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ASpli: Integrative analysis of splicing landscapes through RNA-Seq assays (Mar, btab141, 2021)

bioinformatics 2021年第12期37卷 1783-1783页

作者： Mancini, Estefania Rabinovich, Andres Iserte, Javier Yanovsky, Marcelo Chernomoretz, Ariel Gene Regulation Stem Cells and Cancer Program Centre for Genomic Regulation (CRG) The Barcelona Institute of Science and Technology Barcelona Spain Integrative Systems Biology Fundacion Instituto Leloir Buenos Aires Argentina Instituto de Investigaciones Bioquímicas de Buenos Aires Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Buenos Aires Argentina DStructural Bioinformatics Fundacion Instituto Leloir Buenos Aires Argentina Comparative Genomics of Plant Development Fundacion Instituto Leloir Buenos Aires Argentina

Motivation Genome-wide analysis of alternative splicing has been a very active field of research since the early days of next generation sequencing technologies. Since then, ever-growing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexity of the general splicing repertoire. A large number of splicing analysis methodologies exist, each of them presenting its own strengths and weaknesses. For instance, methods exclusively relying on junction information do not take advantage of the large majority of reads produced in an RNA-seq assay, isoform reconstruction methods might not detect novel intron retention events, some solutions can only handle canonical splicing events, and many existing methods can only perform pairwise *** In this contribution, we present ASpli, a computational suite implemented in R statistical language, that allows the identification of changes in both, annotated and novel alternative-splicing events and can deal with simple, multi-factor or paired experimental designs. Our integrative computational workflow, that considers the same GLM model applied to different sets of reads and junctions, allows computation of complementary splicing signals. Analyzing simulated and real data, we found that the consolidation of these signals resulted in a robust proxy of the occurrence of splicing alterations. While the analysis of junctions allowed us to uncover annotated as well as non-annotated events, read coverage signals notably increased recall capabilities at a very competitive performance when compared against other state-of-the-art splicing analysis *** and implementation ASpli is freely available from the Bioconductor project site https://***/doi:10.18129/*** information Supplementary data are available at bioinformatics online.

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Learning Cellular Objectives from Fluxes by Inverse Optimization

Learning Cellular Objectives from Fluxes by Inverse Optimiza...

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IEEE Annual Conference on Decision and Control

作者： Qi Zhao Arion Stettner Ed Reznik Daniel Segre Ioannis Ch. Paschalidis Dept. of Electrical and Computer Eng. and Division of Systems Engineering Boston University Department of Biomedical Engineering Boston University Bioinformatics Program Department of Biology and Department of Biomedical Engineering Boston University

ISBN: (纸本)9781479978878

Flux Balance Analysis (FBA) is a widely used approach for studying biochemical networks, and in particular the genome-scale metabolic network reconstructions. It formulates the problem of predicting a cell's chemical reaction fluxes as the linear optimization problem of maximizing a cellular objective (e.g., growth) subject to constraints capturing stoichiometry mass balances of the metabolic network and bounds that reflect the composition of the growth medium. In practice, however, reaction fluxes of the cells under specific growth conditions are available to be measured, but the primal FBA objective function is not necessarily known. Understanding its structure can elucidate the cellular metabolic control mechanisms and infer important information regarding an organism's evolution. To that end, we have developed an Inverse Flux Balance Analysis (InvFBA) method which is a novel inverse optimization-based framework for inferring metabolic objective functions. Within this framework, we present three different forms of objective functions: linear, quadratic, and non-parametric. We show that in all cases, the inverse problem is tractable and can be solved efficiently. We provide several numerical examples to show that the inference of the objective function is consistent with simulated flux data and actual measurements.

关键词： Linear programming Kernel Optimization Biochemistry Biomass Biomedical measurement

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Rewriting regulatory DNA to dissect and reprogram gene expression

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Cell 2025年

作者： Martyn, Gabriella E. Montgomery, Michael T. Jones, Hank Guo, Katherine Doughty, Benjamin R. Linder, Johannes Bisht, Deepa Xia, Fan Cai, Xiangmeng S. Chen, Ziwei Cochran, Kelly Lawrence, Kathryn A. Munson, Glen Pampari, Anusri Fulco, Charles P. Sahni, Nidhi Kelley, David R. Lander, Eric S. Kundaje, Anshul Engreitz, Jesse M. Department of Genetics Stanford University School of Medicine Stanford 94305 CA United States Basic Science and Engineering Initiative Stanford Children's Health Betty Irene Moore Children's Heart Center Stanford 94305 CA United States Calico Life Sciences LLC South San Francisco 94080 CA United States Department of Genitourinary Medical Oncology Division of Cancer Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Epigenetics and Molecular Carcinogenesis The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Computer Science Stanford University Stanford 94305 CA United States Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge 02142 MA United States Gene Regulation Observatory Broad Institute of MIT and Harvard Cambridge 02142 MA United States Broad Institute of MIT and Harvard Cambridge 02142 MA United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Quantitative and Computational Biosciences Program Baylor College of Medicine Houston 77030 TX United States Department of Biology MIT Cambridge 02139 MA United States Department of Systems Biology Harvard Medical School Boston 02115 MA United States Stanford Cardiovascular Institute Stanford University Stanford 94305 CA United States

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context. © 2025 Elsevier Inc.

