STARR財団賞（MQZ）、NIHの助成金ES017166（MQZ）、NSFの助成DMS0906593（HX）。

1。 BCPの解析用の入力ファイルを準備する<ol><li>優先短い読み取りアラインメントソフトウェアを使用して、適切なリファレンスゲノムに実行（ChIPと入力ライブラリ）シークエンシングから生産読み取るショートの位置を合わせます。マップされた場所は、拡張可能なデータ（BED）形式13（UCSCゲノムブラウザ、6カラムブラウザに変換する必要があります<a href="http://genome.ucsc.edu/" target="_blank">http://genome.ucsc.edu/</a>開始位置は、マップされたあたり）、タブ区切り線は、マップされた染色体を示す読み出し（0ベース）、終了位置（ハーフオープン）、名前、スコア（オプション）、およびストランドをお読みください。 </li></ol> 2A。プロファイルを読む拡散：前処理チップは、びまん性に富むデータ諸島の検出のための密度を読む<ol><li>所定の断片の長さ、 すなわちにチップと入力マップ場所を拡張します。フラグメントサイズは​​、通常200 bpの周りには、DNAの酵素消化または超音波処理時にターゲットに。フラグメント計数し、その後凝集アール隣接したビンでテッド。デフォルトでは、ビンのサイズは200 bpの推定断片長に設定されています。 </li><li>同一の読み取りカウントを持つビンのセット内の任意の可能な変化点は、最も外側の境界において、最も可能性の高い分類されます。従って、変化点が同じ読み取りカウントを持つ2つのビンの間の内部境界で発生することはありそうにない。だから、同じグループと隣接したビンは、単一のブロック、 すなわちに、ビンごとに読み取ります。 bedGraph形式13。 </li></ol>図2b。点状読むプロフィール：点状のデータのピークの検出のための前処理チップと入力BEDファイル<ol><li>プラスとマイナス鎖チップが別々に読み取るために重複集計を読み取ります。ストランド特定の読み取り密度はプラスとマイナスのピークの二峰性のプロファイルを形成すべきである。最も濃縮されたピークのプラス/マイナスのペアを選択し、ライブラリの断片の長さのための推定値としてそれらの首脳間の距離を使用します。 </li><li> ChIPをシフトして、入力は半分フラグメントルを読み取りシフトと合併し、プラスとマイナス鎖の読み取りの読み取り密度と中心を再計算するngth。断片の長さを推定するためのこの方法論は、チャンら 3から採用されました。同じマージカウントの位置が2a.2をステップと同様のブロックにグループ化する必要があります。 </li></ol> 3。当社BCMIX近似法を用いた各ブロックの事後平均読む密度を推定<ol><li>各ブロックの読み出し密度はガンマ分布の混合後の平均パラメータ、Γ（α、β）と（θt）はPOIを、ポアソン分布としてモデル化され、変化点の事前確率は、任意のブロック境界で発生しているpの。GのコンディショニングPOIS（θt）が（α、β）を効果的にモデルに無期限の状態のHMMをレンダリングします。最大事後確率を用いて、ハイパーパラメータα、β、 および p を推定する 。 </li><li>明示のためのベイズ推定値を計算する各ブロック、θT、としてE（θT |γZ）です 。後部手段を推定するより計算効率有界複雑混合近似で、しばしばHMMのに使用される、より伝統的な前進が、時間がかかり、後方にフィルターを交換して、θC。結果として事後手段は同じで、θcを持つブロックのでおおよその区分的に一定のプロファイルに&quot;平滑化&quot;になり、さらに更新された境界の座標と一緒にブロックする必要があります。 </li></ol>図4a。拡散濃縮のセグメントにポストプロセス臼歯手段：プロファイルを読む拡散<ol><li>入力数（λA）POIS、背景率としてそれぞれの新しいθCブロックごとに読み込みを使用し、ChIP事後平均は、θC、いくつかの閾値δを超えているかどうかに基づいて、単純な仮説検定を用いて濃縮を決定します。 第 90 回&lt;/ SUP&gt;-クオンタイルはデフォルトdである、ほとんどの場合に適しています。 </li><li>シンプルな床形式の単一の領域およびレポートマージ座標に濃縮を超える隣接θCブロックをマージします。あるいは、1は読み取り密度推定の高解像度の詳細を保持するbedGraph形式で、各ブロックのθcを報告することができます。 </li></ol>図4b。点状読むプロフィール：ピーク候補にポストプロセス臼歯手段<ol><li>すべての読み取り回数の平均値（γ2）のように、（λA）POIS、背景率を定義し、しきい値は、dを超えてすべてのブロックを識別します。点状のピークがそれ以上の実質的富化されると予想されるので、デフォルトのδはPOIの99 番目 -変位値（λA）に設定されます。 </li><li>候補ピークの頂上として最大のθCでブロックを設定し、同様の読み取りdenを共有ブロックに隣接する隣接sity（±1は若干のばらつきを考慮するためにカウントを読み取る）。この隣り合う領域は候補結合部位として定義されています。 </li><li> ChIPの候補結合部位と仮説検定の平均読み取りカウントとしてλ2を計算し 、この対の入力背景は帰無仮説があったが、H 0は 、つまり、λ1 p値のしきい値に基づい≥λ2とH 0を棄却。ベッドの形式で出力候補ピーク。 </li></ol>

