Tutorial A5 Annotations Tangermeme V0 1 0 Documentation Let’s start off by loading the beluga model, creating a synthetic sequence that has two ap 1 motifs in it and an etv6 motif, and calculating attributions. after calculating attributions, we are done with the beluga model! annotations are represented in tangermeme as a three column tensor of indexes that reference a tensor of sequences. Essentially, tangermeme aims to implement everything except for the model that you’d like to use, including i o, identifying matched region sets, altering sequences (e.g., inserting a motif or scrambling out a motif), running marginalization experiments, and annotating regions.
Tutorial A5 Annotations Tangermeme V0 1 0 Documentation Getting started tangermeme release history operations tutorial a1: sequence manipulation tutorial a2: predictions tutorial a3: deeplift shap tutorial a4: seqlets tutorial a5: annotations tutorial b1: marginalization tutorial b2: ablation tutorial b3: spacing tutorial b4: in silico saturation mutagenesis (ism) tutorial b5: variant effect. Please see the documentation and tutorials linked at the top of this readme for more extensive documentation. if you only read one vignette, read this one: inspecting what cis regulatory features a model has learned. Documentation and tutorials are available at tangermeme. 267 readthedocs.io en latest , including tutorials that produce the figures shown in this work. While many tools exist for training models on genomic data, tangermeme focuses on the downstream analysis: understanding what models have learned, applying them to new sequences, and using them for discovery and design tasks.
Tutorial A5 Annotations Tangermeme V0 1 0 Documentation Documentation and tutorials are available at tangermeme. 267 readthedocs.io en latest , including tutorials that produce the figures shown in this work. While many tools exist for training models on genomic data, tangermeme focuses on the downstream analysis: understanding what models have learned, applying them to new sequences, and using them for discovery and design tasks. Please see the documentation and tutorials linked at the top of this readme for more extensive documentation. Here, we describe the functionality of tangermeme, a highly optimized toolkit for "everything but the model" when it comes to genomic deep learning, and demonstrate how tangermeme can be used to distill the learned cis regulatory patterns from models into human interpretable insights. Download annotation files to understand what biological entities are represented on applied biosystems genechip arrays. Here, we show some various use cases of the pre computed chrombpnet related analysis products (i.e. accessibility predictions, contribution scores, and motif istances). then, we show how to load the trained cell type specific chrombpnet model to make new predictions.
Tutorial A5 Annotations Tangermeme V0 1 0 Documentation Please see the documentation and tutorials linked at the top of this readme for more extensive documentation. Here, we describe the functionality of tangermeme, a highly optimized toolkit for "everything but the model" when it comes to genomic deep learning, and demonstrate how tangermeme can be used to distill the learned cis regulatory patterns from models into human interpretable insights. Download annotation files to understand what biological entities are represented on applied biosystems genechip arrays. Here, we show some various use cases of the pre computed chrombpnet related analysis products (i.e. accessibility predictions, contribution scores, and motif istances). then, we show how to load the trained cell type specific chrombpnet model to make new predictions.
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