RepeatScou操作步骤(说明)

The purpose of the RepeatScout software is to identify repeat familysequences from genomes where hand-curated repeat databases (a laRepBase update) are not available. In fact, the output of this programcan be used as input to RepeatMasker as a way of automatically maskingnewly-sequenced genomes.

Included in this package is a more or less arbitrary 3 Mb of the human X chromosome for your testing and debugging purposes.

Wget下载，tar -xzvf解压，直接进入make一下即可，安装后文件如下

Running RepeatScout proceeds in four phases.

First, build_lmer_table creates a file that tabulates the frequency of all l-mers in these quence to be analyzed.

第一步：用build_lmer_table命令把整个基因组生成一个频率表格，把所有有过重复的kmer都找出来，

EG:build_lmer_table -sequence genome.fa -freq genome.fq

Second, RepeatScout takes this table and the sequence and produces a fasta file that contains all the repetitive elements that it could find.

第二步：用RepeatScout 这个命令根据生成的频率表格和基因组序列产生一个包含有所有的能找到的重复元件的文件。

EG:RepeatScout -sequence genome.fa -output repeats.fa -freq genome.fq

Third, the "filter-stage-1.prl" scriptis run on the output of RepeatScout to remove low-complexity and tandem elements;

RepeatMasker is run on the sequence of interest using this filtered RepeatScout library.

The program "filter-stage-2.prl"then filters out any repeat element that does not appear a certain number of times (by default, 10).

第三步：用filter-stage-1.prl这个脚本过滤掉低复杂度和串联重复元件。

EG：filter-stage-1.prl repeats.fa &> repeats.fa.filter_1

Finally, the locations of the repeats found by RepeatMasker are used, in conjuction with GFF files that describe segmental duplications or exons or other such "uninteresting" regions to remove sequences from the library that are likely to not be mobile elements; the program "compare-out-to-gff.prl" does exactly this.

第四步

Repeat Scout

RepeatScout is run in several steps using several different programs. This tutorial assumes you are using a fasta file named genome.fa you could, however, use any fasta file simple replacing genome.fa with the name of the fasta file being used. Also, running any of the listed commands without any additional input (or running them with the -h option) will display the help message including a description of the program, the usage, and all options.

1) Count every 12 base pair sequence in the genome, and when it is done, look at the result file:

$build_lmer_table -sequence genome.fa -freq genome.freq $less genome.freq

2) Extend the 12 base pair sequences to form initial consensus repeats. This will take a minute.

$RepeatScout -sequence genome.fa -freq genome.freq -output genome.firstrepeats.lib

3) Filter out simple and tandem repeats from the library

$cat genome.firstrepeats.lib | filter-stage-1.prl > genome.filtered.lib

4) Count the number of times each repeat in the filtered library appears. The output file genome.fa.out will contain a column with the count of each repeat type.

$RepeatMasker -pa [number of processors] -lib

genome.filtered.lib -nolow genome.fa

*The -pa option tells RepeatMasker how many processors to use. If you are only using one processor, do not use the -pa option. If you were running RepeatMasker on 3 processors, you would use -pa 3.

**The -nolow option stops RepeatMasker from masking low complexity repeats. Since we are only concerned with the number of times the generated repeats appear, masking low complexity repeats simply adds more time.

5) Filter out any repeats that appear less than 10 times. Note, because our sample data is so small, not much will be repeated 10 times, so check the results when the first command finishes.

$cat genome.filtered.lib | filter-stage-2.prl --cat genome.fa.out > genome.secondfilter.lib

$Less genome.secondfilter.lib

6) Mask the genome file with the new repeat library.

$RepeatMasker -pa [number of processors] -lib

genome.secondfilter.lib genome.fa

7) To view the length and frequency of repeats, open genome.fa.tbl. Total length, %GC, and percent bases masked (i.e. percent in repeats) are shown. Also shown are number and total length occupied by different kinds of repeats.

8) Because the repeat library made from our five scaffolds was so small, it is possible that the repeat library from step 5 and 7 is blank. Run RepeatMasker again using repeats found in all species so far. These are already in the virtual machine.

$RepeatMasker -pa [number of processors] -species all genome.fa