文件来自于：下载最新版的KEGG信息，并且解析好，如下：

首先在R里面赋值一个变量path2gene_file就是图中的kegg2gene.txt文件，读到R里面去

tmp=read.table(path2gene_file,sep="\t",colClasses=c('character'))

#tmp=toTable(org.Hs.egPATH)

# first column is kegg ID, second column is entrez ID

GeneID2kegg_list<<- tapply(tmp[,1],as.factor(tmp[,2]),function(x) x)

kegg2GeneID_list<<- tapply(tmp[,2],as.factor(tmp[,1]),function(x) x)

这个变量kegg2GeneID_list是一个list，因为是entrez gene ID，需要转换成symbol，我就不多说了，转换后的数据，就是kegg2symbol_list 。

最后对 kegg2symbol_list 输出成gmt文件：

write.gmt <- function(geneSet=kegg2symbol_list,gmt_file='kegg2symbol.gmt'){

sink( gmt_file )

for (i in 1:length(geneSet)){

cat(names(geneSet)[i])

cat('\tNA\t')

cat(paste(geneSet[[i]],collapse = '\t'))

cat('\n')

}

sink()

}

csfastq数据如下，还是四行代表一条read：

起初，我完全蒙圈了，查了资料才勉强了解。

这样只能转为csfasta格式文件和qual文件，需要下载大名鼎鼎的lh写的一个脚本：wget http://www.bbmriwiki.nl/svn/bwa_45_patched/solid2fastq.pl 来转为fastq格式

参考：http://seqanswers.com/forums/showpost.php?p=59156&postcount=4

Sequencer reads have a chance of read error (e.g. spot misidentification), combined with a chance of sequence error (e.g. polymerase misread in the PCR step).

For sequencers that output in base space, both these errors have a similar effect on the base-space mapping.

For sequencers that output in color-space, the read errors result in a somewhat unexpected base-space translation even if the underlying sequence has a perfect match to the reference.

The issues relating to color-space to base-space translation were discussed in the thread you linked to, but here's my take on it (dumped from an email I recently sent to someone else):A color-space sequence is an encoding of adjacent dimers such that unchanging bases are encoded with '0', complementary changes are encoded with '3', the colour '1' is used for a non-complementary base change on the same side of the alphabet (AC, CA, GT, or TG), and the colour '2' is used for a non-complementary base change on a different side of the alphabet (AG, GA, CT, or TC). A table of these changes can be found here:
http://www.ploscompbiol.org/article/...i.1000386.g002
This has a few nice properties (e.g. the reverse-complement of a color-space sequence is the same as the reverse of the color-space sequence, a SNP will have two transitions), but many annoying and nasty properties.

The first is that a color-space sequence in itself is meaningless without a base reference (usually the starting base).

可以看到ENSEMBL的版本特别复杂！！！很容易搞混！

但是UCSC的版本就简单了，就hg18,19,38, 常用的是hg19，但是我推荐大家都转为hg38

看起来NCBI也是很简单，就GRCh36,37,38，但是里面水也很深！

Feb 13 2014 00:00    Directory April_14_2003
Apr 06 2006 00:00    Directory BUILD.33
Apr 06 2006 00:00    Directory BUILD.34.1
Apr 06 2006 00:00    Directory BUILD.34.2
Apr 06 2006 00:00    Directory BUILD.34.3
Apr 06 2006 00:00    Directory BUILD.35.1
Aug 03 2009 00:00    Directory BUILD.36.1
Aug 03 2009 00:00    Directory BUILD.36.2
Sep 04 2012 00:00    Directory BUILD.36.3
Jun 30 2011 00:00    Directory BUILD.37.1
Sep 07 2011 00:00    Directory BUILD.37.2
Dec 12 2012 00:00    Directory BUILD.37.3

