{"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\/pt\/4-recent-advances-in-computational-biology\/","title":{"rendered":"4 Avan\u00e7os recentes em Biologia Computacional"},"content":{"rendered":"

Biologia computacional<\/a> est\u00e1 evoluindo rapidamente com o advento de novas tecnologias, especialmente na forma como coletamos, analisamos e visualizamos os dados. Dr. Ragothaman Yennmalli, um Kolabtree<\/a> freelancer e cientista, examina quatro avan\u00e7os promissores.\u00a0\u00a0<\/em><\/p>\n

Acompanhamento do post introdut\u00f3rio anterior<\/a>Destaco aqui algumas das tend\u00eancias recentes ou avan\u00e7os recentes nas ci\u00eancias biol\u00f3gicas que est\u00e3o transformando a biologia computacional. Estes avan\u00e7os dependem muito de ferramentas e m\u00e9todos computacionais - grandes an\u00e1lises de dados, modelagem em m\u00faltiplas escalas, etc. Alguns deles est\u00e3o listados abaixo.<\/p>\n

1. Grandes Dados<\/strong><\/p>\n

Este \u00e9 um termo bem conhecido na ci\u00eancia da computa\u00e7\u00e3o e foi escolhido por bi\u00f3logos apenas recentemente. Gra\u00e7as \u00e0s t\u00e9cnicas de sequenciamento da pr\u00f3xima gera\u00e7\u00e3o, a seq\u00fc\u00eancia de um genoma pode ser obtida em tempo relativamente mais curto. Por exemplo, a relev\u00e2ncia de gerar dados rapidamente \u00e9 ampliada quando se trabalha com dados metagen\u00f4micos ou com um microbioma. Como se pode gerenciar os dados? E quanto ao armazenamento a longo prazo? Quais s\u00e3o as ferramentas para a an\u00e1lise de dados t\u00e3o volumosos? Estas perguntas surgem e elas t\u00eam respostas. Como mencionado, esta \u00e9 uma tend\u00eancia recente em biologia, mas n\u00e3o em inform\u00e1tica ou f\u00edsica experimental, onde o manuseio e a an\u00e1lise de grandes dados \u00e9 um trabalho de rotina.<\/p>\n

\"big-data-1667184_1280\"
\nOne particular instance where pesquisa<\/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

Leitura adicional sobre grandes dados em computa\u00e7\u00e3o biologia estrutural<\/a>:
\nhttp:\/\/science.sciencemag.org\/content\/355\/6322\/248
\n<\/a><\/p>\n

2. T\u00e9cnicas Cryo-EM e XFEL<\/strong><\/p>\n

Estes dois m\u00e9todos n\u00e3o s\u00e3o novos, como tal. Entretanto, a tecnologia atual e os avan\u00e7os que acontecem nestes dois campos est\u00e3o empurrando os limites da an\u00e1lise da estrutura biomolecular. Cryo-EM <\/a>\u00e9 uma t\u00e9cnica para capturar a estrutura tridimensional da biomol\u00e9cula utilizando o microsc\u00f3pio eletr\u00f4nico em alta resolu\u00e7\u00e3o. Em um dos laborat\u00f3rios pioneiros do NIH, uma estrutura de 2,5\u00c5 foi resolvida. Esta resolu\u00e7\u00e3o \u00e9 geralmente obtida com estrutura de cristal de prote\u00ednas, que rotineiramente envolve pelo menos 1-2 meses de tempo para padronizar o cristal ideal para ser disparado sob feixe de raios X.<\/p>\n

\"alat2-872522_1920\"
\nEm contraste, uma t\u00e9cnica recente que est\u00e1 revolucionando a biologia estrutural \u00e9 XFEL<\/a> que consiste em disparar raios X de alta intensidade sobre microcristais de prote\u00ednas. Devido \u00e0 alta radia\u00e7\u00e3o, os microcristais s\u00e3o literalmente queimados para obter os dados. Dezenas de milhares de microcristais s\u00e3o necess\u00e1rios para se obter dados de cobertura decente. Cada imagem capturada de um microcristal tem que ser analisada com o resto para obter a estrutura 3D da biomol\u00e9cula.<\/p>\n