<ol>
	<li>Park, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChIP-seq:+advantages+and+challenges+of+a+maturing+technology.">ChIP-seq: advantages and challenges of a maturing technology.</a> Nat. Rev. Genet. 10, 669-680 (2009).</li><li>Barski, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+profiling+of+histone+methylations+in+the+human+genome.">High-resolution profiling of histone methylations in the human genome.</a> Cell. 129, 823-837 (2007).</li><li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Model-based+Analysis+of+ChIP-Seq+(MACS).">Model-based Analysis of ChIP-Seq (MACS).</a> Genome Biol. 9, R137 (2008).</li><li>Zang, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+clustering+approach+for+identification+of+enriched+domains+from+histone+modification+ChIP-Seq+data.">A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.</a> Bioinformatics. 25, 1952-1958 (2009).</li><li>Jothi, R., Cuddapah, S., Barski, A., Cui, K., Zhao, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+identification+of+in+vivo+protein-DNA+binding+sites+from+ChIP-Seq+data.">Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data.</a> Nucleic Acids Res. 36, 5221-5231 (2008).</li><li>Qin, Z. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HPeak:+an+HMM-based+algorithm+for+defining+read-enriched+regions+in+ChIP-Seq+data.">HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data.</a> BMC Bioinformatics. 11, 369 (2010).</li><li>Song, Q., Smith, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+dispersed+epigenomic+domains+from+ChIP-Seq+data.">Identifying dispersed epigenomic domains from ChIP-Seq data.</a> Bioinformatics. 27, 870-871 (2011).</li><li>Spyrou, C., Stark, R., Lynch, A. G., Tavar&#233;, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BayesPeak:+Bayesian+analysis+of+ChIP-seq+data.">BayesPeak: Bayesian analysis of ChIP-seq data.</a> BMC Bioinformatics. 10, 299 (2009).</li><li>Lai, T., Xing, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+Bayesian+approach+to+multiple+change-points.">A simple Bayesian approach to multiple change-points.</a> Statistica Sinica. , (2011).</li><li>Robertson, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+profiles+of+STAT1+DNA+association+using+chromatin+immunoprecipitation+and+massively+parallel+sequencing.">Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing.</a> Nat. Methods. 4, 651-657 (2007).</li><li>Stitzel, M. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+epigenomic+analysis+of+primary+human+pancreatic+islets+provides+insights+into+type+2+diabetes+susceptibility+loci.">Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci.</a> Cell Metab. 12, 443-455 (2010).</li><li>Bernstein, B. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+NIH+Roadmap+Epigenomics+Mapping+Consortium.">The NIH Roadmap Epigenomics Mapping Consortium.</a> Nat. Biotechnol. 28, 1045-1048 (2010).</li><li>Karolchik, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+UCSC+Table+Browser+data+retrieval+tool.">The UCSC Table Browser data retrieval tool.</a> Nucleic Acids Res. 32, 493-496 (2004).</li><li>Matys, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TRANSFAC:+transcriptional+regulation,+from+patterns+to+profiles.">TRANSFAC: transcriptional regulation, from patterns to profiles.</a> Nucleic Acids Res. 31, 374-378 (2003).</li><li>Portales-Casamar, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=JASPAR+2010:+the+greatly+expanded+open-access+database+of+transcription+factor+binding+profiles.">JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles.</a> Nucleic Acids Res. 38, D105-D110 (2010).</li></ol>

