{"id":1955,"date":"2014-09-29T12:00:46","date_gmt":"2014-09-29T16:00:46","guid":{"rendered":"https:\/\/blogs.nature.com\/methagora\/?p=1955"},"modified":"2014-09-29T13:25:46","modified_gmt":"2014-09-29T17:25:46","slug":"analyzing-high-throughput-sequencing-data","status":"publish","type":"post","link":"https:\/\/blogs.nature.com\/methagora\/2014\/09\/analyzing-high-throughput-sequencing-data.html","title":{"rendered":"Analyzing high throughput sequencing data"},"content":{"rendered":"

Nature Methods has published popular analysis tools to make sense of the ever-increasing amount of high throughput (HTP) sequencing data. Some tools in this field have a short half life, due to pressure to always improve and innovate, others have staying power. Let\u2019s look back over some of the highlights in our pages.<\/strong><\/p>\n

Mapping and assembling genomic reads<\/b><\/p>\n

One of the first steps in any sequence analysis pipeline is base-calling and in 2008 Yaniv Erlich with Gregory Hannon improved the calling errors in Illumina data with their Alta-Cyclic<\/a> that uses machine learning to reduce noise.<\/p>\n

Once bases are called they most often need to be aligned to a reference, and high speed, sensitivity and accuracy are key requirements for mapping tools. In 2009 Paul Flicek and Ewan Birney discussed the basic principles behind methods for read alignment and assembly<\/a>, and since then many more read mappers have been written. mrsFAST<\/a> is a cache-oblivious, seed and extend, \u00a0short read mapper presented in 2010 by Cenk Sahinalp and colleagues. Bowtie2<\/a> by Ben Langmead and Steven Salzberg, a gapped read aligner,\u00a0 promises exceptional speed and accuracy.\u00a0 The GEM mapper<\/a> by Paolo Ribeca and colleagues combines speed with an exhaustive search that returns all existing matches.<\/p>\n

If no reference genome is available de novo<\/i> assembly is the way to go. Many tools for genome assembly have been published but in 2010 Evan Eichler and colleagues demonstrated some of the limitations of popular assemblers<\/a> used for the human genome. The ongoing high citation level of this paper and other work pointing out limits in current assembly programs highlight that de novo<\/i> read assembly continues to be a challenge.<\/p>\n

Finding structural variants<\/b><\/p>\n

In 2009 Paul Medvedev and Michael Brudno looked at tools to discover structural variants<\/a>\u00a0 and later the same year they presented MoDIL<\/a>, an insertion-deletion (indel) finder that focuses on a size range of 20-50 base pairs. Ken Chen et al. \u00a0published the aptly named BreakDancer<\/a>, a tool to predict a wide variety of structural variation ranging in size from 10 base pairs to 1 megabase. \u00a0In 2011 Evan Eichler and colleagues added Splitread<\/a> to find indels, de novo structural variants and copy number polymorphisms with high specificity and sensitivity. More recently in 2013, DeNovoGear<\/a> from Donald Conrad and colleagues showed high validation rates in finding de novo<\/i> indels in somatic tissue. \u00a0This year Scalpel,<\/a> written by Michael Schatz and colleagues, came on the scene; a combination of mapping and de novo<\/i> assembly allows it to detect transmitted as well as new indels in exome data.<\/p>\n

Handling RNA-seq data<\/b><\/p>\n

In 2008 Mortazavi et al.<\/a> \u00a0and Cloonan et al.<\/a> published one of the first RNA-seq papers in our pages and in 2009 Wold and Mortazavi presented and overview of tools for RNA-seq data<\/a> analysis and the principles behind them. And since then the number of RNA-seq analysis tools has grown steadily throughout the literature.<\/p>\n

To assess differential expression in RNA-seq data Malachi Griffith et al. wrote ALEXA-seq<\/a> in 2010. The same year Chris Burge and colleagues published the MISO model<\/a> to estimate expression of alternatively spliced exons and isoforms. Inanc Birol and colleagues presented Trans-ABySS<\/a> for de novo<\/i> transcriptome assembly. And a year later Manuel Garber and colleagues discussed the challenges in trans<\/ins>sn<\/del>criptome mapping, reconstruction and expression quantification<\/a>.<\/p>\n

Last year Paul Bertone and colleagues from the RGASP consortium compared popular tools for spliced alignment<\/a>.\u00a0 And they looked at the performance of software to reconstruct transcripts<\/a>.<\/p>\n

David Haussler and colleagues showed in 2010 with FragSeq<\/a> that RNA-seq data can also be used to probe the structure of a transcript.\u00a0 And by combining SHAPE with HTP sequencing in their SHAPE-MaP<\/a> approach Kevin Weeks and colleagues showed this year that RNA functional motifs can be discovered in their structure.<\/p>\n

Despite the many computational tools we have published it is still not always easy to predict a priori<\/i> which one will be taken up by the community. We\u2019d love to hear from you what you think makes a top notch analysis tool.<\/p>\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Nature Methods has published popular analysis tools to make sense of the ever-increasing amount of high throughput (HTP) sequencing data. Some tools in this field have a short half life, due to pressure to always improve and innovate, others have staying power. Let\u2019s look back over some of the highlights in our pages.  Read more<\/a> Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":101,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[177,19,12,17],"tags":[66,185,131,31],"class_list":["post-1955","post","type-post","status-publish","format-standard","hentry","category-bioinformatics-2","category-computational","category-geneticsgenomics","category-sequencing","tag-algorithms","tag-genomics","tag-sequencing-2","tag-software"],"_links":{"self":[{"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/posts\/1955","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/users\/101"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/comments?post=1955"}],"version-history":[{"count":0,"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/posts\/1955\/revisions"}],"wp:attachment":[{"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/media?parent=1955"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/categories?post=1955"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.nature.com\/methagora\/wp-json\/wp\/v2\/tags?post=1955"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}