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DICEseq: Dynamic Isoform spliCing Estimator via sequencing data

Project description

About DICEseq

DICEseq (Dynamic Isoform spliCing Estimator via sequencing data) estimates the dynamics of isoform proportions jointly from time series RNA-seq experiments. DICEseq is a Bayesian method based on a mixture model whose mixing proportions represent isoform ratios. It incorporates the correlations from the temporal structure, by coupling the isoform proportions at different times through a latent Gaussian process (GP).

DICEseq provides following functions through command line:

  1. diceseq: estimate the isoform proportions and FPKM for time series data jointly, or for a single time point.

  2. dice-count: fetch reads counts for entile gene, or specific reads counts, e.g. junction reads, for genes with exact one intron. This is special design mainly for yeast.

  3. dice-bias: estimate parameters for sequencing bias, including fragment length distribution, reads sequence and position bias parameter. The output file can be directly used for bias correction in diceseq.

In addition, DICEseq package also provides interface of a set of functions and attributes as an object-oriented python module. Therefore, you could use some of the module e.g., SampleFile to visualize the samples in gzip file in a Gaussian process way, or BiasFile to visualize the bias parameters. Also, the gtf_utils provides a set of ways to load gtf file, choose the genes, or customize the coordinates of exons and introns, add and remove of specific transcripts.

Quick Start

Installation:

  • pip install diceseq

  • or download this repository, and type python setup.py install.

  • You may need to add --user if you don’t have the root permission and you don’t use Anaconda.

Arguments

  • Type command line diceseq -h

Detailed Manual

See the documentation on how to install, to use, to find the annotation data etc.

References

Yuanhua Huang and Guido Sanguinetti. Statistical modeling of isoform splicing dynamics from RNA-seq time series data. Bioinformatics, 2016, 32(19): 2965-2972.

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