STARR Prix de la Fondation (MQZ), NIH ES017166 (MQZ), NSF grant DMS0906593 (HX).

1. Préparation des fichiers d&#39;entrée pour l&#39;analyse PCA <ol><li> Alignez le lit court produite à partir du séquençage pistes (ChIP et les bibliothèques d&#39;entrée) au génome de référence appropriée en utilisant le logiciel préféré courte alignement lecture. Les emplacements mappées devraient être convertis en des 6 données de la colonne navigateur extensibles (LIT) format 13 (UCSC Genome Browser, <a href="http://genome.ucsc.edu/" target="_blank">http://genome.ucsc.edu/</a> ), une ligne délimité par des tabulations par mappé lecture indiquant le chromosome mappé, la position de départ (0-based), position finale (semi-ouvert), lire le nom, le score (en option), et le volet. </li></ol> 2a. Lire diffuser profils: ChIP Prétraitement Lire densités pour la détection des îles enrichi en données diffuses <ol><li> Étendre la puce et emplacements d&#39;entrée mappées à une longueur prédéterminée fragment, c&#39;est à dire. la taille du fragment cible lors de la digestion enzymatique ou sonication de l&#39;ADN, généralement autour de 200 pb. Compte fragments sont ensuite agrégationted dans les bacs adjacents. Par défaut, la taille bin est réglé sur la longueur du fragment estimé à 200 pb. </li><li> Tout changement de points possible dans un ensemble de bacs identiques avec chefs lecture sera très probablement à l&#39;automne des limites les plus externes. Par conséquent, il est improbable qu&#39;un point de changement se produira à une limite interne entre deux bacs avec les comtes de lecture mêmes. Alors, les poubelles groupe adjacents identiques, avec des lectures par bac, en un seul bloc, c&#39;est à dire. Format bedGraph 13. </li></ol> 2b. Ponctuées profils lus: ChIP Prétraitement et fichiers LIT entrée pour la détection de pics dans les données ponctuées <ol><li> Agrégat lectures chevauchantes pour ChIP brin positif et négatif lit séparément. Les brins des densités lues doivent former un profil bimodal de pics positifs et négatifs. Choisissez des paires plus / moins des sommets les plus enrichis et utiliser la distance entre leurs sommets comme une estimation de la longueur des fragments de la bibliothèque. </li><li> Déplacer la puce et l&#39;entrée se lit la moitié du fragment length du centre et de recalculer la densité de lecture de l&#39;, plus décalée et fusionné et moins brin lit. Cette méthode d&#39;estimation de la longueur du fragment a été adoptée à partir de Zhang, et al. 3. Positions dont la numération de fusion identiques doivent être regroupés en blocs, semblables à l&#39;étape 2a.2. </li></ol> 3. Estimer la moyenne a posteriori Lire la densité de chaque bloc à l&#39;aide de notre rapprochement BCMIX <ol><li> Lire la densité de chaque bloc est modélisée comme une distribution de Poisson, Pois (θ t), avec une moyenne de paramètre suivant un mélange de distributions Gamma, Γ (α, β), et une probabilité a priori d&#39;un point de changement se produisant à une limite de bloc de p. climatisé Pois (θ t) sur G (α, β) rend effectivement le modèle HMM un état ​​infini. Estimer l&#39;hyper-paramètres, α, β et p, en utilisant au maximum la probabilité a posteriori. </li><li> Explicitement calculer les estimations de Bayes pourchaque bloc, θ t, E (θ t | γ Z). Remplacez la consommation plus traditionnelle, mais le temps vers l&#39;avant et vers l&#39;arrière filtres souvent utilisés dans les HMMs, avec le rapprochement Mélange complexité de calcul plus efficacement borné à estimer les moyennes a posteriori, θ c. Les moyens résultant postérieures sera &quot;lissé&quot; dans un profil approximatif constante par morceaux afin blocs identiques, θ c, devrait être bloqué avec nouveau contour coordonnées. </li></ol> 4a. Lire diffuser profils: Post-process moyennes a posteriori dans les segments de l&#39;enrichissement diffus <ol><li> Utiliser le numéro d&#39;entrée de lit par chaque nouveau bloc θ c en tant que le taux d&#39;arrière-plan, Pois (λ a) et déterminer l&#39;enrichissement à l&#39;aide d&#39;un test d&#39;hypothèse simple basé sur la moyenne si ChIP postérieure, θ c, δ dépasse un certain seuil. Le 90 e &lt;/ Sup&gt; quantile est le défaut d est approprié dans la plupart des cas. </li><li> Fusionner adjacente θ c blocs qui dépassent l&#39;enrichissement en une seule région et le rapport de fusionner les coordonnées au format LIT simple. Alternativement, on peut signaler le c θ pour chaque bloc au format bedGraph de préserver les détails en haute résolution des estimations de la densité de lecture. </li></ol> 4b. Ponctuée Lire profils: Post-process moyennes a posteriori en pointe candidats <ol><li> Définir la fréquence de fond, Pois (λ a), comme étant la moyenne de tous les chiffres de lecture (γ 2) et identifier tous les blocs qui dépassent le seuil, d. Depuis pics ponctuées devraient être plus largement enrichie, le δ par défaut est réglée sur le 99 e quantile de Pois (λ a). </li><li> Régler le bloc avec le c maximal θ comme le sommet pic candidat et jouxtent blocs adjacents qui partagent un même den lecturesité (± 1 lire comptent pour permettre une légère variation). Cette région attenante est défini comme un site de liaison candidat. </li><li> Calculer λ 2, comme les comptes moyens de lecture dans le site candidat ChIP contraignant et test d&#39;hypothèse dans ce contexte d&#39;entrée en fonction ont fait l&#39;hypothèse nulle, H 0, c&#39;est que λ 1 ≥ λ 2 et rejeter H 0 basé sur un seuil de p-value. Pics candidats de sortie au format LIT. </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>

