naiveBayesCall specifications

Information


Unique identifier OMICS_01152
Name naiveBayesCall
Alternative name BayesCall
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input format INT
Biological technology Illumina
Operating system Unix/Linux
Programming languages Python
License GNU General Public License version 3.0
Computer skills Advanced
Version 0.3
Stability Stable
Requirements
SciPy, NumPy
Source code URL http://bayescall.sourceforge.net/BayesCall_0.3.tgz
Maintained Yes

Versioning


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Maintainer


  • person_outline Yun Song <>

Publications for naiveBayesCall

naiveBayesCall in publications

 (3)
PMCID: 4991838
PMID: 27436340
DOI: 10.1093/dnares/dsw029

[…] adna analysis have not yet been widely accepted, and custom scripts have to be written to adjust for adna specifics. base calling is frequently performed with illumina’s standard base-caller bustard bayescall (flexible model-based tool) and freeibis (utilizing a multiclass support vector machine algorithm). fastqc is typically used for preliminary quality control of reads. adapterremoval, […]

PMCID: 4769523
PMID: 26920804
DOI: 10.1186/s12864-016-2463-2

[…] default basecaller (bustard) is inaccurate []; thus a number of basecallers aimed at achieving better performance have been developed. they either apply a model-based strategy (e.g., ayb [], naivebayescall []) or use supervised learning approaches with an additional training set such as phix174 reads spiked into the run (e.g., ibis [], freeibis []). these approaches in general give […]

PMCID: 3178052
PMID: 21245079
DOI: 10.1093/bib/bbq077

[…] to compare. an advantage of rolexa is that it does not depend on supervised learning, thereby eliminating the need to resequence known templates for training and thereby increasing overall yield., bayescall [] and seraphim [] implement more complex, fully parametric models. in addition to cross-talk, phasing and pre-phasing, they also explicitly model the signal decay. furthermore seraphim […]


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naiveBayesCall institution(s)
Department of EECS, University of California, Berkeley, CA, USA; Department of Statistics, University of California, Berkeley, CA, USA
naiveBayesCall funding source(s)
Supported in part by an NSF CAREER grant (DBI-0846015), an Alfred P. Sloan Research Fellowship, and a Packard Fellowship for Science and Engineering.

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