scholarly journals Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

2020 ◽  
Author(s):  
Simon Höllerer ◽  
Laetitia Papaxanthos ◽  
Anja Cathrin Gumpinger ◽  
Katrin Fischer ◽  
Christian Beisel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Large Scale ◽  
Dynamic Range ◽  
Regulatory Elements ◽  
Major Advance ◽  
Single Experiment ◽  
Quantitative Expression ◽  
Very Large Datasets ◽  
A Site

AbstractPredicting quantitative effects of gene regulatory elements (GREs) on gene expression is a longstanding challenge in biology. Machine learning models for gene expression prediction may be able to address this challenge, but they require experimental datasets that link large numbers of GREs to their quantitative effect. However, current methods to generate such datasets experimentally are either restricted to specific applications or limited by their technical complexity and error-proneness. Here we introduce DNA-based phenotypic recording as a widely applicable and practical approach to generate very large datasets linking GREs to quantitative functional readouts of high precision, temporal resolution, and dynamic range, solely relying on sequencing. This is enabled by a novel DNA architecture comprising a site-specific recombinase, a GRE that controls recombinase expression, and a DNA substrate modifiable by the recombinase. Both GRE sequence and substrate state can be determined in a single sequencing read, and the frequency of modified substrates amongst constructs harbouring the same GRE is a quantitative, internally normalized readout of this GRE’s effect on recombinase expression. Using next-generation sequencing, the quantitative expression effect of extremely large GRE sets can be assessed in parallel. As a proof of principle, we apply this approach to record translation kinetics of more than 300,000 bacterial ribosome binding sites (RBSs), collecting over 2.7 million sequence-function pairs in a single experiment. Further, we generalize from these large-scale datasets by a novel deep learning approach that combines ensembling and uncertainty modelling to predict the function of untested RBSs with high accuracy, substantially outperforming state-of-the-art methods. The combination of DNA-based phenotypic recording and deep learning represents a major advance in our ability to predict quantitative function from genetic sequence.

scholarly journals Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

2020 ◽  
Author(s):  
Nana Ding ◽  
Shenghu Zhou ◽  
Zhenqi Yuan ◽  
Xiaojuan Zhang ◽  
Jing Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Deep Learning ◽  
Translation Initiation ◽  
Rational Design ◽  
Dynamic Range ◽  
Regulatory Elements ◽  
Fine Tuning ◽  
Initiation Rate ◽  
Ribosome Binding

ABSTRACTCurrently, predictive translation tuning of regulatory elements to the desired output of transcription factor based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between the presence and absence of inducer) by adjusting the translation initiation rate of the transcription factor and reporter. However, existing genetically encoded biosensors generally suffer from unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation initiation rate, protein folding and dynamic range, and presented a rational design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship between cRBSs and dynamic range, we established a classification model with the cRBSs and average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors based on convolutional neural network in deep learning. Thus, this work provides a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

scholarly journals Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor

2020 ◽  
Vol 48 (18) ◽  
pp. 10602-10613
Author(s):  
Nana Ding ◽  
Zhenqi Yuan ◽  
Xiaojuan Zhang ◽  
Jing Chen ◽  
Shenghu Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Deep Learning ◽  
Dynamic Range ◽  
Regulatory Elements ◽  
Fine Tuning ◽  
Classification Model ◽  
Ribosome Binding ◽  
Ribosome Binding Sites ◽  
Biosensor System

Abstract Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

scholarly journals A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics

2020 ◽  
Author(s):  
Justin Lakkis ◽  
David Wang ◽  
Yuanchao Zhang ◽  
Gang Hu ◽  
Kui Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Single Cell ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Batch Effect ◽  
Marker Genes ◽  
Deep Learning Model ◽  
Variable Genes

AbstractRecent development of single-cell RNA-seq (scRNA-seq) technologies has led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effect, which is inevitable in studies involving human tissues. Most existing methods remove batch effect in a low-dimensional embedding space. Although useful for clustering, batch effect is still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effect. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Popular methods such as Seurat3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effect in the gene expression space, but MNN can only analyze two batches at a time and it becomes computationally infeasible when the number of batches is large. Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC consistently outperforms scVI, DCA, and MNN. With CarDEC denoising, those non-highly variable genes offer as much signal for clustering as the highly variable genes, suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC’s denoised and batch corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effect. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

scholarly journals Mining influential genes based on deep learning

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Fengjiao Xu ◽  
Mingmin Xu ◽  
Zutan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiling ◽  
Large Scale ◽  
Target Genes ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Genomic Information ◽  
Deep Mine ◽  
Functional Connections

