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NGSデータ解析のワークフロー記述とロボット実験への応用 (2020/10/03 LADEC2020)

396bc88acc93b94735c1c5d47a377c5f?s=47 Haruka Ozaki
October 03, 2020

NGSデータ解析のワークフロー記述とロボット実験への応用 (2020/10/03 LADEC2020)

Laboratory Automation Developers Conference 2020
(LADEC2020) での発表「NGSデータ解析のワークフロー記述とロボット実験への応用」のスライド
https://laboratoryautomation.connpass.com/event/187445/

396bc88acc93b94735c1c5d47a377c5f?s=128

Haruka Ozaki

October 03, 2020
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Transcript

  1. -"%&$ηογϣϯ ̨̣̜σʔλղੳͷ ϫʔΫϑϩʔهड़ͱ ϩϘοτ࣮ݧ΁ͷԠ༻ ඌ࡚ྒྷʢஜ೾େֶʣ !ZVJGV

  2. όΠΦΠϯϑΥϚςΟΫεݚڀࣨˏஜ೾େ w ਓྨ͕σʔλ͔Βੜ໋ͷෳࡶ͞ΛཧղͰ͖ΔΑ͏ʹ͍ͨ͠ w όΠΦΠϯϑΥϚςΟΫεʢੜ෺ؔ࿈σʔλॲཧɾղੳʣ ͷख๏Λͭ͘Δɾ͔ͭ͏ w "ήϊϜ഑ྻͷػೳͷղऍɾ༧ଌٕज़ͷ։ൃ w #̍ࡉ๔ϨϕϧͷΦϛΫεσʔλͷղੳٕज़ͷ։ൃ

    w $ੜ໋ՊֶݚڀͷࣗಈԽ w %ҩྍσʔλղੳ 2
  3. ݚڀͷੜ࢈ੑʺ࣮ݧͷੜ࢈ੑ w όΠΦΠϯϑΥϚςΟΫεɾσʔλղੳͷൺॏ͕૿Ճ w େن໛σʔλΛग़ྗ͢Δ࣮ݧٕज़ͷൃల͕എܠ w ˠ࣮ݧ͚ͩͰͳ͘ɺσʔλղੳ΋ࣗಈԽ͍͖͍ͯͨ͠ 3 ࣮ݧ ૢ࡞ɾܭଌ

    αϯϓϧ σʔλղੳ ࿦จ Ծઆੜ੒ σʔλϕʔε
  4. 4 /(4Λར༻ͨ͠ݚڀͷҰൠతͳྲྀΕ /(4Λ࢖͏ݚڀ͸Կ͕େมͳͷ͔ αϯϓϦϯά ϥΠϒϥϦϓϨοϓ γʔέϯγϯά σʔλղੳ • Πϝʔδ! •

    ػց͕ߴ͍! • σʔλ͕୔ࢁग़Δ! • σʔλղੳ͕Α͘Θ͔Βͳ͍ Tazro Inutano Ohta (2014) https://speakerdeck.com/inutano/introduction-of-next-generation-sequencing-applications-related-public-databases-and-resources
  5. 5 Tazro Inutano Ohta (2014) https://speakerdeck.com/inutano/introduction-of-next-generation-sequencing-applications-related-public-databases-and-resources /(4Λར༻ͨ͠ݚڀͷҰൠతͳྲྀΕ /(4Λ࢖͏ݚڀ͸Կ͕େมͳͷ͔ ࣮ݧσβΠϯ ༧උ࣮ݧ

    αϯϓϦϯά %/"ௐ੔ • ࣮ࡍ! • ҿΈձ͕ԕ͍ ϥΠϒϥϦ࡞੒ γʔέϯε 2$ ϑΟϧλϦϯά NBQQJOHBTTFNCMF 2$ ໨తผղੳ ֬ೝ࣮ݧ ࿦จࣥච σʔλެ։ ࿦จ౤ߘ ϦόΠζ࠶ղੳ ΞΫηϓτ ҿΈձ
  6. ಺༰ w /(4σʔλղੳ w /(4ͬͯͳΜͩʁ w ϫʔΫϑϩʔهड़ w ϫʔΫϑϩʔهड़ͬͯͳΜͩʁ w

    ϩϘοτ࣮ݧ΁ͷԠ༻ͷల๬ 6
  7. ࣍ੈ୅γʔέϯαʔ /FYU(FOFSBUJPO4FRVFODFS/(4  w αϯΨʔ๏ʢୈҰੈ୅ʣҎ߱ɺ೥୅தࠒ͔Βొ৔͠ ͨɺ৽͍͠%/"γʔέϯεٕज़ͷ૯শ w ϋΠεϧʔϓοτͳԘج഑ྻಡΈऔΓʢγʔέϯγϯάʣ͕Ͱ͖Δ w ʮ࣍ੈ୅ʯͱ͍͏ʹ͸ొ৔͔Β͕࣌ؒܦ͍ͪ͗ͯ͢Δ͜ͱ΋͋Γɺ࠷ۙͰ

    ͸)JHI5ISPVHIQVU4FRVFODFS )54 ͱݺ͹ΕΔ͜ͱ΋ 7 DNAʢ෺࣭ʣ NGS DNA഑ྻʢσʔλʣ © 2016 DBCLS TogoTV / CC-BY-4.0 ACGTAAGCCGAAG GATTTTAACCCCC CAATTCCCGGAGC ACGCAAAATTAAG
  8. /(4ͷ෼ྨ ݪཧ͸৭ʑ w ୈೋੈ୅ Ԙج഑ྻܾఆ࣌ʹిؾӭಈΛඞཁͱ͠ͳ͍  w *MMVNJOBͷ)J4FRγϦʔζɺ.J4FR /FYU4FR .JOJ4FR

    J4FR w #(*ͷ#(*4&2γϦʔζ w 2JBHFOͷ(FOF3FBEFS w ୈࡾੈ୅ʢரܕͷ1$3૿෯͕ඞཁͳ͍ʣ w 1BD#JPͷ1BD#JP34 4FRVFM w ୈ࢛ੈ୅ʢܬޫ৭ૉΛ࢖Θͳ͍ʣ w 0YGPSE/BOPQPSF.JO*0/ (SJE*0/9 1SPNFUI*0/3O% 8
  9. /(4൚༻௒ฒྻԘج഑ྻܾఆ૷ஔ w ͋Δੑ࣭Λ࣋ͬͨ%/"΍3/"ΛϥΠϒϥϦ%/"ʹม׵Ͱ ͖Ε͹ɺ/(4Ͱ͞·͟·ͳੜ໋ݱ৅Λ໢ཏతʹଌఆͰ͖Δ 9 RNAΛٯసࣸͯ͠ DNAʹͨ͠΋ͷ NGS సࣸ͞Ε͍ͯͨRNAͷ Ԙج഑ྻ

