Net Promoter Score A Number for Business to Grow By
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自学CHIP-seq分析第七讲~peaks注释经过前面的CHIP-seq测序数据处理的常规分析,我们已经成功的把测序仪下机数据变成了BED格式的peaks记录文件,我选取的这篇文章里面做了4次CHIP-seq实验,分别是两个重复的野生型MCF7细胞系的BAF155 immunoprecipitates和两个重复的突变型MCF7细胞系的 BAF155 immunoprecipitates,这样通过比较野生型和突变型MCF7细胞系的BAF155 immunoprecipitates的结果的不同就知道该细胞系的BAF155 突变,对它在全基因组的结合功能的影响啦。
#我这里直接从GEO里面下载了peaks结果,它们详情如下:wc -l *bed6768GSM1278641_Xu_MUT_rep1_BAF155_MUT.peaks.bed3660GSM1278643_Xu_MUT_rep2_BAF155_MUT.peaks.bed11022GSM1278645_Xu_WT_rep1_BAF155.peaks.bed5260GSM1278647_Xu_WT_rep2_BAF155.peaks.bed49458 GSM601398_Ini1HeLa-peaks.bed24477 GSM601398_Ini1HeLa-peaks-stringent.bed12725 GSM601399_Brg1HeLa-peaks.bed12316 GSM601399_Brg1HeLa-peaks-stringent.bed46412 GSM601400_BAF155HeLa-peaks.bed37920 GSM601400_BAF155HeLa-peaks-stringent.bed30136 GSM601401_BAF170HeLa-peaks.bed25432 GSM601401_BAF170HeLa-peaks-stringent.bed每个BED的peaks记录,本质是就3列是需要我们注意的,就是染色体,以及在该染色体上面的起始和终止坐标,如下:#PeakID chr start end strandNormalized Tag Count region sizefindPeaks Score Clonal Fold Changechr20 52221388 52856380 chr20-8088 41141 +chr20 45796362 46384917 chr20-5152 31612 +chr17 59287502 59741943 chr17-2332 29994 +chr17 59755459 59989069 chr17-66719943 +chr20 52993293 53369574 chr20-7059 12642 +chr1 121482722 121485861 chr1-9959070 +chr20 55675229 55855175 chr20-6524 7592 +chr3 64531319 64762040 chr3-40227213 +chr20 49286444 49384563 chr20-4482 6165 +我们所谓的peaks注释,就是想看看该peaks在基因组的哪一个区段,看看它们在各种基因组区域(基因上下游,5,3端UTR,启动子,内含子,外显子,基因间区域,microRNA区域)分布情况,但是一般的peaks都有近万个,所以需要批量注释,如果脚本学的好,自己下载参考基因组的GFF注释文件,完全可以自己写一个,我这里会介绍一个R的bioconductor包ChIPpeakAnno来做CHIP-seq的peaks 注释,下面的包自带的示例:library(ChIPpeakAnno)bed <- system.file("extdata","MACS_output.bed",package="ChIPpeakAnno")gr1 <- toGRanges(bed, format="BED",header=FALSE)## one can also try import fromrtracklayerlibrary(rtracklayer)gr1.import<- import(bed, format="BED")identical(start(gr1), start(gr1.import))gr1[1:2]gr1.import[1:2] #note the name slot isdifferent from gr1gff <- system.file("extdata","GFF_peaks.gff",package="ChIPpeakAnno")gr2 <- toGRanges(gff, format="GFF",header=FALSE, skip=3)ol <- findOverlapsOfPeaks(gr1, gr2)makeVennDiagram(ol)##还可以用binOverFeature来根据特定的GRanges对象(通常是TSS)来画分布图## Distribution of aggregated peakscores or peak numbers around transcriptstart sites.可以看到这个包使用起来非常简单,只需要把我们做好的peaks 文件(GSM1278641_Xu_MUT_rep1_BAF155_MUT.peaks.bed等等)用toGRanges或者import读进去,成一个GRanges对象即可,上面的代码是比较两个peaks文件的overlap。
【转】生物信息学中的常用词汇【转】生物信息学中的常用词汇 2011年03月13日degeneracy 简并性指某些氨基酸可以被一个以上的三联密码子编码的特性。
denatured protein 变性蛋白质指蛋白质因为受热作用或者去污剂或尿素等化学作用而失去了正常的三级结构和四级结构的结果。
deoxyribonucleic acid (DNA) 脱氧核糖核酸 ( DNA ) 由相连的核苷酸组成的双链生物二聚体,其核苷酸含有脱氧糖基。
DNA是遗传的分子基础。
dipeptide 二肽由一个肽键连成的两个氨基酸。
disulfide bond 二硫键二硫键是蛋白质中两个半胱氨酸侧链之间形成的化学键。
DNA DNA 参见脱氧核糖核酸。
