Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease
- Cem Kuscu, Ş. Arslan, Ritambhara Singh, Jeremy Thorpe, Mazhar Adli
- BiologyNature Biotechnology
- 1 July 2014
Mapping genome-wide binding sites of catalytically inactive Cas9 in HEK293T cells and analysis of off-target binding sites showed the importance of the PAM-proximal region of the sgRNA guiding sequence and that dCas9 binding sites are enriched in open chromatin regions, and it is shown that ChIP-seq allows unbiased detection of Cas9 binding Site-wide.
DeepChrome: deep-learning for predicting gene expression from histone modifications
- Ritambhara Singh, Jack Lanchantin, G. Robins, Yanjun Qi
- Biology, Computer ScienceBioinform.
- 7 July 2016
A unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input and it is shown that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database.
Degree of recruitment of DOT1L to MLL-AF9 defines level of H3K79 Di- and tri-methylation on target genes and transformation potential.
- Aravinda Kuntimaddi, N. Achille, J. Bushweller
- BiologyCell Reports
- 5 May 2015
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
- Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
- Computer Science, BiologybioRxiv
- 1 August 2017
An attention-based deep learning approach that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation, and its attention scores provide a better interpretation than state-of-the-art feature visualization methods such as saliency maps.
Gromov-Wasserstein optimal transport to align single-cell multi-omics data
- Pinar Demetci, Rebecca Santorella, Bjorn Sandstede, William Stafford Noble, Ritambhara Singh
- Computer SciencebioRxiv
- 29 April 2020
Single-Cell alignment using Optimal Transport (SCOT) is presented, an unsupervised learning algorithm that uses Gromov Wasserstein-based optimal transport to align single-cell multi-omics datasets and performs on par with state-of-the-art methods but is faster and requires tuning fewer hyperparameters.
Live cell imaging of low- and non-repetitive chromosome loci using CRISPR-Cas9
- Peiwu Qin, Mahmut Parlak, Mazhar Adli
- BiologyNature Communications
- 14 March 2017
The design of single-guide RNAs integrated with up to 16 MS2 binding motifs to enable robust fluorescent signal amplification enables multicolour labelling of low-repeat-containing regions using a single sgRNA and of non-repetitive regions with as few as four unique sgRNAs.
Jointly Embedding Multiple Single-Cell Omics Measurements
- Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert, William Stafford Noble
- Computer SciencebioRxiv
- 21 May 2019
MMD-MA’s weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data.
Discovery of CTCF-Sensitive Cis-Spliced Fusion RNAs between Adjacent Genes in Human Prostate Cells
- Fujun Qin, Zhenguo Song, Hui Li
- BiologyPLoS Genetics
- 1 February 2015
The results suggest that splicing between neighboring gene transcripts is a rather frequent phenomenon, and it is not a feature unique to cancer cells.
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
- Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi
- Computer SciencePacific Symposium on Biocomputing
- 12 August 2016
A toolkit called the Deep Motif Dashboard (DeMo Dashboard) is proposed which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification, and results indicate that a convolutional-recurrent architecture performs the best among the three architectures.
Unsupervised manifold alignment for single-cell multi-omics data
- Ritambhara Singh, Pinar Demetci, William Stafford Noble
- Computer Science, BiologybioRxiv
- 15 June 2020
To scale the runtime of MMD-MA to a more substantial number of cells, the original implementation is extended to run on GPUs and a method to automatically select one of the user-defined parameters is introduced, thus reducing the hyperparameter search space.
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