    © 2016 DBCLS TogoTV / CC-BY-4.0 ෺࣭ σʔλ λϯύΫ࣭͕݁߹͍ͯ͠Δ ୹͍DNAஅย λϯύΫ࣭͕݁߹͍ͯͨ͠ Ԙج഑ྻ సࣸ λϯύΫ࣭ ݁߹
  10. /(4ࣗମ͸൚༻͚ͩͲɺσʔλલॲཧɾղੳ͸໨తผ ଟ͘ͷιϑτ΢ΣΞΛ૊Έ߹ΘͤͯੜσʔλΛॲཧɾղੳ͢Δඞཁ͕͋Δ 10 ϥΠϒϥϦௐ੔ -JCSBSZQSFQBSBUJPO γʔέϯγϯά 4FRVFODJOH σʔλલॲཧ %BUBQSFQSPDFTTJOH σʔλղੳ

    %BUBBOBMZTJT ໨తผͷ
 γʔέϯγϯάख๏ ໨తผͷղੳ ڞ௨ͷલॲཧ ໨తผͷલॲཧ /(4ͰԘجಡΈऔΓ αϯϓϧ ࣮ݧ ৘ใղੳ σʔλ ஌ࣝ ৭ʑͳιϑτ΢Σ ΞΛ૊Έ͋ΘͤΔ ৭ʑͳιϑτ΢Σ ΞΛ૊Έ͋ΘͤΔ
  11. /(4σʔλղੳʹιϑτ΢ΣΞͷૄ݁߹ w ෳ਺ͷιϑτ΢ΣΞΛॱ࣮࣍ߦ͢Δ w ιϑτ΢ΣΞʴσʔλೖग़ྗ w σʔλ"ˠιϑτ΢ΣΞ̍ˠσʔλ"ˠ ιϑτ΢ΣΞˠσʔλ"ˠʜ w Ұ௚ઢ͚ͩͰͳ͘ɺ෼ذ΍߹ྲྀ΋

    11 https://www.nextflow.io/docs/latest/tracing.html
  12. w ύΠϓϥΠϯʹଟ͘ͷιϑτ΢ΣΞΛ૊Έ߹Θͤͯੜσʔ λΛॲཧɾղੳ͢Δ w ιϑτ΢ΣΞ͕มΘΔͱ݁Ռ͕มΘΔ w ࢀরσʔλ͕มΘΔͱ݁Ռ͕มΘΔ 12 # ؀ڥߏங

