Week 34
Optopace
Weekly summary 14.-20.08.2017 CG
====
Opto PACE
===
Primer for Opto PACE arrived and cloning was started.
Phage propagation of the **unevolved** Dickinson phage
===
Phage supernatant of the unevolved Dickinson phage: target_133_N-term_T7-C was received from Dickinson group. To propagate the phages, 4 ml *E. coli* culture (Stock ID: 47) was cultivated to an OD600 of 0.6 (in LB media + 25 mM Glucose + Amp) and infected with 4 µl of the phage supernatant. Culture was shaked at 37 °C overnight. On the next morning culture was centrifuged at 6,000 g for 5 min and supernatant, which contains the phages, was stored at 4 °C.
A Blue Plaque Assay was performed to determine the phage titer of the supernatant. 143 plaques were counted at the 10-10 dilution, which leads to a phage titer of 1.43*1015 PFU/ml.
A plaque of this plate was picked to infect a 4 ml *E. coli* culture (Stock ID: 47) with an OD600 of 0.4. This culture was cultivated for two hours shaking at 37 °C and was subsequently transferred to 100 ml fresh 2xYT medium. After 1 hour carbenicillin (1000x) was add.
On the next day, culture was centrifuged at 3640 g for 20 min. Supernatant was stored at 4 °C.
A Blue Plaque Assay was performed to determine the phage titer of the supernatant. The monoclonal Dickinson phage target_133_N-term_T7-C exhibited a phage titer of 1.85*109 PFU/ml.
Software
KW34
=====
Word2Vec Embeddings on Proteinsequences
---------------------
We rewrote a word2vec implementation from tensorflows tutorials that implements Efficient Estimation of Word Representations in Vector Space, ICLR 2013 (Mikolov, et. al.). The model is a skipgram model with negative sample that uses custom ops written in C. The code was adapted to our needs, mainly by changing datatypes in the C kernels and writing a different evaluation function based on predicting the nearest words to the most frequent words instead of using analogies. Two new datasets were generated based on both swissprot and uniprot. Training of 4mer embeddings in 50, 100 and 200 dimensions were started but have not been calculated yet.
Visualisation of the first checkpoints is possible via tensorboard [Visualisation of an example embedding via tensorboard](170820ai-vistestemb).
IMPLEMENTATION OF SQUEEZENET Architecture
---------------------------------
With implamentation of a new architecture based on Sequeeze-net (Forrest N. Iandola, 2017), relying on 1x1 convolutions we were able to grasp the 299 as well as the 637 classes dataset. The new model architecture looks the following:
- InputLayer model_valid/input_layer_valid: (64, 20, 1000, 1)
- PadLayer model_valid/block1/pad_layer_valid: paddings:[[0, 0], [0, 0], [3, 3], [0, 0]] mode:CONSTANT
- Conv2dLayer model_valid/block1/cnn_layer_valid: shape:[20, 7, 1, 128] strides:[1, 5, 1, 1] pad:VALID act:prelu
- Conv1dLayer model_valid/block2/cnn_layer_valid: shape:[6, 128, 128] stride:1 pad:SAME act:prelu
- Conv1dLayer model_valid/1x1_I/1x1_valid: shape:[1, 128, 64] stride:1 pad:SAME act:prelu
- BatchNormLayer model_valid/1x1_I/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block3/cnn_layer_valid: shape:[5, 64, 256] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block3/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block3/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block4/cnn_layer_valid: shape:[5, 256, 256] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block4/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block4/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/1x1_II/1x1_valid: shape:[1, 256, 128] stride:1 pad:SAME act:prelu
- BatchNormLayer model_valid/1x1_II/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block5/cnn_layer_valid: shape:[5, 128, 256] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block5/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block5/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block6/cnn_layer_valid: shape:[5, 256, 512] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block6/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block6/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/1x1_III/1x1_valid: shape:[1, 512, 256] stride:1 pad:SAME act:prelu
- BatchNormLayer model_valid/1x1_III/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block7/cnn_layer_valid: shape:[5, 256, 516] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block7/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block7/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block8/cnn_layer_valid: shape:[5, 516, 1024] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block8/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block8/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/1x1_IV/cnn_layer_valid: shape:[1, 1024, 512] stride:1 pad:SAME act:prelu
- BatchNormLayer model_valid/1x1_IV/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/block9/cnn_layer_valid: shape:[5, 512, 1024] stride:1 pad:SAME act:prelu
- PoolLayer model_valid/block9/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool
- BatchNormLayer model_valid/block9/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- Conv1dLayer model_valid/outlayer/cnn_layer_valid: shape:[1, 1024, 637] stride:1 pad:SAME act:prelu
- BatchNormLayer model_valid/outlayer/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False
- MeanPool1d global_avg_pool: filter_size:[7] strides:1 padding:valid
The architecture is fully convolutional, ending in an average pooling layer as outlayer, with the channels dimension corresponding to the number of classes. All inputs were 1-hot encoded and zero padded to a boxsize of 1000 positions.
