plots are generated in real time by python scripts, mod_python and JavaScript libraries from Google charts [].
In order to provide a comprehensive analysis of the distribution and location of the protein binding sites/peaks relative to TSS, and to classify the biological impact of the targets to a specific group of gene in the same metabolic or signal pathway, users can further subdivide the peaks by using six filter criteria based on (a) Distance to nearest TSS, (b) Peak length, (c) Peak location, (d) Gene list, (e) Gene Ontology (GO), and (f) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. ChIPseek uses JavaScript in filter criteria (a) Distance to TSS and (b) Peak length to provide slider bars and text boxes for users to specify a range (bp) to filter the peaks according to distance to TSS or peak length. ChIPseek then generates histogram with mod_python and Google chart libraries for the filtered subset of peaks. For filter criteria (e) GO and (f) KEGG, the relationships between GO terms are downloaded from the GO website (). The functional annotations of KEGG are downloaded from KEGG BRITE database (). KEGG ID conversion API is used to convert the NCBI gene ID and KEGG Orthology (KO) ID. Several in-house scripts are used to integrate all functional hierarchies into tree structures. Fancytree from jQuery Plugin Registry is used to show the relationship between those functional annotation terms [].
ChIPseek employs fastaFromBed to extract corresponding genomic sequences from the corresponding genome. The output FASTA files are converted into tab-delimited text files with in-house python scripts.
For enriched motif identification, ChIPseek integrates findMotifsGenome.pl, a tool from HOMER [] which can identify enriched motifs in genomic regions, and use Weblogo and Ghostscript for sequence logo generation [, ]. The motif identification includes the following steps: extraction of sequences, background selection, GC-content normalization (for background sequences), and finally motif discovery (including novel motifs and known motifs). ChIPseek provides four options (±25&bp, ±100&bp, ±250&bp and ±500&bp) for users to specify the size of the region to search for motifs.
To achieve a better understanding of overall peak distribution in the genome, ChIPseek provides ideograms of chromosomes indicating the relative positions of the peaks associated with them. The ideograms are implemented in JavaScript, and we adapted the ideogram plotting code Ideogram Viewer from MD Anderson Center to ChIPseek []. We also made several modifications: (1) we added options for different colors to mark the p (2) we added a hyperlink to each mark and provide a link to the UCSC (3) we changed the content that is displayed when the mouse pointer and (4) we changed the ends of the chromosome into round ends. The cytoband information used in ideograms is downloaded from UCSC [].
ChIPseek provides the option to compare two set of peaks. The comparison generates the relationship between peaks from the two sets. To obtain these peak relationships, ChIPseek employs several in-house scripts, as well as intersectBed and coverageBed from BEDdtools [].
ChIPseek also provides a binding sites database of transcription factors for human genome assemblies (hg19, hg18 and hg17). This database is generated from the Encyclopedia of DNA Elements (ENCODE) project [], which contains 161 transcription factors in its database. The latest ENCODE dataset (hg19) is obtained from the UCSC Genome Browser download server. We further applied liftOver [] to cover the binding site to obtain corresponding coordinates for hg18 and hg17. All of these databases are available in the comparison section.
ChIPseek is a web-based tool designed for ChIP data analysis. This software includes the following features:
peak peak plotting on
generation of histograms of the peak generation of histograms of the distance to the nearest TSS; generation of pie charts of the genomic location, and peak filtering by peak length, distance to the nearest TSS, peak location, gene list, GO categories, and KEGG pathways, etc. Currently, ChIPseek mainly focuses on the human genome (hg17, hg18 and hg19) but also provides most of the analytic functions for other model organisms, such as mice (mm8, mm9 and mm10), rats (rn4 and rn5), worms (ce6 and ce10), flies (dm3), frogs (xenTro3, xenTro2), zebrafish (danRer7), chickens (galGal4), yeast (sacCer2 and sacCer3), Schizosaccharomyces pombe (ASM294v1), Arabidopsis (tair10) and rice (Oryza sativa, msu6). ChIPseek supports three types of input file formats: text file, BED format and GFF format. Users can upload their own data to perform all supported analyses through the web interface. Here, we demonstrate the usage and analysis functions of ChIPseek with two datasets: (1) the DNA binding sites of four transcriptional activators, ATF2, ATF3, ETS1 and GATA1, and (2) binding sites of the transcriptional repressor CTCF.