domain 域(结构域) 指蛋白质结构中相对独立的、具有特定功能的空间区域。
dot plot 点阵图对两条序列进行图形化比较的方法。
图形中的一系列的斜线对应于序列相似的区域。
dynamic programming 动态规划一种可以有效地探求一定复杂问题的各种可能的解决方案的程序;它将一个问题合理分解成一些小的子问题,然后利用部分计算解得到最终答案。
E enhancer 增强子可以与真核转录因子特异性结合的 DNA 序列片段。
增强子序列可以在任何一个方向上起到逐渐增加转录水平的作用。
enzyme 酶一种生物催化剂(通常是蛋白质),能通过降低活化能使特定的化学反应可以更快地进行。
EST ( Expressed sequence tags ) EST 表达序列标签从 cDNA 的 5' 或 3' 端获取的短的 DNA 片段。
euchromatin 常染色质指真核生物中组蛋白高度甲基化( 乙酰化,)并且 DNA 低度甲基化的开放染色质。
exhaustive search 穷举搜索对问题所有可能的解进行评估。
exon 外显子一个 hnRNA 分子的各个部分,它们被剪接后连在一起形成 mRNA 。
annovar注释sift_scoreAnnovar是一款广泛应用于基因组学研究中的注释工具,它为研究人员提供了丰富的功能和信息。
其中,sift_score是Annovar中一个重要的指标,用于预测非同义突变(non-synonymous mutations)对蛋白质变异的影响程度。
本篇文章将为您介绍sift_score的相关背景、计算方法以及在基因组学研究中的应用。
第一篇:背景介绍:基因组学研究旨在深入了解基因组中的变异对生物体功能和表型的影响。
在这个过程中,注释工具起到了至关重要的作用,帮助研究人员对基因组变异进行解读和分析。
Annovar作为一个常用的注释工具,可以对基因组中的变异进行功能注释和预测。
SIFT Score的定义和计算方法:SIFT(Sorting Intolerant From Tolerant)模型被广泛应用于预测非同义突变对蛋白质功能的影响。
SIFT Score是根据亚突变位点的高度保守性计算得出的。
该分数反映了蛋白质结构域中对氨基酸保守性的破坏程度,越高表示该变异对蛋白质功能的影响越小。
SIFT Score的计算方法基于物种对氨基酸保守性的统计模型。
通过比对不同物种的氨基酸序列,SIFT可以预测出可能导致蛋白质结构和功能改变的氨基酸替换。
SIFT Score值的范围为0.00到1.00,值越低表示变异位点的极端不可容忍性,而值越高则代表变异在进化过程中被允许并且不会对蛋白质功能产生明显影响。
SIFT Score在基因组学研究中的应用:SIFT Score可以用于预测非同义突变对蛋白质功能的影响,帮助研究人员更好地理解变异对基因功能和表型的影响。
在疾病遗传学研究中,SIFT Score可以帮助筛选可能与疾病相关的突变位点。
通过筛选出具有高SIFT Score的变异,研究人员可以更加精确地定位疾病的致病基因。
此外,SIFT Score还可以用于预测药物靶点相关基因的功能性变异。
分子生物学中常用数据库综合数据库:来源:/news/science/article/90048.html生物信息学网址链接:http://www.bioinformatics.ca/links_directory/Nucleic Acid Research Database Issue:/content/vol32/suppl_2/一、蛋白相关数据库蛋白质结构域预测工具Esignal:/esignal/信号传导系统蛋白的结构域预测工具,凡是涉及到信号传导系统的蛋白用这个预测效果最佳SignalP:http://www.cbs.dtu.dk/services/SignalP/信号肽预测工具,适合定位于非胞质位置的蛋白质Emotif:/emotif-search/结构域预测工具,由于其用motif电子学习的方法产生结构域模型,故预测效果比Prosite好Ematrix:/ematrix/是用Matrix的方法创建的结构域数据库,可与emotif互相印证。
其速度快,可快速搜索整个基因组InterPro:/InterProScan/EBI提供的服务,用图形的形式表示出搜索的结构域结果TRRD:http://wwwmgs.bionet.nsc.ru/mgs/gnw/trrd/转录因子结构域预测的最好数据库。
但不会用Protscale:/cgi-bin/protscale.pl可分析该序列的各种性状如活动度、亲水性(Kyte&Doolittle)、抗原性(Hopp&Woods)等通过寻找MOTIF和Domain来分析蛋白质的功能A. MOTIF是蛋白中较小的保守序列片断,其概念比Domain小PROSITE:/tools/scanprosite/是专门搜索蛋白质Motif的数据库,其中signature seqs是最重要的motif信息B. Domain:若干motif可形成一个Domain,每个Domain形成一个球形结构,Domain与Domain之间通常像串珠一样相连Pfam:可以搜索某段序列中的Domain,并以图形化表示出来。
神技能!批量解决哪个转录因子调控你的基因。
果子导读:我的导师曾经跟我讲过,10年前,CELL杂志每期一半以上都是在做转录调控。
10年后,我们发现,在很多杂志,这个现象依然存在。
如果已知转录因子,找他的靶基因,用ChIP-seq就可以搞定。
但是!如果反过来,团队已经确定所研究的基因,那么找出能够调控他的分子,确实是个难题。
所幸!这篇来自嘉因小丫的帖子把这个事情一次性解决。
从此,国自然申请和课题设计,如虎添翼,一日千里。
以下是正文:《哪个蛋白质调控我感兴趣的基因?怎样筛选?基于分析或实验的可行方案V2.1》一文讲了找上游转录因子的策略:•Plan A:基于大量ChIP-seq公共数据挖掘•Plan B:motif分析预测•Plan C:ATAC-seq结合motif分析motif系列答疑帖一步步帮你实现了Plan B1.去哪找motif?史上最全物种转录因子、motif数据库footprintDB2.这段DNA上有我关心的motif吗?点鼠标就能找启动子区的motif | meme-FIMO3.motif scan结果怎样看?互补链上的motif有意义吗?4.motif结果怎样展示到文章里?找到了motif,怎样展示结果?ChIP系列带你实现Plan A,下个系列解决Plan C。
