Canu Quick Start（快速使用Canu）

2023-01-01 22:24:15

Canu Quick Start

Canu Quick Start
PBcR (老版的canu)
CA

Canu specializes in（专门从事） assembling PacBio or Oxford Nanopre sequences. Canu will correct the reads, then trim suspicious regions（修剪可疑区域） (such as remaining SMRTbell adapter), then assemble the corrected and cleaned reads into unitigs（非重复序列区）.（canu是专门用来组装三代reads的，三步走：校正、修剪、组装。）

Brief Introduction简单介绍

Canu has been designed to auto-detect your resources（硬件资源） and scale itself to fit. Two parameters let you restrict the resources used（限制资源的使用）.

maxMemory=XX

maxThreads=XX

Memory is specified in gigabytes（千兆G）. On a single machine, it will restrict Canu to at most this limit, on the grid（集群）, no single job will try to use more than the specified resources.

The input sequences can be FASTA or FASTQ（有质量信息的二代reads） format, uncompressed, or compressed with gz, bz2 or xz（使用格式）.

Running on the grid

Canu is designed to run on grid environments（集群环境） (LSF/PBS/Torque/Slrum/SGE are supported). Currently, Canu will submit itself to the default queue with default time options（所以在运行canu时，如果没有手工设置，就必须要指定grid为false，否则无法提交到集群运行）. You can overwrite this behavior by providing any specific parameters you want to be used for submission as an option. Users should also specify a job name to use on the grid:

gridOptionsJobName=myassembly

"gridOptions=--partition quick --time 2:00"

Assembling PacBio data 组装

Pacific Biosciences released P6-C4 chemistry reads. You can download them directly (7 GB) or from the original page. You must have the Pac Bio SMRTpipe software installed to extract the reads as FASTQ（安装原厂软件将原始数据提出成FASTQ）.

We made a 25X subset FASTQ available here（测试数据）

or use the following curl command:

curl -L -o p6.25x.fastq http://gembox.cbcb.umd.edu/mhap/raw/ecoli_p6_25x.filtered.fastq

Correct, Trim and Assemble（校正、修建、组装）

By default, canu will correct the reads, then trim the reads, then assemble the reads to unitigs（非重复序列区）.

默认是一条龙，全部自动做完。

canu \

 -p ecoli -d ecoli-auto \

 genomeSize=4.8m \

 -pacbio-raw p6.25x.fastq

#PBS -N R498_CANU

#PBS -j oe

#PBS -l nodes=1:ppn=4

#PBS -l mem=30gb

#PBS -q low

cd $PBS_O_WORKDIR

date

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/public/software/gcc-4.9.2/lib64

/public/software/canu-master/Linux-amd64/bin/canu \

         -p ecoli -d ecoli-auto \

         genomeSize=4.8m \

         -pacbio-raw ecoli_p6_25x.filtered.fastq

This will use the prefix（前缀） ‘ecoli’ to name files, compute the correction task（校正任务） in directory ‘ecoli-auto/correction’, the trimming task（修剪任务） in directory ‘ecoli-auto/trimming’, and the unitig construction（contig构建） stage in ‘ecoli-auto’ itself. Output files are described in the next section.

Find the Output

The canu progress chatter records statistics（会记录一些统计量） such as an input read histogram（输入read的直方图）, corrected read histogram（校正后的read直方图）, and overlap types（overlap类型）. Outputs from the assembly tasks are in:

ecoli*/ecoli.trimmedReads.fasta.gz （修剪后的reads）: The sequences after correction and final trimming. The corrected sequences above are overlapped again to identify any missed hairpin adapters or bad sequence that could not be detected in the raw sequences.
ecoli*/ecoli.layout（layout阶段的文件）: The layout provides information on where each read ended up in the final assembly, including contig and positions. It also includes the consensus sequence for each contig.
ecoli*/ecoli.gfa （图文件）: The GFA is the assembly graph generated by Canu. Currently this includes the contigs, associated bubbles, and any overlaps which were not used by the assembly.

