SiRFprima_product_Insert

THE RNAi COMPANYRNAi 产品使用手册上海吉玛制药技术有限公司Shanghai GenePharma Co.,Ltd.Ⅰ. RNAi 简介 1A. RNAi 实验原理B. RNAi 实验流程C. RNAi 实验所需试剂D. 上海吉玛 RNAi 相关产品Ⅱ. siRNA设计7A. 哺乳动物siRNA设计B. 上海吉玛 siRNA 产品特性C. siRNA oligo 技术数据Ⅲ. siRNA 对照9A. 普通阴性对照B. 荧光标记的阴性对照C. siRNA阳性对照D. 转染试剂对照E. 避免off-target对照Ⅳ. siRNA 转染10A.siRNA 转染的方法B.Lipofectamin2000 转染试剂C.Lipofectamin2000适用的细胞类型D.转染前细胞培养E.Lipofectamin：siRNA/DNA比例F.贴壁细胞转染程序G.悬浮细胞siRNA转染程序H.DNA和siRNA共转染细胞程序I. 体内siRNA导入方法J. siRNA转染常见问题与建议Ⅴ. mRNA水平RNAi效果监测15A. siRNA细胞转染条件优化B. Real-Time PCR RNAi 效果检测C. Real-Time PCR 结果分析Ⅵ. 蛋白质水平RNAi效果监测20A. western-blot原理B.western-blot操作步骤wC.estern-blot上样液的制备D.western-blot常用试剂的配制Ⅶ. RNAi实验常见问题解答22Ⅰ. RNAi 简介A. RNAi实验原理RNA干扰（RNA interfering，RNAi）现象是由与靶基因序列同源的双链RNA（double-stranded RNA，dsRNA）引发的广泛存在于生物体内的序列特异性基因转录后的沉默过程。

细胞中的核糖核酸酶III家族成员之一的，dsRNA特异性的核酸酶Dicer将dsRNA裂解成由21-25个核苷酸组成的小干扰RNA (small interfering RNA，siRNA)，随后siRNA作为介导子引起特异性地降解相同序列的mRNA，从而阻断相应基因表达的转录后基因沉默机制。

酶免分析加样系统star操作流程下载提示：该文档是本店铺精心编制而成的，希望大家下载后，能够帮助大家解决实际问题。

文档下载后可定制修改，请根据实际需要进行调整和使用，谢谢!本店铺为大家提供各种类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by this editor. I hope that after you download it, it can help you solve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you! In addition, this shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts, other materials and so on, want to know different data formats and writing methods, please pay attention!酶免分析加样系统STAR操作流程介绍酶免分析加样系统STAR（Sample and Transfer Robot）是一种用于自动化生物化学分析的先进设备。

批量导出芯片探针与基因对应关系r语言代码1. 简介在基因芯片分析中，探针是一种用于检测特定基因表达水平的工具。

而对于芯片数据的分析和处理，了解芯片探针与基因的对应关系是非常重要的。

在r语言中，我们可以使用一些包和函数来批量导出芯片探针与基因的对应关系，方便后续的数据分析和挖掘。

2. 准备工作在进行批量导出芯片探针与基因对应关系之前，首先要确保已经安装了相应的r包，比如"affy"、"pd.hugene.1.0.st.v1"等。

接下来，我们以Affymetrix芯片为例来介绍如何进行批量导出。

3. 导出代码我们需要加载"affy"包，并且使用该包中的函数来读取芯片探针的注释信息。

```Rlibrary(affy)library(pd.hugene.1.0.st.v1)data(pd.hugene.1.0.st.v1)probeAnno <- as.data.frame(pd.hugene.1.0.st.v1PROBEID)```接下来，我们可以使用"select"函数来选择我们感兴趣的基因，并且将其对应的探针信息导出到一个文件中。

```Rgene_list <- c("GeneA", "GeneB", "GeneC")selected_probe <- probeAnno[probeAnno$Gene %in% gene_list, ]write.csv(selected_probe, file = "probe_gene_mapping.csv") ```4. 总结与回顾通过以上的代码，我们可以批量导出芯片探针与基因的对应关系，并且保存到一个csv文件中。

这样一来，我们就可以在后续的数据分析中方便地使用这些对应关系进行基因表达水平的分析和挖掘。

Mitofinder 是一个用于从基因组或转录组数据中自动重建完整线粒体基因组的工具。

Mitofinder 会执行诸如基因注释、线粒体基因组装以及重复序列的鉴定等任务。

为了实现这些功能，它结合了多个生物信息学工具，如SPAdes、BLAST、MAFFT 和HMMER 等。

使用Mitofinder 时，您可以通过命令行指定多种参数来定制分析过程。

下面是一些常见的Mitofinder 参数和它们的功能：- `-i / --input_folder`：指定含有输入数据的文件夹路径。

- `-o / --output_folder`：指定输出结果的文件夹路径。

- `-r / --reference`：提供一个参考基因组用于基因注释过程。

- `-m / --mitogenome`：如果已知线粒体基因组，可以提供该序列用于基因组装。

- `-g / --genetic_code`：指定使用的遗传密码表，默认为动物线粒体密码表。

- `-s / --species`：提供物种名称，用于基因注释。

- `-a / --annotation`：选择要执行的注释类型（例如，全注释、仅蛋白质编码基因等）。

- `-c / --circular`：指定线粒体基因组是否是环形的。

- `-t / --threads`：指定并行运行时使用的CPU线程数。

- `-j / --min_contig_size`：设置最小的组装片段大小，用于基因组装。

- `-k / --keep_blast`：在分析完成后保留BLAST 结果。

- `-v / --verbosity`：控制程序运行时的输出信息量。

请注意，Mitofinder 的参数可能会随着版本更新而改变。

因此，为了获取最新和最准确的参数信息，建议查阅Mitofinder 的官方文档或使用命令行工具查看帮助信息，通常可以通过在命令行中输入`mitofinder -h` 或`mitofinder --help` 来访问。

sratoolkit中prefetch命令参数简介命令参数p,--p i n f o此参数用于显示有关数据库中测序数据的信息，如测序数据的版本、大小、样品信息等。

