We are analyzing https://link.springer.com/article/10.1186/1471-2105-15-27.

Title:
Using single cell sequencing data to model the evolutionary history of a tumor | BMC Bioinformatics
Description:
Background The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor. Results We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations. Conclusions Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Science
Family & Parenting
Education

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We find it hard to spot revenue streams.

Not all websites focus on profit; some are designed to educate, connect people, or share useful tools. People create websites for numerous reasons. And this could be one such example. Link.springer.com might be making money, but it's not detectable how they're doing it.

Keywords {🔍}

mutation, tree, sites, sequencing, cell, order, data, single, mutations, tumor, article, time, cells, pairwise, google, scholar, samples, model, based, pubmed, figure, sample, number, relationships, graph, site, algorithm, mrca, dataset, genotype, minimal, spanning, genotypes, dna, rate, orders, probability, directed, population, prior, analysis, full, errors, branches, trees, earliest, branch, cas, evolutionary, lineages,

Topics {✒️}

/site/kyungin2013/home/muttree-codes muscle-invasive bladder cancer jak2-negative myeloproliferative neoplasm degenerate oligonucleotide-primed pcr multiple displacement amplification single-cell exome sequencing single-cell sequencing data article download pdf high-performance computational capabilities kim & richard simon negative log-posterior probabilities current sequencing technologies essential thrombocythemia generation sequencing technologies single cell sequencing single-cell sequencing privacy choices/manage cookies minimal spanning tree bmc bioinformatics 15 single cell data full size image article kim single human cell population size models cell-lineage-specific mutations recent common ancestor exome sequencing data single degenerate primer multiple α values detect genomic alterations genes’ coding region interesting findings discussed bulk ngs technology full access single cell sequences related subjects intra-tumoral heterogeneity tissue sequencing data single-cell genomics allelic dropout rate sequencing error rate pcr-free libraries authors’ original file called allelic dropout inferring tree models tumour evolution inferred clonal evolutionary models alternate approach exists high error rate constant population size

Schema {🗺️}

WebPage:
      mainEntity:
         headline:Using single cell sequencing data to model the evolutionary history of a tumor
         description:The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor. We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations. Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.
         datePublished:2014-01-24T00:00:00Z
         dateModified:2014-01-24T00:00:00Z
         pageStart:1
         pageEnd:13
         license:http://creativecommons.org/licenses/by/2.0
         sameAs:https://doi.org/10.1186/1471-2105-15-27
         keywords:
            Minimal Span Tree
            Mutation Site
            Essential Thrombocythemia
            Multiple Displacement Amplification
            Much Recent Common Ancestor
            Bioinformatics
            Microarrays
            Computational Biology/Bioinformatics
            Computer Appl. in Life Sciences
            Algorithms
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig1_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig2_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig3_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig4_HTML.jpg
         isPartOf:
            name:BMC Bioinformatics
            issn:
               1471-2105
            volumeNumber:15
            type:
               Periodical
               PublicationVolume
         publisher:
            name:BioMed Central
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Kyung In Kim
               affiliation:
                     name:National Cancer Institute
                     address:
                        name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
                        type:PostalAddress
                     type:Organization
               type:Person
               name:Richard Simon
               affiliation:
                     name:National Cancer Institute
                     address:
                        name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
         isAccessibleForFree:1
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:Using single cell sequencing data to model the evolutionary history of a tumor
      description:The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor. We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations. Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.
      datePublished:2014-01-24T00:00:00Z
      dateModified:2014-01-24T00:00:00Z
      pageStart:1
      pageEnd:13
      license:http://creativecommons.org/licenses/by/2.0
      sameAs:https://doi.org/10.1186/1471-2105-15-27
      keywords:
         Minimal Span Tree
         Mutation Site
         Essential Thrombocythemia
         Multiple Displacement Amplification
         Much Recent Common Ancestor
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig1_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig2_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig3_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-15-27/MediaObjects/12859_2013_Article_6283_Fig4_HTML.jpg
      isPartOf:
         name:BMC Bioinformatics
         issn:
            1471-2105
         volumeNumber:15
         type:
            Periodical
            PublicationVolume
      publisher:
         name:BioMed Central
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Kyung In Kim
            affiliation:
                  name:National Cancer Institute
                  address:
                     name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Richard Simon
            affiliation:
                  name:National Cancer Institute
                  address:
                     name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
      isAccessibleForFree:1
["Periodical","PublicationVolume"]:
      name:BMC Bioinformatics
      issn:
         1471-2105
      volumeNumber:15
Organization:
      name:BioMed Central
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:National Cancer Institute
      address:
         name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
         type:PostalAddress
      name:National Cancer Institute
      address:
         name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Kyung In Kim
      affiliation:
            name:National Cancer Institute
            address:
               name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
               type:PostalAddress
            type:Organization
      name:Richard Simon
      affiliation:
            name:National Cancer Institute
            address:
               name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Biometric Research Branch, National Cancer Institute, Bethesda, USA
      name:Biometric Research Branch, National Cancer Institute, Bethesda, USA

External Links {🔗}(114)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

4.8s.

LINK . SPRINGER . COM {}