bgi webinar june 6, 2014 "genomic big data analysis and customised analysis with rna-seq"

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RNA-Seq による

ゲノム情報の大規模解析とカスタム分析

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東京農工大学「農学系ゲノム科学人材育成プログラム 」

特任教授　石井一夫

BGI ウェビナー：2014年6月13日(金)　14:00~15:00

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本日の内容

１、自己紹介に代えて: ビッグデータ環境によるデータ解析系の構築 ２、RNA-Seqの概要、問題点その対策 ３、今後の展開 基本的にUNIXコマンドを使い始めたばかりの初心者を想定してお話します。 本スライドの内容は、SlideShareに公開しますので、 スクリプトなどが欲しい方はそちらからダウンロードしてください。

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ビッグデータ環境によるデータ解析系 の構築

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我々のビッグデータ処理の新しい産業応用

個別化医療（ライフサイエンス）： 　精神神経系疾患 （うつ病、総合失調症、双極性感情障害）の 　　　　　　　　　　　　　　　　網羅的ゲノム診断法の開発 ゲノム育種（グリーンサイエンス）： 　イネなどの新品種の開発 環境アセスメント（エコサイエンス）： 　環境微生物の分布、分類、生態調査 　

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データの増加

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1,000,000,000

1,000,000,000,000

1,000,000,000,000,000

1,000,000,000,000,000,000

1,000,000,000,000,000,000,000

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ゲノム科学におけるビッグデータ分析 本研究室では、大規模データ解析に対し以下の4方法で対応している。 HPCも、モンテカルロも、クラウドも使えるものは何でも使う主義。 1. モンテカルロシミュレーション：大量データから無作為に サンプルを抽出し、元の全体データをシミュレーションして推測。 2. HPC、Big iron (大容量メモリサーバ)による大量並列処理 HP社の協力により、4TBメモリ、CPU: Xeon E7 (80 コア、 160スレッド)の大容量メモリ解析サーバを使用、 理研、「京」互換SCLS 3. Hadoop による分散処理システム（クラウドを利用）on AWS Amazon Elastic MapReduce（Amazon EMR）プラットフォーム 4. Hadoop によらない分散処理システム シェルスクリプトベースの分散処理。 usp-BOA （USP研究所）を利用。

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１、モンテカルロシミュレーション乱数発生により大量データの一部を抽出し、全体の性状を推定特に、新規に機器を買う必要がなくすぐに気軽に実施できるので、結果をすぐに出したいときに頻用。

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２、BIG IRON, ハイパフォーマンスコンピューティング

(HPC)の利用

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理研（「京」互換スパコンSCLSシステム）の利用

理化学研究所HPCI講習会資料より

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ヒューレットパッカード社の大容量メモリーサーバー利用

80コア、4TBメモリ

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３、Hadoop による分散処理システム（クラウドを利用）on AWS

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BIG　Data 処理のためのフレームワーク

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Hadoop = HDFS + MapReduce

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Hadoop File System (HDFS) コンピュータクラスタ上に、分散させて ファイルを保存 →　分散ファイルシステム これにより、ペタバイト、エクサバイト級のファイルの扱いが可能になる。

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MapReduce 複数のプロセスを、 コンピュータクラスタ内の複数のコンピュータに 分散させて処理

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MapReduce

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個人レベル、１研究室レベルで コンピュータクラスタを使うには

AWS(Amazon Web Service)の利用

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アマゾンウェブサ＝ビス（公共担当　吉荒様）よりの提供資料

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アマゾンウェブサービス（公共担当　吉荒様）よりの提供資料

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Unicage Cluster (BOA) (BOA: Bigdata Oriented Architecture)

4. Hadoop によらない分散処理システム シェルスクリプトベースの分散処理

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Unicage Cluster

Basic Cluster Configuration

boam

boas001

Bubun file system

Infini-band High Speed Network

boas002 boas00n

Shell Script is written only on this server

Master Server

… …

Bubun file system

Bubun file system

Bubun file system

Bubun file system

Slave Servers

Master server: - Unix/linux OS - Unicage commands - Custom shell, ush - Bubun File System Slave server: -Unix/linux OS - Unicage commands - Custom shell, ush - Bubun File System

