[miso-users] MISO with multiple threads?

[Yarden Katz]

Hi Holger,

There is currently no support for multiple processors, though it should be possible to add that via Python's subprocess and multicore modules (this is on our list.)  The built-in support for cluster usage simply generates a bunch of shell scripts that call MISO, and so these could be used as input to a "dispatcher" script that submits executes each shell script on a separate process of the same machine.  It's on our list of things to add, as I mentioned, but we don't have it yet as a feature.

However, we recently released a fast version of MISO, written in the C language, that is 60-100x faster than the pure Python version.  See instructions below for how to use it.  This version comes with a Python interface that is identical to the current MISO interface, so you wouldn't have to change anything on your end, except compiling the C version.  We'd love any comments or feedback you might have on this version.  

Hope this helps.

Best, --Yarden

==
The C version of MISO ("fastmiso") is now available for testing.  The C version of MISO was written by Gábor Csárdi (Harvard Statistics Dept).  

The C code is 60-100x faster than the Python-only version per event/gene and scales much better to high coverage RNA-Seq samples.  A cluster shouldn't be necessary to use MISO anymore, but it still helps considerably since the problem is highly parallelizable.

You can get the new version from the "fastmiso" branch of github (see instructions below for installation.) 

The C version comes with a Python interface which is identical to the original Python-only version of MISO.  The interface and output files are in an identical format, so no change will require on the part of the users apart from compiling the C code (which is invoked under the hood by the Python interface).

There is an R interface to MISO as well (as alternative to Python one), written by Gábor, and documentation for this version will be made available in the future too.

The current version still has some bottlenecks, which all arise from I/O costs (reading of GFF files, accessing of BAM files) and we are trying to optimize these as well.  But the good news is that the actual inference is no longer a bottleneck.

We'd greatly appreciate any comments/issues/questions you might have.  If possible, please check these results against your previous MISO runs -- they should highly correlated.

==
USING THE C VERSION OF MISO:

1. Get the C version from github, as follows.  First check out the MISO repository, if you haven't already:

$ git clone git at github.com:yarden/MISO.git

Then checkout the "fastmiso" branch:

$ git checkout fastmiso

Now compile the relevant packages:

$ make

Then compile the Python interface

$ cd pysplicing/
$ python setup.py install 

Note: if you are compiling on a Unix system where you do not have root access, use the --prefix option, e.g.:

$ python setup.py install --prefix=~/ 

to install the Python interface in your home directory.

Test that the version works through Python:

$ cd ~/
$ python
>> import pysplicing
>> 

If you get an error after the import, something went wrong. 

On Sep 14, 2011, at 3:44 AM, Holger Brandl wrote:

> Hi,
> 
> I've discovered MISO recently, figured out how to prepare a gff3, and did the 
> indexing as described in the manual. When computing the expression estimates 
> using run_events_analysis.py MISO just uses one single core (which takes 
> forever). Is there a way to convince it to use several cores? (similar to the -p 
> option in cufflinks) I'm aware of the cluster-option, but I don't now 
> whether/how this also would/could work locally.
> 
> Best,
> Holger Brandl
> 
> -- 
> Dr. Holger Brandl
> Bioinformatics Service
> Max Planck Institute of Molecular Cell Biology and Genetics
> Pfotenhauerstrasse 108
> 01307 Dresden, Germany
> 
> Tel.:   +49/351/210-2738
> Fax:    +49 351 210 2000
> www:http://www.mpi-cbg.de
> 
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