    $ conda install -c bioconda fastp $ conda install -c bioconda bowtie2 $ mkdir ~/bowtie2_index # Pre-built indexΛೖΕΔͨΊͷσΟϨΫτϦΛ࡞੒͢Δ $ cd ~/bowtie2_index # # Pre-built indexΛೖΕΔͨΊͷσΟϨΫτϦʹҠಈ͢Δ $ wget ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/mm10.zip $ unzip mm10.zip $ cd ~/bowtie2_index $ wget ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Eukaryotes/vertebrates_mammals/ Homo_sapiens/GRCh38/seqs_for_alignment_pipelines/ GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.bowtie_index.tar.gz $ tar xvzf bowtie2_index/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.bowtie_index.tar.gz $ mkdir ~/tools $ cd ~/tools $ python3 -m venv MACS2/ $ source MACS2/bin/activate $ pip install --upgrade pip $ pip install numpy $ pip install cython $ git clone https://github.com/taoliu/MACS.git $ cd MACS $ git checkout remotes/origin/macs2python3 $ python setup_w_cython.py install $ macs2 --help # ϔϧϓϝοηʔδ͕ग़ྗ͞ΕΕ͹OK $ deactivate $ source ~/tools/MACS2/bin/activate $ brew install samtools $ conda install -c bioconda homer $ perl /anaconda3/share/homer-*/configureHomer.pl -install hg38 $ perl /anaconda3/share/homer-*/configureHomer.pl -install mm10 $ conda install -c bioconda deeptools $ deeptools --version $ brew install r $ brew cask install rstudio $ brew install igv $ brew install bedtools $ open -a RStudio > if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") > BiocManager::install("ChIPpeakAnno") $ mkdir ~/gencode $ cd ~/gencode $ wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M20/ gencode.vM20.annotation.gtf.gz # $ gzip -d gencode.vM20.annotation.gtf.gz $ ls gencode.vM20.annotation.gtfɹ# gencode.vM20.annotation.gtf͕Ͱ͖ͨ͜ͱΛ֬ೝ͢Δɻ # σʔλऔಘ $ mkdir ~/chipseq # ChIP-seqղੳ༻ͷσΟϨΫτϦΛ࡞੒͢Δ $ cd ~/chipseq # ChIP-seqղੳ༻ͷσΟϨΫτϦ΁Ҡಈ͢Δ $ mkdir fastq # FASTQϑΝΠϧΛೖΕΔσΟϨΫτϦΛ࡞੒͢Δ $ cd ~/chipseq/fastq $ wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR520/004/SRR5208824/SRR5208824.fastq.gz $ cd ~/chipseq/fastq $ wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR520/008/SRR5208828/SRR5208828.fastq.gz $ cd ~/chipseq/fastq $ wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR520/008/SRR5208838/SRR5208838.fastq.gz $ cd ~/chipseq/fastq $ cp SRR5208824.fastq.gz BRD4_ChIP_IFNy.R1.fastq.gz $ cp SRR5208828.fastq.gz IRF1_ChIP_IFNy.R1.fastq.gz $ cp SRR5208838.fastq.gz Input_DNA.R1.fastq.gz $ cd ~/chipseq # ղੳ $ mkdir fastqc $ cd ~/chipseq $ fastqc -o fastqc fastq/BRD4_ChIP_IFNy.R1.fastq.gz $ cd ~/chipseq $ fastqc -o fastqc fastq/IRF1_ChIP_IFNy.R1.fastq.gz $ fastqc -o fastqc fastq/Input_DNA.R1.fastq.gz $ cd ~/chipseq $ mkdir fastp # fastpͱ͍͏໊લͷσΟϨΫτϦΛ࡞Δ $ cd ~/chipseq $ fastp -i fastq/BRD4_ChIP_IFNy.R1.fastq.gz -o fastp/BRD4_ChIP_IFNy.R1.trim.fastq.gz --html fastp/ BRD4_ChIP_IFNy.fastp.html ɹಉ༷ʹIRF1_ChIP_IFNy.R1.fastq.gzɺInput_DNA.R1.fastq.gzʹରͯ͠fastpΛ࣮ߦ͢Δɻ $ cd ~/chipseq $ fastp -i fastq/IRF1_ChIP_IFNy.R1.fastq.gz -o fastp/IRF1_ChIP_IFNy.R1.trim.fastq.gz --html fastp/ IRF1_ChIP_IFNy.fastp.html $ fastp -i fastq/Input_DNA.R1.fastq.gz -o fastp/Input_DNA.R1.trim.fastq.gz --html fastp/ Input_DNA.fastp.html ɹfastpσΟϨΫτϦ಺ʹҎԼͷϑΝΠϧ͕Ͱ͖ͯΔ͜ͱΛ֬ೝ͢Δɻ $ cd ~/chipseq $ open fastp/BRD4_ChIP_IFNy.fastp.html $ system_profiler SPHardwareDataType | grep Cores $ cd ~/chipseq $ mkdir bowtie2 $ cd ~/chipseq $ bowtie2 -p 2 -x data/external/bowtie2_index/mm10 \ -U fastp/BRD4_ChIP_IFNy.R1.trim.fastq.gz > bowtie2/BRD4_ChIP_IFNy.trim.sam $ cd ~/chipseq $ bowtie2 -p 2 -x data/external/bowtie2_index/mm10 \ -U fastp/IRF1_ChIP_IFNy.R1.trim.fastq.gz > bowtie2/IRF1_ChIP_IFNy.trim.sam $ bowtie2 -p 2 -x data/external/bowtie2_index/mm10 \ -U fastp/Input_DNA.R1.trim.fastq.gz > bowtie2/Input_DNA.trim.sam $ cd ~/chipseq $ samtools view -bhS -F 0x4 -q 42 bowtie2/BRD4_ChIP_IFNy.trim.sam | samtools sort -T bowtie2/ BRD4_ChIP_IFNy.trim - > bowtie2/BRD4_ChIP_IFNy.trim.uniq.bam $ cd ~/chipseq $ samtools view -bhS -F 0x4 -q 42 bowtie2/IRF1_ChIP_IFNy.trim.sam | samtools sort -T bowtie2/ IRF1_ChIP_IFNy.trim - > bowtie2/IRF1_ChIP_IFNy.trim.uniq.bam $ samtools view -bhS -F 0x4 -q 42 bowtie2/Input_DNA.trim.sam | samtools sort -T bowtie2/Input_DNA.trim - > bowtie2/Input_DNA.trim.uniq.bam $ cd ~/chipseq $ samtools index bowtie2/BRD4_ChIP_IFNy.trim.uniq.bam $ samtools index bowtie2/IRF1_ChIP_IFNy.trim.uniq.bam $ samtools index bowtie2/Input_DNA.trim.uniq.bam $ cd ~/chipseq $ mkdir macs2 # MACS2ͷग़ྗ݁ՌΛอଘ͢ΔσΟϨΫτϦΛ࡞੒͢ΔʢඞਢͰ͸ͳ͍ʣ $ cd ~/chipseq $ source ~/tools/MACS2/bin/activate # MACS2͕Πϯετʔϧ͞Εͨ؀ڥ΁੾Γସ͑Δ $ macs2 callpeak -t bowtie2/BRD4_ChIP_IFNy.trim.uniq.bam \ -c bowtie2/Input_DNA.trim.uniq.bam -f BAM -g mm -n BRD4_ChIP_IFNy --outdir macs2 -B -q 0.01 $ macs2 callpeak -t bowtie2/IRF1_ChIP_IFNy.trim.uniq.bam \ -c bowtie2/Input_DNA.trim.uniq.bam -f BAM -g mm -n IRF1_ChIP_IFNy --outdir macs2 -B -q 0.01 $ deactivate # ݩͷ؀ڥ΁੾Γସ͑Δɻ $ head macs2/BRD4_ChIP_IFNy_peaks.narrowPeak $ head macs2/BRD4_ChIP_IFNy_summits.bed $ cd ~/chipseq $ wc -l macs2/*_peaks.narrowPeak $ cd ~/chipseq $ bedtools intersect -u -a macs2/BRD4_ChIP_IFNy_peaks.narrowPeak -b macs2/ IRF1_ChIP_IFNy_peaks.narrowPeak > macs2/ BRD4_ChIP_IFNy_peaks.overlapped_with_IRF1_ChIP_IFNy_peaks.narrowPeak $ cd ~/chipseq $ bedtools intersect -u -a macs2/IRF1_ChIP_IFNy_peaks.narrowPeak -b macs2/ BRD4_ChIP_IFNy_peaks.narrowPeak > macs2/ IRF1_ChIP_IFNy_peaks.overlapped_with_BRD4_ChIP_IFNy_peaks.narrowPeak $ cd ~/chipseq $ bedtools intersect -v -a macs2/BRD4_ChIP_IFNy_peaks.narrowPeak -b macs2/ IRF1_ChIP_IFNy_peaks.narrowPeak > macs2/ BRD4_ChIP_IFNy_peaks.not_overlapped_with_IRF1_ChIP_IFNy_peaks.narrowPeak $ bedtools intersect -v -a macs2/IRF1_ChIP_IFNy_peaks.narrowPeak -b macs2/ BRD4_ChIP_IFNy_peaks.narrowPeak > macs2/ IRF1_ChIP_IFNy_peaks.not_overlapped_with_BRD4_ChIP_IFNy_peaks.narrowPeak $ cd ~/chipseq $ wc -l macs2/*overlapped*.narrowPeak $ cd ~/chipseq $ mkdir deeptools # deepToolsͷग़ྗ݁ՌΛอଘ͢ΔσΟϨΫτϦΛ࡞੒͢Δ $ cd ~/chipseq $ bamCoverage -b bowtie2/BRD4_ChIP_IFNy.trim.uniq.bam -o deeptools/BRD4_ChIP_IFNy.trim.uniq.bw -of bigwig --normalizeUsing CPM $ cd ~/chipseq $ bamCoverage -b bowtie2/IRF1_ChIP_IFNy.trim.uniq.bam -o deeptools/IRF1_ChIP_IFNy.trim.uniq.bw -of bigwig --normalizeUsing CPM $ bamCoverage -b bowtie2/Input_DNA.trim.uniq.bam -o deeptools/Input_DNA.trim.uniq.bw -of bigwig -- normalizeUsing CPM $ igv $ cd ~/chipseq $ mkdir homer $ cd ~/chipseq $ mkdir homer/BRD4_ChIP_IFNy $ findMotifsGenome.pl macs2/BRD4_ChIP_IFNy_summits.bed mm10 homer/BRD4_ChIP_IFNy -size 200 -mask $ cd ~/chipseq $ mkdir homer/IRF1_ChIP_IFNy $ findMotifsGenome.pl macs2/IRF1_ChIP_IFNy_summits.bed mm10 homer/IRF1_ChIP_IFNy -size 200 -mask $ cd ~/chipseq $ computeMatrix scale-regions \ --regionsFileName ~/gencode/gencode.vM20.annotation.gtf \ --scoreFileName deeptools/BRD4_ChIP_IFNy.trim.uniq.bw \ --outFileName deeptools/BRD4_ChIP_IFNy.trim.uniq.matrix_gencode_vM20_gene.txt.gz \ --upstream 1000 --downstream 1000 \ --skipZeros $ cd ~/chipseq $ plotProfile -m deeptools/BRD4_ChIP_IFNy.trim.uniq.matrix_gencode_vM20_gene.txt.gz \ -out deeptools/metagene_BRD4_ChIP_IFNy_gencode_vM20_gene.pdf \ --plotTitle "GENCODE vM20 genes" $ cd ~/chipseq $ plotHeatmap -m deeptools/BRD4_ChIP_IFNy.trim.uniq.matrix_gencode_vM20_gene.txt.gz \ -out deeptools/heatmap_BRD4_ChIP_IFNy_gencode_vM20_gene.pdf \ --plotTitle "GENCODE vM20 genes" $ cd ~/chipseq $ computeMatrix scale-regions \ --regionsFileName ~/gencode/gencode.vM20.annotation.gtf \ --scoreFileName deeptools/IRF1_ChIP_IFNy.trim.uniq.bw \ --outFileName deeptools/IRF1_ChIP_IFNy.trim.uniq.matrix_gencode_vM20_gene.txt.gz \ --upstream 1000 --downstream 1000 \ --skipZeros $ plotProfile -m deeptools/IRF1_ChIP_IFNy.trim.uniq.matrix_gencode_vM20_gene.txt.gz \ -out deeptools/metagene_IRF1_ChIP_IFNy_gencode_vM20_gene.pdf \ --plotTitle "GENCODE vM20 genes" $ plotHeatmap -m deeptools/IRF1_ChIP_IFNy.trim.uniq.matrix_gencode_vM20_gene.txt.gz \ -out deeptools/heatmap_IRF1_ChIP_IFNy_gencode_vM20_gene.pdf \ --plotTitle "GENCODE vM20 genes" $ cd ~/ $ computeMatrix reference-point \ --regionsFileName macs2/IRF1_ChIP_IFNy_summits.bed \ --scoreFileName deeptools/BRD4_ChIP_IFNy.trim.uniq.bw \ --referencePoint center \ --upstream 1000 \ --downstream 1000 \ --outFileName deeptools/BRD4_ChIP_IFNy.trim.IRF1_ChIP_IFNy_summits.matrix.txt.gz \ --skipZeros $ plotProfile -m deeptools/BRD4_ChIP_IFNy.trim.IRF1_ChIP_IFNy_summits.matrix.txt.gz \ -out deeptools/aggregation_BRD4_ChIP_IFNy.trim.IRF1_ChIP_IFNy_summits.pdf \ --regionsLabel "IRF1_ChIP_IFNy Peaks" $ plotHeatmap -m deeptools/BRD4_ChIP_IFNy.trim.IRF1_ChIP_IFNy_summits.matrix.txt.gz \ -out deeptools/heatmap_BRD4_ChIP_IFNy.trim.IRF1_ChIP_IFNy_summits.pdf \ --samplesLabel "BRD4_ChIP_IFNy" \ --regionsLabel "IRF1_ChIP_IFNy Peaks" $ computeMatrix scale-regions \ --regionsFileName ../data/gencode/gencode.vM20.annotation.gtf \ --scoreFileName deeptools/BRD4_ChIP_IFNy.trim.uniq.bw \ deeptools/IRF1_ChIP_IFNy.trim.uniq.bw \ --outFileName deeptools/chipseq_matrix_gencode_vM20_gene.txt.gz \ --upstream 1000 --downstream 1000 \ --skipZeros $ plotHeatmap -m deeptools/chipseq_matrix_gencode_vM20_gene.txt.gz \ -out deeptools/heatmap_BRD4_ChIP_IFNy_gencode_vM20_gene.k3.pdf \ --kmeans 3 \ --plotTitle "GENCODE vM20 genes" $ cd ~/chipseq $ cut -f 1,2,3,4,5,6 macs2/BRD4_ChIP_IFNy_peaks.narrowPeak > macs2/BRD4_ChIP_IFNy_peaks.narrowPeak.bed $ cut -f 1,2,3,4,5,6 macs2/IRF1_ChIP_IFNy_peaks.narrowPeak > macs2/IRF1_ChIP_IFNy_peaks.narrowPeak.bed $ cd ~/chipseq $ head macs2/*.narrowPeak.bed $ cd ~/chipseq $ open -a RStudio > library(ChIPpeakAnno) > gr1 <- toGRanges("macs2/BRD4_ChIP_IFNy_peaks.narrowPeak", format="narrowPeak", header=FALSE) > gr2 <- toGRanges("macs2/IRF1_ChIP_IFNy_peaks.narrowPeak", format="narrowPeak", header=FALSE) > ol <- findOverlapsOfPeaks(gr1, gr2) # ϐʔΫಉ࢜ͷॏͳΓΛௐࠪ͢Δ > makeVennDiagram(ol, NameOfPeaks=c(“BRD4”, “IRF1”)) # ॏͳΓΛϕϯਤͱͯ͠ՄࢹԽ͢Δ > BiocManager::install("TxDb.Mmusculus.UCSC.mm10.ensGene") # Ϛ΢εήϊϜmm10ͷҨ఻ࢠϞσϧͷύοέʔδΛμ ΢ϯϩʔυ͢Δ > library(TxDb.Mmusculus.UCSC.mm10.ensGene) # Ϛ΢εήϊϜmm10ͷҨ఻ࢠϞσϧͷύοέʔδΛϩʔυ͢Δ > annoData <- toGRanges(TxDb.Mmusculus.UCSC.mm10.ensGene) > seqlevelsStyle(gr1) <- seqlevelsStyle(annoData) # છ৭ମ໊ͷελΠϧΛἧ͑Δ > anno1 <- annotatePeakInBatch(gr1, AnnotationData=annoData) # ϐʔΫΛ࠷΋͍ۙసࣸ։࢝఺ʢTSSʣʹׂΓ౰ͯΔ > pie1(table(anno1$insideFeature), main="BRD4") # ϐʔΫ͕Ҩ఻ࢠ͔ΒΈͯͲͷྖҬʹஔ͍ͨͷ͔Λɺԁάϥϑͱͯ͠ දࣔ͢Δ > seqlevelsStyle(gr2) <- seqlevelsStyle(annoData) # છ৭ମ໊ͷελΠϧΛἧ͑Δ > anno2 <- annotatePeakInBatch(gr2, AnnotationData=annoData) # ϐʔΫΛ࠷΋͍ۙసࣸ։࢝఺ʢTSSʣʹׂΓ౰ͯΔ > pie1(table(anno2$insideFeature), main="IRF2") # ϐʔΫ͕Ҩ఻ࢠ͔ΒΈͯͲͷྖҬʹஔ͍ͨͷ͔Λɺԁάϥϑͱͯ͠ දࣔ͢Δ > overlaps <- ol$peaklist[["gr1///gr2"]] > aCR <- assignChromosomeRegion(overlaps, nucleotideLevel=FALSE, precedence=c("Promoters", "immediateDownstream", "fiveUTRs", "threeUTRs", "Exons", "Introns"), TxDb=TxDb.Mmusculus.UCSC.mm10.ensGene) > pie1(aCR$percentage, main="BRD4 & IRF1") > BiocManager::install("EnsDb.Mmusculus.v79") > library(EnsDb.Mmusculus.v79) > anno1$feature[is.na(anno1$feature)] <- "." # ΤϥʔΛආ͚ΔͨΊʹ NA ΛϐϦΦυʹม͑Δ > anno1$geneName <- mapIds(EnsDb.Mmusculus.v79, keys=anno1$feature, column = "GENENAME", keytype="GENEID") > anno1[1:2] if(!dir.exists("ChIPpeakAnno")) dir.create("ChIPpeakAnno") df_anno1 <- as.data.frame(anno1) write.table(df_anno1, "ChIPpeakAnno/BRD4_ChIP_IFNy_peaks.annot.txt", sep="\t", quote=F) ChIP-seqղੳͷྫ >200ߦͷίϚϯυ https://github.com/yuifu/ngsdat2_epigenome_chipseq/blob/master/chipseq.md
  13. ιϑτ΢ΣΞͷΠϯετʔϧ΋น ґଘੑ஍ࠈͳͲʹΑͬͯιϑτ΢ΣΞͷٕज़ಋೖʹ΋ϋʔυϧ 13 https://twitter.com/yuifu/status/1178877099580379136