Perform actual processing

USP研究所2013年米国MIT発表資料より

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Human DNA Sequance Quality Check

- Data: FASTQ sequences of human genome a person's DNA sequence is about 35GB 1: @ABCD:77:64U9X:6:3:11306:1000 2:N: 2: TTCCAGTACTTCCGCCAGGCACG 3: +B-=DFFBB<BF2:=*68B?BD>DCF=@=? 4: @AE3A?:BB2B<BB+B:8C7=<##### /Data resource : Prof. Ishii at Tokyo University of Agriculture and Technology/

Quality Score

DNA Base Squence Squence Name

Squence Name

USP研究所2013年米国MIT発表資料より

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非定形データ

定形データ

数理モデル化

説明変数の選択

モデル最適化

モデル検証

ビッグデータ解析のワークフロー例 Hadoop MapReduceを用いた並列分散処理、 シェルスクリプトを用いた分散テキスト処理、 NoSQL によるデータ処理、モンテカルロ法による無作為抽出

RDMS（MySQL、PostgreSQLなど）によるデータ処理、集計

統計学的有意差検定（ステューデントのt検定、マン ホイットニーのU検定）、スパースモデリング

疾患患者の識別 判別分析、サポートベクトルマシン、機械学習、ベイズフィルタリング

線形モデル、ロジスティック回帰モデル、両者の混合モデル など

ウィルクスラムダ、AIC（赤池情報量規準）、BIC（ベイズ情報量基準）など

クロスバリデーション（リーブワンアウト法を含む）

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２、RNA-Seqの概要、問題点その対策

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RNA-Seq

1. Reference がある場合 　1.1　発現定量解析　臨床データ、モデル生物 　1.2　RNA多型解析　臨床データ、モデル生物 2. Reference がない場合 　2.1　発現定量解析　非モデル生物 　2.2　RNA多型解析　非モデル生物 small RNAについては方法などが異なるので今回は 除外します。主に発現定量解析に限定して議論します。

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BGI JAPAN website より 転載

Referenceありの場合の RNA-Seq ワークフロー

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Referenceがない場合のRNA-Seq ワークフロー

BGI JAPAN website より 転載

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RNA-Seq を始めるにあたって

検出感度、反復回数、網羅性、コントロールなど 基本的問題を考えてみます。

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問題点：検出感度（検出限界）

経験的な一般論として、 検出感度 　　RNA-Seq ＜ マイクロアレイ　＜　定量PCR 網羅性 　　RNA-Seq ＞ マイクロアレイ　＞　定量PCR RNA-Seq はマイクロアレイや定量PCRに比べて 検出感度がよくない傾向。検出感度 を上げるためには多くのリード数が必要。

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問題点：リード数

リード数が必要はどれだけ必要か？ 大体、最低でも1000万リードはあったほうがよい。 １種類の生物に約10000個の遺伝子があったと仮定して 1000万リードのデータがある場合、 平均して１遺伝子あたり、1000リードの配列データが マッピングされる。解析の目的にもよるが、数千万リード は必要な場合が多い。

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検出感度

検出感度ギリギリの遺伝子の定量は困難。 リードカウントが１0以下では離散型データになり変動比などの 比較が困難

　　　　　　　　　　　　

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統計学的な評価をする場合の反復回数の問題

反復回数が１〜２回の場合：　統計学的評価（検定は困難） 反復回数が少ない場合（３〜５回）： 　 Student t検定では、大量の擬陽性データが検出される。 　 Mann-Whiteney U検定では最低でも、４回以上の反復がないと、 　　有意差は出ない。 臨床検体など、検体数が多く確保できる場合を除き、一般的な基礎実験 では、RNA-Seqで統計な議論をすることは困難。 RのパッケージなどでDEG(発現変動遺伝子)を検出してくれるものもある が、現実問題としては他の方法（例えば、qPCRなど）で確認することが 望ましい。 DEGやFDRなどの問題については書籍「トランスクリプトーム解析」 門田幸二、共立出版（2014）などに詳しいので、そちらを参照してください。 ここでは、深くは立ち入りません。