1.原理2.在线快速查看结果3.局限性4.速查表5.从哪里下载数据6.7.怎样批量处理数据8.怎样展示结果用上篇《Plan A详细步骤1234》找到转录因子的小伙伴可以跳过本篇,直接看7,下篇讲7。
本文讲56,适用于从几十上百套ChIP-seq中找上游调控因子的情况。
如果在嘉因公众号讲这篇,需要铺垫太多基础知识,读者也未必愿意看。
思来想去,还是放到生信技能树发布吧。
书接上文《Plan A详细步骤1234》,如果您关注的细胞类型有几十甚至几百套ChIP-seq,用肉眼挨个看哪个track有peak,就要疯掉了。
这时就需要我们懂生信的出手了,批量下载,批量处理。
外显子、内含子、mRNA、CDS、ORF区别与联系1、DNA复制:以DNA为模板,在DNA聚合酶的催化作用下,将四种游离的dNTP按照碱基互补配对原则合成新链DNA转录:以DNA为模版,在DNA指导的RNA聚合酶的作用下,将四种游离的NTP按照碱基互补配对的原则合成RNA翻译:以mRNA为模板,在核糖体内合成蛋白质的过程特点:DNA复制:模板为双链DNA,合成的新链与模板链一模一样,原料为四种dNTP,为半保留复制,需要引物转录:模板为双链DNA,为半不连续转录需要引物,原料为四种NTP,合成的新链除了把DNA上的T改为U外,其他一样翻译:模板为mRNA,原料为20中游离的氨基酸,3个碱基决定一个氨基酸2、mRNAmRNA (messenger RNA,信使RNA)信使RNA是由DNA经hnRNA剪接而成,携带遗传信息的能指导蛋白合成的一类单链核糖核酸。
3、基因DNA分为编码区和非编码区,编码区包含外显子和内含子,一般非编码区具有基因表达的调控功能,如启动子在非编码区。
编码区则转录为mRNA并最终翻译成蛋白质。
外显子和内含子都被转录到mRNA前体hnRNA中,当hnRNA 进行剪接变为成熟的mRNA时,内含子被切除,而外显子保留。
实际上真正编码蛋白质的是外显子,而内含子则无编码功能,内含子存在于DNA中,在转录的过程中,DNA上的内含子也会被转录到前体RNA中,但前体RNA上的内含子会在RNA离开细胞核进行翻译前被切除。
4、CDS Sequence coding for amino acids in protein 蛋白质编码区 CDS是Coding sequence的缩写,是编码一段蛋白产物的序列,是结构基因组学术语。
与开放读码框ORF的区别开放读码框是从一个起始密码子开始到一个终止密码子结束的一段序列;不是所有读码框都能被表达出蛋白产物,或者能表达出占有优势或者能产生生物学功能的蛋白。
CDS,是编码一段蛋白产物的序列。
基因工程综合实验_浙江大学中国大学mooc课后章节答案期末考试题库2023年1.使用超净台进行无菌操作前,开启紫外灯照射杀菌()答案:20-30 min2.琼脂糖的溶解温度是()答案:96℃3.抽提RNA后,紫外分析其纯度,如无污染,A260/A280应在()值附近?答案:2.04.制作固体平板时,加入抗生素时所用培养基的适宜温度一般为()答案:50-60 ℃5.在亲和层析中,使用谷胱甘肽作为亲和吸附剂,其提取靶蛋白需要具有()答案:GST标签6.对于胞外分泌蛋白的获取,以下哪一步是不需要进行的()答案:破碎收集上清7.实验中IPTG的使用作用是()答案:诱导靶基因的表达作用8.蛋白质生物合成中的rRNA的作用是()答案:提供蛋白质合成场所9.反密码子是位于()答案:tRNA10.真核生物启动子不存在的序列是()答案:Pribnow框11.Western Blotting用于杂交结合反应的是()答案:抗体-抗原12.蛋白质吸收紫外光能力的大小,主要取决于()答案:酪氨酸、色氨酸与苯丙氨酸的含量13.常规PCR扩增合成DNA不需要()答案:ddNTP14.关于多克隆位点MCS的描述,正确的是答案:具有多种酶的识别序列不同酶的识别序列可重叠人工合成后添加入载体中15.关于感受态细胞性质的描述,下面说法正确的是()答案:具有可诱导性不同菌株出现感受态比例不同具有可转移性16.表达宿主细胞一般有以下要求()答案:具有限制-修饰系统缺陷具有特殊筛选性质,如抗生素抗性、蓝白斑等17.在化学发光的Western杂交实验结果中出现了高背景的现象,即常说的显影后背景太脏,请问可能的原因有()答案:显影时膜过曝,曝光时间过长非特异性位点封闭不足显影前膜干燥18.真核生物mRNA上的起始密码子为AUG,其上游具有一段富含嘧啶的序列答案:错误19.DNA分子在琼脂糖凝胶电泳中是从正极向负极迁移答案:错误20.抗生素筛选中,能在含有抗生素的平板上生长的菌落是成功导入重组质粒的菌落答案:错误21.蛋白质的半衰期与多肽N端的氨基酸种类有关答案:正确。
Net Promoter Score: A Number for Business to Grow ByR. Eric Reidenbach and Reginald Goeke February 26, 2010Organizations looking for a silver bullet customer metric look no further than the Net Promoter Score (NPS). Developed by Frederick Reichhold and his colleagues at Bain & Co., NPS provides an economical and concise metric for assessing how loyal a company’s customers are. Managing that measurement is the key to future growth.What Is NPS?NPS is both simple and compelling. Customers are asked, on a scale of 1 to 10, how likely they are to recommend a company to a friend. Respondents fall into one of three categories:∙Promoters (9 or 10) –customers who are highly likely to recommenda company or product∙Passives (7 or 8) –satisfied but unenthusiastic customers looking for a better