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1. Identification of histone post-translational modifications in three tissues of Mozambique tilapia (Oreochromis mossambicus)

   Mojica, EA*; Fu, Y; Kültz, D (January 2021, SOCIETY FOR INTEGRATIVE AND COMPARATIVE BIOLOGY 2021 VIRTUAL ANNUAL MEETING)

   null (Ed.)

   Histone post-translational modifications (PTMs) are epigenetic marks that modify the state of chromatin and lead to alterations in gene expression. Advances in mass spectrometry have enabled the high-throughput analysis of histone PTMs without the need for prior knowledge of individual PTMs of interest. In this study, the global histone PTM landscape was analyzed in the gills, kidney, and testes of Mozambique tilapia (Oreochromis mossambicus) through tandem mass spectrometry using data dependent acquisition (DDA-LCMS2) and PTM mapping approaches. PTM assignment to a specific amino acid was validated using A-score and localization probability scores that are based on the detection of diagnostic MSMS ions. These values signify the robustness of PTM assignment to a specific residue within the protein sequence. For PTMs that were represented by both modified and unmodified versions of the corresponding peptide, the stoichiometry was calculated and compared between tissues. We have identified multiple types of histone PTMs and assigned them to specific residues in each tissue. These PTMs include acetylation, methylation, demethylation, trimethylation, phosphorylation/ dehydration, and ubiquitination. Our results indicate that the gills, kidney, and testes each display a unique profile of histone PTMs. These data provide a strong basis for the generation of spectral libraries that enable high-throughput quantitative analyses of histone PTM stoichiometry on a global scale in tilapia exposed to diverse environmental and developmental contexts. 

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2. Handling Missing Data with Graph Representation Learning

   You, Jiaxuan; Ma, Xiaobai; Ding, Daisy Yi; Kochenderfer, Mykel; Leskovec, Jure. (January 2020, Neural Information Processing Systems (NeurIPS)

   Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges. Under the GRAPE framework, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. These tasks are then solved with Graph Neural Networks. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods. 

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3. PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software

   https://doi.org/10.1145/3643991.3644907  

   Jiang, W; Yasmin, J; Jones, J; Synovic, N; Kuo, J; Bielanski, N; Tian, Y; Thiruvathukal, G K; Davis, J C (May 2024, 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR))

   The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse.This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset’s comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model’s training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and cross-cutting questions.Our artifact is available at https://github.com/PurdueDualityLab/PeaTMOSS-Artifact. Our dataset is available at https://transfer.rcac.purdue.edu/file-manager?origin_id=ff978999-16c2-4b50-ac7a-947ffdc3eb1d&origin_path=%2F. 

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4. PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software

   Jiang, W; Yasmin, J; Jones, J; Synovic, N; Kuo, J; Bielanski, N; Tian, Y; Thiruvathukal, G K; Davis, J C (May 2024, 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR))

   The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset’s comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model’s training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and cross-cutting questions. Our artifact is available at https://github.com/PurdueDualityLab/PeaTMOSS-Artifact. Our dataset is available at https://transfer.rcac.purdue.edu/file-manager?origin_id=ff978999-16c2-4b50-ac7a-947ffdc3eb1d&origin_path=%2F. 

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5. Sparse Convolutional Denoising Autoencoders for Genotype Imputation

   https://doi.org/10.3390/genes10090652  

   Chen, Junjie; Shi, Xinghua (September 2019, Genes)

   Genotype imputation, where missing genotypes can be computationally imputed, is an essential tool in genomic analysis ranging from genome wide associations to phenotype prediction. Traditional genotype imputation methods are typically based on haplotype-clustering algorithms, hidden Markov models (HMMs), and statistical inference. Deep learning-based methods have been recently reported to suitably address the missing data problems in various fields. To explore the performance of deep learning for genotype imputation, in this study, we propose a deep model called a sparse convolutional denoising autoencoder (SCDA) to impute missing genotypes. We constructed the SCDA model using a convolutional layer that can extract various correlation or linkage patterns in the genotype data and applying a sparse weight matrix resulted from the L1 regularization to handle high dimensional data. We comprehensively evaluated the performance of the SCDA model in different scenarios for genotype imputation on the yeast and human genotype data, respectively. Our results showed that SCDA has strong robustness and significantly outperforms popular reference-free imputation methods. This study thus points to another novel application of deep learning models for missing data imputation in genomic studies. 

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