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実験デザインの問題（基本の基本なのですが、、）

１、ネガティブコントロールを入れ忘れる人が結構います。 例えば、ある生物（細胞）に対するAという薬剤とBという薬剤の効果を 比較したい場合、薬剤処理群だけのデータをとり、薬剤非処理群のデー タをとり忘れるケースです。 相対的な定量値で判断しますので、ネガティブコントロールは必須です。 実験デザイン上、常にネガティブコントロールを意識する必要があります。 ２、実際のデータの解析でも、ネガティブコントロールを無視したような 解析、解釈をする人がいます。 上記の例のAという薬剤とBという薬剤の処理群の絶対量のみで比較 しようとするケースです。

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サンプルの質の問題（これも、基本の基本なのですが、、）

採取した生物、細胞のクオリティ、RNA のクオリティなど、配列解析 を実施する材料のバリデーションなどはきちんと行ったほうがいいと 思います。 １、バイアビリティの悪いと思われる細胞や、クオリティの悪いと見られる サンプルを用いて解析を行い、？？？な結論を出していると思われる例 もたまに見られます。 ２、材料のコンタミやサンプルの取り違えなどが、 シーケンス後に検出される場合も。

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メタゲノムまたは、2種類以上の生物の混じった検体のRNA-Seq

種によって特異的な遺伝子の定量は可能。 種によって配列の類似する遺伝子の定量は、困難。 メジャーな生物の遺伝子変動に飲み込まれ、マイナーな生物 の遺伝子の変動をみることは困難。 A A A G G メジャーな生物とマイナーな生物の間の多型を手がかりに定量

メジャーな生物

マイナーな生物

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一般的な RNA-Seqの ワークフロー

http://bioinfo.mc.vanderbilt.edu/ NGS/rna-seq.htmlより

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データ解析の方針

自作スクリプト、コマンド　＞　フリーソフト　＞　商用ソフト 基本的に車輪の再発明を奨励しています。 自作スクリプトやコマンドで処理できるようにしておけば、 いろいろな場面で応用が効くようになるので、 いろいろな解決法を身につけておいたほうがいいと思います。 各研究者の事情によりますがLinux環境での使用を奨励して います。 また、実際の解析の際には、単にプログラムのみでなくウェットの 実験のセンスや経験も必要になってきます。 ウェブでの情報も充実してきていますので、独学でもかなり できるようになります。

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クオリティチェック

解析結果に結構影響が出ますので、必要な行程です。 FastQC, HTQC, qrpc, ShortRead, FASTXToolkitなどが使えます。 クオリティチェックは、FastQCの操作そのものが易しいのでこれを使います。 インストール法： wget http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/fastqc_v0.8.0.zip unzip fastqc_v0.8.0.zip cd Fastqc chmod 755 fastqc インストール後fastqcのパスを通します 使用法： ワイルドカードが使えますので、コマンド一行で、ファイル内の複数データを一気に全部 処理できます。 fastqc　/path/to/*.fastq

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クオリティコントロール

クオリティの悪いデータを除く行程です。 cutadapt, FASTX-toolkit, PRINSEQ, ShortReadなどが使えます。 ただし、この行程は、シェルのコマンドや、Perl, Python, Rubyなどのスクリプトで 簡単に自作できます。また、そのほうがいろいろ融通が利きます。 私たちは、cutadaptを使うほか、自作スクリプトで対応しています。 その他の問題として： マルチプレックスのサンプルでデマルチプレックスを失敗しているケースがあります。 デマルチプレックスはegrepコマンドを用いたコマンド１行で、自在にできますので、 それで処理します。