deal∙Detractors (0 to 6) – people who are less likely to recommend a company or productNPS is the percentage of promoters minus the percentage of detractors.The Benefits of a High NPSHaving a high NPS, and loyal customers, means growth. Promoters account for 80 percent to 90 percent of positive word of mouth, according to “Reichheld’s New Metric: The Net Promoter Score,” an article on . The net promoter leader grows at more than 2.5 times the rate of its competitors.Bain & Co. publishes a list of selected NPS stars, all with scores of 50 percent to 80 percent. To achieve such stardom, a company needs to manage its NPS. This includes narrowing the focus for scores and considering the influence of value on loyalty.Focusing on a Product/MarketIf Dell has an NPS of 50 percent, does that refer to laptops sold to business people or desktops sold to home users, or servers sold to medium or large businesses? Many organizations serve more than one market and offer more than one product or service. Promoters and detractors will be specific to a product/market. By approaching NPS this way, companies can drill down and look at how to improve low NPSs and leverage high NPSs for greater market share and top line revenue. The following matrix provides a systematic way to view the NPSs of different products/markets.The intersection of a product line with a market segment creates a product/market – products bought by specific segments. So NPSAA corresponds to the NPS for product A sold to Segment A, NPSBBis product B sold to segment B, and so forth.It is likely that a company may find a NPSAAthat may be 30 points higherthan NPSBB but 20 points lower than NPSAC. Each product/market will haveits own success requirements. No single lever will change NPSs across all groups. Each must be managed according to the dynamics operating within the product/market that drive customers’ willingness to recommend it.The Influence of ValueWhat is the best predictor of whether a customer is willing to recommend a product or service to a friend? Not customer satisfaction. Reichhold’s research shows it is difficult to discern a strong correlation between high customer satisfaction scores and outstanding sales growth.What does correlate highly with profitability and sales is loyalty –exactly what NPS measures. But what is loyalty? Reichhold defines loyalty as “the willingness of someone – a customer, an employee, a friend tomake an investment or personal sacrifice in order to strengthen a relationship. For a customer, that can mean sticking with a supplier who treats him well and gives him good value in the long term even if the supplier does not offer the best price in a particular transaction.”Value is the key word. It is conceptually defined as the relationship between a