From 77295a23eedd05b5e757043ece3699f3f69acb9f Mon Sep 17 00:00:00 2001 From: lixin <48940428+lixin856@users.noreply.github.com> Date: Sat, 6 Jul 2019 18:51:31 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B3=A8=E9=87=8A=E5=9F=BA=E5=9B=A0=E7=BB=84?= =?UTF-8?q?=E5=8C=BA=E5=9F=9F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 注释基因组区域 --- .../Annotating_Genomic_Ranges.Rmd | 198 +++++++++--------- 1 file changed, 99 insertions(+), 99 deletions(-) diff --git a/BiocWorkflow_todo/05annotation_en/Annotating_Genomic_Ranges.Rmd b/BiocWorkflow_todo/05annotation_en/Annotating_Genomic_Ranges.Rmd index 8153eb1..3833371 100644 --- a/BiocWorkflow_todo/05annotation_en/Annotating_Genomic_Ranges.Rmd +++ b/BiocWorkflow_todo/05annotation_en/Annotating_Genomic_Ranges.Rmd @@ -12,7 +12,7 @@ vignette: > %\VignetteEngine{knitr::rmarkdown} --- -# Version Info +# 版本信息 ```{r, echo=FALSE, results="hide", warning=FALSE} suppressPackageStartupMessages({ library('annotation') @@ -26,66 +26,66 @@ library('annotation') **Package version**: `r packageVersion("annotation")`