Aucun conflit d&#39;intérêt déclaré.

Nous avons cherché à développer un modèle d&#39;analyse de données permettant d&#39;identifier ChIPseq deux structures ponctuées et diffuse des données aussi bien. Jusqu&#39;à présent, les régions de l&#39;enrichissement, notamment des régions diffuses, qui reflètent les attentes présupposé de taille grande île, ont été difficiles à identifier. Pour résoudre ces problèmes, nous avons utilisé les plus récentes avancées en matière de technologie HMM, qui possèdent de nombreux avantages par rapport...

<table border="1"><tbody><tr><td> Nom du réactif </td><td> Entreprise </td><td> Numéro de catalogue </td><td> Commentaires (optionnel) </td></tr><tr><td> Linux station de travail </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 est une technique largement utilisée pour étudier les interactions protéine-ADN. Des profils de densité de lecture sont générés à l&#39;aide de la prochaine séquence d&#39;ADN lié à une protéine et l&#39;alignement du lit court à un génome de référence. Régions enrichies sont révélés comme des pics, qui diffèrent souvent de façon spectaculaire dans la forme, en fonction de la protéine cible 1. Par exemple, des facteurs de transcription se lient souvent dans un site et la séquence de manière spécifique et ont tendance à produire des pics ponctuées, alors que les modifications des histones sont plus omniprésents et sont caractérisés par de larges îles diffuses d&#39;enrichissement 2. De manière fiable l&#39;identification de ces régions a été au centre de notre travail. Algorithmes pour l&#39;analyse des données ChIPseq ont eu recours à diverses méthodes heuristiques, à partir de 3-5 pour plus rigoureuses modèles statistiques, p.ex. modèles de Markov cachés (HMM) 6-8. Nous avons cherché une solution qui réduit au minimum la nécessité de difficile à définir, ad hoc paramètres qui sont souventcompromettre la résolution et de réduire l&#39;utilisation intuitive de l&#39;outil. En ce qui concerne les méthodes basées sur HMM, nous avons cherché à limiter les procédures d&#39;estimation de paramètres et simples, les classifications à états finis qui sont souvent utilisés. En outre, l&#39;analyse des données ChIPseq conventionnelle implique catégorisation de la lecture devrait profils de densité que soit ponctuée diffuse ou suivie par l&#39;application ultérieure de l&#39;outil approprié. Nous avons également pour but de remplacer la nécessité pour ces deux modèles distincts avec un seul modèle plus polyvalent, qui peut répondre avec compétence l&#39;ensemble du spectre des types de données. Pour atteindre ces objectifs, nous avons d&#39;abord construit un cadre statistique qui, naturellement, modelés ChIPseq structures de données en utilisant une avance fine pointe de la HMM 9, qui utilise uniquement des formules explicites, une innovation cruciale pour ses avantages de performance. Des modèles plus sophistiqués, puis heuristiques, notre HMM accueille infinies états cachés par unModèle bayésien. Nous l&#39;avons appliquée à l&#39;identification des points de changement de la densité raisonnables lire, ce qui a encore définir des segments de l&#39;enrichissement. Notre analyse a révélé la façon dont notre Changement de point de Bayes (BCP) algorithme a une complexité de calcul réduite, témoigne d&#39;un moment de l&#39;exécution abrégée et empreinte mémoire. L&#39;algorithme PCA a été appliquée avec succès à la fois de pointe et ponctuée d&#39;identification île diffuse avec une précision robuste et limités paramètres définis par l&#39;utilisateur. Cette illustré à la fois sa polyvalence et sa facilité d&#39;utilisation. Par conséquent, nous croyons qu&#39;il peut être mis en œuvre facilement à travers de larges gammes de types de données et les utilisateurs finaux d&#39;une façon qui est facilement comparés et contrastés, ce qui en fait un excellent outil pour ChIPseq analyse de données qui peuvent aider à la collaboration et à la corroboration entre les groupes de recherche. Ici, nous démontrons l&#39;application de la BCP au facteur de transcription existante et les données épigénétiques 10,11 12 à illustrer son utilité.