The binding sites of transcriptional factors ATF2, ATF3, ETS1 and GATA1 are generated by the ENCODE project. We downloaded the BED files from the UCSC download server and used them as a demo dataset for ChIPseek. We uploaded four BED files and selected Human/hg19 as our genome assembly version from the multiple file upload page. The input files are checked and then annotated by ChIPseek. The results are shown in the annotation tables (as shown in Figure&). If users upload multiple files, then the annotation tables for each file are listed in separate tabs. The annotation results are separated according to different chromosomes. The annotation table for each chromosome indicates start, end, location annotation, distance to the nearest TSS, nearest RefSeq, nearest gene name and link to the UCSC genome browser. Users can also link to external databases, NCBI and UniProt [, ], by clicking on the RefSeq or gene symbol. The total number of peaks is shown on top of each tab. In our demo samples, there are a total of 2, 13737 and 20310 peaks for ATF2, ATF3, ETS1 and GATA1, respectively. Due to limited space on the browser, seven columns of peak information are displayed. The detailed information of the peaks can be downloaded by clicking the hyperlink “Download all annotation results” on the top-left corner within each separate tab. The full annotation results include the distance to the nearest TSS, nearest promoter ID, Entrez Gene ID, nearest Uniqene ID, nearest Refseq, nearest Ensembl gene name, gene symbol, gene aliases, gene description, genomic location annotation, and hyperlinks to external databases in text format.
Annotation results for binding sites of ATF2, ATF3, ETS1 and GATA1. ChIPseek separates annotation tables into separate tabs for each upload file. In each tab, ChIPseek shows annotation results for each chromosome in an annotation table. Here, a partial annotation result for binding sites of ATF3 in chromosome 13 is shown. For this TF, the total number of peaks (23,095 peaks) is shown above the annotation table followed by a link for downloading the full annotation table (highlighted by the red box). Within the table, each column shows the location of peaks, genomics location annotation, distance to the nearest TSS, nearest RefSeq, gene name etc. The user can click on the title of each column to sort that column. In the table, words in light blue are hyperlinks leading to external databases or a genome browser, i.e., NCBI RefSeq database, UniProt database and the UCSC genome browser, for each peak. User may also specify the regions of interest and visit those particular regions using the text boxes above the annotation table (highlighted by the blue box).
ChIPseek provides links to the UCSC genome browser and displays uploaded peak data on the genome browser. UCSC integrates many biological information sources such as SNP, expression profile and regulation elements [, ], allowing users to access and explore peaks of interest at many levels. In the last column of the annotation table, ChIPseek provides a hyperlink for each peak, which can lead users to the corresponding region for that particular peak. By default, the region extends 1,000&bp upstream and downstream. Users can directly select a peak and explore other biological feature tracks in adjacent regions on the UCSC genome browser. ChIPseek also provides the flexibility for users to explore a specific region of interest with the genome browser. As shown in Figure&, users can enter a specified region in the text boxes above the annotation table, click on the ‘go’ button, and be linked to the UCSC genome browser, focusing on that particular region in a new window.