特別な利害関係は宣言されません。

我々は、同様にうまく斑点およびびまん性両方のデータ構造を識別することができるChIPseqデータを分析するためのモデルを開発するために着手した。今までは、濃縮の地域では、大きな島の大きさの前提期待を反映して、特に拡散領域は、特定することは困難であった。これらの問題に対処するために、我々は既存のヒューリスティック·モデルと少ない革新的なHMMを比べて多くの利点を持?...

<table border="1"><tbody><tr><td> 試薬の名称 </td><td> 会社 </td><td> カタログ番号 </td><td> コメント（オプション） </td></tr><tr><td> Linuxベースのワークステーション</td><td></td><td></td><td></td></tr></tbody></table>

a novel bayesian change-point algorithm for genome-wide analysis of diverse chipseq data types

ChIPseqは、タンパク質-DNA相互作用を調査するために広く使われているテクニックです。読む密度プロファイルは、タンパク質に結合したDNAの次世代シーケンシングを使用しており、リファレンスゲノムに読む短いを整列させることによって生成されます。濃縮された領域は、多くの場合、標的タンパク質1に応じて、形状が大幅に異なるピークとして明らかにされています。例えば、転写因子は、多くの場合、サイトと配列特異的に結合し、ヒストン修飾がより普及していると濃縮2の広い、びまん性の島々によって特徴付けられる一方で、点状のピークを生成する傾向があります。確実にこれらの地域を特定することが我々の仕事の中心だった。 ChIPseqデータを解析するためのアルゴリズムはヒューリスティクス3月5日から、より厳密な統計モデルに様々な方法、 例えば 、隠れマルコフモデル（HMM）6-8を採用してきた。我々は困難な定義、アドホックパラメータそれほど頻繁にする必要性を最小限に抑えるソリューションを求めて解像度を妥協し、ツールの直感的な使いやすさを軽減。 HMMベースの方法につきましては、パラメータ推定の手順としばしば利用されているシンプルな、有限状態分類を抑制することを目的とした。 さらに、従来のChIPseqデータ分析はの分類を含む適切なツールのその後のアプリケーションに続いて点状またはびまん性のいずれかとして密度プロファイルを読んで期待した。我々は、さらに上手にデータ型のスペクトル全体を扱うことができ、単一の、より汎用性の高いモデルで、これら二つの異なるモデルの必要性を置き換えることを目的とした。 これらの目的を満たすために、我々は最初だけ明示的な式-その性能の優位性に不可欠な技術革新を利用したHMM 9における最先端の進歩を使用して、統計的枠組み自然にモデル化したChIPseqデータ構造を構築した。ヒューリスティックモデルより洗練された、私たちのHMMはを通して無限隠れ状態に対応しベイズモデル。我々は、さらに濃縮のセグメントを定義読ん密度で合理的な変更点を識別に適用した。我々の分析では、私たちのベイジアン変化点（BCP）のアルゴリズムは演算量の増加を抑え、証明抄訳実行時間とメモリフットプリントを持っていた方法で明らかにした。 BCPのアルゴリズムが正常に堅牢な正確さと限られたユーザー定義のパラメータを使用して点状のピークおよびびまん島の識別の両方に適用した。これは、その汎用性と使いやすさの両方を示す。従って、我々はそれがそれの研究グループ間の連携と確証するのを助けることができるChIPseqデータ解析のための偉大なツールとなって、簡単に比較し、対比された方法でのデータ型およびエンドユーザの幅広い範囲にわたって容易に実現できると信じています。ここでは、その有用性を説明するために、既存の転写因子10,11およびエピジェネティックなデータ12にBCPのアプリケーションを示しています。

BCPは、ヒストン修飾データに幅広い濃縮の領域を同定するに優れています。基準点として、我々は以前SICER 3、強力な性能を実証した既存のツールの方々に結果を比較した。最高のBCPの利点を説明するために、我々はよく成功率を評価するための基盤を確立するために検討されていたヒストン修飾を検討した。それは積極的に転写された遺伝子の本体（ 図1）と強く結合?...