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市販のソフトによるクオリティチェック、クオリティコントロールなど

CLCゲノミクスワークベンチなどの市販のソフトでも可能です。 きめ細かな、クオリティチェック、クオリティコントロールのため には、自作スクリプトやコマンドのパイプラインが強力ですので、 慣れてきたら、FastQCでざっとクオリティをチェックして、 作り捨ての一行コマンドで 除いてしまったほうが早いような気がします。

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マッピング

シーケンシング後のリードをリファレンスゲノム対してアラインメントします。 Bowtie2、BWA、TopHat などのプログラムを利用します。 我々の場合は、 ゲノムリファレンスに対しては、TopHatでマッピングし、 Cufflinkで計数します。 トランスクリプトームリファレンスに対しては、Bowtie2でマッピングし、計数します。 計数はRのパッケージなども使えますが、出力データであるsamファイルからコマンド群を使って 容易に抽出できますので、学生の練習の意味で awk, grepなどのコマンド群で計数します。 最近は、マルチコア、大量メモリのサーバが使えるので、Bowtie2などのマッピングは数分で 終了します。また、シェルスクリプトで並列処理も簡単にできますので、多検体データも簡単に 処理できます。

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TopHat(インストール)

いろいろな人が解説をウェブに上げていますが、まず以下を読みましょう。 http://ccb.jhu.edu/software/tophat/index.shtml インストール： 1. TopHatのインストールの前に、bowtie2 (or bowtie) とsamtoolsをインストールし、 以下のコマンドのパスを通します。 bowtie2 (or bowtie) bowtie2-build (or bowtie-build) bowtie2-inspect (or bowtie-inspect) samtools 2. Tophatをダウンロードし解凍後、パスを通します。 wget http://ccb.jhu.edu/software/tophat/downloads/tophat-2.0.11.Linux_x86_64.tar.gz tar zxvf tophat-2.0.11.Linux_x86_64.tar.gz 3. Cufflinksもダウンロードし解凍後、パスを通します。 yum install –y rpmlib でバイナリパッケージをインストール後 wget http://cufflinks.cbcb.umd.edu/downloads/cufflinks-2.1.1.Linux_x86_64.tar.gz tar zxvf cufflinks-2.1.1.Linux_x86_64.tar.gz

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TopHat（続き）(インストール) 4. bowtie2のインストール　（１〜３より先に行う） wget http://sourceforge.net/projects/bowtie-bio/files/bowtie2/2.1.0/bowtie2-2.1.0-source.zip unzip bowtie2-2.1.0-source.zip bowtie2-2.1.0-source.zip make 5. Samtoolsのインストール（１〜３より先に行う） sudo yum –y ncurses sudo yum –y ncurses-devel wget http://sourceforge.net/projects/samtools/files/samtools/0.1.18/samtools-0.1.18.tar.bz2 tar jxvf samtools-0.1.18.tar.bz2 cd samtools-0.1.18 vi Makefile Makefileの以下の行を以下に示すように書き換えます。 original: LIBCURSES= -lcurses # -lXCurses changed: LIBCURSES= -lncurses # -lXCurses 　 その後 make 作成されたsamtools をパスを通します。

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TopHat（使い方）

インデックス有りの場合のマッピング 1.  Bowtie2によるインデックス作成 bowtie2-build <リファレンスファイル>.fa <リファレンスファイル> 2. TopHat によるマッピング tophat -p 14 –G <GTFファイル> -o <出力ファイル> <インデックスファイル>　<入力ファイル> 3. cufflinksによる計数 cufflinks -p 14 –G <GTFファイル> -o <出力ファイル> <インデックスファイル>　<入力ファイル> cuffdiff, cuffmergeを使う方法などもあるが（以下のサイトなど参照)、 我々はcufflinksを使って出した計数値を、Rかコマンドをつかって マージし、変動比、t検定、マンホイットトニーU検定などを行っている。 http://cat.hackingisbelieving.org/lecture/biwako/NGS-R-Bioconductor-2nd.html http://crusade1096.web.fc2.com/katei.html#3-2 ここではイルミナの場合の方法のみ示したが、その他の種のデータについては以下を参照 http://cell-innovation.nig.ac.jp/wiki/tiki-index.php?page=TopHat