product’s quality and the price paid for the pro duct. New research by Reidenbach and Goeke also indicates that the brand and/or corporate image may play a significant role in the value definition. Value, like the NPS, is specific to a product/market and must be managed accordingly. It is the best predictor of loyalty and, therefore, NPS.The following figure depicts a customer value model for an insurance company offering retirement services to large businesses. Data was obtained from a survey of benefit managers.Figure 1: Customer Value Model: Retirement Services/LargeBusinessesThe model clearly points out how this company can improve its NPS within this specific product/market by examining value. The right-hand side of the model is the predictive side, showing the three value drivers – CQI (customer quality index), image and price – and their relative contributions to value. The model is generated using a regression algorithm and indicates a high degree of robustness (R2= .85). In other words, the three value drivers capture about 85 percent of what largebusinesses define as value with regard to retirement services offered by insurance companies. The greatest increases in value, and subsequently in loyalty and the NPS, will come from positive changes in the quality component, not price changes – a finding that amazes many managers.The left side of the model identifies the critical-to-quality (CTQ) factors that have the greatest impact on value and subsequently on the NPS. Increases in NPS will come from improvements in the company’s capacity to anticipate needs, provide strong relationships, value adding solutions and having services available on the Internet.The relationship between value and NPS is made even clearer by looking at the following customer loyalty matrix.Figure 2: Customer Loyalty Matrix (Source: Competing for Customers and Winning With Value [ASQ Quality Press, 2006])The matrix is comprised of the two main drivers of value: quality (CQI) on the vertical axis and price on the horizontal axis. Price is measured as a reaction to the various price offerings of the competitors in terms of its fairness, competitiveness and appropriateness. The matrix is divided into four quadrants by the mean scores for quality and price:∙Outstanding value – superior quality offered at a highly satisfactory price∙Expensive relationship –superior quality but at an unsatisfactory price∙Poor value – inferior quality offered at an unsatisfactory priceDiscount relationship – inferior quality but a satisfactory price.The circles indicate groups of customers and relative sizes of the groups. For example, the outstanding value group is made up of about 26 percent of this company’s customer base within the retirement services/large business