The scores (height) of custom tracks indicated in the UCSC genome browser are either from users’ uploaded files or given by ChIPseek. If users uploaded BED files, GFF files or text files with score information, then the score will be shown on the UCSC genome browser. If there is no score information in the uploaded text file, ChIPseek will assign all peaks a score of 1. In that case, all peaks will have the same height on the UCSC genome browser. Moreover, the peaks in a single track on the UCSC genome browser cannot overlap. Therefore, if there are overlapping peaks in a single uploaded file, ChIPseek will merge those overlapping peaks and use the average score as a representation for the final merged peak.To visualize the peak distribution within the genome, the examination of location distribution is usually the first step in analyzing ChIP data. The location represents where each peak located and the value includes the 3′UTR (untranslated region), 5′UTR, exon, intergenic region, intron, non-coding, promoter-TSS and transcription termination site (TTS). Both pie charts and bar charts for visualizing the overall peak distribution are available in ChIPseek. If users upload multiple files, separate pie charts for each dataset and a bar chart containing all datasets for comparison are presented as shown in Figure&. Users may choose a suitable chart to present their data. If users are interested in the detailed composition of each category, then the raw data of genomic location is also available in the full annotation table as mentioned previously.To display the frequency and distribution of the distance to the nearest TSS or distribution of peak lengths, histograms are used. As shown in Figure&, the x-axis for the histogram of distance to the nearest TSS is centered at 0 and divided into 100 bins. The x-axis for the histogram of the length distribution starts at 0 and is divided into 50 bins. One of the unique features of ChIPseek is that it offers six filters allowing users to obtain a subset of peaks for further analysis. These six filters are established based on the following criteria: distance to the nearest TSS, lengths of peaks, genomic location, uploaded gene list, selected GO terms, and KEGG groups. Because the transcription factors are likely to be located near the TSS of regulated genes, we can filter the binding sites of ATF2 and ATF3 based on the “Distance to the nearest TSS”. Using this filter criterion, only the peaks located between -200 and +200&bp of the nearest TSS are selected. After this step, a total of 2,847 out of 26,026 ATF2 peaks and 3,999 out of 23,095 ATF3 peaks fit this criterion. A new histogram of the subset peaks in real time as shown in Figure&. Two new files containing the two subsets of peaks will be generated by and saved in ChIPseek, and will be automatically named ATF2_-200-200_TSS and ATF3_-200-200_TSS, respectively. The naming system starts with the original uploaded file name, followed by the range and then “TSS”, which shows the filter criteria based on the distance to TSS. These two saved files can be used in the subsequent analysis.
Pie chart and bar chart of genomic location distribution. (A) Pie chart of the genomic location distribution of transcription factor ATF2. This plot shows the percentage for each genomic location category. The categories are sorted by descending percentage. The exact percentage for each category appears if the mouse pointer hovers over a pie slice. (B) Bar chart of the genomic location distribution of four transcription factors, ETS1, ATF2, ATF3 and GATA1. All uploaded files are combined into the same bar chart. This bar chart reveals the actual number of peaks for each category when the mouse pointer hovers over each bar.
Histogram of distance to the nearest TSS and of peak lengths. (A) The distribution of distance to the nearest TSS. This example is of the transcription factor binding sites for ATF2. The x-axis of the histogram is centered at 0 and divided into 100 bins that cover the largest and smallest values of distance. As shown in this histogram, most of the binding sites are located near the TSS. The exact number of peaks for each range of distance appears when the mouse pointer hovers over that bar. (B) The distribution of the peak lengths of transcription factor binding sites of ATF2. Most of the peaks have a length smaller than 600&bp. Again, if users are interested in the exact number of peaks within each range, hovering over that range will reveal the value. (C) The user may use filter criteria to select a subset of peaks. There are two ways to filter the peaks (highlighted by the red boxes). The first is the slider bar and the second is the text box. In this example, we use text boxes to filter out peaks with distance to the nearest TSS larger than +200 or smaller than -200. After this operation, 2,847 peaks are left. After the selection step, the histogram is refreshed with this subset of peaks in real time. After this filter step, we save the filtered subset with the “save” button above the histogram.