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TopHat（続き）（使い方）

インデックス有りの場合のマッピング注意点 １、インデックスファイル作成は、ヒトなどの場合非常に時間がかかりますので、 以下のサイトなどからダウンロードしたほうが楽です。 http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml ２、GTFファイル（または、GFFファイル）の出来不出来が解析の正否を左右します。 以下などからきちんと動作するGTFファイルを入手します。 http://support.illumina.com/sequencing/sequencing_software/igenome.ilmn ３、GTFファイルがない場合は、ゲノム配列とGenbankファイルから、 Bioperlのライブラリ(bp_genbank2gff.pl)を使って自分で作成します。

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Bowtie2によるマッピング

トランスクリプトームデータへのマッピング 1. bowtie2-buildによるインデックス作成 bowtie2-build <リファレンスファイル>.fa <リファレンスファイル> 2. bowtie2によるマッピング シングルエンドリード bowtie2 [options]* -x <インデックスファイル> -U <リードファイル> -S　＜出力ファイル＞ ペアエンドリード bowtie2 [options]* -x <インデックスファイル> -１<リード１> -2　<リード2> -S　＜出力ファイル＞

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マッピングの問題点

リファレンスによりかなり結果が異なる。 例えば、 bowtieでトランスクリプトームリファレンスにマッピングした場合の変動比と tophat でゲノムトランスクリプトームリファレンスにマッピングした場合の変動比が異なるという ことをしばしば経験する。この場合、トランスクリプトームリファレンスにマッピングした場合の ほうが変動比が大きくなる傾向がある（いつもではない。あくまで傾向で、すべてそうという わけではない）。 特に生物種によっては、トランスクリプトームのリファレンスと、ゲノムのリファレンスへのマップ 状況が異なり、リードがゲノムのリファレンスへ90％以上マッピングされるのに、 トランスクリプトームのリファレンスへ50％強しかマッピングされないような生物種が少なからず 存在する。　 →　非モデル生物に関しては、トランスクリプトームのデータをもとに自分で独自にアノテーション ファイル（GFFファイル）を作り直すなどの対策をしたほうがいいかもしれない。

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並列処理と逐次処理

スパコンやクラウドなどでは、OpenMPやMPIなどのしくみを 使って並列処理を実現する。 これらは、特殊なプログラミングを使ってメッセージ交換など を行う必要がある。（詳細は省略します。理研のサイトなどに 詳しい解説があります。）ただ、その環境を使える人は多くな いと思われる。 ただし、通常のマルチコアのサーバでも、シェルのコマンドを つかって逐次処理、並列処理が簡単に出来るので、適宜利用 するとデータ解析はかなり楽になるので、紹介する。 本ラボでは、並列処理で、マッピングは多検体（25検体とか）でも 数分で終了することが普通

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理研のスパコン利用

理化学研究所HPCI講習会資料より

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並列処理と逐次処理の実際例

並列処理 ＆の利用: 以下のように＆でコマンドを連結させることで５個のbowtie のプロセスを同時に 実施できます。 bowtie2 –p -x index1.fa –U test1.fastq –S out1.sam & Bowtie2 –p -x index2.fa –U test2.fastq –S out2.sam & Bowtie2 –p -x index3.fa –U test3.fastq –S out3.sam & Bowtie2 –p -x index4.fa –U test4.fastq –S out4.sam & Bowtie2 –p -x index5.fa –U test5.fastq –S out5.sam 逐次処理 ＆＆の利用:マッピングのあとのsamtoolsの連続処理や、tophat-cufflinks などのように連続した ルーチンワークが続くときは、＆＆を利用した逐次処理で実施できます。 samtools view -bS aln.sam > aln.bam && samtools sort aln.bam aln && samtools index aln.bam && samtools faidx ref.fa && samtools mpileup -uf ref.fa aln.bam | bcftools view -cg – tophat -p 14 –G <GTFファイル> -o <出力ファイル> <インデックスファイル>　<入力ファイル>　&& cufflinks -p 14 –G <GTFファイル> -o <出力ファイル> <インデックスファイル>　<入力ファイル> http://jehupc.exblog.jp/15729095/ など参照

