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arXiv 2019年

作者： Silverman, Justin D. Roche, Kimberly Holmes, Zachary C. David, Lawrence A. Mukherjee, Sayan Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Medical Scientist Training Program Duke University DurhamNC27708 United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University DurhamNC27708 United States Center for Genomic and Computational Biology Duke University DurhamNC27708 United States Department of Molecular Genetics and Microbiology Duke University DurhamNC27708 United States

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to handling small data sets due to the non-conjugacy of the multinomial and logistic-normal distributions. We introduce MLN models which can be written as marginally latent matrix-t process (LTP) models. Marginally LTP models describe a flexible class of generalized linear regression, non-linear regression, and time series models. We develop inference schemes for Marginally LTP models and, through application to MLN models, demonstrate that our inference schemes are both highly accurate and often 4-5 orders of magnitude faster than MCMC. Copyright © 2019, The Authors. All rights reserved.

关键词： Multivariant analysis

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Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects

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Journal of Proteome Research 2024年第1期23卷 301-315页

作者： Wang, Wei-Hsuan Kao, Yi-Chun Hsieh, Chiao-Hui Tsai, Shin-Yu Cheung, Chantal Hoi Yin Huang, Hsuan-Cheng Juan, Hsueh-Fen Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei 106 Taiwan Department of Life Science National Taiwan University Taipei 106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei 106 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 112 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106 Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei 106 Taiwan

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1’s inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition. © 2023 American Chemical Society.

关键词： Mdivi-1 multiomics neuroblastoma phosphoproteome proteome targeted metabolome

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Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM

Donor Selection for Hematopoietic Stem Cell Transplant Using...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Adarsh Sivasankaran Vladimir Cherkassky Mark Albrecht Eric Williams Martin Maiers Biomedical Informatics and Computational Biology Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota U.S.A. Bioinformatics Research National Marrow Donor Program Minneapolis Minnesota U.S.A.

ISBN: (纸本)9781509002887

Donor selection for Hematopoietic Stem Cell Transplant often requires physicians to manually select 3 to 5 donors from a list of 100s of genetically compatible donors as identified by HLA-based matching algorithms. The decision process is complicated by a lack of strict guidelines governing a "secondary" selection process, which is based upon non-HLA donor attributes. Our research is aimed at modeling this "secondary" decision process which can help physicians choose the right donors, based on donor attributes and historical choice behavior. Proposed black box models will help in improving selection consistency.

关键词： Medical services Support vector machines Data models Stem cells Mathematical model Numerical models Training

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Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis (vol 56, pg 627, 2024)

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NATURE GENETICS 2024年第6期56卷 1319-1319页

作者： Mitra, Sneha Malik, Rohan Wong, Wilfred Rahman, Afsana Hartemink, Alexander J. Pritykin, Yuri Dey, Kushal K. Leslie, Christina S. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA Rye Country Day School Rye NY USA Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA Hunter College City University of New York New York City NY USA Department of Computer Science Duke University Durham NC USA Program in Computational Biology and Bioinformatics Duke University Durham NC USA Center for Genomic and Computational Biology Duke University Durham NC USA Department of Computer Science Princeton University Princeton NJ USA Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC–seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene–peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

关键词： Epigenetics Population genetics Software

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Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

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BMC proceedings 2016年第SUPPL 7期10卷 275-281页

作者： Dongni Zhang Hongzhu Cui Dmitry Korkin Zheyang Wu Mathematics Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA. Computer Science Department Bioinformatics and Computational Biology Program Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA.

Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.

关键词： Likelihood Ratio Test Real Data Analysis Burden Score Data Sample Size Genomic Inflation Factor

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Autofix for backward-fit sidechains: Using MolProbity and real-space refinement to put misfits in their place

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Journal of Structural and Functional Genomics 2009年第1期10卷 83-93页

作者： Headd, Jeffrey J. Immormino, Robert M. Keedy, Daniel A. Emsley, Paul Richardson, David C. Richardson, Jane S. Department of Biochemistry Duke University Medical Center 211 Nanaline Duke Building Durham NC 27710 United States Computational Biology and Bioinformatics Program Duke University Durham NC 27708 United States Department of Immunology University of Washington School of Medicine Seattle WA 98195 United States Laboratory of Molecular Biophysics Department of Biochemistry University of Oxford Oxford Oxfordshire 0X1 3QU United Kingdom

Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to address, but a large class of systematic errors are quite common even in well-ordered regions, resulting in sidechains fit backwards into local density in predictable ways. The MolProbity web site is effective at diagnosing such errors, and can perform reliable automated correction of a few special cases such as 180° flips of Asn or Gln sidechain amides, using all-atom contacts and H-bond networks. However, most at-risk residues involve tetrahedral geometry, and their valid correction requires rigorous evaluation of sidechain movement and sometimes backbone shift. The current work extends the benefits of robust automated correction to more sidechain types. The Autofix method identifies candidate systematic, flipped-over errors in Leu, Thr, Val, and Arg using MolProbity quality statistics, proposes a corrected position using real-space refinement with rotamer selection in Coot, and accepts or rejects the correction based on improvement in MolProbity criteria and on χ angle change. Criteria are chosen conservatively, after examining many individual results, to ensure valid correction. To test this method, Autofix was run and analyzed for 945 representative PDB files and on the 50S ribosomal subunit of file 1YHQ. Over 40% of Leu, Val, and Thr outliers and 15% of Arg outliers were successfully corrected, resulting in a total of 3,679 corrected sidechains, or 4 per structure on average. Summary Sentences: A common class of misfit sidechains in protein crystal structures is due to systematic errors that place the sidechain backwards into the local electron density. A fully automated method called "Autofix" identifies such errors for Leu, Val, Thr, and Arg and corrects over one third of them, using MolProbity validation criteria and Coot real-space refinement

