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前言及背景

什么是基因組de?novo測序？其是對某一物種進行高通量測序，利用高性能計算平臺和生物信息學方法，在不依賴于參考基因組的情況下進行組裝，從而繪制該物種的全基因組序列圖譜。針對基因組的特性，基因組常被分為兩類：普通（簡單）基因組和復雜基因組。簡單基因組指單倍體，純合二倍體或者雜合度<0.5%，且重復序列含量<50%，GC含量為35%到65%之間的二倍體。復雜基因組則指雜合率＞0.5%，重復序列含量＞50%，GC含量處于異常的范圍（GC含量＜35%或者GC含量＞＝65%的二倍體，多倍體。諾禾致源對二倍體復雜基因組進一步細分為微雜合基因組（0.5%＜雜合率＜＝0.8%、高雜合基因組（雜合率＞0.8%）以及高重復基因組（重復序列比例>50%）。復雜基因組的組裝一直以來都是一個讓科研工作者為之頭疼的問題?？蒲泄ぷ髡咭矠榻鉀Q這個問題一直努力著。隨著三代測序平臺的更迭，測序subreads的長度不斷的增長，以及光學圖譜技術的出現（Hic,10xGenomic等）。重復序列導致的組裝困難逐步被緩解。但高雜合這一特性任一直困擾著科研工作者。

為了解決雜合組裝，各式各樣的方案不斷被提出。總體思路有下：1.設計實驗方案獲取單倍型（例如種間雜種；案例：Sequencing a Juglans regia?×?J. microcarpa hybrid yields high-quality genome assemblies of parental species；2.設計適合于雜合基因組組裝的軟件，優化組裝的算法；3.組裝之后，再用去雜合軟件去除雜合序列。

第一個思路很清晰，獲取單倍型再測序，就解決了高雜合這個難點，可有時單倍體的獲取真不是一般的難，局限性很大。

基于第二個思路，已有很多軟件開發。有NOVOheter, Plantanus, MSR-CA，Plantanus-allee（Plantanus的升級版，支持Hic，10xGenomics等數據）等。此外還有一些軟件有支持高雜合組裝的模塊，如Canu，SPAdes, Falon等。但實測的經驗來看，效果都不是很好。

第三種思路的核心就是，居于相似性cut，目前接觸過的有Redundans，Haplomerger2，Purge_haplogs。Purge_haplogs除了考慮相似性，還有一大特性，就是通過分析比對read的覆蓋度決定誰去誰留。此外，Haplomerger2和Purge_haplogs還支持重復序列部分的屏蔽。

今年我接手到了一個基因組大小為86M,雜合率約2.4%，重復序列率約為20%左右的真菌。測序策略為pacbio seq I + PE 150 。本來打算再做一個Hic，但咨詢一些測序公司之后，均表示真菌沒有太多成功經驗，且投入與產出可能不對等。故打消測Hic的念頭。

基于已有的數據，我先采取了canu （單倍型組裝；canu多倍體模式，"batOptions=-dg 3 -db 3 -dr 1 -ca 500 -cp 50"組裝），MECAT2，MaSuRCA，Falon，flye，wtdbg2 等組裝軟件進行了組裝。接著使用 Purge_haplogs 、Redundans和Haplomerger2進行去雜合，然后再使用 FinisherSC進行基因組升級，最后使用nextpolish 進行 polish。經過測試，canu，MECAT2（經過nextpolish 進行 polish之后）， MaSuRCA的三個軟件的表現相對較好。去雜軟件Purge_haplogs的適用性優于Redundans和Haplomerger2，去雜前后BUSCO評估基本不變。由于涉及到文章。故暫時只能先提供最優的腳本，等文章出了之后會進一步深入。

組裝案例


#測序數據Bam to Fastq or Fasta

samtools fastq -0 012m.subreads.fq -@ 32 subreads.bam

#Assessment of genome size and heterozygosity(raw PE reads)

mkdir genomescope

cd genomescope

ln -s ../012m_L1_?.fq ./

jellyfish count -C -m 21 -s 1000000000 -t 32 *.fq -o reads.jf

jellyfish histo -t 32 reads.jf > reads.histo

Rscript /opt/biosoft/genomescope/genomescope.R reads.histo 21 150 output

# 二代數據質控

mkdir Trimmomatic

cd Trimmomatic

java -jar /opt/biosoft/Trimmomatic-0.36/trimmomatic-0.36.jar PE -threads 32 -phred33 ../012m_L1_1.fq ../012m_L1_2.fq 012m_Trimmomatic.1.fq 012m_Trimmomatic.unpaired.1.fq 012m_Trimmomatic.2.fq 012m_Trimmomatic.unpaired.2.fq ILLUMINACLIP:/opt/biosoft/Trimmomatic-0.36/adapters/TruSeq3-PE-2.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:75 TOPHRED33