使用示例：p r ef et ch-p<a cc ess i on_n um be r>v,--v e r s i o n此参数用于显示正在使用的s ra to ol ki t版本信息。

使用示例：p r ef et ch-vX,--q u a l此参数用于获取测序数据的质量分数信息。

使用示例：p r ef et ch-X<a cc ess i on_n um be r>-a s c p-p a t h此参数用于指定高速下载工具AS CP（A sp e ra）的路径。

使用示例：p r ef et ch--as cp-pa t h<pa th_t o_as cp>-a s c p-o p t i o n s此参数用于指定使用A SC P下载时的附加选项。

使用示例：p r ef et ch--as cp-op t io ns"<op ti on s>"-u s e-f t p此参数用于强制使用F TP下载测序数据。

使用示例：p r ef et ch--us e-ftp<ac ce ss io n_nu mbe r>-h t t p s此参数用于下载测序数据时使用H TT PS协议。

使用示例：p r ef et ch--ht tp s<a c ce ss io n_nu mb er>-f a s t e r q-d u m p-p at h此参数用于指定f ast e rq-d um p工具的路径，该工具用于快速将SR A 格式转换为F AS TQ格式。

使用示例：p r ef et ch--fa st erq-du mp-p at h<pa th_t o_fa st er q-du mp>用法示例下载测序数据使用以下命令下载指定测序数据：p r ef et ch<a cc es sio n_n um be r>查看测序数据信息使用以下命令查看指定测序数据的信息：p r ef et ch-p<a cc ess i on_n um be r>获取测序数据的质量分数信息使用以下命令获取指定测序数据的质量分数信息：p r ef et ch-X<a cc ess i on_n um be r>强制使用F T P下载测序数据使用以下命令强制使用F TP下载指定测序数据：p r ef et ch--us e-ftp<ac ce ss io n_nu mbe r>下载测序数据时使用H T T P S协议使用以下命令下载测序数据时使用HT TPS协议：p r ef et ch--ht tp s<a c ce ss io n_nu mb er>结论通过SR AT oo lk it中的p re fe tc h命令及其各种参数，我们可以方便地从NC BI的S R A数据库中下载原始测序数据。

罗氏应用科学部/分子诊断部rLightCycler® 480实时荧光定量PCR系统操作快速指南 V 2.0说明及版本历史1.基本界面及图标1.1.Overview界面1.2.New Experiment 界面1.3.Navigator 界面2.PCR及结果分析2.1.开机2.2.运行一次PCR实验2.2.1.PCR程序的设定2.2.2.样本的编辑2.2.3.程序的运行2.3.实验结果的分析2.3.1.绝对定量分析2.3.2.相对定量分析2.3.3.基因分型分析2.3.4.Tm calling2.4.结果报告3.数据管理及模块化操作3.1.历史数据的处理3.2.应用Template和Macros简化实验流程4.用户管理5.日常维护6.Troubleshooting本指南用于本指南用于简要说明简要说明LightCycler® 480 系统软件系统软件的界面及操作要点的界面及操作要点的界面及操作要点，，对初学者了解系统软件有所帮助统软件有所帮助。

但荧光定量PCR 是一个相对较为复杂和精确的定量方法是一个相对较为复杂和精确的定量方法，，要求使用者对仪器仪器、、软件和实验设计有比较系统深入的了解软件和实验设计有比较系统深入的了解，，方能对方能对实验的设计实验的设计实验的设计、、操作及分析运用自如操作及分析运用自如。

因此请各位使用者在初步了解本软件的操作要点后因此请各位使用者在初步了解本软件的操作要点后，，尽快通过完整的操作手册尽快通过完整的操作手册，，熟悉系统相关软件和实验设计关软件和实验设计、、分析分析。

版本历史1.0 2007-3-272.02007-8-22第一部分 基本基本界面及界面及界面及图标图标概述概述：：一、 O verview 界面Exit ：关闭LC480软件Log off ：从目前使用的数据库中退出并可登陆其他的数据库Overview ：点击该图标进入点击该图标进入““Overview ”界面Navigator ：点击该图标进入点击该图标进入““Navigator ”界面界面，，可进行数据的导入导出等操作可进行数据的导入导出等操作，，详见详见。

解码位置代码在一个设备显现故障时，您需要明白该设备在系统中的物理位置，以即能够改换它。

errpt或lscfg命令提供了一个位置代码，指定了故障设备所在的位置。

有了位置代码和效劳器手册，或包括您的型号的IBM® Redbooks®，乃至您能够访问IBM Web 信息中心取得更有效的信息，您就能够够识别设备所在的确切位置。