product/market. Fifty-one percent of thi s company’s customers in this product/market feel they are receiving only average value.What is particularly compelling about this analysis is the degree of loyalty each group of customers has toward this product and company.The outstanding value group is the most loyal and has the highest NPS score. NPS scores continue to drop as customers’ evaluation of the value they receive from this company drops.Using NPS to Improve LoyaltyThe largest group of customers in this product/market is the fence sitters, with a NPS of 22 percent. How do you improve the loyalty of this group of customers? Simply knowing its NPS is insufficient to provide any systematic focused action. To improve those customers’ loyalty, it is critical to understand the factors that are driving this score. The following table provides a breakdown of the scores on each of the significant quality factors taken from the value model of thisproduct/market. These scores are mean scores reported on a 10-point scale anchored by “excellent performance” (10) and “poor performance” (1).Compared to the outstanding value customers, performance ratings provided by the fence sitters are significantly lower. Moreover, the primary source of failure for these customers is the “anticipates needs” CTQ (6.28).A careful examination of the CTQ performance criteria (questionnaireat tributes underlying “anticipates needs”) will reveal the specific aspects that must be managed more effectively to move this group to outstanding value customers – with substantially higher NPSs.Similarly, customers in the Poor 4 group provide much lower performance ratings on all three CTQs than do their counterparts in the outstanding value group, as anticipated. The primary source of failure for this group, however, is the customer relationship CTQ. Changing this group of customers from detractors to promoters will require managerial focus on that performance criteria. Some specific ideas:∙Profiling those customers on the basis of specific demographic criteria, which may reveal those customers are primarily associated with a geographic region or specific field representative ∙Tapping transactional reporting systems to determine whether there are any specific types of transactions leading systematically to poor evaluations of performance?Growing RecommendationsThe effectiveness of NPS increases with the degree of focus. By looking at the NPS of each product/market, companies can determine which groups need attention to improve their loyalty and which groups can be leveraged to help grow the company. Then, measuring the components of value, quality, image and price will provide essential insight into those segments, and into the dynamics of loyalty and its potential to help drive growth.。