cd ../

# correct Illumina reads | Assessment of genome size and heterozygosity

mkdir Finderrors

source ~/.bash.pacbio

# ErrorCorrectReads.pl 為 ALLPATHS-LG 的一個perl 程序

ErrorCorrectReads.pl PHRED_ENCODING=33 READS_OUT=012m FILL_FRAGMENTS=0 KEEP_KMER_SPECTRA=1 PAIRED_READS_A_IN=../fastuniq/012m.fastuniq.1.fastq PAIRED_READS_B_IN=../fastuniq/012m.fastuniq.2.fastq PLOIDY=2 PAIRED_SEP=251 PAIRED_STDEV=48

# ErrorCorrectReads.pl也可以對基因組特性進行評估，但針對我的菌株來說，同已發布的鄰近菌株比較，GenomeScope的結果要準確一些。

# kmer plot

cd 012m.fastq.kspec

perl -p -i -e 's/\@fns = \(\"frag_reads_filt.25mer.kspec\", \"frag_reads_edit.24mer.kspec\", \"frag_reads_corr.25mer.kspec\"\);\n//' /opt/biosoft/ALLPATHS-LG/bin/KmerSpectrumPlot.pl

KmerSpectrumPlot.pl SPECTRA=1 FREQ_MAX=255

perl -p -i -e 's/\@fns = \(\"frag_reads_filt.25mer.kspec\", \"frag_reads_edit.24mer.kspec\", \"frag_reads_corr.25mer.kspec\"\);\n//' /opt/biosoft/ALLPATHS-LG/bin/KmerSpectrumPlot.pl

convert kmer_spectrum.cumulative_frac.log.lin.eps kmer_spectrum.cumulative_frac.log.lin.png

convert kmer_spectrum.distinct.lin.lin.eps kmer_spectrum.distinct.lin.lin.png

convert kmer_spectrum.distinct.log.log.eps kmer_spectrum.distinct.log.log.png

cd ../

# Using LoRDEC to modify PacBio Reads

mkdir LoRDEC

cd LoRDEC

lordec-correct -2 ../Finderrors/012m.paired.A.fastq ../Finderrors/012m.paired.B.fastq -i ../012m.subreads.fq -k 19 -o pacbio.LoRDEC.corrected.fasta -s 3 -T 32 &> lordec-correct.log

seqkit seq -u pacbio.LoRDEC.corrected.fasta > pacbio.corrected.fasta**

cd ../

###Genome assembly###

mkdir MaSuRCA

cd MaSuRCA

echo '# example configuration file

DATA

#Illumina paired end reads supplied as <two-character prefix> <fragment mean> <fragment stdev> <forward_reads> <reverse_reads>

#if single-end, do not specify <reverse_reads>

#MUST HAVE Illumina paired end reads to use MaSuRCA

PE= pe 251 48 /home/bioinfo/data/012m/012m_L1_1.fq  /home/bioinfo/data/012m/012m_L1_2.fq

#Illumina mate pair reads supplied as <two-character prefix> <fragment mean> <fragment stdev> <forward_reads> <reverse_reads>

#JUMP= sh 3600 200 /FULL_PATH/short_1.fastq  /FULL_PATH/short_2.fastq

#pacbio OR nanopore reads must be in a single fasta or fastq file with absolute path, can be gzipped

#if you have both types of reads supply them both as NANOPORE type

PACBIO=/home/bioinfo/data/012m/LoRDEC/pacbio.corrected.fasta

#NANOPORE=/FULL_PATH/nanopore.fa

#Other reads (Sanger, 454, etc) one frg file, concatenate your frg files into one if you have many

#OTHER=/FULL_PATH/file.frg

#synteny-assisted assembly, concatenate all reference genomes into one reference.fa; works for Illumina-only data

#REFERENCE=/FULL_PATH/nanopore.fa

END

PARAMETERS

#PLEASE READ all comments to essential parameters below, and set the parameters according to your project

#set this to 1 if your Illumina jumping library reads are shorter than 100bp

EXTEND_JUMP_READS=0

#this is k-mer size for deBruijn graph values between 25 and 127 are supported, auto will compute the optimal size based on the read data and GC content

GRAPH_KMER_SIZE = auto

#set this to 1 for all Illumina-only assemblies

#set this to 0 if you have more than 15x coverage by long reads (Pacbio or Nanopore) or any other long reads/mate pairs (Illumina MP, Sanger, 454, etc)

