{"id":2288,"date":"2017-07-07T12:23:36","date_gmt":"2017-07-07T12:23:36","guid":{"rendered":"https:\/\/www.kolabtree.com\/blog\/?p=2288"},"modified":"2020-02-10T14:12:18","modified_gmt":"2020-02-10T14:12:18","slug":"4-recent-advances-in-computational-biology","status":"publish","type":"post","link":"https:\/\/www.kolabtree.com\/blog\/fr\/4-recent-advances-in-computational-biology\/","title":{"rendered":"4 Progr\u00e8s r\u00e9cents en biologie computationnelle"},"content":{"rendered":"

Biologie computationnelle<\/a> \u00e9volue rapidement avec l'av\u00e8nement des nouvelles technologies, notamment dans la mani\u00e8re dont nous collectons, analysons et visualisons les donn\u00e9es. Le Dr. Ragothaman Yennmalli, une Kolabtree<\/a> freelance et scientifique, examine quatre avanc\u00e9es prometteuses.\u00a0\u00a0<\/em><\/p>\n

Dans le prolongement de la poste d'introduction pr\u00e9c\u00e9dent<\/a>Dans ce num\u00e9ro, je vais mettre en lumi\u00e8re certaines des tendances ou avanc\u00e9es r\u00e9centes dans les sciences biologiques qui transforment la biologie computationnelle. Ces avanc\u00e9es reposent en grande partie sur des outils et des m\u00e9thodes informatiques - analyse de donn\u00e9es massives, mod\u00e9lisation multi-\u00e9chelle, etc. Certaines d'entre elles sont \u00e9num\u00e9r\u00e9es ci-dessous.<\/p>\n

1. Le Big Data<\/strong><\/p>\n

Ce terme, bien connu en informatique, n'a \u00e9t\u00e9 repris que r\u00e9cemment par les biologistes. Gr\u00e2ce aux techniques de s\u00e9quen\u00e7age de nouvelle g\u00e9n\u00e9ration, la s\u00e9quence d'un g\u00e9nome peut \u00eatre obtenue en un temps relativement court. Ainsi, la pertinence de g\u00e9n\u00e9rer des donn\u00e9es rapidement est amplifi\u00e9e lorsqu'on travaille avec des donn\u00e9es m\u00e9tag\u00e9nomiques ou un microbiome. Comment g\u00e9rer les donn\u00e9es ? Qu'en est-il du stockage \u00e0 long terme ? Quels sont les outils pour analyser des donn\u00e9es aussi massives ? Ces questions se posent et elles ont des r\u00e9ponses. Comme nous l'avons mentionn\u00e9, il s'agit d'une tendance r\u00e9cente en biologie, mais pas en informatique ou en physique exp\u00e9rimentale, o\u00f9 la manipulation et l'analyse de donn\u00e9es massives sont des t\u00e2ches courantes.<\/p>\n

\"big-data-1667184_1280\"
\nOne particular instance where recherche<\/a> is happening is in the file formats of biological big data. In the case of protein structure file format, the current standard is the .pdb format, a column dependent format that is parseable and both human and machine readable. However, this format fails when describing mega structures, such as the ribosome or full viral capsids. Hence, a new format has been proposed called the .pdbx format that overcomes the previous format’s limitations. There is also another format called MMTF format that spees up the loading time for structures with more then 20 million atoms within seconds.<\/p>\n

Autres lectures sur le big data en informatique biologie structurelle<\/a>:
\nhttp:\/\/science.sciencemag.org\/content\/355\/6322\/248
\n<\/a><\/p>\n