Abstract Background Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome. Results Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information. Conclusions We believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.

scholarly journals Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Zrimec ◽  
Christoph S. Börlin ◽  
Filip Buric ◽  
Azam Sheikh Muhammad ◽  
Rhongzen Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Regulatory Elements ◽  
Mrna Abundance ◽  
Model Organisms ◽  
Mrna Levels ◽  
Regulatory Structure ◽  
Expression Levels ◽  
Coding Regions ◽  
Gene Regulatory

AbstractUnderstanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

scholarly journals Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shengqiao Gao ◽  
Lu Han ◽  
Dan Luo ◽  
Gang Liu ◽  
Zhiyong Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Large Scale ◽  
Prediction Models ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Genetic Profile ◽  
Drug Induced ◽  
Drug Mechanism

Abstract Background Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach. Results In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs. Conclusion GPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.

scholarly journals Transcription terminator-mediated enhancement in transgene expression in maize: preponderance of the AUGAAU motif overlapping with poly(A) signals

2020 ◽  
Author(s):  
Po-Hao Wang ◽  
Sandeep Kumar ◽  
Jia Zeng ◽  
Robert McEwan ◽  
Terry R. Wright ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transgene Expression ◽  
Brachypodium Distachyon ◽  
Fold Increase ◽  
Regulatory Elements ◽  
Gene Promoters ◽  
Transcription Terminator ◽  
Polyubiquitin Gene ◽  
A Site ◽  
Polyadenylation Signals

AbstractThe selection of transcription terminators (TTs) for pairing with high expressing constitutive promoters in chimeric constructs is crucial to deliver optimal transgene expression in plants. In this study, the use of the native combinations of four polyubiquitin gene promoters and corresponding TTs resulted in up to >3-fold increase in transgene expression in maize. Of the eight polyubiquitin promoter and TT regulatory elements utilized, seven were novel and identified from the polyubiquitin genes of Brachypodium distachyon, Setaria italica, and Zea mays. Furthermore, gene expression driven by the Cassava mosaic virus promoter was studied by pairing the promoter with distinct TTs derived from the high expressing genes of Arabidopsis. Of the three TTs studied, the polyubiquitin10 gene TT produced the highest transgene expression in maize. Polyadenylation patterns and mRNA abundance from eight distinct TTs were analyzed using 3’-RACE and next-generation sequencing. The results exhibited one to three unique polyadenylation sites in the TTs. The poly(A) site patterns for the StPinII TT were consistent when the same TT was deployed in chimeric constructs irrespective of the reporter gene and promoter used. Distal to the poly(A) sites, putative polyadenylation signals were identified in the near-upstream regions of the TTs based on previously reported mutagenesis and bioinformatics studies in rice and Arabidopsis. The putative polyadenylation signals were 9 to 11 nucleotides in length. Six of the eight TTs contained the putative polyadenylation signals that were overlaps of either canonical AAUAAA or AAUAAA-like polyadenylation signals and AUGAAU, a top-ranking-hexamer of rice and Arabidopsis gene near-upstream regions. Three of the polyubiquitin gene TTs contained the identical 9-nucleotide overlap, AUGAAUAAG, underscoring the functional significance of such overlaps in mRNA 3’ end processing. In addition to identifying new combinations of regulatory elements for high constitutive trait gene expression in maize, this study demonstrated the importance of TTs for optimizing gene expression in plants. Learning from this study could be applied to other dicotyledonous and monocotyledonous plant species for transgene expression.

scholarly journals Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 121 ◽  
Author(s):  
Enrico Ferrero
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Regulatory Elements ◽  
New Drugs ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Expression Studies ◽  
Regulatory Genomics

The identification of therapeutic targets is a critical step in the research and developement of new drugs, with several drug discovery programmes failing because of a weak linkage between target and disease. Genome-wide association studies and large-scale gene expression experiments are providing insights into the biology of several common and complex diseases, but the complexity of transcriptional regulation mechanisms often limit our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacers across different tissues and cell types. In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.

scholarly journals Assessing the Gene Regulatory Landscape in 1,188 Human Tumors

2017 ◽  
Author(s):  
C Calabrese ◽  
K Lehmann ◽  
L Urban ◽  
F Liu ◽  
S Erkek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Large Scale ◽  
Human Cancer ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Whole Genome ◽  
Somatic Variation ◽  
Specific Expression ◽  
Large Scale Assessment

AbstractCancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-Seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.

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