  14. /(4σʔλղੳͷ՝୊ ݚڀʹ͓͚ΔόΠΦΠϯϑΥϚςΟΫεɾσʔλղੳͷൺॏ͕૿Ճ͍ͯ͠ΔͷͰ w ࣮ߦ༰қੑɿ͙࢝͢ΊΒΕΔ͔ w ࠶ݱੑɿಉ݁͡Ռ͕ฦͬͯ͘Δ͔ w ιϑτ΢ΣΞͷόʔδϣϯʹΑͬͯ݁Ռ͕ҟͳΔ w ࢀরσʔλͷόʔδϣϯʹΑͬͯ݁Ռ͕ҟͳΔ

    w ιϑτ΢ΣΞɾࢀরσʔλͷόʔδϣϯͷʮ૊Έ߹Θͤʯͷ؅ཧ͕ඞཁ w Մൖੑɿผͷܭࢉػ؀ڥͰ΋Ͱ͖Δ͔ 14
  15. ϫʔΫϑϩʔ؅ཧͷߟ͑ํ

  16. ϫʔΫϑϩʔͷهड़ w ϫʔΫϑϩʔͷఆٛʹૉϓϩηεͷ࿈ͳΓ w ૉϓϩηεʹπʔϧʢεΫϦϓτʣʴೖग़ྗ w πʔϧιϑτ΢ΣΞɺόʔδϣϯɺΦϓγϣϯ w ೖग़ྗೖྗϑΝΠϧͱग़ྗϑΝΠϧ 16

    ೖྗ ग़ྗ πʔϧ ೖྗ ग़ྗ πʔϧ ೖྗ ग़ྗ πʔϧ ೖྗ ग़ྗ ૉϓϩηε ૉϓϩηε ૉϓϩηε
  17. %BUBGMPXQSPHSBNNJOHNPEFM w 8JMMJBN34VUIFSMBOE   w ϓϩάϥϜΛ༗޲άϥϑͰද ݱ w ʮૢ࡞ʯͷؒΛσʔλ͕ྲྀΕ͍ͯ͘

    w ྫ w 4*4"- 4"$ "QBDIF#FBN  5FOTPSqPX 17 Sutherland, W.: On-Line Graphical Specification of Computer Procedures. (1966) https://en.wikipedia.org/wiki/Bert_Sutherland
  18. ϫʔΫϑϩʔݴޠ͕஫໨͞Ε͍ͯΔ w ϫʔΫϑϩʔͷ࠶ݱੑ୲อͱڞ༗ͷखஈ w ਺ඦ͔Β਺ઍͷσʔλϑΝΠϧΛѻ͑Δ w ਐḿͷ؂ࢹ΍Τϥʔॲཧ΋ߦ͏ w SFFOUSBODZʢલճͷ࣮ߦ࣌ʹࢭ·ͬͨεςοϓ͔Β࠶ ࣮ߦͰ͖Δʣ

    w εέʔϥϏϦςΟ͕͋ΔʢϥοϓτοϓͰ࣮ߦ͍ͯͨ͠ ύΠϓϥΠϯΛΫϥελʔܭࢉػ΍Ϋϥ΢υͰͦͷ··࣮ ߦ͢ΔͳͲʣ w εςοϓ͝ͱʹܭࢉػ؀ڥΛࢦఆͰ͖Δʢྫɿ$POEBͳ Ͳͷύοέʔδ؅ཧπʔϧɺ%PDLFSͳͲͷίϯςφγες Ϝʣ w ϫʔΫϑϩʔ࣮ߦͷϨϙʔτΛੜ੒Ͱ͖Δ 18 https://www.nature.com/articles/d41586-019-02619-z
  19. ϫʔΫϑϩʔݴޠϫʔΫϑϩʔΤϯδϯ w ϫʔΫϑϩʔݴޠ w ϫʔΫϑϩʔΛهड़͢Δํ๏ w ϫʔΫϑϩʔ࣮ߦΤϯδϯ w ϫʔΫϑϩʔݴޠͰهड़͞ΕͨϫʔΫϑϩʔΛ࣮ߦ͢Διϑτ΢ΣΞ w

    ˞ͱ͸։ൃݩ͕ಉ͡৔߹΋͋Ε͹ҧ͏৔߹΋͋Δ 19
  20. ϫʔΫϑϩʔݴޠ w υϝΠϯݻ༗ݴޠܕ ʢ%4-ʣ w ௕ॴॊೈͳهड़͕Մೳ w ୹ॴಠࣗͷจ๏Λशಘ͢Δඞཁ ͕͋Δ w