Tais t\u00e9cnicas dependem muito de softwares automatizados que utilizam algoritmos de processamento de imagem e, at\u00e9 certo ponto, abordagens de aprendizado de m\u00e1quina para identificar o sinal do ru\u00eddo ao redor. Isto<\/span>\u00a0s\u00e3o grandes dados, pois a diversidade e a velocidade com que a informa\u00e7\u00e3o \u00e9 adquirida \u00e9 astron\u00f4mica.<\/span><\/p>\n

3. Modelagem em m\u00faltiplas escalas<\/strong><\/p>\n

Ao contr\u00e1rio da modelagem de uma \u00fanica estrutura biomolecular e da extrapola\u00e7\u00e3o para um sistema mais complexo, a modelagem em m\u00faltiplas escalas envolve mais de 200.000 \u00e1tomos e a din\u00e2mica obtida revela intera\u00e7\u00f5es de longo prazo e comportamento complexo dos m\u00faltiplos componentes (homog\u00eaneos ou heterog\u00eaneos). Os dados gerados por tais experimentos s\u00e3o massivos devido ao n\u00famero de pontos de dados obtidos, tamb\u00e9m devido a m\u00faltiplas execu\u00e7\u00f5es para obter um significado estat\u00edstico.<\/p>\n

Um exemplo em que a modelagem em m\u00faltiplas escalas tem sido usada \u00e9 na compreens\u00e3o dos dyanmics do Celulosoma, uma estrutura complexa bacteriana feita de prote\u00ednas heterog\u00eaneas e enzimas que se ligam \u00e0 celulose. O celulosoma \u00e9 industrialmente importante na \u00e1rea de biocombust\u00edveis, especificamente na produ\u00e7\u00e3o de bioetanol.<\/p>\n

Leitura adicional:\u00a0http:\/\/www.ks.uiuc.edu\/Research\/biofuels\/<\/a><\/p>\n

4. Seq\u00fcenciamento de c\u00e9lula \u00fanica<\/strong><\/p>\n

Em vez de olhar para v\u00e1rias c\u00e9lulas, a t\u00e9cnica mais recente \u00e9 isolar cada c\u00e9lula individual e extrair o RNA e sequenci\u00e1-las. Esta t\u00e9cnica recente \u00e9 chamada de seq\u00fcenciamento de RNA de c\u00e9lula \u00fanica ou scRNA-seq<\/a>. Neste artigo da Natureza, discutindo o m\u00e9todo e suas vantagens, eles mencionam que<\/p>\n

\u00c9 muito mais dif\u00edcil manipular c\u00e9lulas individuais do que grandes popula\u00e7\u00f5es, e como cada c\u00e9lula produz apenas uma pequena quantidade de RNA, n\u00e3o h\u00e1 espa\u00e7o para erros. Outro problema \u00e9 analisar as enormes quantidades de dados que resultam - at\u00e9 porque as ferramentas utilizadas podem ser pouco intuitivas.<\/p><\/blockquote>\n

Uma excelente revis\u00e3o do fluxo de trabalho e ferramentas para o scRNA-seq \u00e9 dada aqui: https:\/\/doi.org\/10.3389\/fgene.2016.00163<\/span><\/a><\/p>\n

\n

Precisa de ajuda com a consultoria de Bi\u00f3logo Computacional<\/a>? Alugue um Biologia Computacional Freelance<\/a> especialista<\/b>\u00a0em Kolabtree. \u00c9 gr\u00e1tis para postar seu projeto e receber or\u00e7amentos.<\/b><\/p>\n

Quer consultar a Dra. Yennamalli sobre um projeto? Entre em contato com ele em Kolabtree aqui<\/a>.<\/p>\n

Peritos relacionados:
\nContratar um bioinform\u00e1tico<\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Contratar um Bi\u00f3logo Molecular\u00a0<\/a> \u00a0 \u00a0 Contratar um bioestat\u00edstico<\/a><\/p>","protected":false},"excerpt":{"rendered":"

Computational biology is rapidly evolving with the advent of new technologies, especially in the way we collect, analyze and visualize data. Dr. Ragothaman Yennmalli, a Kolabtree freelancer and scientist, examines four promising advances.\u00a0\u00a0 Following up on the previous introductory post, here I will highlight some of the recent trends or recent advances in the biological<\/p>\n