In addition t.o generating plots for exploring the properties of peaks, ChIPseek also extracts the sequences of the peaks. These sequences can be used for further analysis such as TFsearch, DMEME, CentriMo or other ChIP data analysis tools etc. [–]. After clicking the “Peak sequences” on the menu, users can obtain a table of partial extracted sequences. The extracted sequences are presented in two forms: FASTA format and tab-delimited text format. The tab-delimited format contains 5 columns: chromosome, start position, end position, length of sequences and the DNA sequences. Our tools can also extract sequences for the subsets of uploaded files. As we already saved a subset of peaks (ATF2_-200-200_TSS and ATF3_-200-200_TSS) in the previous step, we can retrieve the sequences for these two saved subsets by clicking on “Get peak sequences” on the menu.
ChIPseek further allows peak comparison between two peak files. As a demonstration, we performed a comparison using the two saved subsets of peaks, ATF2_-200-200_TSS and ATF3_-200-200_TSS. In Figure&, the results of the comparison are displayed in four separate tabs. The first tab is a Venn diagram that shows the number of unique peaks for each dataset and the number of overlapped peaks between the two datasets. Users should note that the overlapped peak section might have redundant peaks. For example, if one peak from ATF3_-200-200_TSS is overlapped with two peaks in ATF2_-200-200_TSS, then they will be counted twice. Therefore, the total number of unique peaks plus overlapped peaks may not necessarily be equal to the total number of peaks in the original datasets. The second and third tabs show tables of unique peaks for each dataset. As shown in Figure&, the final tab contains tables of various chromosomes with the listed overlapped peaks. In addition to directly comparing the users’ datasets, ChIPseek also includes a database of binding sites for 161 transcription factors generated by ChIP-seq from the ENCODE project []. Users may compare their datasets with the experimentally based transcription factor binding sites. As the ENCODE project is focused on human genome, the comparison of transcription factor binding sites is only available for human datasets.
Comparison between binding sites of ATF2 and ATF3 and Venn diagram. After selecting ATF2_-200-200_TSS and ATF3_-200-200_TSS for comparison, ChIPseek compares peaks from these two datasets. The overall comparison result is shown as a Venn diagram in the first tab. As shown here, a total of 1,359 peaks are ATF2 unique, 2,499 peaks are ATF3 unique and 1,499 peak pairs are overlapped between the biding sites of ATF2 and ATF3. The detailed peak information for unique peaks and overlapped peak pairs can be found in the following three tabs.
Overlapped peak pairs. At the top of this page is shown how many peak pairs are found, and a link is provided to download all peak pairs with their annotation. All overlapped peak pairs are separated into different tables according to their location. As shown here, the table lists peak pairs located on chromosome X. The first four columns list the start and end positions of peaks. The fifth column shows the relative positions of the peak pairs. The last column provides links to the UCSC genome browser for that region of interest. Clicking on the title of each column can sort that column.
CTCF is known as a DNA methylation-dependent chromatin insulator. Aberrant methylation pattern of the CTCF binding sites of an imprint gene Igf2/H19 causes the development of Beckwith-Wiedemann Syndrome (BWS) [–]. Children with BWS have an over-growth syndrome and predispositions to develop pediatric tumors including Wilms tumor []. We downloaded the binding sites of CTCF as our demo dataset. The CTCF dataset is uploaded as a BED file. To demonstrate how to filter the dataset by the length of peaks, we used “Peak length” filter criteria to obtain a subset of peaks that have length range from 100&bp to 150&bp. After that, a subset of 67 peaks out of the original 162,209 peaks is selected and saved. ChIPseek automatically named this subset “CTCF_100-150_len”. The suffix “len” indicates that the peaks in this subset have been filtered according to the peak length.