The fasta output is split into three types:

1.ecoli*/asm.contigs.fasta （最终的contig文件，里面有些标签头）

Everything which could be assembled and is part of the primary assembly, including both unique and repetitive elements. Each contig has several flags included on the fasta def line:

>tig######## len=<integer> reads=<integer> covStat=<float> gappedBases=<yes|no> class=<contig|bubble|unassm> suggestRepeat=<yes|no> suggestCircular=<yes|no>

>tig00000000 len=110432 reads=231 covStat=181.52 gappedBases=no class=contig suggestRepeat=no suggestCircular=no TGAAAACACCAGTCGGTGGCAGACAAGGCGTCGGTCGGTGGAAGTGTAGACGCCCAACAACGGCAGCATAATAGGTCAGCCGTGCAGGCGGAGACACCAG
下面是对上面标签的细致化解释：
len: Length of the sequence, in bp.
reads: Number of reads used to form the contig. （组装这条contig用了多少个reads）
covStat （log（不同的contig/two-copy）？？？）: The log of the ratio of the contig being unique（唯一的） versus being two-copy（）, based on the read arrival rate（）. Positive values indicate more likely to be unique, while negative values indicate more likely to be repetitive. See Footnote 24 in Myers et al., A Whole-Genome Assembly of Drosophila.
gappedBases: If yes, the sequence includes all gaps in the multialignment.
class: Type of sequence. Unassembled sequences are primarily low-coverage sequences spanned by a single read.
suggestRepeat: If yes, sequence was detected as a repeat based on graph topology or read overlaps to other sequences.
suggestCircular: If yes, sequence is likely circular. Not implemented.

2.ecoli*/asm.bubbles.fasta （bubble信息）

alternate paths in the graph which could not be merged into the primary assembly.

3.ecoli*/asm.unassembled.fasta （没有进入组装，也不是bubble）

reads which could not be incorporated into the primary or bubble assemblies.

Correct, Trim and Assemble, Manually 手动运行

Sometimes, however, it makes sense to do the three top-level tasks by hand. This would allow trying multiple unitig construction parameters on the same set of corrected and trimmed reads.

First, correct the raw reads:

canu -correct \

  -p ecoli -d ecoli \

  genomeSize=4.8m \

  -pacbio-raw  p6.25x.fastq

Then, trim the output of the correction:

canu -trim \

  -p ecoli -d ecoli \

  genomeSize=4.8m \

  -pacbio-corrected ecoli/correction/ecoli.correctedReads.fasta.gz

And finally, assemble the output of trimming, twice（为啥要两次？）:

canu -assemble \

  -p ecoli -d ecoli-erate-0.013 \

  genomeSize=4.8m \

  errorRate=0.013 \

  -pacbio-corrected ecoli/trimming/ecoli.trimmedReads.fasta.gz

canu -assemble \

  -p ecoli -d ecoli-erate-0.025 \

  genomeSize=4.8m \

  errorRate=0.025 \

  -pacbio-corrected ecoli/trimming/ecoli.trimmedReads.fasta.gz

The directory layout for correction and trimming is exactly the same as when we ran all tasks in the same command. Each unitig construction task needs its own private work space, and in there the ‘correction’ and ‘trimming’ directories are empty. The error rate always specifies the error in the corrected reads which is typically <1% for PacBio data and <2% for Nanopore data (<1% on newest chemistries).

Assembling Oxford Nanopore data

A set of E. coli runs were released by the Loman lab. You can download one directly or any of them from the original page.（下载测试数据）

or use the following curl command:

curl -L -o oxford.fasta http://nanopore.s3.climb.ac.uk/MAP006-PCR-1_2D_pass.fasta

Canu assembles any of the four available datasets into a single contig but we picked one dataset to use in this tutorial. Then, assemble the data as before:

canu \

 -p ecoli -d ecoli-oxford \

 genomeSize=4.8m \

 -nanopore-raw oxford.fasta

The assembled identity is >99% before polishing.