    ਓ਺͕ݶΒΕͨνʔϜ಺ w ྫɿ/FYUqPXɺ4OBLFNBLF w ϚʔΫΞοϓݴޠܕ ʢ.-ʣ w ௕ॴػցՄಡɺଞͷهड़ܗࣜͱ ͷม׵͕༻ҙ w ୹ॴɿෳࡶͳॲཧʢʙϧʔϓͱ ͔ʣΛॻ͘ͷ͕ۤख w ྫɿ(BMBYZɺ$PNNPO8PSLqPX -BOHVBHF $8- 20
  21. /FYUGMPX w %4-ܕͷϫʔΫϑϩʔ؅ཧγεςϜ w ϫʔΫϑϩʔݴޠ Τϯδϯ w ৚݅෼ذɺϧʔϓɺෳࡶͳΦϓγϣ ϯͷ૊Έ߹ΘͤɺϦιʔε؅ཧͳ Ͳॊೈͳૢ࡞͕Մೳ

    w %BUBqPXQSPHSBNNJOHNPEFMʹج ͮ͘ 21 https://www.nextflow.io/docs/latest/tracing.html
  22. /FYUGMPXϫʔΫϑϩʔͷߏ੒ཁૉ w 1SPDFTTιϑτ΢ΣΞΛಈ͔͢εςοϓΛهड़ w $IBOOFMQSPDFTT͸DIBOOFMΛ௨ͯ͡΍ΓऔΓ͢Δ ʢϫʔΫϑϩʔఆٛʣ w 0QFSBUPSTDIBOOFMؒΛ઀ଓͨ͠Γɺม׵͢Δ w &YFDVUPST֤1SPDFTTͷ࣮ߦํ๏Λهड़

    w ϩʔΧϧɺδϣϒεέδϡʔϥɺΫϥελʔܭࢉɺΫϥ΢υ 22
  23. 23 process _05_bamCoverage { input: set sample_id, file(ibam), file(ibai) from

    bam_out output: set sample_id, file("${sample_id}.bw") into bamCoverage_out script: """ bamCoverage -b $ibam -o ${sample_id}.bw - of bigwig \\ --normalizeUsing CPM """ } ೖग़ྗΛ໌ࣔతʹهड़͠ɺ҉ʹϫʔΫϑϩʔΛఆٛ JOQVU PVUQVU TDSJQU process _03_bowtie2 { tag { "${sample_id}"} publishDir "products/_03_bowtie2/${sample_id}" input: set sample_id, file(ifastq1), index_id, bowtie2_index, file(index) from bowtie2_conditions output: set sample_id, file("${sample_id}.trim.uniq.bam"), file("${sample_id}.trim.uniq.bam.bai") into bam_out script: def obam1 = "${sample_id}.trim.uniq.bam" """ mkdir -p $sample_id $fastqc -o $sample_id $ifastq1 $bowtie2 -p 4 -x $bowtie2_index -U $ifastq1 > osam1.sam $samtools view -bhS -F 0x4 -q 42 osam1.sam | $samtools sort -T $sample_id - > $obam1 $samtools index $obam1 """ } JOQVU PVUQVU TDSJQU Process A Process B Channel
  24. /FYUGMPXδϣϒͷ࣮ߦ 24 -δϣϒ࣮ߦΛࢦࣔ -δϣϒ։࢝ -δϣϒऴྃ -δϣϒ׬ྃΛड͚औΔ ᶃJOQVU֬ೝ ᶄδϣϒ։࢝ࢦྩ ᶆδϣϒऴྃड৴ ᶅεέδϡʔϥʔ͕࣮ߦ

    ᶇPVUQVU֬ೝ Executors ϩʔΧϧɺδϣϒεέ δϡʔϥɺΫϥελʔܭ ࢉɺΫϥ΢υ
  25. ίϯςφٕज़ͱͷ࿈ܞͰΠϯετʔϧΛճආ w ίϯύΠϧࡁΈͷΠϝʔδ Λ͍͖ͳΓ࣮ߦͰ͖Δ w %PDLFS 4JOHVMBSJUZ͕༗ ໊ w ίϯςφΠϝʔδͷϨϙδ

    τϦ͕ॆ࣮ w όΠΦܥઐ໳ϨϙδτϦ΋ IUUQTCJPDPOUBJOFSTQSP 25
  26. ࣮ྫɿ̍ࡉ๔3/"TFRσʔλ2$ͷࣗಈԽ σʔλ2$͸࣮ݧ͕ऴΘͬͨΒ଎΍͔ʹ࣮ߦ͍ͨ͠ w ཧݚɾೋ֊ಊݚͱͷڞಉݚڀ w ̍ࡉ๔3/"TFRɿ୯Ұࡉ๔͝ͱʹ໢ཏతҨ఻ࢠൃݱܭଌ w σʔλ2$ɿ࣮ݧ͕͏·͍͍ͬͯ͘Δ͔Λσʔλ͔Β֬ೝ ͢Δεςοϓ w

    ˠσʔλ2$Λͯ͠ॳΊ࣮ͯݧͷ૗ޭ͕൑அͰ͖Δ 26
  27. 3BN%"2 ΒΉͩͬ͘ چ൛ w ඌ࡚͕΋ͱ΋ͱख࡞ۀͰ΍ͬͨ͜ ͱΛશ෦࣮૷ͨ͠ w ֦ுੑΛॏࢹ w ෳ਺ͷήϊϜɾҨ఻ࢠΞϊςʔγϣϯͷόʔ

    δϣϯͰಉ࣌ʹղੳ͕Ͱ͖Δ w Φϓγϣϯͷઃఆ w ίϯϑΟάϑΝΠϧΛॻ͖׵͑Δ͜ͱͰɺ ༷ʑͳσʔλʹରԠͰ͖Δ 27 https://github.com/rikenbit/RamDAQ
  28. 3BN%"2ʢچ൛ʣͷ໰୊఺ w ࣗ෼͕ٱʑʹ࢖ͬͨΒ࢖͍ͮΒ͔ͬͨ w σʔλͷछྨʹΑͬͯҟͳΔϫʔΫϑϩʔͷϑΝΠϧΛબͿඞཁ͕͋ͬͨ w ίϯϑΟάϑΝΠϧΛॻ͖׵͑Δͷ໘౗ͩͬͨ w ϚχϡΞϧΛಡΊ͹Θ͔Δ͚Ͳɺ௚ײతͳઃఆɾૢ࡞͕Ͱ͖ͳ͍ w

    ΞϊςʔγϣϯσʔλΛࣗ෼Ͱ४උ͢Δͷ͕໘౗ͩͬͨ 28
  29. ۪ऀ͸ܦݧʹֶͿ 29 ࢓༷ΛܾΊΔ ઃܭΛܾΊΔ Մൖੑͷςετ Πϯϑϥͷਓ͕ Ϋϥ΢υͰ࣮ߦͰ͖Δ͔ ࣮૷͢Δ ςετ͢Δ ࣮ߦ༰қੑͷςετ

