112 research outputs found
Graph algorithms for predicting subcellular localization at the pathway level
Protein subcellular localization is an important factor in normal cellular
processes and disease. While many protein localization resources treat it as
static, protein localization is dynamic and heavily influenced by biological
context. Biological pathways are graphs that represent a specific biological
context and can be inferred from large-scale data. We develop graph algorithms
to predict the localization of all interactions in a biological pathway as an
edge-labeling task. We compare a variety of models including graph neural
networks, probabilistic graphical models, and discriminative classifiers for
predicting localization annotations from curated pathway databases. We also
perform a case study where we construct biological pathways and predict
localizations of human fibroblasts undergoing viral infection. Pathway
localization prediction is a promising approach for integrating publicly
available localization data into the analysis of large-scale biological data.Comment: 35 pages, 14 figure
Bayes Optimal Informer Sets for Early-Stage Drug Discovery
An important experimental design problem in early-stage drug discovery is how
to prioritize available compounds for testing when very little is known about
the target protein. Informer based ranking (IBR) methods address the
prioritization problem when the compounds have provided bioactivity data on
other potentially relevant targets. An IBR method selects an informer set of
compounds, and then prioritizes the remaining compounds on the basis of new
bioactivity experiments performed with the informer set on the target. We
formalize the problem as a two-stage decision problem and introduce the Bayes
Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a
flexible model of the initial bioactivity data, a relevant loss function, and
effective computational schemes to resolve the two-step design problem. We
evaluate BOISE and compare it to other IBR strategies in two retrospective
studies, one on protein-kinase inhibition and the other on anti-cancer drug
sensitivity. In both empirical settings BOISE exhibits better predictive
performance than available methods. It also behaves well with missing data,
where methods that use matrix completion show worse predictive performance. We
provide an R implementation of BOISE at
https://github.com/wiscstatman/esdd/BOISEComment: 18 pages, 6 figure
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
Gene expression is a readily-observed quantification of transcriptional activity and cellular state that enables the recovery of the relationships between regulators and their target genes. Reconstructing transcriptional regulatory networks from gene expression data is a problem that has attracted much attention, but previous work often makes the simplifying (but unrealistic) assumption that regulator activity is represented by mRNA levels. We use a latent tree graphical model to analyze gene expression without relying on transcription factor expression as a proxy for regulator activity. The latent tree model is a type of Markov random field that includes both observed gene variables and latent (hidden) variables, which factorize on a Markov tree. Through efficient unsupervised learning approaches, we determine which groups of genes are co-regulated by hidden regulators and the activity levels of those regulators. Post-processing annotates many of these discovered latent variables as specific transcription factors or groups of transcription factors. Other latent variables do not necessarily represent physical regulators but instead reveal hidden structure in the gene expression such as shared biological function. We apply the latent tree graphical model to a yeast stress response dataset. In addition to novel predictions, such as condition-specific binding of the transcription factor Msn4, our model recovers many known aspects of the yeast regulatory network. These include groups of co-regulated genes, condition-specific regulator activity, and combinatorial regulation among transcription factors. The latent tree graphical model is a general approach for analyzing gene expression data that requires no prior knowledge of which possible regulators exist, regulator activity, or where transcription factors physically bind
Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package
High-throughput, āomicā methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of āomicā data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.National Institutes of Health (U.S.) (grant U54CA112967)National Institutes of Health (U.S.) (grant U01CA184898)National Institutes of Health (U.S.) (grant U54NS091046)National Institutes of Health (U.S.) (grant R01GM089903
Sharing Information to Reconstruct Patient-Specific Pathways in Heterogeneous Diseases
Advances in experimental techniques resulted in abundant genomic, transcriptomic, epigenomic, and proteomic data that have the potential to reveal critical drivers of human diseases. Complementary algorithmic developments enable researchers to map these data onto protein-protein interaction networks and infer which signaling pathways are perturbed by a disease. Despite this progress, integrating data across different biological samples or patients remains a substantial challenge because samples from the same disease can be extremely heterogeneous. Somatic mutations in cancer are an infamous example of this heterogeneity. Although the same signaling pathways may be disrupted in a cancer patient cohort, the distribution of mutations is long-tailed, and many driver mutations may only be detected in a small fraction of patients. We developed a computational approach to account for heterogeneous data when inferring signaling pathways by sharing information across the samples. Our technique builds upon the prize-collecting Steiner forest problem, a network optimization algorithm that extracts pathways from a protein-protein interaction network. We recover signaling pathways that are similar across all samples yet still reflect the unique characteristics of each biological sample. Leveraging data from related tumors improves our ability to recover the disrupted pathways and reveals patient-specific pathway perturbations in breast cancer.United States. Army Research Office (Institute for Collaborative Biotechnologies Grant W911NF-09-0001)National Institutes of Health (U.S.) (Grant U54-CA112967)Future & Emerging Technologies (Program) (Open Grant 265496)European Research Council (Grant 267915
Backup in gene regulatory networks explains differences between binding and knockout results
The complementarity of gene expression and proteināDNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
Gene expression is a readily-observed quantification of transcriptional activity and cellular state that enables the recovery of the relationships between regulators and their target genes. Reconstructing transcriptional regulatory networks from gene expression data is a problem that has attracted much attention, but previous work often makes the simplifying (but unrealistic) assumption that regulator activity is represented by mRNA levels. We use a latent tree graphical model to analyze gene expression without relying on transcription factor expression as a proxy for regulator activity. The latent tree model is a type of Markov random field that includes both observed gene variables and latent (hidden) variables, which factorize on a Markov tree. Through efficient unsupervised learning approaches, we determine which groups of genes are co-regulated by hidden regulators and the activity levels of those regulators. Post-processing annotates many of these discovered latent variables as specific transcription factors or groups of transcription factors. Other latent variables do not necessarily represent physical regulators but instead reveal hidden structure in the gene expression such as shared biological function. We apply the latent tree graphical model to a yeast stress response dataset. In addition to novel predictions, such as condition-specific binding of the transcription factor Msn4, our model recovers many known aspects of the yeast regulatory network. These include groups of co-regulated genes, condition-specific regulator activity, and combinatorial regulation among transcription factors. The latent tree graphical model is a general approach for analyzing gene expression data that requires no prior knowledge of which possible regulators exist, regulator activity, or where transcription factors physically bind
An Open-Publishing Response to the COVID-19 Infodemic
The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript\u27s figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and
structural information. Meanwhile, there exists tremendous knowledge curated by
humans in the text format describing proteins' high-level functionalities. Yet,
whether the incorporation of such text data can help protein design tasks has
not been explored. To bridge this gap, we propose ProteinDT, a multi-modal
framework that leverages textual descriptions for protein design. ProteinDT
consists of three subsequent steps: ProteinCLAP which aligns the representation
of two modalities, a facilitator that generates the protein representation from
the text modality, and a decoder that creates the protein sequences from the
representation. To train ProteinDT, we construct a large dataset,
SwissProtCLAP, with 441K text and protein pairs. We quantitatively verify the
effectiveness of ProteinDT on three challenging tasks: (1) over 90\% accuracy
for text-guided protein generation; (2) best hit ratio on 10 zero-shot
text-guided protein editing tasks; (3) superior performance on four out of six
protein property prediction benchmarks
Not a Waste: Wastewater Surveillance to Enhance Public Health
Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics
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