简介碰到设备故障确信是一件麻烦事。

故障设备的类型可能是热插拔硬件，比如风扇冷却装置或热插拔外设组件互连（PCI）卡。

在这两种情形下，都需要明白设备的物理位置，然后才能改换它。

因此，您需要明白设备的位置代码。

故障设备会显示在错误报告中（利用 errpt 命令），该报告还会发布物理位置代码。

另外，利用 lscfg 命令能够取得您设备的物理位置。

在取得该位置后，应该如何定位设备？AIX 内部代码和物理代码AIX 提供了两种不同的代码，它们别离是：IBM AIX®（内部）位置系统代码物理位置代码AIX 内部位置代码可与物理代码配合利用来识别设备，咱们稍后会在本文这了解这种用法。

利用AIX 生成的那些代码引用了某些特定的设备，例如：10-80-00-3,0 SCSI CD Drive10-80-00-2,0 SCSI disk02-08-00 SAS disk上面的代码是访问实际设备的内部途径，能够利用 lsdev 命令来查看它们。

另一个咱们专门感爱好的位置代码是物理类型的，由固件生成。

例如：U789C.001.DQD3F62-P2-D3 SAS Disk Drive自从几年前发布了IBM POWER5 处置器以后，物理位置代码就成了定位设备的首选方式。

作为一项体会法那么，您需要的一般是物理代码。

这也是本文的重点。

表1 中提供的命令使您能够取得有关设备的各类信息。

表1. 取得有关您的设备的信息的命令命令描述lsdev -C -H -F "name status physloc location description"获得AIX（如果存在）和物理位置代码。

ncbi的accession numberprjna开头
NCBI的Accession number是指NCBI数据库中数据记录唯一标识符，可用于在数据库中快速检索和识别特定数据记录。

以prjna开头的Accession number一般指的是NCBI的Sequence Read Archive (SRA)数据库中的项目号，这些编号标识了已经提交的原始测序数据集和相关的元数据。

在SRA中，研究人员可以将测序数据、分析结果、实验信息等数据上传至NCBI数据库，成为公共资源以供更多的研究人员使用。

SRA 包含了海量的测序数据，可以为各种生物学研究提供必要的基础数据支持。

因此，SRA为科学家们带来了极大的便利，可以节省大量的时间和精力。

Accession number通常具有一定的系统性和规律性，例如，NCBI 数据记录的Accession number由字母和数字组成，其前缀的含义和后缀的含义各不相同。

其中，以prjna开头的Accession number表示一个项目编号，后面的数字则代表该项目的唯一标识符。

使用prjna开头的Accession number，可以快速查询NCBI数据库中的项目信息、原始数据、分析结果等相关信息。

此外，该Accession number也可以在相关文章中使用，为读者提供精确的数据来源信息，保证科研成果的可信度和可靠性。

总而言之，NCBI的Accession number是唯一标识符，可以在NCBI数据库中快速查找特定数据记录。

以prjna开头的Accession number是NCBI SRA数据库中的项目编号，为研究人员分享测序数据提供了便捷的方式，也为科学家们提供了有效的研究资源支持。

resfams的使用
Resfams是一个用于鉴定全基因组中的转座子和外源基因的工具。

它是一个集成了大量的转座子和外源基因家族的数据库，可以根据这些家族的特征来识别全基因组序列中的相似序列。

使用Resfams，你可以按照以下步骤进行：
1. 下载Resfams数据库：你可以从Resfams网站
（https://cge.cbs.dtu.dk/services/Resfams/）下载Resfams数据库的最新版本。

2. 安装HMMER：Resfams使用HMMER软件来进行序列比对，因此你需要在计算机上安装HMMER。

你可以从HMMER官
方网站（https://hmmer.dev/）下载并按照说明进行安装。

3. 运行Resfams：使用HMMER和Resfams数据库，你可以运
行Resfams脚本来鉴定全基因组序列中的转座子和外源基因。

你需要将待鉴定的序列与Resfams数据库中的HMM文件进行
比对，并根据比对结果来识别转座子和外源基因。

除了使用命令行进行鉴定外，Resfams还提供了一个基于图形
界面的工具ResfamsScan，可以更方便地运行和可视化结果。

需要注意的是，Resfams主要用于鉴定转座子和外源基因，对
于其他类型的基因可能效果较差。

因此，在使用Resfams前，最好明确你希望鉴定的基因类型，并了解Resfams是否适用。

此外，随着科学研究的不断发展，Resfams数据库也会进行更新和改进，因此在使用过程中，建议经常检查并下载最新版本的数据库。

lipofectamine rnaimax转染试剂的原理
Lipofectamine RNAiMAX是一种RNA干扰实验中常用的转染
试剂。

其原理主要涉及以下几个步骤：
1. 形成转染复合物：将RNA干扰试剂（如siRNA或miRNA）与Lipofectamine RNAiMAX转染试剂按照一定比例混合，形
成转染复合物。

