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  6. How Much Does Bmcbioinformatics.biomedcentral.com Make
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We are analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-595.

Title:
BIGSdb: Scalable analysis of bacterial genome variation at the population level | BMC Bioinformatics | Full Text
Description:
Background The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner. Results The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into
Website Age:
25 years and 11 months (reg. 1999-08-06).

Matching Content Categories {📚}

  • Education
  • Science
  • Technology & Computing

Content Management System {📝}

What CMS is bmcbioinformatics.biomedcentral.com built with?

Custom-built

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

Traffic Estimate {📈}

What is the average monthly size of bmcbioinformatics.biomedcentral.com audience?

🚀 Good Traffic: 50k - 100k visitors per month


Based on our best estimate, this website will receive around 50,219 visitors per month in the current month.

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How Does Bmcbioinformatics.biomedcentral.com Make Money? {💸}


Display Ads {🎯}


The website utilizes display ads within its content to generate revenue. Check the next section for further revenue estimates.

Ads are managed by yourbow.com. Particular relationships are as follows:

Direct Advertisers (3)
yourbow.com, google.com, doceree.com

How Much Does Bmcbioinformatics.biomedcentral.com Make? {💰}


Display Ads {🎯}

$700 per month
Our analysis indicates Bmcbioinformatics.biomedcentral.com generates between $464 and $1,276 monthly online from display ads.

Keywords {🔍}

pubmed, sequence, data, article, google, scholar, genome, loci, central, cas, database, analysis, isolate, typing, sequences, bigsdb, bacterial, population, isolates, mlst, number, databases, information, genomes, multilocus, allele, sequencing, locus, defined, maiden, bmc, allowing, schemes, multiple, single, studies, approach, software, system, species, bioinformatics, nucleotide, authentication, provenance, figure, authors, jolley, streptococcus, individual, blast,

Topics {✒️}

springer nature home page[http implementation bigsdb jolley & martin cj maiden methicillin-resistant staphylococcus aureus availability enabling fine-grained control multi-locus sequence typing main index page author information authors open access article client-side javascript makes main results tables tab-delimited text format authors scientific editing multilocus sequence typing factor h-binding protein web-accessible database system markowitz vm short-read solexa data fine-grained authentication results design philosophy conserved genomic sequence high-quality reference genome standard query interface cross-reference typing methods privacy choices/manage cookies identifying gene-length regions allele content continents reveals hypervirulent article jolley authors’ original file extended-fasta format suitable reference gene-based analysis specimen-centric view required multilocus typing data illumina solexa reads existing genomic methods //perl-md5-login define variable regions multilocus sequence data high-throughput genome sequencing allowing additional features client-side javascript extended sequence typing laboratory samples disease management multi-fasta format existing sequence typing org/software/database/bigsdb/

Schema {🗺️}

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         description:The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner. The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses. Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches. LIMS functionality of the software enables linkage to and organisation of laboratory samples. The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database. Some of the applications of BIGSDB are illustrated with the genera Neisseria and Streptococcus. The BIGSDB source code and documentation are available at http://pubmlst.org/software/database/bigsdb/ . Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies. BIGSDB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.
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      description:The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner. The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses. Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches. LIMS functionality of the software enables linkage to and organisation of laboratory samples. The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database. Some of the applications of BIGSDB are illustrated with the genera Neisseria and Streptococcus. The BIGSDB source code and documentation are available at http://pubmlst.org/software/database/bigsdb/ . Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies. BIGSDB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.
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         Computer Appl. in Life Sciences
         Algorithms
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