MISO (Mixture of Isoforms) is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples. By modeling the generative process by which reads are produced from isoforms in RNA-Seq, the MISO model uses Bayesian inference to compute the probability that a read originated from a particular isoform.

MISO uses the inferred assignment of reads to isoforms to quantitate the abundances of the underlying set of alternative mRNA isoforms. Confidence intervals over estimates can be obtained, which quantify the reliability of the estimates.



Version on CSC's Servers

Taito: 0.4.8, 0.5.2
FGI grid: 0.4.7


Running MISO batch jobs in Taito

In Taito, MISO is initialized with command:

module load miso

In Taito, MISO jobs should be run as batch jobs. Below is a sample batch job file, for running a MISO job in Taito:

#!/bin/bash -l
#SBATCH -J miso
#SBATCH -o miso%j.txt
#SBATCH -e miso%j.txt
#SBATCH -t 24:00:00
#SBATCH --mem-per-cpu=12288

module load python-env/2.7.3
module load miso

## Compute Psi values for sample 1 --run=events/ inputBAM/test.sorted.bam \
--output-dir miso_output --read-len 76 --use-cluster

## Summarize the output --summarize-samples miso-output/ summaries/ 

In the batch job example above, the command is launched with command line option --use-cluster. This option automatically split the task into number of sub-tasks that can be run in parallel fashion in the Taito cluster. This speeds up the analysis significantly.

You can submit the batch job file to the batch job system with command:

sbatch batch_job_file.bash

See the chapter 3 of the Taito user guide for more information about running batch jobs.