Assembling With Multiple Technologies/Files多类型数据组装

Canu takes an arbitrary number of input files/formats. We made a mixed dataset of about 10X of a PacBio P6 and 10X of an Oxford Nanopore run available here（测试数据）

or use the following curl command:

curl -L -o mix.tar.gz http://gembox.cbcb.umd.edu/mhap/raw/ecoliP6Oxford.tar.gz

tar xvzf mix.tar.gz

Now you can assemble all the data:

canu \

 -p ecoli -d ecoli-mix \

 genomeSize=4.8m \

 -pacbio-raw pacbio*fastq.gz \

 -nanopore-raw oxford.fasta.gz

Assembling Low Coverage Datasets组装低覆盖度的数据

When you have 30X or less coverage, it helps to adjust the Canu assembly parameters. Typically, assembly 20X of single-molecule data outperforms hybrid methods with higher coverage. You can download a 20X subset of S. cerevisae（下载测试数据）

or use the following curl command:

curl -L -o yeast.20x.fastq.gz http://gembox.cbcb.umd.edu/mhap/raw/yeast_filtered.20x.fastq.gz

and run the assembler adding sensitive parameters (errorRate=0.035):

canu \

 -p asm -d yeast \

 genomeSize=12.1m \

 errorRate=0.035 \

 -pacbio-raw yeast.20x.fastq.gz

After the run completes, we can check the assembly statistics:

tgStoreDump -sizes -s 12100000 -T yeast/unitigging/asm.tigStore 2 -G yeast/unitigging/asm.gkpStore

lenSuggestRepeat sum     160297 (genomeSize 12100000)

lenSuggestRepeat num         12

lenSuggestRepeat ave      13358

lenUnassembled ng10       13491 bp   lg10      77   sum    1214310 bp

lenUnassembled ng20       11230 bp   lg20     176   sum    2424556 bp

lenUnassembled ng30        9960 bp   lg30     290   sum    3632411 bp

lenUnassembled ng40        8986 bp   lg40     418   sum    4841978 bp

lenUnassembled ng50        8018 bp   lg50     561   sum    6054460 bp

lenUnassembled ng60        7040 bp   lg60     723   sum    7266816 bp

lenUnassembled ng70        6169 bp   lg70     906   sum    8474192 bp

lenUnassembled ng80        5479 bp   lg80    1114   sum    9684981 bp

lenUnassembled ng90        4787 bp   lg90    1348   sum   10890099 bp

lenUnassembled ng100       4043 bp   lg100   1624   sum   12103239 bp

lenUnassembled ng110       3323 bp   lg110   1952   sum   13310167 bp

lenUnassembled ng120       2499 bp   lg120   2370   sum   14520362 bp

lenUnassembled ng130       1435 bp   lg130   2997   sum   15731198 bp

lenUnassembled sum   16139888 (genomeSize 12100000)

lenUnassembled num       3332

lenUnassembled ave       4843

lenContig ng10      770772 bp   lg10       2   sum    1566457 bp

lenContig ng20      710140 bp   lg20       4   sum    3000257 bp

lenContig ng30      669248 bp   lg30       5   sum    3669505 bp

lenContig ng40      604859 bp   lg40       7   sum    4884914 bp

lenContig ng50      552911 bp   lg50      10   sum    6571204 bp

lenContig ng60      390415 bp   lg60      12   sum    7407061 bp

lenContig ng70      236725 bp   lg70      16   sum    8521520 bp

lenContig ng80      142854 bp   lg80      23   sum    9768299 bp

lenContig ng90       94308 bp   lg90      33   sum   10927790 bp

lenContig sum   12059140 (genomeSize 12100000)

lenContig num         56

lenContig ave     215341

Consensus Accuracy

While Canu corrects sequences and has 99% identity or greater with PacBio or Nanopore sequences, for the best accuracy we recommend polishing with a sequence-specific tool. We recommend Quiver for PacBio and Nanopolish for Oxford Nanpore data.（专业校正）

If you have Illumina sequences available, Pilon can also be used to polish either PacBio or Oxford Nanopore assemblies.

Futher Reading

See the FAQ page for commonly-asked questions and the release. notes page for information on what’s changed and known issues.

码农公寓

Canu Quick Start

Brief Introduction简单介绍

Running on the grid

Assembling PacBio data 组装

Correct, Trim and Assemble（校正、修建、组装）

Find the Output

Correct, Trim and Assemble, Manually 手动运行

Assembling Oxford Nanopore data

Assembling With Multiple Technologies/Files多类型数据组装

Assembling Low Coverage Datasets组装低覆盖度的数据

Consensus Accuracy

Futher Reading

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