A package for simulating RNA-seq library preparation with parameter estimation Long Description (required) The rlsim package is a collection of tools for simulating RNA-seq library construction, aiming to reproduce the most important factors which are known to introduce significant biases in the currently used protocols: hexamer priming, PCR amplification and size selection. It allows for a systematic exploration of the effects of the individual biasing factors and their interactions on downstream applications by simulating data under a variety of parameter sets.
The implicit simulation model implemented in the main tool (rlsim) is inspired by the actual library preparation protocols and it is more general than the models used by the bias correction methods hence it allows for a fair assessment of their performance.
Although the simulation model was kept as simple as possible in order to aid usability, it still has too many parameters to be inferred from data produced by standard RNA-seq experiments. However, simulating datasets with properties similar to specific datasets is often useful. To address this, the package provides a tool (effest) implementing simple approaches for estimating the parameters which can be recovered from standard RNA-seq data (GC-dependent amplification efficiencies, fragment size distribution, relative expression levels). None Step 1: Use the attribute tree to add new attributes or remove pre-selected attributes to describe the simulator. Every sub-attribute is selected Not all sub-attributes are selected Fill Clear Expand Collapse Reset Summary of Proposed Changes Step 2: Review list of proposed attribute addition(s) and subtraction(s). Can't Find the Attribute You Are Looking For?
If you would like to propose an attribute that you cannot find in the tree above, or if you would like to add a clarification to one or more attributes for this simulator (e.g. a specific file format for attribute /Output/File Format/Other), please list them in the
Additional Comment box of the Submit tab. Summary of Proposed Changes Current Citations/Applications ,
Realistic simulations reveal extensive sample-specificity of RNA-seq biases Quantitative Biology. Genomics,