    ΢Σοτͷਓ͕ ϩʔΧϧͰ࣮ߦͰ͖Δ͔
  30. ԿΛ΍Δ͔ʢ΍Βͳ͍͔ʣΛܾΊΔ w ϖϧιφΛઃఆ͢Δ w ίϐϖͰίϚϯυ࣮ߦ͢Δੈքதͷݚڀऀɾٕज़ऀ w ֦ுੑΛఘΊΔ w Ұ౓ʹ୯ҰͷήϊϜɾҨ఻ࢠΞϊςʔγϣϯͷόʔδϣϯͰղੳ w

    ˠ͋Μ·Γχʔζͷͳ͍ػೳͩͬͨ w γϯϓϧʹ͢Δ w Φϓγϣϯͷઃఆ͸ίϚϯυϥΠϯҾ਺ͰઃఆͰ͖ΔΑ͏ʹ͢Δ w ίϯϑΟάͷॻ͖׵͑Λۃྗආ͚Δ w σʔλͷछྨʹ͔͔ΘΒͣϫʔΫϑϩʔͷϑΝΠϧΛબͿඞཁ͕͋ͬͨ 30
  31. ԿΛ΍Δ͔ʢ΍Βͳ͍͔ʣΛܾΊΔ 31 Read trimming FastQC FastQC Genome mapping .html Reporting

    Mapping QC infer_experime nt.py ReadCoverage.jl read_distributio n.py BigWig FeatureCounts Read QC Read count Read QC MultiQC Gene expression table Fastq-mcf HISAT2 BAM bam2wig FASTQ FASTQ BigWig conversion .tsv Report
  32. ԿΛ΍Δ͔ʢ΍Βͳ͍͔ʣΛܾΊΔ w ରԠ͢Δγʔέϯγϯάઓུ w 4JOHMFFOE1BJSFEFOE w 6OTUSBOEFE4USBOEFE w ରԠ͢Δछ w

    .PVTF)VNBO 32
  33. ઃܭΛܾΊΔ ศརͳ΋ͷʹ͸৐͔ͬΔ w OGDPSF/FYUqPXͰॻ͔ΕͨύΠϓϥΠϯͷϨϙδτϦ IUUQTOGDPSF w OGDPSFςϯϓϨʔτΛఏڙ w ίϚϯυϥΠϯҾ਺Ͱ৭ʑΦϓγϣϯΛॻ͖׵͑ΒΕΔ w

    ˠ͜Εʹ৐͔ͬΔ 33
  34. SBNEBROGʢΒΉͩͬ͘Ͳͬͱ͑͵͑;ʣ 34 Ԙج഑ྻͷੜσʔλ '"452 2$Ϩϙʔτ )5.- ݁Ռ w/ Mika YOSHIMURA

  35. ͜Μͳײ͡ 35

  36. ֤εςοϓͷܭࢉ࣌ؒͷࣗಈܭଌ 36

  37. $16ɾϝϞϦͳͲϦιʔεͷ࢖༻ྔͷࣗಈܭଌ 37

  38. Մൖੑͷςετʢچ൛ʣ 38 w/ Akihiro MATSUSHIMA & Manabu ISHII Table 1:

    AWS cost Condition Computation time (min.) On- demand (USD) File size (GB) Data trans- fer (USD) Total cost (USD) sc SE 96 61.1 2.98 56 6.38 9.37 sc PE 96 257 12.5 79 9.01 21.5 bulk PE 96 680 33.2 198 22.6 55.8 w ܭࢉ࣌ؒͱܦࡁੑ
  39. ϫʔΫϑϩʔݴޠͷ͝རӹͱࠓޙͷ՝୊ w ͝རӹ w ܁Γฦ͢͜ͱ͕ಘҙʢσʔλϕʔεେن໛ղੳɺγʔέϯγϯάηϯλʔʣ w πʔϧͷΠϯετʔϧΛίϯςφʹ୲ΘͤΔ͜ͱͰɺπʔϧͷΠϯετʔϧɾ؅ཧΛ෼཭Ͱ͖Δ w ˠΠϯϑϥͱόΠΦΠϯϑΥͷઐ໳ੑΛ෼཭͕Ͱ͖Δ w

    ίϚϯυ̍ͭˠணखͷϋʔυϧΛԼ͛Δ w ࠓޙͷ՝୊ w ݻఆͱॊೈੑͷόϥϯε w Χόʔ͢Δσʔλͷଟ༷ੑͷൣғ w ϓϩτλΠϐϯά 39
  40. ϩϘοτ࣮ݧ΁ͷԠ༻ͷల๬

  41. ݚڀࣗಈԽʹαΠόʔɾϑΟδΧϧϫʔΫϑϩʔ w αΠόʔɾϑΟδΧϧϫʔΫϑϩʔʢϩϘοτɾਓؒɾܭ ࢉػ͔Β੒Δ࣮ݧ͔Βσʔλղੳ·ͰؚΊͨϫʔΫϑ ϩʔʣΛهड़ɾ࣮ߦ͢ΔͨΊͷϑϨʔϜϫʔΫ͕ඞཁ 41 ࣮ݧϩϘοτ ࣮ݧ(ิॿ)ऀ ܭࢉػ αϯϓϧ

    ݁Ռ ղऍɾԾઆੜ੒
  42. σʔλϑϩʔ͔Βαϯϓϧϑϩʔ΁ w αΠόʔɾϑΟδΧϧϫʔΫϑϩʔ͸αϯϓϧϑϩʔϓϩάϥϛϯάͱ͠ ͯهड़ ֤ૢ࡞Λαϯϓϧͷೖग़ྗͱͯ͠௚ײతʹهड़ w ϫʔΫϑϩʔ؅ཧϑϨʔϜϫʔΫͷ֦ு΁ͷظ଴ ރΕٕͨज़ ؂ࢹۀ຿͔Βͷղ์ʢ%BUBqPXQSPHSBNNJOHNPEFMͷ͝རӹʣ ϫʔΫϑϩʔ؅ཧਓͱ֤ϓϩηε؅ཧਓͷ෼཭

    w ҰͭҰͭͷ࣮ݧखॱϩϘοτ࣮ݧઐ໳Ո w ࣮ݧखॱΛ࿈ͶͯϓϩτίϧશମΛهड़ͯ͠ྲྀ͢ਓϫʔΫϑϩʔઐ໳Ո 42
  43. σʔλϑϩʔ͔Βαϯϓϧϑϩʔ΁ w αΠόʔɾϑΟδΧϧϫʔΫϑϩʔ͸αϯϓϧϑϩʔϓϩ άϥϛϯάͱͯ͠هड़ ֤ૢ࡞Λαϯϓϧͷೖग़ྗͱͯ͠௚ײతʹهड़ 43 طଘͷϫʔΫϑϩʔ ܭࢉखॱ σʔλ δϣϒεέδϡʔϥ