2. Techniques Cryo-EM et XFEL<\/strong><\/p>\n

Ces deux m\u00e9thodes ne sont pas nouvelles, en tant que telles. Toutefois, la technologie actuelle et les progr\u00e8s r\u00e9alis\u00e9s dans ces deux domaines repoussent les limites de l'analyse de la structure biomol\u00e9culaire. Cryo-EM <\/a>est une technique permettant de capturer la structure tridimensionnelle de la biomol\u00e9cule \u00e0 l'aide d'un microscope \u00e9lectronique \u00e0 haute r\u00e9solution. Dans l'un des laboratoires pionniers du NIH, une structure de 2,5\u00c5 a \u00e9t\u00e9 r\u00e9solue. Cette r\u00e9solution est habituellement obtenue avec la structure cristalline des prot\u00e9ines, ce qui implique au moins 1 \u00e0 2 mois de temps pour normaliser le cristal optimal \u00e0 photographier sous le faisceau de rayons X.<\/p>\n

\"alat2-872522_1920\"
\nEn revanche, une technique r\u00e9cente qui r\u00e9volutionne la biologie structurelle est la suivante XFEL<\/a> qui consiste \u00e0 envoyer des faisceaux de rayons X de haute intensit\u00e9 sur des microcristaux de prot\u00e9ines. En raison du rayonnement \u00e9lev\u00e9, les microcristaux sont litt\u00e9ralement br\u00fbl\u00e9s pour obtenir les donn\u00e9es. Des dizaines de milliers de microcristaux sont n\u00e9cessaires pour obtenir des donn\u00e9es d'une couverture d\u00e9cente. Chaque image captur\u00e9e \u00e0 partir d'un microcristal doit \u00eatre analys\u00e9e avec les autres pour obtenir la structure 3D de la biomol\u00e9cule.<\/p>\n

Ces techniques d\u00e9pendent fortement de logiciels automatis\u00e9s qui utilisent des algorithmes de traitement d'images et, dans une certaine mesure, des approches d'apprentissage automatique pour identifier le signal dans le bruit environnant. Ce site<\/span>\u00a0est le big data, car la diversit\u00e9 et la vitesse \u00e0 laquelle les informations sont acquises sont astronomiques.<\/span><\/p>\n

3. Mod\u00e9lisation multi-\u00e9chelle<\/strong><\/p>\n

Contrairement \u00e0 la mod\u00e9lisation d'une structure biomol\u00e9culaire unique et \u00e0 l'extrapolation \u00e0 un syst\u00e8me plus complexe, la mod\u00e9lisation multi-\u00e9chelle implique plus de 200 000 atomes et la dynamique obtenue r\u00e9v\u00e8le des interactions \u00e0 long terme et un comportement complexe des multiples composants (homog\u00e8nes ou h\u00e9t\u00e9rog\u00e8nes). Les donn\u00e9es g\u00e9n\u00e9r\u00e9es par de telles exp\u00e9riences sont massives en raison du nombre de points de donn\u00e9es obtenus, mais aussi en raison de l'ex\u00e9cution de plusieurs cycles pour obtenir une signification statistique.<\/p>\n

Un exemple d'utilisation de la mod\u00e9lisation multi-\u00e9chelle est la compr\u00e9hension de la dynamique du cellulosome, une structure bact\u00e9rienne complexe compos\u00e9e de prot\u00e9ines h\u00e9t\u00e9rog\u00e8nes et d'enzymes qui se fixent \u00e0 la cellulose. Les cellulosomes sont importants sur le plan industriel dans le domaine des biocarburants, notamment pour la production de bio\u00e9thanol.<\/p>\n

Pour en savoir plus :\u00a0http:\/\/www.ks.uiuc.edu\/Research\/biofuels\/<\/a><\/p>\n

4. S\u00e9quen\u00e7age de cellules uniques<\/strong><\/p>\n

Au lieu d'examiner plusieurs cellules, la derni\u00e8re technique consiste \u00e0 isoler chaque cellule individuelle, \u00e0 en extraire l'ARN et \u00e0 les s\u00e9quencer. Cette technique r\u00e9cente est appel\u00e9e s\u00e9quen\u00e7age de l'ARN d'une seule cellule ou scRNA-seq<\/a>. Dans cet article de Nature, discutant de la m\u00e9thode et de ses avantages, ils mentionnent que<\/p>\n