-# Background +# 背景 -Bioconductor can import diverse sequence-related file types, including fasta, -fastq, BAM, VCF, gff, bed, and wig files, among others. Packages support common -and advanced sequence manipulation operations such as trimming, transformation, -and alignment. Domain-specific analyses include quality assessment, ChIP-seq, -differential expression, RNA-seq, and other approaches. Bioconductor includes -an interface to the Sequence Read Archive (via the -[SRAdb](/packages/release/bioc/html/SRAdb.html) package). +Bioconductor可以导入各种序列相关的文件类型， +包括 fasta、fastq、BAM、VCF、gff、bed 和 wig 文件等。 +Bioconductor 包支持常规和高级的序列操作，例如 trimming，转换和对齐； +也支持特定领域的分析，包括质量评估、ChIP-seq、差异表达、RNA-seq等。 +Bioconductor包含序列读取存档的接口（通过 +[SRAdb](/ packages / release / bioc / html / SRAdb.html)包）。 -This workflow walks through the annotation of a generic set of ranges with -Bioconductor packages. The ranges can be any user-defined region of interest or -can be from a public file. -# Data Preparation +此工作流程将介绍用 Bioconductor 包进行通用区域的注释， +区域可以是任何用户定义的感兴趣区域，也可以来自公共文件。 + + +# 数据准备 ## Human hg19 -As a first step, data are put into a GRanges object so we can take -advantage of overlap operations and store identifiers as -metadata columns. +第一步，将数据放入 GRanges 对象中，以便利用 +overlap operations，以及存储标识符作为元数据列。 + + +第一组区域是来自 dbSNP 的 VCF 文件。该文件可以从 NCBI 的 ftp 站点下载 +[ftp://ftp.ncbi.nlm.nih.gov/snp/](ftp://ftp.ncbi.nlm.nih.gov/snp/）， +然后使用 VariantAnnotation 包中的 readVcf() 导入；或者作为 AnnotationHub +中预解析的 VCF 对象使用。 -The first set of ranges are variants from a dbSNP Variant Call Format (VCF) -file. This file can be downloaded from the ftp site at NCBI -[ftp://ftp.ncbi.nlm.nih.gov/snp/](ftp://ftp.ncbi.nlm.nih.gov/snp/) and -imported with readVcf() from the VariantAnnotation package. Alternatively, -the file is available as a pre-parsed VCF object in the AnnotationHub. ```{r, echo=FALSE} -suppressPackageStartupMessages(library(annotation)) +suppressPackageStartupMessages(library(annotation)) #导入"annotation"包，屏蔽导入时的信息输出。（译者注：如果之前尚未安装 annotation 包，可参考 https://bioconductor.org/install/ 安装） ``` -The Hub returns a VcfFile object with a reference to the file on disk. +返回一个引用磁盘上文件的 VcfFile 对象--hub。 ```{r} hub <- AnnotationHub() ``` -Query the Hub for clinvar VCF files build GRCh37: +查询 hub，返回 clinvar VCF 文件 GRCh37 版本的 sourceurl（vcf文件的原始URL）： ```{r} mcols(query(hub, "clinvar.vcf", "GRCh37"))[,"sourceurl", drop=FALSE] ``` -Retrieve one of the files: +检索其中一个文件： ```{r} fl <- query(hub, "clinvar.vcf", "GRCh37")[[1]] ``` -Read the data into a VCF object: +将数据读入 VCF 对象： ```{r} vcf <- readVcf(fl, "hg19") dim(vcf) ``` -Overlap operations require that seqlevels and the genome of the objects match. -Here the VCF seqlevels are modified to match the TxDb. +Overlap operations 要求 seqlevels 和对象的基因组匹配。 +这里修改 VCF seqlevels 以匹配 TxDb。 ```{r} @@ -95,64 +95,64 @@ seqlevels(vcf) seqlevels(vcf) <- paste0("chr", seqlevels(vcf)) ``` -For this example we'll annotate chromosomes 3 and 18: +这里我们以注释染色体 3 和 18 为例： ```{r} seqlevels(vcf, pruning.mode="coarse") <- c("chr3", "chr18") seqlevels(txdb_hg19) <- c("chr3", "chr18") ``` -Sanity check to confirm we have matching seqlevels. +检查确认 seqlevels 是匹配的。 ```{r} intersect(seqlevels(txdb_hg19), seqlevels(vcf)) ``` -The genomes already match so no change is needed. +基因组已经匹配，因而无需进行任何更改。 ```{r} unique(genome(txdb_hg19)) unique(genome(vcf)) ``` +使用'rowRanges()'提取 VCF 对象中的 GRanges。 -The GRanges in a VCF object is extracted with 'rowRanges()'. ```{r} gr_hg19 <- rowRanges(vcf) ``` ## Mouse mm10 -The second set of ranges is a user-defined region of chromosome 4 in mouse. -The idea here is that any region, known or unknown, can be annotated with -the following steps. +第二组注释区域是用户自定义的小鼠染色体 4 的区域。 +这里是想说明，任何已知或未知的区域都可以通过以下 +步骤进行注释。 -Load the TxDb package and keep only the standard chromosomes. +加载 TxDb 包且只保留标准染色体。 ```{r} txdb_mm10 <- keepStandardChromosomes(TxDb.Mmusculus.UCSC.mm10.ensGene) ``` -We are creating the GRanges from scratch and can specify the seqlevels -(chromosome names) to match the TxDb. + +我们从头开始创建 GRanges 并指定 seqlevels（染色体名称）以匹配 TxDb。 ```{r} head(seqlevels(txdb_mm10)) gr_mm10 <- GRanges("chr4", IRanges(c(4000000, 107889000), width=1000)) ``` -Now assign the genome. +分配基因组。 ```{r} unique(genome(txdb_mm10)) genome(gr_mm10) <- "mm10" ``` -# Location in and Around Genes +# 区域内及周围的基因 -locateVariants() in the VariantAnnotation package annotates ranges -with transcript, exon, cds and gene ID's from a TxDb. Various -extractions are performed on the TxDb (exonsBy(), transcripts(), -cdsBy(), etc.) and the result is overlapped with the ranges. An appropriate -GRangesList can also be supplied as the annotation. Different variants -such as 'coding', 'fiveUTR', 'threeUTR', 'spliceSite', 'intron', -'promoter', and 'intergenic' can be searched for by passing the appropriate -constructor as the 'region' argument. See ?locateVariants for details. +VariantAnnotation 包中的 locateVariants() 使用来自 TxDb +的 transcript，exon，cds 和 gene ID 来注释基因区域。可以 +对 TxDb 执行各种提取（exonsBy()，transcript()，cdsBy()等 +），注释结果与提取范围对应。适当的 GRangesList 也可以提供 +作为注释。通过将适当的构造函数作为 “region” 的参数传递， +可以搜索不同的变体，例如 'coding'，'fiveUTR'，'threeUTR' +，'spliceSite'，'intron'，'promoter' 和 'intergenic'。详 +细信息，请参阅 ?locateVariants。 ```{r} loc_hg19 <- locateVariants(gr_hg19, txdb_hg19, AllVariants()) @@ -161,10 +161,10 @@ loc_mm10 <- locateVariants(gr_mm10, txdb_mm10, AllVariants()) table(loc_mm10$LOCATION) ``` -# Annotate by ID +# 通过 ID 注释 -The ID's returned from locateVariants() can be used in select() to map -to ID's in other annotation packages. +从 locateVariants() 返回的 ID 可以在 select() 中用于 +映射到其他注释包中的 ID。 ```{r} @@ -173,8 +173,8 @@ keys <- na.omit(unique(loc_hg19$GENEID)) head(select(org.Hs.eg.db, keys, cols, keytype="ENTREZID")) ``` -The 'keytype' argument specifies that the mouse TxDb contains -Ensembl instead of Entrez gene id's. +参数 'keytype' 指定小鼠 TxDb 包含 Ensembl 而不是 +Entrez gene id。 ```{r} @@ -182,16 +182,16 @@ keys <- unique(loc_mm10$GENEID) head(select(org.Mm.eg.db, keys, cols, keytype="ENSEMBL")) ``` -# Annotate by Position +# 通过位置注释 -Files stored in the AnnotationHub have been pre-processed into -ranged-based R objects such as a GRanges, GAlignments and VCF. -The positions in our GRanges can be overlapped with the ranges in -the AnnotationHub files. This allows for easy subsetting -of multiple files, resulting in only the ranges of interest. +存储在 AnnotationHub 中的文件已经预处理为基于 +范围的 R 对象，例如 GRanges，GAlignments 和 +VCF。GRanges 中的位置可以与 AnnotationHub 文 +件中的范围重叠，这对多个文件取子集很容易，从 +而仅产生感兴趣的范围。 -Create a 'hub' from AnnotationHub and filter the files based -on organism and genome build. +从 AnnotationHub 创建一个 'hub' ，并根据生物 +和基因组版本过滤文件。 ```{r} hub <- AnnotationHub() @@ -200,7 +200,7 @@ hub_hg19 <- subset(hub, length(hub_hg19) ``` -Iterate over the first 3 files and extract ranges that overlap 'gr_hg19'. +迭代前 3 个文件并提取与 'gr_hg19' 重叠的范围。 ```{r, echo=FALSE} ov_hg19 <- lapply(1:3, function(i) subsetByOverlaps(hub_hg19[[i]], gr_hg19)) ``` @@ -208,21 +208,21 @@ ov_hg19 <- lapply(1:3, function(i) subsetByOverlaps(hub_hg19[[i]], gr_hg19)) ov_hg19 <- lapply(1:3, function(i) subsetByOverlaps(hub_hg19[[i]], gr_hg19)) ``` -Inspect the results. +检查结果。 ```{r} names(ov_hg19) <- names(hub_hg19)[1:3] lapply(ov_hg19, head, n=3) ``` -Annotating the mouse ranges in the same fashion is left -as an exercise. +作为练习，以相同的方式注释小鼠范围。 + -# Annotating Variants +# 注释变体 + +

