Informative priors based on transcription factor structural class improve de novo motif discovery

MOTIVATION: An important problem in molecular biology is to identify the locations at which a transcription factor (TF) binds to DNA, given a set of DNA sequences believed to be bound by that TF. In previous work, we showed that information in the DNA sequence of a binding site is sufficient to predict the structural class of the TF that binds it. In particular, this suggests that we can predict which locations in any DNA sequence are more likely to be bound by certain classes of TFs than others. Here, we argue that traditional methods for de novo motif finding can be significantly improved by adopting an informative prior probability that a TF binding site occurs at each sequence location. To demonstrate the utility of such an approach, we present priority, a powerful new de novo motif finding algorithm. RESULTS: Using data from TRANSFAC, we train three classifiers to recognize binding sites of basic leucine zipper, forkhead, and basic helix loop helix TFs. These classifiers are used to equip priority with three class-specific priors, in addition to a default prior to handle TFs of other classes. We apply priority and a number of popular motif finding programs to sets of yeast intergenic regions that are reported by ChIP-chip to be bound by particular TFs. priority identifies motifs the other methods fail to identify, and correctly predicts the structural class of the TF recognizing the identified binding sites

Centralization of lung cancer surgery in the Netherlands: differences in care and survival of patients with stage I non-small cell lung cancer between hospitals with and without in-house lung cancer surgery

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Modelling non-homogeneous dynamic Bayesian networks with piece-wise linear regression models

In statistical genomics and systems biology non-homogeneous dynamic Bayesian networks (NH-DBNs) have become an important tool for learning regulatory networks and signalling pathways from post-genomic data, such as gene expression time series. This chapter gives an overview of various state-of-the-art NH-DBN models with a variety of features. All NH-DBNs, presented here, have in common that they are Bayesian models that combine linear regression with multiple changepoint processes. The NH-DBN models can be used for learning the network structures of time-varying regulatory processes from data, where the regulatory interactions are subject to temporal change. We conclude this chapter with an illustration of the methodology on two applications, related to morphogenesis in Drosophila and synthetic biology in yeast

The role of surgery in the treatment of oligoprogression after systemic treatment for advanced non-small cell lung cancer

Objectives: Patients with advanced stage non-small cell lung cancer (NSCLC) are generally considered incurable. The mainstay of treatment for these patients is systemic therapy. The addition of local treatment, including surgery, remains controversial. Oligoprogression is defined as advanced stage NSCLC with limited progression of disease after a period of prolonged disease stabilisation or after a partial or complete response on systemic therapy. In this retrospective study we evaluated outcome and survival of patients who underwent a resection for oligoprogression after systemic therapy for advanced stage NSCLC. Materials and Methods: Patients with oligoprogression after systemic treatment for advanced NSCLC who were operated in the Antoni van Leeuwenhoek Hospital were included. Patient and treatment characteristics were collected in relation to progression free survival (PFS) and overall survival (OS). Results: Between January 2015 and December 2019, 28 patients underwent surgery for an oligoprogressive lesion (primary tumor lung (n = 12), other metastatic site (n = 16)). Median age at time of resection was 60 years (39-86) and 57% were female. Postoperative complications were observed in 2 patients (7%). Progression of disease after resection of the oligoprogressive site was observed in 17 patients (61%). Median PFS was 7 months since date of resection (95% CI 6.0-25.0) and median OS was not reached. Seven patients (25%) died during follow-up. Age was predictive for OS and clinical T4 stage was predictive for PFS. M1 disease at initial presentation was predictive for better PFS compared to patients who were diagnosed with M0 disease initially. Patients who underwent resection because of oligoprogression of the primary lung tumour had a better PFS, when compared to oligoprogression of another metastastic site. Conclusion: Surgical resection of an oligoprogressive lesion in patients with advanced NSCLC treated with systemic treatment is feasible and might be considered in order to achieve long term survival.Pathogenesis and treatment of chronic pulmonary disease

Learning Signaling Network Structures with Sparsely Distributed Data

Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs “Markov neighborhoods” for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.Leukemia & Lymphoma Society of AmericaNational Institutes of Health (U.S.) (grant AI06584)National Institutes of Health (U.S.) (grant GM68762)Burroughs Wellcome FundNational Institutes of Health (U.S.) (grant N01-HV-28183)National Institutes of Health (U.S.) (U19 AI057229)National Institutes of Health (U.S.) (2P01 AI36535)National Institutes of Health (U.S.) (U19 AI062623)National Institutes of Health (U.S.) (R01-AI065824)National Institutes of Health (U.S.) (2P01 CA034233-22A1)National Institutes of Health (U.S.) (HHSN272200700038C)National Institutes of Health (U.S.) (NCI grant U54 RFA-CA-05-024)National Institutes of Health (U.S.) (LLS grant 7017-6