Il est beaucoup plus difficile de manipuler des cellules individuelles que de grandes populations, et comme chaque cellule ne produit qu'une quantit\u00e9 infime d'ARN, il n'y a pas de place pour l'erreur. Un autre probl\u00e8me est l'analyse des \u00e9normes quantit\u00e9s de donn\u00e9es qui en r\u00e9sultent, notamment parce que les outils utilis\u00e9s peuvent \u00eatre peu intuitifs.<\/p><\/blockquote>\n

Une excellente revue du flux de travail et des outils pour scRNA-seq est donn\u00e9e ici : https:\/\/doi.org\/10.3389\/fgene.2016.00163<\/span><\/a><\/p>\n

\n

Besoin d'aide pour le conseil de Biologiste informaticien<\/a>? Engagez un freelance Biologie computationnelle<\/a> expert<\/b>\u00a0sur Kolabtree. Il est gratuit de publier votre projet et d'obtenir des devis.<\/b><\/p>\n

Vous souhaitez consulter le Dr. Yennamalli sur un projet ? Contactez-le sur Kolabtree. ici<\/a>.<\/p>\n

Experts connexes :
\nEmbaucher un bioinformaticien<\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Embaucher un biologiste mol\u00e9culaire\u00a0<\/a> \u00a0 \u00a0 Engager un biostatisticien<\/a><\/p>","protected":false},"excerpt":{"rendered":"

La biologie computationnelle \u00e9volue rapidement avec l'av\u00e8nement des nouvelles technologies, notamment dans la mani\u00e8re dont nous collectons, analysons et visualisons les donn\u00e9es. Le Dr Ragothaman Yennmalli, freelance et scientifique de Kolabtree, examine quatre avanc\u00e9es prometteuses. Dans le prolongement du pr\u00e9c\u00e9dent billet d'introduction, je vais ici mettre en lumi\u00e8re certaines des tendances ou avanc\u00e9es r\u00e9centes dans le domaine de la biologie.<\/p>\n

Lire la suite<\/a><\/div>","protected":false},"author":26,"featured_media":2324,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[442,398,247,435],"tags":[],"acf":[],"yoast_head":"\nComputational biology: 4 recent advances - Kolabtree Blog<\/title>\n<meta name=\"description\" content=\"Recent advances in computational biology that are changing the way we look at genomic data, structural biology and RNA sequencing.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kolabtree.com\/blog\/fr\/4-recent-advances-in-computational-biology\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"4 Recent Advances in Computational Biology\" \/>\n<meta property=\"og:description\" content=\"Recent advances in computational biology that are changing the way we look at genomic data, structural biology and RNA sequencing.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.kolabtree.com\/blog\/fr\/4-recent-advances-in-computational-biology\/\" \/>\n<meta property=\"og:site_name\" content=\"The Kolabtree Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/kolabtree\" \/>\n<meta property=\"article:published_time\" content=\"2017-07-07T12:23:36+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-02-10T14:12:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2017\/07\/alat2-872522_1920.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"745\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Ragothaman Yennamalli\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@kolabtree\" \/>\n<meta name=\"twitter:site\" content=\"@kolabtree\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ragothaman Yennamalli\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Computational biology: 4 recent advances - 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He conducted postdoctoral research at Iowa State University (2009-2011), University of Wisconsin-Madison (2011-2012), and Rice University (2012-2014). Currently he is an Assistant Professor at Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India.","url":"https:\/\/www.kolabtree.com\/blog\/fr\/author\/ragothaman\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/posts\/2288"}],"collection":[{"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/users\/26"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/comments?post=2288"}],"version-history":[{"count":19,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/posts\/2288\/revisions"}],"predecessor-version":[{"id":6896,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/posts\/2288\/revisions\/6896"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/media\/2324"}],"wp:attachment":[{"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/media?parent=2288"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/categories?post=2288"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/fr\/wp-json\/wp\/v2\/tags?post=2288"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}