可以看到，有37.1,   37.2，  37.3 等等，不过这种版本一般指的是注释在更新，基因组序列一般不会更新！！！

反正你记住hg19基因组大小是3G，压缩后八九百兆即可！！！

如果要下载GTF注释文件，基因组版本尤为重要！！！

对NCBI：ftp://ftp.ncbi.nih.gov/genomes/H_sapiens/GFF/          ##最新版（hg38）

对于ensembl：

变幻中间的release就可以拿到所有版本信息：ftp://ftp.ensembl.org/pub/

对于UCSC，那就有点麻烦了：

1. Navigate to http://genome.ucsc.edu/cgi-bin/hgTables

2. Select the following options:
clade: Mammal
genome: Human
assembly: Feb. 2009 (GRCh37/hg19)
group: Genes and Gene Predictions
track: UCSC Genes
table: knownGene
region: Select "genome" for the entire genome.
output format: GTF - gene transfer format
output file: enter a file name to save your results to a file, or leave blank to display results in the browser

3. Click 'get output'.

 现在重点来了，搞清楚版本关系了，就要下载呀！

UCSC里面下载非常方便，只需要根据基因组简称来拼接url即可：

http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/chromFa.tar.gz

http://hgdownload.cse.ucsc.edu/goldenPath/mm9/bigZips/chromFa.tar.gz

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz

http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/chromFa.tar.gz

for i in $(seq 1 22) X Y M;
do echo $i;
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/chromosomes/chr${i}.fa.gz;

## 这里也可以用NCBI的：ftp://ftp.ncbi.nih.gov/genomes/M_musculus/ARCHIVE/MGSCv3_Release3/Assembled_Chromosomes/chr前缀
done
gunzip *.gz
for i in $(seq 1 22) X Y M;
do cat chr${i}.fa >> hg19.fasta;
done
rm -fr chr*.fasta