2. 结合到细胞膜表面：转染复合物中的Lipofectamine能与细
胞膜表面的磷脂结合，形成Lipofectamine-RNA复合物。

3. 转染复合物进入细胞：通过细胞膜的内吞作用或细胞膜破损，转染复合物能够被细胞摄入。

4. 转染复合物释放RNA：一旦进入细胞，转染复合物会释放
出RNA干扰试剂。

5. RNA干扰效应：释放的RNA干扰试剂能够与细胞内的靶向mRNA相互结合，通过RNA酶PAS复合物介导的靶向降解或抑制翻译的机制，达到基因沉默或抑制的效果。

总之，Lipofectamine RNAiMAX转染试剂通过将RNA干扰试
剂转染入细胞内，调控目标基因的表达，从而实现基因沉默或抑制。

【转】Isoform expre...Exon-centric DEDSGseq summary:This programs uses gapped alignments to the genome to generate differential splicing for groups of technical and biological replicates in two treatments. You can't compare just two samples, two samples per group is the minimum.It generates a ranking of differentially spliced genes using their negative binomial statistic which focuses of difference in expression. The NB statistic is provided per gene and per exon. A threshold used in the paper is NB > 5. The program doesn't support reconstruction of isoforms or quantification of specific isoforms, which apparently is computationally harder.I found it easy to get it to run using the example data provided and the instructions. You need to run a preparation step on the gene annotation. Starting from BAM files, you also need to run two preparation steps on each library, first to convert it to BED, and then to get the counts.While the paper clearly says that transcript annotation information is not necessary for the algorithm, you do need to provide a gene annotation file in refFlat format, which the output is based on.The developers are unresponsive so no help is at hand if you get stuck.DEXseq summaryThis is similar to DSGseq and Diffsplice insofar as the isoform reconstruction and quantification are skipped and differential exon expression is carried out. Whereas the other two tools say that they don't need an annotation for their statistics, this program is based on only annotated exons, and uses the supplied transcript annotation in the form of a GFF file.It also needs at least two replicates per group.I found the usage of this program extremely tedious (as a matlab person). To install it you need to also install numpy and HTSeq. For preparing the data (similarly to DSGseq) you need to do a preparation step on the annotations, and another preparation step for every sample separately which collects the counts (both using python scripts). Then you switch to R, where you need to prepare something called an ExonCountSet object. To do this you need to first make a data.frame R object with the files that come out of the counting step. Yo also need to define a bunch of parameters in the R console. Then you can finally run the analysis. Despite the long instructional PDF, all this is not especially clear, and it's a rather tedious process compared to the others I've tried so far. In the end, I ran makeCompleteDEUAnalysis, and printed out a table of the results. I tried to plot some graphics too, but couldn't because "semi-transparency is not supported on this device". However, there's an extremely useful function that creates a browsable HTML directory with the graphics for all the significant genes. If anyone wants a copy of the workflow I used, send me a message, trying to figure it out might take weeks, but after you get the hang of it, this program is really useful.DiffSplice summaryThis is a similar approach for exon-centric differential expression to DEXseq and DSGseq (no attempt to reconstruct or quantify specific isoforms). Also supports groups of treatments, minimum 2 samples per group. The SAM inputs and various rather detailed parameters are supplied in two config files. I found this very convenient. In the data config file you can specify treatment group ID, individual IDs, and sample IDs, which determine how the shuffling in their permuation test is done. It was unclear to me what the sample IDs are (as opposed to the individual ID).DiffSplice prefers alignments that come from TopHat or MapSplice because it looks for the XS (strand) tag which BWA doesn't create. There's no need to do a separate preparation step on the alignments. However, if you want you can separate the three steps of the analysis using parameters for selective re-running. This program is user friendly and the doc page makes sense.On the downside, when the program has bad inputs or stops in the middle there's no errors or warnings - it just completes in an unreasonably short time and you get no results.Diffsplice appears to be sensitive to rare deviations from the SAM spec, because while I'm able to successfully run it on mini datasets, the whole datasets are crashing it. I ran Picard's FixMateInformation and ValidateSamFile tools to see if they will make my data acceptable (mates are fine, and sam files are valid! woot), but no dice. It definitely isn't due to the presence of unaligned reads.SplicingCompass summary:SplicingCompass would be included together with DEXseq, DiffSplice, and DSGseq, insofar as it's an exon-centric differential expression tool. However, unlike DEXseq and DSGseq, it provides novel junctions as well. Unlike DiffSplice, it does use an annotation. The annotation + novel detection feature of this program is pretty attractive.This is an R package, though as far as i can tell, it's not included in bioconductor. Personally I find it tedious to type lines upon lines of commands into R, and would much prefer to supply a configuration file and run one or a few things on the command line. Alas. Here, at least the instructions are complete, step by step, and on a "for dummies" level. Great.This tool is based on genome alignments. You basically have to run Tophat, because the inputs are both bam files and junction.bed files which Tophat provides. A downside is that you basically have to use the GTF annotation that they provide which is based on UCSC ccds genes. If you want to use ensembl or something else, you meed to email the developer for an untested procedure that might get you a useable annotation at the end (directly using an ensembl GTF doesn't work).Another problem is that I got no significant exons at the end of the analysis:>sc=initSigGenesFromResults(sc,adjusted=TRUE,threshold=0.1)Error in order(df[, pValName]) : argument 1 is not a vectorI'm still unsure as to whether this is due to some mistake or because this tool is extremely conservative.Transcriptome based reconstruction and quantificationeXpress summary:This program can take a BAM file in a stream, or a complete SAM or BAM file.It produces a set of isoforms and a quantification of said isoforms. There is no built in differential expression function (yet) so they recommend inputting the rounded effective counts that eXpress produces into EdgeR or DEGSeq. No novel junctions or isoforms are assembled.I used bowtie2 for the alignments to the transcriptome. Once you have those, using eXpress is extremely simple and fun. There's also a cloud version available on Galaxy, though running from the command line is so simple in this case I don't see any advantage to that. Definite favorite!SailFish summary:This program is unique insofar as it isn't based on read alignment to the genome or the transcriptome. It is based on k-mer alignment, which is based on a k-merized reference transcriptome. It is extremely fast. The first, indexing step took about 20 minutes. This step only needs to be run once per reference transcriptome for a certain k-mer size. The second, quant step took from 15 minutes to 1.5 hours depending on the library. The input for the quant step is fastq's as opposed to bam files. No novel junctions or isoforms are assembled.Like eXpress, there is no built in differential expression function. I used the counts from the non-bias-corrected (quant.sf) output file as inputs for DESeq and got reasonable results.The method is published on arXiv, and has been discussed in Lior Pachter's blog. According to the website the manuscript has been submitted for publication. The program is quite user friendly.RSEM +EBSeq summary:This also generates isoforms and quantifies them. It also needs to be followed by an external cont-based DE tool - they recommend EBSeq, which is actually included in the latest RSEM release, and can be run from the command line easily.RSEM can't tolerate any gaps in your transcriptome alignment, including the indels bowtie2 supports. Hence, you either need to align ahead of time with bowtie and input a SAM/BAM, or use the bowtie that's built into the RSEM call and input a fsta/fastq. For me this was unfortunate because we don't keep fastq files on hand (only illumina qseq files) which bowtie doesn't take as inputs. However, it does work! I successfully followed the instructions to execute EBSeq, which is conveniently included as an RSEM function, and gives intelligible results. Together, this workflow is complete.An advantage of RSEM is that it supplies expression relative to the whole transcriptome (RPKM, TPM) and, if supplied with a transcript-to-gene mapping, it also supplies relative expression of transcripts within genes (PSI). ie. transcript A comprises 70% of the expression of gene X, transcript B comprises 20 %, etc. MISO is the only other transcript-based program, as far as I know, that provides this useful information.BitSeq summary:This, like DEXSeq, is an R bioconductor package. I found the manual a lot easier to understand than DEXSeq.They recalculate the probability of each alignment, come up with a set of isoforms, quantify them, and also provide a DE function. In this way, it is the most complete tool I've tried so far, since all the other tools have assumed, skipped, or left out at least one of these stages. Also, BitSeq automatically generates results files, which is useful for people that don't know R. One annoying thing is that (as far as I know) you have to use sam files.For running BitSeq I used the same bowtie2 alignments to the transcriptome as for eXpress. You need to run the function getExpression on each sample separately. Then you make a list of the result objects in each treatment group and run the function getDE on those.Genome based reconstruction and quantificationiReckon summary:iReckon generates isoforms and quantifies them. However, this is based on gapped alignment to the genome (unlike eXpress, RSEM and BitSeq which are based on alignments to the transcriptome). It doesn't have a built in DE function, so each sample is run separately.This tool is a little curious because it requires both a gapped alignment to the genome, and the unaligned reads in fastq or fasta format with a reference genome. Since it requires a BWA executable, it's doing some re-alignment. iReckon claims to generate novel isoforms with low false positives by taking into consideration a whole slew of biological and technical biases.One irritating thing in getting the program running is that you need to re-format your refgene annotation file using an esoteric indexing tool from the Savant genome browser package. If you happen to use IGV, this is a bit tedious. Apparently, this will change in the next version. Also, iReckon takes up an enormous amount of memory and scratch space. For a library with 350 million reads, you would need about 800 G scratch space. Apparently everything (run time, RAM, and space) is linear to the number of reads, so this program would be a alright for a subset of the library or for lower coverage libraries.Cufflinks + cuffdiff2 summary:This pipeline, like iReckon, is based on gapped alignment to the genome. It requires the XS tag, so if you're not using tophat to align your RNA, you need to add that tag. I also found out that our gapped aligner introduces some pesky 0M and 0N's in the cigars, since cufflinks doesn't tolerate these. But with these matters sorted out, it's pretty easy to use.I like the versatility. You can run cufflinks for transcriptome reconstruction and isoform quantification in a variety of different modes. For example, with annotations and novel transcript discovery, with annotations and no novel discovery, with no annotations, and with annotations to be ignored in the output. For differential expression, cuffdiff 2 can be run with the results of the transcript quantification from cufflinks to include novel transcripts, or, it can be run directly from the alignment bam files with an annotation. Unlike the exon-based approaches, you don't need to have more than one library in each treatment group, (ie. you can do pairwise comparisons) though if you do it's better to keep them separate than to merge them. The problem here is that the results of cuffdiff are so numerous that it's not easy to figure out what you need in the end. Also, not all the files include the gene/transcript names so you need to do a fair bit of command line munging. There's also cummeRbund, which is a visualization package in R that so far seems to work ok.。

