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R语言 rtfbs包 score.ms()函数中文帮助文档(中英文对照)

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发表于 2012-9-28 23:15:48 | 显示全部楼层 |阅读模式
score.ms(rtfbs)
score.ms()所属R语言包:rtfbs

                                        Score sequences against a PWM
                                         针对PWM分数序列

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

Score all potential binding sites in an MS object. If a PWM has N rows, then score every observed N-mer in the MS object. The score is given by the log likelihood of the N-mer given the PWM, minus the log likelihood of the N-mer under the Markov model specified by mm. By default, only potential binding sites with scores > 0 are returned, but this can be modified with the threshold argument.
得分MS对象所有可能的结合位点。如果一个PWM有n行,然后打分每观察到的N-MER在MS的对象。的得分由下式给出给定的N-聚体的对数似然的PWM,减去根据所指定毫米的马尔科夫模型的N-聚体的对数似然。默认情况下,唯一的潜在结合位点的分数> 0返回,但可以threshold参数进行修改。


用法----------Usage----------


  score.ms(ms, pwm, mm, conservative = TRUE, threshold = 0,
    strand = "best")



参数----------Arguments----------

参数:ms
MS object containing at least one sequence
MS对象,其中包含至少一个序列


参数:pwm
Position Weight matrix representing transcription factor motif
位置权重矩阵表示转录因子的主题


参数:mm
Markov Model associated with given sequences, which represents the null model
与给定序列,它代表了空模型的马尔可夫模型


参数:conservative
(Logical value) If TRUE, sequences containing N's are given a log likelihood of negative infinity under the PWM model. If FALSE, any 'N' encountered does not contributes to the score.
(逻辑值),如果设置为TRUE,含有N的序列是负无穷大的给定的对数似然下的PWM模式。如果为FALSE,任何遇到的N没有的得分。


参数:threshold
(Numeric value) Only sites with scores above this threshold are returned (default = 0)
得分超过这个阈值(数值)只有网站返回(默认值= 0)


参数:strand
One of "best", "both", "+", or "-" specifying which strand(s) to return results for. If "both" search for binding sites in both directions, return all results found. If "best" search for binding sites in both directions, but for each N-mer, return the maximum score over either strand. If "+" look only on the forward strand, and if "-" look only on the reverse strand.
一个“最佳”,“两者”,“+”或“ - ”指定链()返回结果为。如果“都”的结合位点在两个方向上的搜索,返回所有结果发现。如果“最好的”在两个方向上的结合位点,但对于每一个N-Mer的搜索,返回的最大比分超过要么链。如果“+”看仅在正向链,如果是“ - ”看仅在反向链。


值----------Value----------

Scores and locations for possible binding sites returned as a feature object.
成绩和可能的结合位点的位置作为一个功能对象返回。


注意----------Note----------

If a PWM file contains multiple PWMs, then read.pwm will return a list of PWMs.  This function takes a single PWM.
如果PWM文件包含多个脉冲宽度调制器,,然后read.pwm将返回一个列表的PWM。这个函数接受一个单一的PWM。


参见----------See Also----------

read.ms split.ms groupByGC.ms   build.mm read.pwm
read.ms split.ms groupByGC.ms   build.mm read.pwm


实例----------Examples----------


require("rtfbs")
exampleArchive <- system.file("extdata", "NRSF.zip", package="rtfbs")
seqFile <- "input.fas"
unzip(exampleArchive, seqFile)
# Read in FASTA file "input.fas" from the examples into an [阅读在FASTA的文件“input.fas”中的例子成]
#   MS (multiple sequences) object[多个序列(MS)对象]
ms <- read.ms(seqFile);
pwmFile <- "pwm.meme"
unzip(exampleArchive, pwmFile)
# Read in Position Weight Matrix (PWM) from MEME file from[阅读中的位置权重矩阵(PWM)MEME文件]
#  the examples into a Matrix object[到Matrix对象的例子]
pwm <- read.pwm(pwmFile)
# Build a 3rd order Markov Model to represent the sequences[建立一个3阶Markov模型来表示序列]
#   in the MS object "ms".  The Model will be a list of[在MS对象“MS”。该模型将一个列表]
#   matrices  corrisponding in size to the order of the [矩阵的大小的顺序corrisponding]
#   Markov Model[马尔可夫模型]
mm <- build.mm(ms, 3);
# Match the PWM against the sequences provided to find[对找到的序列匹配PWM]
#   possible transcription factor binding sites.  A [可能的转录因子结合位点。一]
#   Features object is returned, containing the location[返回对象特点的,包含位置]
#   of each possible binding site and an associated score.[每个可能的结合位点和一个相关的得分。]
#   Sites with a negative score are not returned unless [除非网站不会返回一个负的成绩]
#   we set threshold=-Inf as a parameter.[我们作为一个参数设置的阈值=-INF。]
score.ms(ms, pwm, mm)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注:
注1:为了方便大家学习,本文档为生物统计家园网机器人LoveR翻译而成,仅供个人R语言学习参考使用,生物统计家园保留版权。
注2:由于是机器人自动翻译,难免有不准确之处,使用时仔细对照中、英文内容进行反复理解,可以帮助R语言的学习。
注3:如遇到不准确之处,请在本贴的后面进行回帖,我们会逐渐进行修订。
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