    αΠόʔϑΟδΧϧ ࣮ݧखॱ αϯϓϧ ϑΟδΧϧδϣϒεέδϡʔϥ ʢσδλϧπΠϯɺϩϘοτͷAPIʣ
  44. /FYUGMPXδϣϒͷ࣮ߦ 44 -δϣϒ࣮ߦΛࢦࣔ -δϣϒ։࢝ -δϣϒऴྃ -δϣϒ׬ྃΛड͚औΔ ᶃJOQVU֬ೝ ᶄδϣϒ։࢝ࢦྩ ᶆδϣϒऴྃड৴ ᶅεέδϡʔϥʔ͕࣮ߦ

    ᶇPVUQVU֬ೝ Executors ϩʔΧϧɺδϣϒεέ δϡʔϥɺΫϥελʔܭ ࢉɺΫϥ΢υ
  45. /FYUGMPXδϣϒͷ࣮ߦʢ࣮ۭؒͰ͸ʁʣ 45 -δϣϒ࣮ߦΛࢦࣔ -δϣϒ։࢝ -δϣϒऴྃ -δϣϒ׬ྃΛड͚औΔ ᶃJOQVU֬ೝ ᶄδϣϒ։࢝ࢦྩ ᶆδϣϒऴྃड৴ ᶅεέδϡʔϥʔ͕࣮ߦ

    ᶇPVUQVU֬ೝ ͜ͷ෦෼͕ϥοϓ͞Ε ͍ͯΕ͹ϩϘοτͰ΋ ΋ਓؒͰ΋Α͍ʁ
  46. ϩϘοτʹΑΔδϣϒ࣮ߦ 46 -δϣϒ࣮ߦΛࢦࣔ -δϣϒ։࢝ -δϣϒऴྃ -δϣϒ׬ྃΛड͚औΔ ᶃσδλϧπΠϯͰ
 JOQVU֬ೝ ᶄδϣϒ։࢝ࢦྩ ᶆδϣϒऴྃड৴

    ᶅϩϘοτ؅ཧεέδϡʔϥ ͕࣮ߦ ᶇσδλϧπΠϯͰ PVUQVU֬ೝ ɾAPIͰ௨৴ ɾϩϘοτͷavailabilityΛ֬ೝ
  47. ਓؒʹΑΔδϣϒ࣮ߦ 47 -δϣϒ࣮ߦΛࢦࣔ -δϣϒ։࢝ -δϣϒऴྃ -δϣϒ׬ྃΛड͚औΔ ᶄδϣϒ։࢝ࢦྩ ᶆδϣϒऴྃड৴ ᶇσδλϧπΠϯͰ PVUQVU֬ೝ

    ᶃσδλϧπΠϯͰ
 JOQVU֬ೝ ᶅਓؒ؅ཧεέδϡʔϥ͕
 ࣮ߦ ɾAPIͰ௨৴ ɾਓؒͷavailabilityΛ֬ೝ ɾ੠ɺจࣈͰࢦྩ
  48. طଘͷϫʔΫϑϩʔ؅ཧϑϨʔϜϫʔΫͷ֦ு w ϩϘοτɺਓؒ؅ཧε έδϡʔϥͷ։ൃ w σδλϧπΠϯɺηϯλʔ ʹΑΔϞχλϦϯά w طଘϫʔΫϑϩʔ؅ཧ ϑϨʔϜϫʔΫ͕ͦΕ

    Βͷεέδϡʔϥͱ௨ ৴Ͱ͖ΔΑ͏ʹ֦ு 48 ϫʔΫϑϩʔ ֦ு ϩϘοτ σδλϧπΠϯ ࣮ݧϩϘοτ ࣮ݧ(ิॿ)ऀ εέδϡʔϥ ਓؒ ଘࡏɺόΠλϧ ܭࢉػ ܭࢉػ ࣮ಇ ݁Ռ
  49. ϩϘοτ࣮ݧ΁ͷԠ༻ʹඞཁͳ΋ͷ w ϦιʔεʢϩϘοτɾػثʣͷ੍ޚܥͷڞ௨Խ w Ͳ͏΍ͬͯϫʔΫϑϩʔΤϯδϯ͔ΒϩϘοτɾػثʹ໋ྩ͢Δ͔ w ϦιʔεʢϩϘοτɾػثʣͷ؂ࢹܥͷڞ௨Խ w ੍࣌ؒ໿΁ͷରԠ w

    ੜ໋Պֶ࣮ݧͷ৔߹ɺʮࢭΊΒΕͳ͍εςοϓʯ͕ଟ͍ w ܭࢉػͷ৔߹͸Ϧιʔε͕ͳ͔ͬͨΒ͍ͭ·Ͱ΋଴ͯΔ 49
  50. ੍࣌ؒ໿Λߟྀͨ͠࠷దεέδϡʔϦϯά w ෳ਺ͷϩϘοτɾػثؒͰαϯϓϧΛॱ࣍ॲཧ͍ͯ͘͠ w 03ͷδϣϒγϣοϓ໰୊ʢϦιʔε഑෼໰୊ʣͷ֦ுɿઢܗܭը໰୊ 50 w/ Takeshi ITOH ࣮ݧϓϩτίϧ

    ࣮ݧࣨͷઃඋ εέδϡʔϦϯά݁Ռ Scheduler
  51. ·ͱΊ w ϫʔΫϑϩʔ؅ཧʹϫʔΫϑϩʔͷهड़ͱ࣮ߦ w %BUBqPXQSPHSBNNJOHNPEFM w ݚڀࣗಈԽ΁ͷ՝୊͸αΠόʔɾϑΟδΧϧϫʔΫϑϩʔ ͷ؅ཧ w σʔλϑϩʔ͔Βαϯϓϧϑϩʔ΁

    w طଘϫʔΫϑϩʔ؅ཧϑϨʔϜϫʔΫͷ֦ு ϩϘοτɾਓؒɾܭࢉػ؅ ཧεέδϡʔϥͰαϯϓϧϑϩʔΛ؅ཧ 51
  52. ँࣙ w 8PSLqPX.FFUVQIUUQTHJUIVCDPNXPSLqPXNFFUVQKQXPSLqPXNFFUVQ w ϫʔΫϑϩʔγεςϜɺ࠶ݱੑɺࣗಈԽɺٴͼͦΕʹؔ࿈͢Δ༷ʑͳࣄฑ ʹ͍ͭͯɺ஌ݟΛަ׵ͨ͠Γɺٕज़ΛߴΊΔͨΊͷݚڀձ w ཧԽֶݚڀॴੜ໋ػೳՊֶݚڀηϯλʔόΠΦΠϯϑΥ ϚςΟΫεݚڀ։ൃνʔϜ w

    +45ະདྷࣾձ૑଄ࣄۀʮϩϘςΟοΫόΠΦϩδʔʹΑΔ ੜ໋ՊֶͷՃ଎ʯ 52