Watch this Scientific Journal Video about A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types at JoVE.com

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

当社ベイジアン変化点（BCP）のアルゴリズムは変化点隠れマルコフモデルを経由してモデリングの最先端の進歩に基づいて構築され、クロマチン免疫沈降シーケンス（ChIPseq）データ解析に適用します。 BCPは、広範かつ点状の両方のデータ·タイプではうまく実行されますが、正確にびまん性ヒストン濃縮の堅牢で再現性の島々を識別するのに優れています。

多様ChIPseqデータ型のゲノムワイドな解析のための新規ベイジアン変化点アルゴリズム

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

a-novel-bayesian-change-point-algorithm-for-genome-wide-analysis

Research

JoVE Journal

Biology

11.2K ビュー.  Stony Brook University. 当社ベイジアン変化点（BCP）のアルゴリズムは変化点隠れマルコフモデルを経由してモデリングの最先端の進歩に基づいて構築され、クロマチン免疫沈降シーケンス（ChIPseq）データ解析に適用します。 BCPは、広範かつ点状の両方のデータ·タイプではうまく実行されますが、正確にびまん性ヒストン濃縮の堅牢で再現性の島々を識別するのに優れていま?...

ビデオ: A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

神経毒性の高いコンテンツ分析のための神経とアストロサイト共培養アッセイ

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

生物学

Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both prokaryotic and eukaryotic cells lead to translational regulation which is a major cellular mechanism of stress response. Familiar examples are the stringent response in E. coli and the Gcn2 stress response pathway in yeast ([2-6]). Recent work in E. coli and S. cerevisiae have shown that tRNA charging patterns are highly dynamic and depends on the type of stress experienced by cells [1, 6, 7]. The highly dynamic, variable nature of tRNA charging makes it essential to determine changes in tRNA charging levels at the genomic scale, in order to fully elucidate cellular response to environmental variations. In this review we present a method for simultaneously measuring the relative charging levels of all tRNAs in S. cerevisiae . While the protocol presented here is for yeast, this protocol has been successfully applied for determining relative charging levels in a wide variety of organisms including E. coli and human cell cultures[7, 8].

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.

マイクロアレイを用いたtRNAのアミノアシル化（充電）レベルのゲノムワイド解析

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

アイソフォーム特異的遺伝子標的を同定するためのチップを使用してゲノムワイド解析

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, genome-wide cDNA library cloned into the 3'UTR downstream from the dual-selection fusion protein, thymidine kinase-zeocin (TKzeo). The first round of selection is for stable transformants, followed with introduction of a miRNA of interest, and finally, selecting for cDNAs containing the miRNA's target. Selected cDNAs are identified by sequencing (see Figure 1-3 for Target ID Library Workflow and details).
To ensure broad coverage of the human transcriptome, Target ID Library cDNAs were generated via oligo-dT priming using a pool of total RNA prepared from multiple human tissues and cell lines. Resulting cDNA range from 0.5 to 4 kb, with an average size of 1.2 kb, and were cloned into the p3&#900;TKzeo dual-selection plasmid (see Figure 4 for plasmid map). The gene targets represented in the library can be found on the Sigma-Aldrich webpage. Results from Illumina sequencing (Table 3), show that the library includes 16,922 of the 21,518 unique genes in UCSC RefGene (79%), or 14,000 genes with 10 or more reads (66%).

The Target ID Library is a plasmid-based, genome-wide collection of cloned cDNA used to identify miRNA targets. Here we demonstrate its use and application.

ミッションターゲットIDのライブラリを使用してmiRNAのターゲットのゲノムワイドスクリーン

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, ...

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis. 
	Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
	We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

We describe a protocol using C. elegans and RNAi feeding libraries that allows automated measurement of multiple parameters such as fluorescence, size and opacity of individual worms in a population. We give one example of a screen to identify genes involved in anti-fungal innate immunity in C. elegans.

