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BPnet é o portal de informação do Banco de Portugal, onde pode consultar as instruções e cartas circulares que regulam as operações bancárias e financeiras. Veja as últimas atualizações sobre depósitos e levantamentos de notas e moedas de euro, sistemas inteligentes de neutralização e outros temas.
BPnet é o portal do Banco de Portugal para a submissão de pedidos de autorização para o exercício de funções em entidades sujeitas à supervisão do Banco de Portugal. Saiba como aceder ao portal, quais os serviços disponíveis e quais as instituições que devem usar o Portal IMAS.
- Overview
- Getting started
- Main commands
- Main python classes
- Installation
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BPNet is a python package with a CLI to train and interpret base-resolution deep neural networks trained on functional genomics data such as ChIP-nexus or ChIP-seq. It addresses the problem of pinpointing the regulatory elements in the genome:
Specifically, it aims to answer the following questions:
•What are the sequence motifs?
•Where are they located in the genome?
•How do they interact?
For more information, see the BPNet manuscript:
Main documentation of the bpnet package and an end-to-end example higlighting the main features are contained in the following colab notebook https://colab.research.google.com/drive/1VNsNBfugPJfJ02LBgvPwj-gPK0L_djsD. You can run this notebook yourself by clicking on 'Open in playground'. Individual cells of this notebook can be executed by pressing the Shift+Enter keyboard shortcut.
To learn more about colab, visit https://colab.research.google.com and follow the 'Welcome To Colaboratory' notebook.
Compute data statistics to inform hyper-parameter selection such as choosing to trade off profile vs total count loss (lambda hyper-parameter):
Train a model on BigWig tracks specified in dataspec.yml using an existing architecture bpnet9 on 200 bp sequences with 6 dilated convolutional layers:
Compute contribution scores for regions specified in the dataspec.yml file and store them into contrib.scores.h5
Export BigWig tracks containing model predictions and contribution scores
Discover motifs with TF-MoDISco using contribution scores stored in contrib.scores.h5, premade configuration modisco-50k and restricting the number of seqlets per metacluster to 20k:
Determine motif instances with CWM scanning and store them to motif-instances.tsv.gz
•bpnet.seqmodel.SeqModel - Keras model container specified by implementing output 'heads' and a common 'body'. It contains methods to compute the contribution scores of the input sequence w.r.t. differnet output heads.
•bpnet.BPNet.BPNetSeqModel - Wrapper around SeqModel consolidating profile and total count predictions into a single output per task. It provides methods to export predictions and contribution scores to BigWig files as well as methods to simulate the spacing between two motifs.
•bpnet.cli.contrib.ContribFile - File handle to the HDF5 containing the contribution scores
•bpnet.modisco.files.ModiscoFile - File handle to the HDF5 file produced by TF-MoDISco.
•bpnet.modisco.core.Pattern - Object containing the PFM, CWM and optionally the signal footprint
•bpnet.modisco.core.Seqlet - Object containing the seqlet coordinates.
Supported python version is 3.6. After installing anaconda (download page) or miniconda (download page), create a new bpnet environment by executing the following code:
Alternatively, you could also start a fresh conda environment by running the following
BPNet is a toolkit to train and interpret base-resolution neural networks on ChIP-nexus or ChIP-seq data. It can identify regulatory elements, motifs, and interactions in the genome using contribution scores and TF-MoDISco.
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