As mentioned in the previous section, users can use multiple filter criteria to narrow down the number of regions that they are interested in. For example if the user want to narrow down the peaks located at promoter regions due to the fact that the transcription factor binding site near the “promoter-TSS” is consider to have a greater biological impact on the activity of the promoter. By selecting peaks located within “promoter-TSS” under the “Peak location” in the filter criteria, the user can filter a total of 11,328 out of 162,209 peaks. On the other hand, if users already have lists of genes which they are interested in, they could also upload lists of genes and filter peaks based on these genes. In addition to use a list of genes for filtering, ChIPseek also offers built-in functional annotations downloaded from GO and KEGG [, ] which allow users to generate gene lists based on their interested biological function or pathways. As the annotation functions from GO and KEGG are both constructed in tree structures, users may select interested functions one by one or directly select a whole branch from the annotation tree. It should be noted that, nodes that are belonged to more than one parent, will be listed multiple times under different parents separately in the annotation tree. By allowing this kind of redundancy, ChIPseek can guarantee that the created gene list includes all possible genes related to these selected function groups. Subsets of filtered peaks can be used in further analysis or filtered again by other filter criteria. Users can also remove selected files too if they found some results are not necessary for further analysis.To illustrate the distribution of peaks on chromosomes, users may click “Plot peaks on ideogram” on the menu. After that, users may select the dataset and the color bar (with 10 different colors), and click “show locations on chromosomes”. Due to the resolution limitation, only datasets having less than 1000 peaks can be displayed in this way. Users may display up to 10 different datasets in different colors on the ideograms. In our demo data, dataset “CTCF_100-150_len” is selected, and the locations of 67 peaks are indicated on the chromosomes by blue bars as shown in Figure&. The chromosome ideogram provides an intuitive way to visualize the overall genomic distribution of all peaks. Currently, the chromosome ideogram is only available for human assemblies (hg17, hg18 and hg19).To identify the enriched motif of CTCF biding sites, users may click on “find motif” in the menu. The users need to specify the range of the enriched motifs. ChIPseek utilizes HOMER for motif identification. According to the user manual of HOMER, 50&bp (±25&bp around the centers of peaks) should be enough to identify the motif for a transcription factor. Alternatively, users may choose 200&bp (±100&bp around the center), which should be enough to find both the primary and “co-enriched” motifs for a transcription factor. As for histone marked regions, ranges from 500–1000&bp (±250- ± 500&bp around the center) may be suitable regions to search. To satisfy all ChIP studies focused on transcription factors, histone modifications and CpG islands, ChIPseek provides four different options: ±25&bp, ±100&bp, ±250&bp and ±500&bp. Users can specify how many nucleotides around the centers of peaks to use as target regions for domain identification according to the users’ experimental purpose. It is worth noting that motif identification is time-consuming and the larger the selected regions the longer it will take to identify the consensus sequences. In our case, because CTCF is a DNA binding protein with known consensus sequence CTCCC, we selected ±25&bp around the center of peaks to search for motifs and the results are shown in Figure&.
Chromosome ideograms of CTCF. Peaks are plotted on the chromosome ideograms at their positions. The information of exact position and nearest gene will appear if the mouse pointer hovers over the peak. Clicking on the peak will link to the UCSC genome browser. There are 10 different colors available for selection: blue, pink, green, yellow, gold, purple, aqua, fuchsia, silver, and red. The user may plot different datasets on the same ideogram with different colors. To plot additional peaks, the user can clear all marks with the “clear all peaks” button.
Motif identification result for binding site of CTCF. Here is the result of motif enrichment analysis for ±25&bp around the center of CTCF binding sites. The identified motifs are sorted according to their p-values. Clicking on “more information” will display the details for that enriched motif. The original HOMER prediction result is available from the hyperlink provided above the table.
