There is no 'one size-fits-all' monitoring well, but there is a tool-box or ensemble of solutions that can achieve a broad range of relevant monitoring objectives given constraints of site characteristics and budgetary limitations.
Although we focus on SpCas9, particularly its use in therapeutic applications and the development of next-generation transformational drugs in T cells, the general outline described here applies to the larger ensemble of CRISPR/Cas9 tools.
Here we report on GimmeMotifs, a pipeline that incorporates an ensemble of computational tools to predict motifs de novo from ChIP-sequencing (ChIP-seq) data.
For the first-principles study, an ensemble of theoretical tools was employed within the framework of periodic density functional theory (DFT) using the Vienna Ab Initio Simulation Package (VASP).
Since ensembles of NER tools often outperform individual tools [28], we also measure the performance of two ensembles produced by merging the results of tmChem and ChemSpot.
Here, we report on GimmeMotifs, a motif prediction pipeline using a ensemble of existing computational tools (Supplementary Fig. S1).
The combined data for this study showed that at a moderate level of sequencing coverage, an ensemble of platforms and tools can be applied together to maximize the accurate detection of sequence and structural variants.
Furthermore, our results suggest that, at the level of resolution and sequencing coverage found in the present study, an ensemble of platforms and tools can be used to maximize the detection of SV; however, the false positive rates should be controlled by applying threshold settings and performing subset validation experiments.
As of May 2015, the machine learning tools offered cover a range of tasks, including classification, clustering, generalized linear models, statistical analysis, ensembles, optimization tools, data preprocessing options and deep neural networks [143].
