[Bioc-sig-seq] counts differences among multiple RNA-seq samples

[Nicolas Delhomme]

Hi Kasper,

You are correct, this sounds like a perfect Genominator use case.  
However, while working on my package, I realized that you can achieve  
the same with straight out of the box IRanges and Rsamtools/ShortRead  
functions, without having to format the data back and forth. This was  
important for me as I use many IRanges functionalities in my  
downstream analyses.

Cheers,

Nico

---------------------------------------------------------------
Nicolas Delhomme

High Throughput Functional Genomics Center

European Molecular Biology Laboratory

Tel: +49 6221 387 8310
Email: nicolas.delhomme at embl.de
Meyerhofstrasse 1 - Postfach 10.2209
69102 Heidelberg, Germany
---------------------------------------------------------------




On 15 Apr 2010, at 17:43, Kasper Daniel Hansen wrote:

> If you are mainly interested in counting, you should check out
> Genominator which has been capable of doing this for a large number of
> samples for a long time.  It should be fairly easy to use, with the
> biggest huddle usually being reading in the data at first.
>
> Kasper
>
> On Thu, Apr 15, 2010 at 11:23 AM, Nicolas Delhomme  
> <delhomme at embl.de> wrote:
>> Hi Kunbin,
>>
>> I'm currently developing an R package that does something close to  
>> what you
>> describe. Maybe we can discuss more in details what you need, off  
>> list, to
>> see if I can help you out? If it turns out to be the case, then  
>> we'll post
>> back the result to the list.
>>
>> Cheers,
>>
>> ---------------------------------------------------------------
>> Nicolas Delhomme
>>
>> High Throughput Functional Genomics Center
>>
>> European Molecular Biology Laboratory
>>
>> Tel: +49 6221 387 8310
>> Email: nicolas.delhomme at embl.de
>> Meyerhofstrasse 1 - Postfach 10.2209
>> 69102 Heidelberg, Germany
>> ---------------------------------------------------------------
>>
>>
>>
>>
>> On 3 Apr 2010, at 05:48, Kunbin Qu wrote:
>>
>>> Hi,
>>>
>>> I have run RNA-seq on 4 human samples, and I'd like to look at the  
>>> count
>>> number from each sample at regions where any of the sample has  
>>> some read
>>> coverage (say, threshold of 5 reads). What is the best way to do  
>>> this? It is
>>> basically to examine the differentially expression regions across  
>>> the
>>> transcriptome, not just limited to known annotated regions. I  
>>> having been
>>> trying to use IRanges and related packages, but things start to  
>>> get hairy
>>> when come to cluster the reads, condense them (within certain bp  
>>> range),
>>> back-track the identities. I also looked at Cufflink, but it does  
>>> not seem
>>> to be for this purpose, isn't it? Any advice is highly appreciated.
>>>
>>> -Kunbin
>>>
>>>
>>>
>>>
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