关键词： Automation Crystallography Protein/RNA interactions Sidechain rotamers Structure improvement

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SeqScreen: a biocuration platform for robust taxonomic and biological process characterization of nucleic acid sequences of interest

SeqScreen: a biocuration platform for robust taxonomic and b...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Dreycey Albin Dan Nasko R. A. Leo Elworth Jacob Lu Advait Balaji Christian Diaz Nidhi Shah Jeremy Selengut Chris Hulme-Lowe Pravin Muthu Gene Godbold Mikael Lindvall Madeline Diep Adam Porter Mihai Pop Krista Ternus Todd J. Treangen Program in Systems Synthetic and Physical Biology Rice University Center for Bioinformatics and Computational Biology University of Maryland Department of Computer Science Rice University Signature Science LLC Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland

ISBN: (纸本)9781728118680

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at .

关键词： Biological Process interest Base Sequence Nucleic acids Version control protein database Database synthetic biology ANNOTATIONS Leverage

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MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

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BMC Research Notes 2010年第1期3卷 312-312页

作者： Sucaet, Yves Wurtele, Eve Syrkin Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Interdepartmental Program in Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

Background. Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results. MetNetAPI is a versatile Application programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions. An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, *** and R programming environments and offers flexible query and bro

关键词： Application program Interface Molecular Entity Biological Database Command Line Interface Network Object

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Genetic correlates of autoreactivity and autoreactive potential in human Ig heavy chains

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Immunome Research 2009年第1期5卷 1页

作者： Volpe, Joseph M Kepler, Thomas B Center for Computational Immunology Duke University Durham NC United States Departments of Biostatistics and Bioinformatics and Immunology Duke University Medical Center Durham NC United States Computational Biology and Bioinformatics Graduate Program Institute for Genome Sciences and Policy Duke University Durham NC United States

Background. Immature bone marrow B cells are known to have longer CDR3 than mature peripheral B cells, and this genetic characteristic has been shown to correlate with autoreactivity in these early cells. B-cell Central tolerance eliminates these cells, but it is known that autoreactive B cells nevertheless appear commonly in healthy human blood. We examined over 7,300 Ig genes from Genbank, including those annotated by their discoverers as associated with autoreactivity, to determine the genetic correlates of autoreactivity in mature B cells. Results. We find differential biases in gene segment usage and higher mutation frequency in autoreactivity-associated Ig genes, but the CDR3 lengths do not differ between autoreactive and non-autoreactive Ig genes. The most striking genetic signature of autoreactivity is an increase in the proportion of N-nucleotides relative to germline-encoded nucleotides in CDR3 from autoreactive genes. Conclusion. We hypothesize that peripheral autoreactivity results primarily from somatic mutation, and that the genetic correlates of autoreactivity in mature B-cells are not the same as those for autoreactivity in immature B cells. What is seen in mature autoreactive B cells are the correlates of autoreactive potential, not of autoreactivity per se. The autoreactive potential is higher for V(D)J rearrangements encoded to a large extent by N-nucleotides rather than by the gene segments that, we posit, have been selected in germline evolution for their suppression of autoreactive potential.

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Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models

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Journal of Structural and Functional Genomics 2011年第2期12卷 137-147页

作者： Zimmermann, Michael T. Leelananda, Sumudu P. Gniewek, Pawel Feng, Yaping Jernigan, Robert L. Kloczkowski, Andrzej Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011-0320 United States L.H.Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50011-3020 United States Bioinformatics and Computational Biology Interdepartmental Graduate Program Iowa State University Ames IA 50011 United States Laboratory of Theory of Biopolymers Faculty of Chemistry University of Warsaw 02-093 Warsaw Pasteura 1 Poland Battelle Center for Mathematical Medicine Research Institute at Nationwide Children's Hospital Columbus OH 43205 United States Department of Pediatrics Ohio State University College of Medicine Columbus OH 43205 United States

We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Combining four-body non-sequential, four-body sequential and pairwise short range potentials with optimized weights for each term, our coarse-grained potential improved recognition of native structure among misfolded decoys, outperforming all other contact potentials for CASP8 decoy sets and performance comparable to the fully atomic empirical DFIRE potentials. By combing statistical contact potentials with entropies from elastic network models of the same structures we can compute free energy changes and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and dynamics should improve protein structure prediction and refinement of computational models. This work is the first to combine coarse-grained multibody potentials with an entropic model that takes into account contributions of the entire structure, investigating native-like decoy selection. © 2011 Springer Science+Business Media B.V.

关键词： 4-Body potentials Elastic network models Multibody potentials Protein dynamics Protein structure prediction Protein structure refinement

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