首先，我们说官网，肯定可以找到，不然这种芯片出来就没有意义了！

然后，我们看看NCBI下载的，会比较大

这两种方法都比较麻烦，需要一个个的来！

所以我接下来要讲的是用R的bioconductor包来批量得到芯片探针与gene的对应关系！

一般重要的芯片在R的bioconductor里面都是有包的，用一个R包可以批量获取有注释信息的芯片平台，我选取了常见的物种，如下：

        gpl           organism                  bioc_package

1     GPL32       Mus musculus                        mgu74a

2     GPL33       Mus musculus                        mgu74b

3     GPL34       Mus musculus                        mgu74c

6     GPL74       Homo sapiens                        hcg110

7     GPL75       Mus musculus                     mu11ksuba

8     GPL76       Mus musculus                     mu11ksubb

9     GPL77       Mus musculus                     mu19ksuba

10    GPL78       Mus musculus                     mu19ksubb

11    GPL79       Mus musculus                     mu19ksubc

12    GPL80       Homo sapiens                        hu6800

13    GPL81       Mus musculus                      mgu74av2

14    GPL82       Mus musculus                      mgu74bv2

15    GPL83       Mus musculus                      mgu74cv2

16    GPL85  Rattus norvegicus                        rgu34a

17    GPL86  Rattus norvegicus                        rgu34b

18    GPL87  Rattus norvegicus                        rgu34c

19    GPL88  Rattus norvegicus                         rnu34

20    GPL89  Rattus norvegicus                         rtu34

22    GPL91       Homo sapiens                      hgu95av2

23    GPL92       Homo sapiens                        hgu95b

24    GPL93       Homo sapiens                        hgu95c

25    GPL94       Homo sapiens                        hgu95d

26    GPL95       Homo sapiens                        hgu95e

27    GPL96       Homo sapiens                       hgu133a

28    GPL97       Homo sapiens                       hgu133b

29    GPL98       Homo sapiens                     hu35ksuba

30    GPL99       Homo sapiens                     hu35ksubb

31   GPL100       Homo sapiens                     hu35ksubc

32   GPL101       Homo sapiens                     hu35ksubd

36   GPL201       Homo sapiens                       hgfocus

37   GPL339       Mus musculus                       moe430a

38   GPL340       Mus musculus                     mouse4302

39   GPL341  Rattus norvegicus                       rae230a

40   GPL342  Rattus norvegicus                       rae230b

41   GPL570       Homo sapiens                   hgu133plus2

42   GPL571       Homo sapiens                      hgu133a2

43   GPL886       Homo sapiens                     hgug4111a

44   GPL887       Homo sapiens                     hgug4110b

45  GPL1261       Mus musculus                    mouse430a2

49  GPL1352       Homo sapiens                       u133x3p

50  GPL1355  Rattus norvegicus                       rat2302

51  GPL1708       Homo sapiens                     hgug4112a

54  GPL2891       Homo sapiens                       h20kcod

55  GPL2898  Rattus norvegicus                     adme16cod

60  GPL3921       Homo sapiens                     hthgu133a

63  GPL4191       Homo sapiens                       h10kcod

64  GPL5689       Homo sapiens                     hgug4100a

65  GPL6097       Homo sapiens               illuminaHumanv1

66  GPL6102       Homo sapiens               illuminaHumanv2

67  GPL6244       Homo sapiens   hugene10sttranscriptcluster

68  GPL6947       Homo sapiens               illuminaHumanv3

69  GPL8300       Homo sapiens                      hgu95av2

70  GPL8490       Homo sapiens   IlluminaHumanMethylation27k

71 GPL10558       Homo sapiens               illuminaHumanv4

72 GPL11532       Homo sapiens   hugene11sttranscriptcluster

73 GPL13497       Homo sapiens         HsAgilentDesign026652

74 GPL13534       Homo sapiens  IlluminaHumanMethylation450k

75 GPL13667       Homo sapiens                        hgu219

76 GPL15380       Homo sapiens      GGHumanMethCancerPanelv1

77 GPL15396       Homo sapiens                     hthgu133b

78 GPL17897       Homo sapiens                     hthgu133a

这些包首先需要都下载

gpl_info=read.csv("GPL_info.csv",stringsAsFactors = F)

### first download all of the annotation packages from bioconductor

for (i in 1:nrow(gpl_info)){

  print(i)

  platform=gpl_info[i,4]

  platform=gsub('^ ',"",platform) ##主要是因为我处理包的字符串前面有空格

  #platformDB='hgu95av2.db'

  platformDB=paste(platform,".db",sep="")

  if( platformDB  %in% rownames(installed.packages()) == FALSE) {

    BiocInstaller::biocLite(platformDB)

    #source("http://bioconductor.org/biocLite.R");

    #biocLite(platformDB )

  }

}

for (i in 1:nrow(gpl_info)){

  print(i)

  platform=gpl_info[i,4]

  platform=gsub('^ ',"",platform)

  #platformDB='hgu95av2.db'

  platformDB=paste(platform,".db",sep="")

  if( platformDB  %in% rownames(installed.packages()) != FALSE) {

    library(platformDB,character.only = T)

    #tmp=paste('head(mappedkeys(',platform,'ENTREZID))',sep='')

    #eval(parse(text = tmp))

###重点在这里，把字符串当做命令运行

    all_probe=eval(parse(text = paste('mappedkeys(',platform,'ENTREZID)',sep='')))

    EGID <- as.numeric(lookUp(all_probe, platformDB, "ENTREZID"))

##自己把内容写出来即可

  }

}

参考：http://blog.sina.com.cn/s/blog_62b37bfe0101jbuq.html

我的数据如下，需要自己创建成一个GRanges对象

library(GenomicRanges)

pfam.hg38 <- GRanges(seqnames=Rle(a[,2]),
               ranges=IRanges(a[,3], a[,4]),
               strand=a[,5])

这样就OK拉，虽然这只是一个很简陋的GRanges对象，但是这个GRanges对象可以通过R的liftover方法来转换坐标啦。