DeepCRISPR: sgRNA Off-Targets

Chuai et al. |

DeepCRISPR: sgRNA Off-Targets

Omics

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API Example

Step 1: Upload your data

Upload Source Molecules

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Your file can be in the following formats:csv

Your data requires the following column headings: 1. target_seq 2. target_ctcf 3. target_dnase 4. target_h3k4me3 5. target_rrbs 6. off_target_seq 7. off_target_ctcf 8. off_target_dnase 9. off_target_h3k4me3 10. off_target_rrbs Epigenetic features are represented by sequences of As and Ns where A represents 1 meaning a signal located and N represents 0 meaning no signal. For example: +------------+------+------+ | target_seq | ctcf | rrbs | +------------+------+------+ | CTT… | AAA… | NNN… | +------------+------+------+

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Step 2: Set Parameters

Prediction Method

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Step 3: Complete run profile

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An efficient and extendable computational model for prediction of CRISPR sgRNA off-target profile, facilitating optimized sgRNA design with high sensitivity and specificity

Example use case: Epigenetics research

Limitations: Different off-target assays may have different sensitivities

Technology: Convolutional neural network-based deep learning network, trained on two different cell types: 293-related cell lines (18 sgRNAs) and K562 t (12 sgRNAs)

Metrics: As reported by Chuai et al.

Citation:

Guohui Chuai, Qi Liu et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. 2018

Released:
Sep-14-2022

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