Leia mais<\/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\/pt\/4-recent-advances-in-computational-biology\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\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\/pt\/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=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ragothaman Yennamalli\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutos\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Computational biology: 4 recent advances - Kolabtree Blog","description":"Recent advances in computational biology that are changing the way we look at genomic data, structural biology and RNA sequencing.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.kolabtree.com\/blog\/pt\/4-recent-advances-in-computational-biology\/","og_locale":"pt_BR","og_type":"article","og_title":"4 Recent Advances in Computational Biology","og_description":"Recent advances in computational biology that are changing the way we look at genomic data, structural biology and RNA sequencing.","og_url":"https:\/\/www.kolabtree.com\/blog\/pt\/4-recent-advances-in-computational-biology\/","og_site_name":"The Kolabtree Blog","article_publisher":"https:\/\/www.facebook.com\/kolabtree","article_published_time":"2017-07-07T12:23:36+00:00","article_modified_time":"2020-02-10T14:12:18+00:00","og_image":[{"width":1920,"height":745,"url":"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2017\/07\/alat2-872522_1920.jpg","type":"image\/jpeg"}],"author":"Ragothaman Yennamalli","twitter_card":"summary_large_image","twitter_creator":"@kolabtree","twitter_site":"@kolabtree","twitter_misc":{"Escrito por":"Ragothaman Yennamalli","Est. tempo de leitura":"4 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/#article","isPartOf":{"@id":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/"},"author":{"name":"Ragothaman Yennamalli","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/person\/61d4584c2ca630dcee91e7a79c417693"},"headline":"4 Recent Advances in Computational Biology","datePublished":"2017-07-07T12:23:36+00:00","dateModified":"2020-02-10T14:12:18+00:00","mainEntityOfPage":{"@id":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/"},"wordCount":805,"commentCount":0,"publisher":{"@id":"https:\/\/www.kolabtree.com\/blog\/#organization"},"articleSection":["Biotechnology","Data Science","Guest posts","Research"],"inLanguage":"pt-BR","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/","url":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/","name":"Computational biology: 4 recent advances - Kolabtree Blog","isPartOf":{"@id":"https:\/\/www.kolabtree.com\/blog\/#website"},"datePublished":"2017-07-07T12:23:36+00:00","dateModified":"2020-02-10T14:12:18+00:00","description":"Recent advances in computational biology that are changing the way we look at genomic data, structural biology and RNA sequencing.","breadcrumb":{"@id":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.kolabtree.com\/blog\/4-recent-advances-in-computational-biology\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.kolabtree.com\/blog\/"},{"@type":"ListItem","position":2,"name":"4 Recent Advances in Computational Biology"}]},{"@type":"WebSite","@id":"https:\/\/www.kolabtree.com\/blog\/#website","url":"https:\/\/www.kolabtree.com\/blog\/","name":"The Kolabtree Blog","description":"Expert Views on Science, Innovation and Product Development","publisher":{"@id":"https:\/\/www.kolabtree.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.kolabtree.com\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/www.kolabtree.com\/blog\/#organization","name":"Kolabtree","url":"https:\/\/www.kolabtree.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/logo\/image\/","url":"","contentUrl":"","caption":"Kolabtree"},"image":{"@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/kolabtree","https:\/\/twitter.com\/kolabtree","https:\/\/instagram.com\/kolabtree","https:\/\/www.linkedin.com\/company\/kolabtree","https:\/\/en.m.wikipedia.org\/wiki\/Kolabtree"]},{"@type":"Person","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/person\/61d4584c2ca630dcee91e7a79c417693","name":"Ragothaman Yennamalli","image":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2017\/06\/raghu_bt-96x96.jpg","contentUrl":"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2017\/06\/raghu_bt-96x96.jpg","caption":"Ragothaman Yennamalli"},"description":"Dr. Ragothaman Yennamalli completed his PhD in Computational Biology and Bioinformatics in 2008 from Jawaharlal Nehru University, New Delhi. 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\/pt\/author\/ragothaman\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/posts\/2288"}],"collection":[{"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/users\/26"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/comments?post=2288"}],"version-history":[{"count":19,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/posts\/2288\/revisions"}],"predecessor-version":[{"id":6896,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/posts\/2288\/revisions\/6896"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/media\/2324"}],"wp:attachment":[{"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/media?parent=2288"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/categories?post=2288"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/pt\/wp-json\/wp\/v2\/tags?post=2288"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}