Users should notice that ChIPseek can identify enriched motifs even if they input random data due to the large scale of input data. A significant p-value may be achieved just by random chance. Because ChIPseek uses HOMER to identify potential motifs, ChIPseek only reports motifs that have p-values smaller than 1e-10, which is the cutoff suggested by the HOMER website. However, there may still be some false positives after this filter. Therefore, it is still very important for the users to determine which patterns may truly be recognized by the binding protein. Users may identify most promising candidates by checking the p-value distribution and setting a customized p-value cutoff. As ChIPseek shows p-values in ascending order, the p-value followed by another dramatically increased p-value may be the proper cutoff. Another clue for candidate motif selection is that different offsets of a true motif may appear in the report several times. For example, in Figure&, the pattern “GGTGG” shows up several times at the top four sequence logos. Therefore, this GGTGG is likely to be part of a real motif. As the binding consensus sequence of CTCF in the human genome has already been investigated in a previous study [], we found this pattern to indeed be present in the consensus binding sequences of CTCF. Meanwhile, many motifs have been identified, and their consensus sequences are well known. ChIPseek also uses HOMER to perform motif enrichment analysis for those known motifs. Users may also explore known motifs by following the hyperlink “Known Motif Enrichment Results” above the table.
Here we present ChIPseek as a web tool for analyzing ChIP-seq and ChIP-chip data. By integrating HOMER and BEDTools, ChIPseek is an easy-to-use software package for the first-time user with only basic computer skills who wants to analyze ChIP data. ChIPseek guides the users step-by-step to obtain the peak annotation, locations, sequences, and useful statistics as charts and histograms for visualizing the properties of the peaks. ChIPseek also provides UCSC genome browser links so that the users can investigate peaks further. A unique feature of our tool is that it includes filter tools which allow users to select interested peak subsets based on peak lengths, distance to the nearest TSS, peak location, user uploaded gene lists or genes belong to user selected functions. For human datasets, users may also use ChIPseek to plot peaks on an ideogram, which offers an overview of genomic distribution across different chromosomes. When comparing two datasets, ChIPseek generates a Venn diagram, lists of unique peaks for each dataset and overlapped peak pairs with their relative positions. We also showed that ChIPseek can help users identify enriched binding motifs for transcription factors and DNA binding protein factors. In addition to these convenient analysis tools, ChIPseek also has a built-in database of human transcription factor binding sites available for comparison. In summary, ChIPseek includes many desired functions that are available through an intuitive user interface. ChIPseek is a free, web-based service. The use of ChIPseek requires no knowledge of Linux, no programming, no installation and no login. The ChIP data analysis tools and databases provided by ChIPseek will offer invaluable assistance for biologists whose studies focus on protein-DNA interaction, gene transcription regulation, epigenetics, chromatin organization and cancer biology.
ChIPseek is freely available at
and all the source codes of pipelines and parsers are also available upon request. Currently, ChIPseek can support most commonly used browsers such as Chrome, Firefox, Internet Explorer and Safari.
Ting-Wen Chen, Hsin-Pai Li contributed equally to this work.
Chromatin immunoprecipitation
Transcription start sites
Untranslated region
Transcription termination site
Bechwith-Wiedemann Syndrome.
We thank Mr. Chi-Yang for his help on modifying the code of Ideogram Viewer. This work is supported by grants from the Chang Gung Memorial Hospital (CMRPD to PT and CMRPD1A0352 to HPL); the Ministry of Education, Taiwan and the Ministry of Science and Technology, Taiwan (NSC102-2319-B-400-001 to PT).
Below are the links to the authors’ original submitted files for images.
Authors’ original file for figure 1
Authors’ original file for figure 2
Authors’ original file for figure 3
Authors’ original file for figure 4
Authors’ original file for figure 5
Authors’ original file for figure 6
Authors’ original file for figure 7
The authors declare that they have no competing interests.TWC and CYL built the pipeline. CY, THW and CCL designed and constructed the website. TWC and HPL wrote the manuscript together. RCG and PJH maintain the system. PT supervised and revised the manuscript. All authors read and approved the final manuscript.
(1)Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan(2)Bioinformatics Center, Chang Gung University, Taoyuan, Taiwan(3)Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan(4)Department of Microbiology and Immunology, Medical School of Chang Gung University, Taoyuan, Taiwan(5)Department of Biological Science and Technology, National Chiao Tung University, HsinChu, Taiwan(6)Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
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