Freiburg RNA Tools
CRISPRloci - Results
BIF
IFF
CRISPRloci 0475185

Input and runtime details for job 0475185 (precomputed example)

Input Parameters

? Sequence(s)[.fa]
? Sequence type CRISPR repeat(s) only (FASTA format)

Genome information

? DNA sequence completeness complete

Parameters concerning CRISPR arrays

? CRISPR array orientation prediction Yes
? ML model to use all
? Detect the IS-element Yes
? Compute degenerated repeat Yes
? Fast run mode No
? Enhancement of the predicted array Yes
? Enhancement of the start and end of the array Yes
? Min. repeat length in the array21
? Max. repeat length in the array55
? Min. spacer length in the array18
? Max. spacer length in the array78
? Min. number of repeats in the array3
? Max. edit distance for evaluated array enhancement6
? Max. number of identical spacers in the array4
? Max. number of consecutive identical spacers in the array3
? Max. length of the spacer's margin for the degenerated search30

Parameters concerning Cas genes

? ML model to run combination of both
? Select the classifiers ERT
? Select the regressors ERT
? Max. number of contiguous gaps in a cassette2

Parameters for CRISPR repeat input

? Hit sensitivity (e-value threshold)0.01

Parameters for Virus DNA/RNA input

? Hit sensitivity (e-value threshold)0.000001

Job ID 0475185 (server version trunk)

?Job Submitted & Queued@ Fri Dec 18 08:00:22 CET 2020
?CRISPRloci Started@ Fri Dec 18 08:00:39 CET 2020
?CRISPRloci Finished & Post-Processing@ Fri Dec 18 08:01:02 CET 2020
?Job Completed@ Fri Dec 18 08:01:05 CET 2020
 DIRECT ACCESS: http://rna.informatik.uni-freiburg.de/RetrieveResults.jsp?jobID=0475185&toolName=CRISPRloci ( 30 days expiry )

Description of the job

Repeat mode

Output

Download an archive file containing the complete results produced by the job in [.zip] format.

Results file content

Namespace(cross_validation=False, input_files=['/scratch/rna/bisge001/RNA_results/CARNA-result//CRISPRloci_0475185//input.fa'], model_path='Models/model_r.h5', output_folder='Results', repeat_type=True, training=False, usecols=[0, 5]) Model: "model_1" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 4, 38, 1)] 0 __________________________________________________________________________________________________ conv2d_1 (Conv2D) (None, 1, 35, 32) 512 input_1[0][0] __________________________________________________________________________________________________ conv2d_2 (Conv2D) (None, 1, 34, 32) 640 input_1[0][0] __________________________________________________________________________________________________ conv2d_3 (Conv2D) (None, 1, 33, 32) 768 input_1[0][0] __________________________________________________________________________________________________ conv2d_4 (Conv2D) (None, 1, 31, 32) 1024 input_1[0][0] __________________________________________________________________________________________________ conv2d_5 (Conv2D) (None, 1, 29, 32) 1280 input_1[0][0] __________________________________________________________________________________________________ activation_1 (Activation) (None, 1, 35, 32) 0 conv2d_1[0][0] __________________________________________________________________________________________________ activation_2 (Activation) (None, 1, 34, 32) 0 conv2d_2[0][0] __________________________________________________________________________________________________ activation_3 (Activation) (None, 1, 33, 32) 0 conv2d_3[0][0] __________________________________________________________________________________________________ activation_4 (Activation) (None, 1, 31, 32) 0 conv2d_4[0][0] __________________________________________________________________________________________________ activation_5 (Activation) (None, 1, 29, 32) 0 conv2d_5[0][0] __________________________________________________________________________________________________ batch_normalization_1 (BatchNor (None, 1, 35, 32) 128 activation_1[0][0] __________________________________________________________________________________________________ batch_normalization_2 (BatchNor (None, 1, 34, 32) 128 activation_2[0][0] __________________________________________________________________________________________________ batch_normalization_3 (BatchNor (None, 1, 33, 32) 128 activation_3[0][0] __________________________________________________________________________________________________ batch_normalization_4 (BatchNor (None, 1, 31, 32) 128 activation_4[0][0] __________________________________________________________________________________________________ batch_normalization_5 (BatchNor (None, 1, 29, 32) 128 activation_5[0][0] __________________________________________________________________________________________________ global_max_pooling2d_1 (GlobalM (None, 32) 0 batch_normalization_1[0][0] __________________________________________________________________________________________________ global_max_pooling2d_2 (GlobalM (None, 32) 0 batch_normalization_2[0][0] __________________________________________________________________________________________________ global_max_pooling2d_3 (GlobalM (None, 32) 0 batch_normalization_3[0][0] __________________________________________________________________________________________________ global_max_pooling2d_4 (GlobalM (None, 32) 0 batch_normalization_4[0][0] __________________________________________________________________________________________________ global_max_pooling2d_5 (GlobalM (None, 32) 0 batch_normalization_5[0][0] __________________________________________________________________________________________________ cutoff_layer (Concatenate) (None, 160) 0 global_max_pooling2d_1[0][0] global_max_pooling2d_2[0][0] global_max_pooling2d_3[0][0] global_max_pooling2d_4[0][0] global_max_pooling2d_5[0][0] __________________________________________________________________________________________________ dense_1 (Dense) (None, 256) 41216 cutoff_layer[0][0] __________________________________________________________________________________________________ activation_6 (Activation) (None, 256) 0 dense_1[0][0] __________________________________________________________________________________________________ dropout_1 (Dropout) (None, 256) 0 activation_6[0][0] __________________________________________________________________________________________________ dense_2 (Dense) (None, 32) 8224 dropout_1[0][0] __________________________________________________________________________________________________ activation_7 (Activation) (None, 32) 0 dense_2[0][0] __________________________________________________________________________________________________ dropout_2 (Dropout) (None, 32) 0 activation_7[0][0] __________________________________________________________________________________________________ dense_3 (Dense) (None, 1) 33 dropout_2[0][0] __________________________________________________________________________________________________ activation_8 (Activation) (None, 1) 0 dense_3[0][0] ================================================================================================== Total params: 54,337 Trainable params: 54,017 Non-trainable params: 320 __________________________________________________________________________________________________ Reading the files... GTTCACTGCCGTACAGGCAGCTAAGAAA I-F 0.9993531107902527 CTTTCCTTCTACTAATCCCGGCGATCGGGACTGAAAC I-D 0.924616813659668 GTCTCCACTCGTAGGAGAAATTAATTGATTGGAAAC III-B 0.8701797723770142 GATCGATACCCACCCCGAAGAAAAGGGGACGAGAAC III-A 0.9270436763763428 GTTGCACCGGCCCGAAAGGGCCGGTGAGGATTGAAAC I-C 0.9510350227355957 ATTCGCGAGCAAGATCCATTAAAACAAGGATTGAAAC I-B 0.9011879563331604 0.5833333333333334 ################################################################################### ########## CRISPR: Master Script ######### ################################################################################### Directory already exists: /scratch/rna/bisge001/RNA_results/CARNA-result//CRISPRloci_0475185/

When using CRISPRloci please cite :

Results are computed with CRISPRloci version 1.1.0