生物博士论文利用CRISPR Cas9系统建立基因修饰猪以及在人细胞中对顺式作用元件做注释生物博士论文：利用CRISPR Cas9系统建立基因修饰猪以及在人细胞中对顺式作用元件做注释摘要：本论文旨在探讨利用CRISPR Cas9系统建立基因修饰猪的可行性，并研究该系统在人类细胞中对顺式作用元件的注释情况。

通过对CRISPR Cas9系统的细致研究，我们可以了解其应用于基因编辑和遗传疾病研究的潜力和局限性，并为相关研究提供理论和实践参考。

第一章：引言1.1 研究背景随着基因工程技术的发展，人们对基因修饰动物的研究日益深入。

CRISPR Cas9系统作为一种新兴的基因编辑工具，具有高效、精准和低成本等优势，被广泛应用于研究领域。

本研究旨在利用CRISPR Cas9系统建立基因修饰猪模型，并进一步研究其应用于人类细胞中的潜力。

1.2 研究意义基因修饰猪模型在农业、医学和生物学等领域具有重要意义。

通过对猪基因进行修饰，我们可以模拟人类遗传疾病，并进一步研究疾病的发生机制以及新药的开发。

此外，CRISPR Cas9系统在人类细胞中的应用也为研究顺式作用元件提供了新的思路和方法。

第二章：CRISPR Cas9系统原理与技术2.1 CRISPR Cas9系统简介CRISPR Cas9系统是一种原核细菌天然拒绝外源DNA侵入的免疫机制，后被开发为基因编辑技术。