BCP excelle à identifier les régions d&#39;enrichissement large dans les données de modification des histones. Comme point de référence, nous avons déjà comparé nos résultats à ceux de SICER 3, un outil existant qui a réalisé une excellente performance. Pour mieux illustrer les avantages de BCP, nous avons examiné une modification des histones qui avait été bien étudié pour établir une base pour évaluer les taux de réussite. Dans cet esprit, nous avons ensuite analysé H3K36me3, car il a ?...

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

Notre point de changement bayésien (BCP) algorithme s&#39;appuie sur l&#39;état de l&#39;art des progrès de la modélisation ruptures par modèles de Markov cachés et les applique à immunoprécipitation de la chromatine séquençage (ChIPseq) l&#39;analyse des données. BCP se comporte bien dans les types de données à la fois larges et ponctuées, mais il excelle dans l&#39;identification précise robustes, reproductibles îles de l&#39;enrichissement d&#39;histone diffuse.

A Novel bayésienne changement de point Algorithme pour l&#39;échelle du génome Analyse des données ChIPseq divers types de

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 vues.  Stony Brook University. Notre point de changement bayésien (BCP) algorithme s'appuie sur l'état de l'art des progrès de la modélisation ruptures par modèles de Markov cachés et les applique à immunoprécipitation de la chromatine séquençage (ChIPseq) l'analyse des données. BCP se comporte bien dans les types de données à la fois larges et ponctuées, mais il excelle dans l'identification précise robustes, reproductibles îles de l'enrichissement d'histone...

Vidéo: 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.

Un neuronale et astrocytes de co-culture de dosage pour l&#39;analyse du contenu haute de la neurotoxicité

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

Recherche

Biologie

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.

L&#39;échelle du génome Analyse des aminoacylation (recharge) Niveaux d&#39;ARNt utilisant des 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 ...

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.

Analyse du génome entier en utilisant ChIP d&#39;identifier les gènes cibles spécifiques Isoform

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.

L&#39;échelle du génome d&#39;écran pour cibles des miRNA l&#39;aide de l&#39;ID de bibliothèque MISSION cible

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.

Quantitatifs et automatisé à haut débit du génome à l&#39;échelle écrans ARNi dans C. elegans</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.

La délétion de gènes dans le génome entier<em&gt; Streptococcus sanguinis</em&gt; Par PCR à haut débit

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.

Polysome Fractionnement et analyse des mammifères Translatomes sur une échelle de l&#39;ensemble du génome

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).

La cartographie du génome à l&#39;échelle du médicament-ADN interactions dans les cellules avec COSMIC (réticulation de petites molécules pour isoler la chromatine)

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.

Purification de l&#39;ensemble du génome de l&#39;ADN extrachromosomique circulaire à partir de cellules eucaryotes

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.

Flux de travail complet pour le génome-large d’Identification et de la Meta-analyse de l’Expression de la famille des gènes ATL E3 ubiquitine Ligase à Grapevine

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