Page 53

Trinity

De novo RNA-Seq アセンブリ用ソフトウェア 1. インストール wget http://sourceforge.net/projects/trinityrnaseq/files/trinityrnaseq_r20140413p1.tar.gz zxvf trinityrnaseq_r20140413p1.tar.gz cd trinityrnaseq_r20140413p1 make 注意点：バージョンによってはJava のバージョンがあわず makeが通らないことがある。 そのときは、複数バージョンのJava をインストールして alternatives コマンドでバージョンを変更して コンパイルする。 2. 使い方 Trinity --seqType fq --JM 100G --left reads_1.fq --right reads_2.fq --CPU 6 注意点：最近コマンド名が変わったので、ウェブ上の古いマニュアルを見るときは注意。

Page 54

De novo RNA-Seq の方法論

アセンブリ、アノテーション：　読んだリードをデノボアセンブリし、それにアノテーションをつける。 マッピング：　これをリファレンスにマッピングを行う。 ゲノムの部分配列など他の情報があれば、これにアノテーションをつけて、マッピングすることも 考えられる。 読んだリードをデノボアセンブリは、Trinity, Velvet-Oases がよく用いられる。 出力結果は、Fasta 形式である。 Fastaファイルは、シェルコマンドやRなどを用いて、平均リード長や、N50、リード長さの分布など を調べる。 また、コンティグをリファレンスにしてマッピングし、そのマッピング情報により、samtoolsを用いて カバー率、深度などを調べる。多型情報などにより、アセンブリエラーを見つけたり、 適当な配列編集ソフトで正確な配列に修正することも可能。

Page 55

Velvet-Oases

De novo RNA-Seq アセンブリ用ソフトウェア Velvet のインストール: wget http://www.ebi.ac.uk/~zerbino/velvet/velvet_1.2.08.tgz tar zxvf velvet_1.2.08.tgz $ cd velvet_1.2.08 make 'MAXKMERLENGTH=101’ ./velveth ./velvetg パスを通しておく。 Oasesのインストール: wget http://www.ebi.ac.uk/~zerbino/oases/oases_0.2.08.tgz tar zxvf oases_0.2.08.tgz # cd oases_0.2.08 gmake 'VELVET_DIR=/path/to/velvet/velvet_0.2.08' 'MAXKMERLENGTH=101’ ./oases パスを通しておく。

Page 56

Velvet-Oases(使い方)

ManualのFor Impatient People の項目は非常によく出来ているのでこれを参考にする。 velveth directory 21,23 data/reads.fa velvetg directory_21 -read_trkg yes oases directory_21 ls directory_21 velvetg directory_23 -read_trkg yes oases directory_23 velveth mergedAssembly 23 -long directory*/transcripts.fa velvetg mergedAssembly -read_trkg yes -conserveLong yes oases mergedAssembly -merge Or use the python script oases pipeline.py. python oases_pipeline.py -m 21 -M 23 data/reads.fa

Page 57

ビューア　IGV

ブロードで開発されたIGV がよく使われています。 IGV インストール wget http://www.broadinstitute.org/igv/projects/downloads/IGV_2.2.5.zip unzup IGV_2.2.5.zip cd IGV_2.2.5 ./igv.sh パスは、igv.sh と igv.jarの２つを通す必要があります。 使い方 下記の方法で、インデックスを作成した後に、閲覧します。 samtools view -bS aln.sam > aln.bam && samtools sort aln.bam aln && samtools index aln.bam && samtools faidx ref.fa https://www.youtube.com/watch?v=5kkPnCV06dE