该系统由Cas9蛋白和RNA引导序列组成，通过引导序列的识别和结合，实现对目标基因的特异性靶向剪切和修饰。

2.2 CRISPR Cas9系统的应用CRISPR Cas9系统在基因编辑、遗传疾病研究和农业改良等领域具有广泛的应用前景。

通过调整RNA引导序列，可以实现对基因组中特定基因的修饰和删除，从而研究基因功能和疾病机制。

第三章：基因修饰猪模型的建立3.1 基因编辑策略的设计在建立基因修饰猪模型之前，我们需要进行合理的基因编辑策略设计。

通过分析目标基因的结构和功能，选择合适的靶点和RNA引导序列，确定CRISPR Cas9系统的使用方法和引物设计。

验证内核糖体插入位点IRES序列活性的双
荧光素酶报告系统
自查报告。

标题，验证内核糖体插入位点IRES序列活性的双荧光素酶报告系统。

自查人，XXX。

自查时间，XXXX年XX月XX日。

自查内容，本次自查主要是对内核糖体插入位点IRES序列活性的双荧光素酶报告系统进行验证，以确保实验结果的准确性和可靠性。

自查步骤：
1. 确认实验材料的准备情况，包括质粒、细胞培养物等，确保实验所需材料齐全。

2. 对实验操作流程进行复查，确保每个步骤都按照标准操作程序进行。

3. 对实验结果进行初步分析，比对预期结果和实际结果，查找可能的偏差和误差。

4. 对实验数据进行统计分析，确保数据的可靠性和准确性。

5. 检查实验设备和仪器的运行情况，确保仪器的准确性和稳定性。

自查结果：
经过自查，发现实验操作流程符合标准程序，实验材料准备充分，实验数据统计分析结果与预期结果一致。

实验设备和仪器运行正常，无异常情况发生。

自查结论：
经过自查，内核糖体插入位点IRES序列活性的双荧光素酶报告系统的实验结果可靠，符合预期要求。

实验结果具有较高的可信度和准确性，可以用于后续的研究和分析工作。

自查人签名，XXX 日期，XXXX年XX月XX日。

使用MAKER进行注释学习MAKER的配置参数MAKER是一种用于基因组注释的自动化工具，可将基因组序列映射到已知的蛋白质和ncRNA数据库，并预测新的基因和基因功能。

在进行基因组注释之前，我们需要了解和熟悉MAKER的配置参数，以便对它进行正确的设置和使用。

MAKER的配置参数分为三个级别：全局参数、库参数和个体参数。

全局参数适用于整个基因组注释过程，而库参数和个体参数是根据数据集和实验需求进行设置的。

首先，让我们来了解一些全局参数。

全局参数主要用于控制MAKER的整体行为。

其中一些重要的全局参数包括：1. genome：这是指要进行注释的基因组的FASTA格式文件路径。

该文件应该是未注释的基因组序列。

2. est：这是指EST或mRNA序列的FASTA格式文件路径。

这些序列通常用于辅助基因预测。

3. protein：这是指已知蛋白质序列的FASTA格式文件路径。

这些序列通常用于初始的基因预测和注释。

4. model_org：这是一个字符串，用于指定与我们研究物种的近缘关系较近的参考物种。

根据参考物种的相似性，MAKER可以更好地预测与目标基因组相关的基因和注释。

5. repeat_library：这是指已知重复序列的FASTA格式文件路径。

重复序列可能会干扰基因预测和注释，因此在进行注释之前需要进行去重复处理。

接下来是库参数。

库参数用于特定类型的注释数据，如EST和蛋白质序列，以及非编码RNA序列等。

库文件参数以一种库类型的标识符开头，如EST或protein，后面跟着“_db”和文件路径。

下面是一些常见的库参数：1. est_db：用于指定EST序列的库文件路径。

2. protein_db：用于指定蛋白质序列的库文件路径。

3. snp_db：用于指定SNP注释的库文件路径。

除了库参数，MAKER还可以使用一些特定用途的参数来优化注释结果。

这些参数通常在个体层面上设置，用于调整和改进注释的特定方面。

promass for xcalibur密钥-回复promass for xcalibur密钥是什么？Promass for Xcalibur密钥是一种用于科学领域中基因测序分析的软件工具。

Xcalibur是由Thermo Fisher Scientific开发的质谱仪控制软件，而Promass是一款专门为质谱仪数据处理和分析而设计的软件。

Promass for Xcalibur密钥则是一个可以为用户提供更高级功能和分析工具的许可证。

在这篇文章中，我们将一步一步地回答有关Promass for Xcalibur 密钥的问题，以帮助读者了解更多信息。

第一步：什么是Xcalibur？Xcalibur是一款由Thermo Fisher Scientific开发的质谱仪控制软件。

它被广泛应用于科学研究、生物医学和制药领域中的质谱分析。

Xcalibur具有使用简便、数据处理快速和结果准确可靠等优势，因此被许多研究人员和实验室所采用。

第二步：什么是Promass？Promass是一款专门为质谱仪数据处理和分析而设计的软件。

它具有强大的功能，可以帮助用户从原始质谱数据中提取出有用的信息，进行分析和解释。

Promass能够处理质谱仪生成的大量数据，并提供多种数据解释工具和分析模型，从而帮助用户更好地理解样本的组成、结构和功能。

第三步：为什么需要Promass for Xcalibur密钥？Promass for Xcalibur密钥是为了让用户能够使用更高级功能和分析工具而设立的许可证。