氨基酸编码变化

+对于落在编码区中的一组 dbSNP 变体，可以计算氨基酸变化。 +一个变体-转录本的匹配输出一行，这导致每个变体可能输出 +多行。 -

Amino acid coding changes

-For the set of dbSNP variants that fall in coding regions, amino -acid changes can be computed. The output contains one line for each -variant-transcript match which can result in multiple lines for -each variant. ```{r} @@ -233,34 +233,34 @@ head(predictCoding(vcf, txdb_hg19, Hsapiens), 3) sessionInfo() ``` -# Exercises - -Exercise 1: VCF header and reading data subsets. - -VCF files can be large and it's often the case that only a subset of -variables or genomic positions are of interest. The scanVcfHeader() -function in the VariantAnnotation package retrieves header information -from a VCF file. Based on the information returned from scanVcfHeader() -a ScanVcfParam() object can be created to read in a subset of data from -a VCF file. -* Use scanVcfHeader() to inspect the header information in the - 'chr22.vcf.gz' file in VariantAnnotation package. -* Select a few 'info' or 'geno' variables and create a ScanVcfParam object. -* Use the ScanVcfParam object as the 'param' argument to readVcf() - to read in a subset of data. -Note that the header() accessor operates on VCF objects in the R -workspace. Try header(vcf) on the dbSNP file from AnnotationHub. - -Exercise 2: Annotate the mouse ranges in 'gr_mm10' with AnnotationHub files. -* Create a new 'hub' and filter on organism. -* Isolate the files for the appropriate genome build and perform overlaps. +# 练习 + +练习 1 ：VCF 头部和读取数据子集。 + +VCF 文件可能很大，而且通常情况下只有一部分变量或 +基因组位置是有意义的。 VariantAnnotation 包中的 +scanVcfHeader() 函数从 VCF 文件中检索标头信息。根 +据 scanVcfHeader() 返回的信息，可以创建 ScanVcfParam() +对象以从 VCF 文件中读取数据子集。 + +* 使用 scanVcfHeader() 检查 VariantAnnotation 包中 'chr22.vcf.gz' +文件中的标题信息。 +* 选择一些 'info' 或 'geno' 变量并创建一个 ScanVcfParam 对象。 +* 使用 ScanVcfParam 对象作为 readvcf() 的 'param' 参数来读入 +数据子集。 +请注意，header() 访问器对 R 工作空间中的 VCF 对象进行操作。 +在 AnnotationHub 的 dbSNP 文件中尝试 header(vcf)。 + +练习 2 ：使用 AnnotationHub 文件在 'gr_mm10' 中注释小鼠区域。 +* 创建一个新的 “hub” 并过滤 organism。 +* 从适当基因组版本提取出文件并执行 overlaps。 -Exercise 3: Annotate a gene range from Saccharomyces Scerevisiae. -* Load TxDb.Scerevisiae.UCSC.sacCer3.sgdGene and extract the - gene ranges. (Hint: use transcriptsBy() and range()). -* Isolate the range for gene "YBL086C". -* Create a new 'hub' from AnnotationHub and filter by organism. - (You should see >= 39 files.) -* Select the files for 'sacCer3' and perform overlaps. +练习 3 ：注释来自 Saccharomyces Scerevisiae 的基因区域。 +* 加载 TxDb.Scerevisiae.UCSC.sacCer3.sgdGene 并提取基因 +范围。 （提示：使用 transcriptsBy() 和 range()）。 +* 分离基因 “YBL086C” 的区域。 +* 从 AnnotationHub 创建一个新的 “hub” 并按 organism 过滤。 +（你应该看到 >= 39 个文件。） +* 选择 'sacCer3' 的文件并执行 overlaps。

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