Page 58

IGVの使用例

Page 59

アノテーション BLAST, BLAST+がよく用いられる。 インストール方法： BLAST バイナリパッケージをダウンロードして解凍後, パスを通しておく. wget ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/LATEST/blast-2.2.26-x64-linux.tar.gz tar zxvf blast-2.2.26-x64-linux.tar.gz BLAST+ バイナリパッケージをダウンロードして解凍後, パスを通しておく. wget ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ncbi-blast-2.2.28+-x64-linux.tar.gz tar zxvf ncbi-blast-2.2.28+-x64-linux.tar.gz 使い方：コマンドのみ blastall -p blastx –d Osativa_193_peptide.fa -i Consensus.fa -m 9 > Consensus_Osativa_193_peptide_BLASTXOUT.txt 以下のサイトなどを参考にしてください。出力結果をシェルコマンドやRを用いて集計します。 http://www.geocities.jp/ancientfishtree/LocalBlast_JI.html

Page 60

パスウェイ解析、GO解析

GUIツールですので、リンク先を示すにとどめます。 DAVID：　パスウェイ解析、GO解析が簡単に出来るウェブツールです。 http://david.abcc.ncifcrf.gov/ 使い方は、GUIでそんなに難しくないので以下などを参考にしてください。 http://togotv.dbcls.jp/20090925.html BLAST2GO:　GO解析が簡単に出来ます。 http://blast2go.com/b2ghome GSEA: パスウェイ解析、GO解析を行います。 http://array.cell-innovator.com/?p=2030 GOなどは、自分でデータべースのテーブルをダウンロードしてデータベース化し、 クエリで得られたデータをフッシャーの正確確率検定などをやった方がうまく情報が引き出せる こともあるので、ツールに頼らず自力の解析を試みてみるのもよい。

Page 61

R 統計解析環境で、データ解析には欠かせません。Rはバージョンアップが頻繁で、RedHat系 Linuxでは、バイナリパッケージの提供が遅いので、以下の方法で自分でコンパイルしています。 インストール方法（＊はバージョン番号が入ります。） wget http://cran.ism.ac.jp/src/base/R-3/R-*.tar.gz tar zxvf R-*.tar.gz mkdir R_* cd R-* sudo yum install readline readline-devel sudo yum install libXt libXt-devel sudo yum install libX11 libX11-devel sudo yum install cairo cairo-devel sudo yum install libjpeg libjpeg-devel sudo yum install libpng libpng-devel sudo yum install libdiff libdiff-devel sudo yum install texlive* sudo yum install texinfo wget http://genome.lab.tuat.ac.jp/~kazuoishii/files/inconsolata.sty ./configure --prefix=/home/genome/Packages/R/R_* sudo make sudo make install

Page 62

R Rは Haskellや、Lispのような関数型言語で、 Lispの方言のSchemeのセマンテックをもとに、 ベル研究所で開発されたSをまねて作られています。 ベクトルの処理のしかたなどが、 Rubyににています。 なので、Rubyや、Lispを勉強するとRの理解が深まりますので、是非勉強してください。 意識して書くと、Lispそっくりのコードになります。 生物学的パッケージがたくさんそろっており、便利ですが、 現在のところ、マルチコアで動作することができない。メモリダンプを起こしやすく、 動作が遅いなど、実はビッグデータ処理には向いていません。 以下で、インタラクティブな使い方に慣れてきたら、出来るだけ、 関数やスクリプトを書いて、バッチ処理や並列処理で対応してください。 http://cse.naro.affrc.go.jp/takezawa/r-tips/r.html http://www.iu.a.u-tokyo.ac.jp/~kadota/r.html http://www.iu.a.u-tokyo.ac.jp/~kadota/r_seq.html 以下にバッチ処理の汚い例で、申し訳有りませんがご紹介しています。 http://www.ospn.jp/press/20130124no32-1-useit-oss.html

Page 63

Rの使用例

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0 5 10 15

−10

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