Re: [PATCH] gnu: Add r-centipede.

[Ben Woodcroft <b.woodcroft@uq.edu.au>]

On 03/05/16 00:49, Ricardo Wurmus wrote:
> * gnu/packages/bioinformatics.scm (r-centipede): New variable.
> ---
>   gnu/packages/bioinformatics.scm | 21 +++++++++++++++++++++
>   1 file changed, 21 insertions(+)
>
> diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm
> index 7d025ef..d7957cf 100644
> --- a/gnu/packages/bioinformatics.scm
> +++ b/gnu/packages/bioinformatics.scm
> @@ -441,6 +441,27 @@ pybedtools extends BEDTools by offering feature-level manipulations from with
>   Python.")
>       (license license:gpl2+)))
>   
> +(define-public r-centipede
> +  (package
> +    (name "r-centipede")
> +    (version "1.2")
> +    (source (origin
> +              (method url-fetch)
> +              (uri (string-append "http://download.r-forge.r-project.org/"
> +                                  "src/contrib/CENTIPEDE_" version ".tar.gz"))
> +              (sha256
> +               (base32
> +                "1hsx6qgwr0i67fhy9257zj7s0ppncph2hjgbia5nn6nfmj0ax6l9"))))
> +    (build-system r-build-system)
> +    (home-page "http://centipede.uchicago.edu/")
> +    (synopsis "Predict transcription factor binding sites")
> +    (description
> +     "Centipede fits a bayesian hierarchical mixture model to learn
> +transcription-factor-specific distribution of experimental data on a
> +particular cell-type for a set of candidate binding sites described by a
> +genetic motif.")
Perhaps this is just personal opinion but I prefer not to make the 
suggestion that experiments can only be done in the lab.

Also I don't think that sentence makes sense grammatically - 
s/distribution/distributions/ but even then, it doesn't learn the 
experimental data.
Maybe steal from the website, cut down a bit from this?
 >CENTIPEDE applies a hierarchical Bayesian mixture model to infer 
regions of the genome that are bound by particular transcription 
factors. It starts by identifying a set of candidate binding sites 
(e.g., sites that match a certain position weight matrix (PWM)), and 
then aims to classify the sites according to whether each site is bound 
or not bound by a TF. CENTIPEDE is an unsupervised learning algorithm 
that discriminates between two different types of motif instances using 
as much relevant information as possible.

Thanks,
ben