SeqGenome (Index of Posts):
We design this categoy of explainable deep learning methods working on genomics..
This includes:
01 Jun 2020
Title: FastSK: Fast Sequence Analysis with Gapped String Kernels
Abstract
Gapped k-mer kernels with Support Vector Machines (gkm-SVMs)
have achieved strong predictive performance on regulatory DNA sequences
on modestly-sized training sets. However, existing gkm-SVM algorithms
suffer from the slow kernel computation time, as they depend
exponentially on the sub-sequence feature-length, number of mismatch
positions, and the task’s alphabet size.
In this work, we introduce a fast and scalable algorithm for
calculating gapped k-mer string kernels. Our method, named FastSK,
uses a simplified kernel formulation that decomposes the kernel
calculation into a set of independent counting operations over the
possible mismatch positions. This simplified decomposition allows us
to devise a fast Monte Carlo approximation that rapidly converges.
FastSK can scale to much greater feature lengths, allows us to
consider more mismatches, and is performant on a variety of sequence
analysis tasks. On 10 DNA transcription factor binding site (TFBS)
prediction datasets, FastSK consistently matches or outperforms the
state-of-the-art gkmSVM-2.0 algorithms in AUC, while achieving
average speedups in kernel computation of 100 times and speedups of
800 times for large feature lengths. We further show that FastSK
outperforms character-level recurrent and convolutional neural
networks across all 10 TFBS tasks. We then extend FastSK to 7
English medical named entity recognition datasets and 10 protein
remote homology detection datasets. FastSK consistently matches or
outperforms these baselines.
Our algorithm is available as a Python package and as C++ source code.
(Available for download at https://github.com/Qdata/FastSK/.
Install with the command make or pip install)
Citations
@article{10.1093/bioinformatics/btaa817,
author = {Blakely, Derrick and Collins, Eamon and Singh, Ritambhara and Norton, Andrew and Lanchantin, Jack and Qi, Yanjun},
title = "{FastSK: fast sequence analysis with gapped string kernels}",
journal = {Bioinformatics},
volume = {36},
number = {Supplement_2},
pages = {i857-i865},
year = {2020},
month = {12},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa817},
url = {https://doi.org/10.1093/bioinformatics/btaa817},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_2/i857/35337038/btaa817.pdf},
}
Having trouble with our tools? Please contact Yanjun Qi and we’ll help you sort it out.
01 May 2020
Title: Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data
Abstract
Motivation
Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the chromatin profile. Deep neural networks have achieved state of the art performance on chromatin profile prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the chromatin profiles because modeling the 3D genome is challenging.
Results
In this work, we introduce ChromeGCN, a graph convolutional network for chromatin profile prediction by fusing both local sequence and long-range 3D genome information. By incorporating the 3D genome, we relax the independent and identically distributed assumption of local windows for a better representation of DNA. ChromeGCN explicitly incorporates known long-range interactions into the modeling, allowing us to identify and interpret those important long-range dependencies in influencing chromatin profiles. We show experimentally that by fusing sequential and 3D genome data using ChromeGCN, we get a significant improvement over the state-of-the-art deep learning methods as indicated by three metrics. Importantly, we show that ChromeGCN is particularly useful for identifying epigenetic effects in those DNA windows that have a high degree of interactions with other DNA windows.
Citations
@article{10.1093/bioinformatics/btaa793,
author = {Lanchantin, Jack and Qi, Yanjun},
title = "{Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data}",
journal = {Bioinformatics},
volume = {36},
number = {Supplement_2},
pages = {i659-i667},
year = {2020},
month = {12},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa793},
url = {https://doi.org/10.1093/bioinformatics/btaa793},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_2/i659/35336695/btaa793.pdf},
}
Having trouble with our tools? Please contact Jack and we’ll help you sort it out.
11 Dec 2017
Jack’s DeepMotif paper (Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks ) have received the “best paper award“ at NIPS17 workshop for Transparent and interpretable Machine Learning in Safety Critical Environments. Big congratulations!!!
10 Dec 2017
Prototype Matching Networks : A novel deep learning architecture for Large-Scale Multi-label Genomic Sequence Classification
Abstract
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as “motifs” and (2) interactions among TFs known as co-binding effects. In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN model automatically extracts prototypes (“motif”-like features) for each TF through a novel prototype-matching loss. Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences. On a reference TFBS dataset with 2.1 million genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically. To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large-scale TFBS prediction. Not only is the proposed architecture accurate, but it also models the underlying biology.
Citations
@article{lanchantin2017prototype,
title={Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification},
author={Lanchantin, Jack and Sekhon, Arshdeep and Singh, Ritambhara and Qi, Yanjun},
journal={arXiv preprint arXiv:1710.11238},
year={2017}
}
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12 Jun 2017
Abstract
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as “motifs”. However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning over inputs and supporting sets. In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites. Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or nonbinding site.
Citations
@article{lanchantin2017memory,
title={Memory Matching Networks for Genomic Sequence Classification},
author={Lanchantin, Jack and Singh, Ritambhara and Qi, Yanjun},
journal={arXiv preprint arXiv:1702.06760},
year={2017}
}
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11 Jun 2017
Abstract:
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.
Citations
@inproceedings{lanchantin2017deep,
title={Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks},
author={Lanchantin, Jack and Singh, Ritambhara and Wang, Beilun and Qi, Yanjun},
booktitle={PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017},
pages={254--265},
year={2017},
organization={World Scientific}
}
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20 Jun 2016
Abstract:
String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (Σ) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to O(ΣM). We propose a \textbf{fast} algorithm for calculating \underline{Ga}pped k-mer \underline{K}ernel using \underline{Co}unting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger Σ and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the ΣM term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets), respectively.
Citations
@inproceedings{singh_gakco:_2017,
location = {Cham},
title = {GaKCo: A Fast Gapped k-mer String Kernel Using Counting},
isbn = {978-3-319-71249-9},
pages = {356--373},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
publisher = {Springer International Publishing},
author = {Singh, Ritambhara and Sekhon, Arshdeep and Kowsari, Kamran and Lanchantin, Jack and Wang, Beilun and Qi, Yanjun},
editor = {Ceci, Michelangelo and Hollmén, Jaakko and Todorovski, Ljupčo and Vens, Celine and Džeroski, Sašo},
date = {2017}
}
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15 Jun 2016
Tool TSK: Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Abstract
Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, therefore, propose a method called “Transfer String Kernel” (TSK) that achieves improved prediction of transcription factor binding site (TFBS) using knowledge transfer via cross-context sample adaptation. TSK maps sequence segments to a high-dimensional feature space using a discriminative mismatch string kernel framework. In this high-dimensional space, labeled examples of the source context are re-weighted so that the revised sample distribution matches the target context more closely. We have experimentally verified TSK for TFBS identifications on fourteen different TFs under a cross-organism setting. We find that TSK consistently outperforms the state-of the-art TFBS tools, especially when working with TFs whose binding sequences are not conserved across contexts. We also demonstrate the generalizability of TSK by showing its cutting-edge performance on a different set of cross-context tasks for the MHC peptide binding predictions.
Citations
@article{singh2016transfer,
title={Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction},
author={Singh, Ritambhara and Lanchantin, Jack and Robins, Gabriel and Qi, Yanjun},
journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
year={2016},
publisher={IEEE}
}
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