の定量と自動ハイスループットゲノムワイドRNAiスクリーニング C.エレガンス</em

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

ゲノムワイドな遺伝子欠失で<em&gt;ストレプトコッカス血統</em&gt;ハイスループットPCRによる

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

ゲノム規模で哺乳類Translatomesのソーム分画および分析

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to dose-limiting toxicity and many adverse side effects. Targeting the genome with sequence-specific small molecules may enable molecules with increased therapeutic index and fewer off-target effects. N-methylpyrrole/N-methylimidazole polyamides are molecules that can be rationally designed to target specific DNA sequences with exquisite precision. And unlike most natural transcription factors, polyamides can bind to methylated and chromatinized DNA without a loss in affinity. The sequence specificity of polyamides has been extensively studied in vitro with cognate site identification (CSI) and with traditional biochemical and biophysical approaches, but the study of polyamide binding to genomic targets in cells remains elusive. Here we report a method, the crosslinking of small molecules to isolate chromatin (COSMIC), that identifies polyamide binding sites across the genome. COSMIC is similar to chromatin immunoprecipitation (ChIP), but differs in two important ways: (1) a photocrosslinker is employed to enable selective, temporally-controlled capture of polyamide binding events, and (2) the biotin affinity handle is used to purify polyamide&#8211;DNA conjugates under semi-denaturing conditions to decrease DNA that is non-covalently bound. COSMIC is a general strategy that can be used to reveal the genome-wide binding events of polyamides and other genome-targeting chemotherapeutic agents.

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Identifying the direct targets of genome-targeting molecules remains a major challenge. To understand how DNA-binding molecules engage the genome, we developed a method that relies on crosslinking of small molecules to isolate chromatin (COSMIC).

COSMICと細胞の薬剤-DNA相互作用のゲノムワイドなマッピング（小分子の架橋は、クロマチンを分離するために）

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to ...

Genome-wide Purification of Extrachromosomal Circular DNA from Eukaryotic Cells

Extrachromosomal circular DNAs (eccDNAs) are common genetic elements in Saccharomyces cerevisiae and are reported in other eukaryotes as well. EccDNAs contribute to genetic variation among somatic cells in multicellular organisms and to evolution of unicellular eukaryotes. Sensitive methods for detecting eccDNA are needed to clarify how these elements affect genome stability and how environmental and biological factors&#160;induce their formation in eukaryotic cells. This video presents a sensitive eccDNA-purification method called Circle-Seq. The method encompasses column purification of circular DNA, removal of remaining linear chromosomal DNA, rolling-circle amplification of eccDNA, deep sequencing, and mapping. Extensive exonuclease treatment&#160;was&#160;required for sufficient linear chromosomal DNA degradation. The rolling-circle amplification step by &#966;29 polymerase enriched for circular DNA over linear DNA. Validation of the Circle-Seq method on three S. cerevisiae CEN.PK populations of 1010 cells detected hundreds of eccDNA profiles in sizes larger than 1 kilobase. Repeated findings of ASP3-1, COS111, CUP1, RSC30,&#160;HXT6, HXT7 genes on circular DNA in both S288c and CEN.PK suggests that DNA circularization is conserved between strains at these loci. In sum, the Circle-Seq method has broad applicability for genome-scale screening for eccDNA in eukaryotes as well as for detecting specific eccDNA types.

This paper presents a sensitive method called Circle-Seq for purifying extrachromosomal circular DNA (eccDNA). The method encompasses column purification, removal of remaining linear chromosomal DNA, rolling-circle amplification and high-throughput sequencing. Circle-Seq is applicable to genome-scale screening of eukaryotic eccDNA and studying genome instability and copy-number variation.

真核細胞から体外の環状DNAのゲノムワイドな精製

Extrachromosomal circular DNAs (eccDNAs) are common genetic elements in Saccharomyces cerevisiae and are reported in other eukaryotes as well. EccDNAs ...

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

ゲノムワイドな探索とグレープバインで ATL E3 ユビキチンリ ガーゼ遺伝子家族の表現メタ分析のための包括的なワークフロー

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...