USE_LINKING_MATES = 0

#specifies whether to run the assembly on the grid

USE_GRID=0

#specifies grid engine to use SGE or SLURM

GRID_ENGINE=SGE

#specifies queue (for SGE) or partition (for SLURM) to use when running on the grid MANDATORY

GRID_QUEUE=all.q

#batch size in the amount of long read sequence for each batch on the grid

GRID_BATCH_SIZE=500000000

#use at most this much coverage by the longest Pacbio or Nanopore reads, discard the rest of the reads

#can increase this to 30 or 35 if your reads are short (N50<7000bp)

LHE_COVERAGE=25

#set to 0 (default) to do two passes of mega-reads for slower, but higher quality assembly, otherwise set to 1

MEGA_READS_ONE_PASS=0

#this parameter is useful if you have too many Illumina jumping library mates. Typically set it to 60 for bacteria and 300 for the other organisms

#LIMIT_JUMP_COVERAGE = 300

#these are the additional parameters to Celera Assembler.  do not worry about performance, number or processors or batch sizes -- these are computed automatically.

#CABOG ASSEMBLY ONLY: set cgwErrorRate=0.25 for bacteria and 0.1<=cgwErrorRate<=0.15 for other organisms.

CA_PARAMETERS =  cgwErrorRate=0.15

#CABOG ASSEMBLY ONLY: whether to attempt to close gaps in scaffolds with Illumina  or long read data

CLOSE_GAPS=1

#auto-detected number of cpus to use, set this to the number of CPUs/threads per node you will be using

NUM_THREADS = 32

#this is mandatory jellyfish hash size -- a safe value is estimated_genome_size*20**

JF_SIZE = 1740000000

#ILLUMINA ONLY. Set this to 1 to use SOAPdenovo contigging/scaffolding module.  Assembly will be worse but will run faster. Useful for very large (>=8Gbp) genomes from Illumina-only data

SOAP_ASSEMBLY=0

#Hybrid Illumina paired end + Nanopore/PacBio assembly ONLY.  Set this to 1 to use Flye assembler for final assembly of corrected mega-reads.  A lot faster than CABOG, at the expense of some contiguity. Works well even when MEGA_READS_ONE_PASS is set to 1.  DO NOT use if you have less than 15x coverage by long reads.

FLYE_ASSEMBLY=0

END ' > config.txt

/opt/biosoft/MaSuRCA-3.3.4/bin/masurca config.txt

./assemble.sh

mkdir purge_haplogs

cd purge_haplogs

minimap2 -t 32 -ax map-pb ../final.genome.scf.fasta /home/bioinfo/data/012m/canu/012m.correctedReads.fasta.gz | samtools view -hF 256 - | samtools sort -@ 32 -m 2G -o aligned.bam

purge_haplotigs  hist  -b aligned.bam  -g ../final.genome.scf.fasta -t 20

purge_haplotigs contigcov -i aligned.bam.gencov -o coverage_stats.csv -l 18 -m 76 -h 134

purge_haplotigs purge -g ../final.genome.scf.fasta -c coverage_stats.csv -b aligned.bam -t 4 -a 50

mkdir finisherSC

cd finisherSC

ln -s ../curated.fasta ./contigs.fasta

ln -s ~/data/012m/canu/012m.correctedReads.fasta ./raw_reads.fasta

#這一步不要設置多線程，不然可能報錯，使用mummer4的速度要遠高于mummet3

python /opt/biosoft/finishingTool/finisherSC.py ./ /opt/biosoft/mummer4/bin/

mkdir NextPolish

cd NextPolish

ls ~/data/012m/Finderrors/012m.paired.?.fastq > sgs.fofn

echo '/home/bioinfo/data/012m/canu/012m.correctedReads.fasta' > lgs.fofn

echo '[General]

job_type = local

job_prefix = nextPolish

task = default

rewrite = yes

rerun = 10

parallel_jobs = 5

multithread_jobs = 6

genome = ../improved3.fasta

genome_size = auto

workdir = ./01_rundir

polish_options = -p {multithread_jobs}

[sgs_option]

sgs_fofn = ./sgs.fofn

sgs_options = -max_depth 100 -bwa

[lgs_option]

lgs_fofn = ./lgs.fofn

lgs_options = -min_read_len 10k -max_read_len 150k -max_depth 60

lgs_minimap2_options = -x map-pb

[polish_options]

-ploidy 2 ' > run.cfg

/opt/biosoft/NextPolish/nextPolish run.cfg

cat ./01_rundir/01.kmer_count/*polish.ref.sh.work/polish_genome*/genome.nextpolish.part*.fasta > genome.nextpolish.fasta