通过获得Promass for Xcalibur密钥，用户可以解锁软件中的额外功能，更好地满足其特定研究需求。

这些额外功能可以包括更高级的数据处理算法、更多的数据解释工具、更大容量的数据存储等等。

因此，Promass for Xcalibur密钥可以帮助用户获得更多的分析选项和更深入的数据解释能力。

第四步：如何获取Promass for Xcalibur密钥？要获取Promass for Xcalibur密钥，用户可以直接联系Thermo Fisher Scientific或其授权代理商进行咨询和购买。

qScript™ One-Step SYBR® Green qRT-PCR Kit, Low ROX™Cat No. 95089-050Size: 50 x 50-µL reactions Store at -25ºC to - 15°Cprotected from light 95089-200200 x 50-µL reactionsDescriptionThe qScript One-Step SYBR Green qRT-PCR Kit, Low ROX is a convenient and highly sensitive solution for reverse transcription quantitative PCR (RT-qPCR) of RNA templates using SYBR Green I dye detection and gene-specific primers on Applied Biosystems 7500, 7500 Fast, ViiA™ 7, or Stratagene MX series of real-time PCR systems. cDNA synthesis and PCR amplification are carried out in the same tube without opening between procedures. The system has been optimized to deliver maximum RT-qPCR efficiency, sensitivity, and specificity. The proprietary reaction buffer has been specifically formulated to maximize activities of both reverse transcriptase and Taq DNA polymerase while minimizing the potential for primer-dimer and other non-specific PCR artifacts. The kit is compatible with both fast and standard qPCR cycling protocols. Highly specific amplification is essential for successful RT-qPCR with SYBR Green I technology, since this dye binds to any dsDNA generated during amplification. AccuSta rt™ Taq DNA polymerase contains monoclonal antibodies that bind to the polymerase and keep it inactive prior to the initial PCR denaturation step. Upon heat activation at 95ºC, the antibodies denature irreversibly, releasing fully active, unmodified Taq DNA polymerase. Instrument CompatibilityDifferent real-time PCR systems employ different strategies for the normalization of fluorescent signals and correction of well-to-well optical variations. It is critical to match the appropriate qPCR reagent and internal reference dye to your specific instrument. The qScript Custom One-Step SYBR Green qRT-PCR Kit, Low ROX provides seamless integration on the Applied Biosystems 7500, 7500 Fast, ViiA 7, or Stratagene MX series of real-time PCR systems. Please visit our web site at to find the optimal kit for your instrument platform.ComponentsReagent Description 95088-050 95088-200qScript One-Step Reverse Transcriptase Optimized 50X formulation of recombinant MMLV reverse transcriptase for one-step RT-PCR.1 x 50 µL 1 x 200 µLOne-Step SYBR Green Master Mix, Low ROX (2X) 2X reaction buffer containing dNTPs, magnesium chloride, AccuStart Taq DNApolymerase, stabilizers, ROX dye (for 580-585 nm excitation) and SYBR Green I dye1 x 1.25 mL 4 x 1.25 mLNuclease-free water 1 x 1.5 mL 4 x 1.5 mLStorage and StabilityStore components in a constant temperature freezer at -25°C to -15°C protected from light upon receipt.For lot specific expiry date, refer to package label, Certificate of Analysis or Product Specification Form.Guidelines for One-Step SYBR Green qRT-PCR▪Primer design is critical for successful one-step RT-qPCR with SYBR Green. The use of software tools for PCR primer design and RNA secondary structure analysis can aide in the design of specific and efficient primers for one-step RT-qPCR. Primers should be designed according to standard qPCR guidelines with a length of 18 - 25 nucleotides and a GC content of 40-65%. Avoid internal secondary structure, and complementation at 3’ ends within each primer and primer pair. 3’-end terminal stability should be kept low to maximize primer specificity (3’-pentamer ΔGº > -8.0 kcal/mol or have no more than 2 to 3 Cs or Gs in the last 5 bases).▪Regions of RNA secondary structure should be avoided as this can interfere with annealing of the reverse primer for cDNA synthesis and/or impede procession of the reverse transcriptase. Programs for RNA structure prediction, such as the mfold web server (/), are useful for selecting regions of relaxed RNA structure for qRT-PCR primer design.▪Ideally, primer Tm should be between 58 and 60ºC for a typical 2-step qPCR cycling protocol. Estimation of primer Tm varies widely with different methods and analysis parameters. We recommend using a program that calculates Tm based on nearest-neighbor thermodynamic models at 50 mM monovalent salt and 50 nM primer concentration. Primers with melting temperatures outside of this range may require optimization of PCR cycling conditions.▪PCR product size should be between 70- 200 bp. Ideally, the amplified sequence should span intronic sequence to minimize the potential to amplify genomic DNA sequence. Design primers to anneal to exons that bracket intronic sequence or within exon / exon boundaries of the speci fic mRNA. NCBI’s Primer-BLAST program (/tools/primer-blast/index.cgi?LINK_LOC=BlastHomeAd) can facilitate the design of RNA-specific primer sets.Control reactions that lack reverse transcriptase (minus RT) should always be included to verify that amplification signal is due to the presence of RNA target and not genomic DNA.▪ A final concentration of 200 nM each primer is recommended as a general starting point. Optimal results may require titration of primer concentration between 100 and 500 nM. PCR efficiency is often improved with higher primer concentration (300 to 500 nM). In some cases, higher concentration of the reverse primer alone may improve RT-PCR efficiency without compromising specificity. We highly recommend including a post PCR dissociation analysis step (melt curve) to distinguish specific from non-specific amplification product(s) (i.e. primer-dimer).▪Thaw all components, except the qScript One-Step RT, at room temperature. Mix by gently vortexing, then centrifuge to collect contents to the bottom of the tube before using. Place all components on ice after thawing.▪To maximize assay specificity and sensitivity reactions should be assembled on ice and kept cold until placed in your real-time PCR system. Centrifugation steps should be carried out in a refrigerated centrifuge. AccuStart Taq DNA polymerase is inactive prior to high temperature activation; however, reverse transcriptases are active at lower temperatures and can use single strand DNA as a template.Guidelines for One-Step SYBR Green qRT-PCR continued:▪ First-strand synthesis can be carried out between 42°C and 52°C. Optimal results are generally obtained with a 5-minute incubation at 50°C. We recommend a 2-5 minute incubation at 95°C to fully inactivate the RT prior to PCR cycling.▪Preparation of a reaction cocktail is recommended to reduce pipetting errors and maximize assay precision. Assemble the reaction cocktail with all required components except RNA template and dispense equal aliquots into each reaction tube. Add RNA to each reaction as the final step. Addition of sample as5 to 10-µL volumes will improve assay precision.▪Suggested input quantities of template are: 1 pg to 100 ng total RNA; 10 fg to 100 ng poly A(+) RNA; 10 to 1x108 copies viral RNA.▪After sealing each reaction, vortex gently to mix contents. Centrifuge briefly to collect components at the bottom of the reaction tube.Reaction AssemblyComponent Volume for 50-μL rxn. Final ConcentrationOne-Step SYBR Green Master Mix, Low ROX (2X) 25 µL 1XForward primer Variable 200 – 300 nMReverse primer Variable 200 – 300 nMNuclease-free water VariableRNA template 5 – 10 µL VariableqScript One-Step RT * 1 µL 1XFinal Volume (μL) 50 µLNote: Reaction volume can be scaled from 5 to 50 µL depending on the reaction plate (i.e. 384-well vs. 96-well) and qPCR system. Scale all component volumes proportionally. * Omit addition of qScript One-Step RT in minus RT control reactions.Reaction ProtocolIncubate the complete reaction mix in a real-time thermal detection system as follows:Fast qPCR Cycling Standard qPCR Cycling 3-Step PCR Cycling cDNA Synthesis 50°C, 5 min 48 – 50°C, 10 min 48 – 50°C, 10 minTaq Activation 95°C, 2 min 95°C, 5 min 95°C, 5 minPCR cycling (30 - 45 cycles) 95°C, 3s 95°C, 10s 95°C, 10s60°C, 30s (data collection) 60°C, 30s (data collection) 55 – 65°C, 20s68 – 72°C, 30 to 60s (data collection)Melt Curve (dissociation stage): See instrument instructions See instrument instructions See instrument instructionsOptimal cycling conditions will vary for different primer sets. A 3-step cycling protocol may improve assay specificity with some primer sets.Quality ControlKit components are free of contaminating DNase and RNase. The qScript One-Step SYBR Green qRT-PCR Kit, Low ROX is functionally tested in RT-qPCR. Kinetic analysis must demonstrate linear resolution over six orders of dynamic range (r2 > 0.995) and an RT-PCR efficiency > 90%Limited Label LicensesUse of this product signifies the agreement of any purchaser or user of the product to the following terms:1.The product may be used solely in accordance with the protocols provided with the product and this manual and for use with components contained in the kitonly. QIAGEN Beverly, Inc. grants no license under any of its intellectual property to use or incorporate the enclosed components of this kit with any components not included within this kit except as described in the protocols provided with the product, this manual, and additional protocols available at . Some of these additional protocols have been provided by Quantabio product users. These protocols have not been thoroughly tested or optimized by QIAGEN Beverly, Inc.. QIAGEN Beverly, Inc. neither guarantees them nor warrants that they do not infringe the rights of third-parties.2.Other than expressly stated licenses, QIAGEN Beverly, Inc. makes no warranty that this kit and/or its use(s) do not infringe the rights of third-parties.3.This kit and its components are licensed for one-time use and may not be reused, refurbished, or resold.4.QIAGEN Beverly, Inc. specifically disclaims any other licenses, expressed or implied other than those expressly stated.5.The purchaser and user of the kit agree not to take or permit anyone else to take any steps that could lead to or facilitate any acts prohibited above. QIAGEN Beverly,Inc. may enforce the prohibitions of this Limited License Agreement in any Court, and shall recover all its investigative and Court costs, including attorney fees, in any action to enforce this Limited License Agreement or any of its intellectual property rights relating to the kit and/or its components.©2018 QIAGEN Beverly Inc. 100 Cummings Center Suite 407J Beverly, MA 01915Quantabio brand products are manufactured by QIAGEN, Beverly Inc.Intended for molecular biology applications. This product is not intended for the diagnosis, prevention or treatment of a disease.qScript and AccuStart are trademarks of QIAGEN Beverly, Inc. SYBR is a registered trademark of Molecular Probes, Inc. ViiA and ROX are trademarks of Life Technologies Corporation. Stratagene, MX3000P, MX3005P and MX4000 are trademarks of Stratagene Corporation。