关键词： CRISPR enhancers gene regulation high-throughput screening non-coding variants predictive models prime editing RNA FlowFISH sequence design transcription factors

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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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An Application of Monotone Functions Decomposition to the Reconstruction of Gene Regulatory Networks

An Application of Monotone Functions Decomposition to the Re...

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Annual International Conference of the IEEE Engineering in Medicine and biology Society

作者： H. Chang G. Richard A. A. Julius C. Belta S. Amar Dept. of Mechanical Engineering Boston University Program in Bioinformatics Boston University Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Dept. of Periodontology and Oral Biology Boston University

ISBN: (纸本)9781424441211

We describe the reconstruction of a gene regulatory network involved with the Toll-like Receptor signaling pathways. By applying our recent identification algorithm to a time series gene expression dataset, we identify regulatory interactions between genes and construct discrete-time piecewise affine regulatory functions. Our validation shows that our model predicts the expression levels of the genes involved in the network with good accuracy.

关键词： Gene Regulatory Networks Reconstructive Surgical Procedures MONOTONE FUNCTIONS Monotone Reconstructions Regulatory Gene Expression Regulation Gene Expression Profiles Ectopic Gene Expression decomposition

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RETRACTION: RETRACTED: Site-specific recombination of nitrogen-fixation genes in cyanobacteria by XisF-XisH-XisI complex: Structures and models (Retraction of Vol 86, Pg 1, 2014)

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PROTEINS-STRUCTURE FUNCTION AND bioinformatics 2018年第2期86卷 268-268页

作者： Hwang, William C. Golden, James W. Pascual, Jaime Xu, Dong Cheltsov, Anton Godzik, Adam Joint Center for Structural Genomics http://*** USA Bioinformatics and Systems Biology Program Sanford Burnham Medical Research Institute 10901 North Torrey Pines Road La Jolla CA 92037 USA.

The above article from the Proteins: Structure, Function, and bioinformatics, published online on 1 September 2014 in Wiley Online Library as Accepted Article (http://***/doi/10.1002/prot.24679/full), has been retracted by agreement between William C. Hwang, James W. Golden, Jaime Pascual, Dong Xu, Anton Cheltsov, Adam Godzik, the Editor‐in‐Chief, Bertrand E. Garcia‐Moreno, and Wiley Periodicals, Inc. The retraction has been agreed because submission was made without agreement from co‐author Adam Godzik.

关键词： endonuclease excisionase fdxN element recombinase recombination directionality factor

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Biwhitening reveals the rank of a count matrix

arXiv

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arXiv 2021年

作者： Landa, Boris Zhang, Thomas T.C.K. Kluger, Yuval Program in Applied Mathematics Yale University United States Department of Electrical and Systems Engineering University of Pennsylvania United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University United States Department of Pathology Yale University School of Medicine United States

Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA. Significant progress on this task was achieved using random matrix theory by characterizing the spectral properties of large noise matrices. However, utilizing such tools is not straightforward when the data matrix consists of count random variables, e.g., Poisson, in which case the noise can be heteroskedastic with an unknown variance in each entry. In this work, we consider a Poisson random matrix with independent entries, and propose a simple procedure termed biwhitening for estimating the rank of the underlying signal matrix (i.e., the Poisson parameter matrix) without any prior knowledge. Our approach is based on the key observation that one can scale the rows and columns of the data matrix simultaneously so that the spectrum of the corresponding noise agrees with the standard Marchenko-Pastur (MP) law, justifying the use of the MP upper edge as a threshold for rank selection. Importantly, the required scaling factors can be estimated directly from the observations by solving a matrix scaling problem via the Sinkhorn-Knopp algorithm. Aside from the Poisson, our approach is extended to families of distributions that satisfy a quadratic relation between the mean and the variance, such as the generalized Poisson, binomial, negative binomial, gamma, and many others. This quadratic relation can also account for missing entries in the data. We conduct numerical experiments that corroborate our theoretical findings, and showcase the advantage of our approach for rank estimation in challenging regimes. Furthermore, we demonstrate the favorable performance of our approach on several real datasets of single-cell RNA sequencing (scRNA-seq), High-Throughput Chromosome Conformation Capture (Hi-C), and document topic *** Codes 62H12, 62H25. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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The Genomic Landscape of Wilms' Tumor 1 (WT1) Mutant Acute Myeloid Leukemia

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BLOOD 2020年第Sup1期136卷 28-28页

作者： Awada, Hassan Durmaz, Arda Gurnari, Carmelo Zawit, Misam Kongkiatkamon, Sunisa Pagliuca, Simona Kerr, Cassandra M. Carraway, Hetty E. Nazha, Aziz Sekeres, Mikkael A. Maciejewski, Jaroslaw P. Visconte, Valeria Department of Translational Hematology and Oncology Research Taussig Cancer Institute Cleveland Clinic Foundation Cleveland OH Department of Systems biology and Bioinformatics CWRU cleveland Department of Translational Hematology and Oncology Taussig Cancer Institute Cleveland OH Department of Translational Hematology and Oncology Research Taussig Cancer Institute Cleveland Clinic Cleveland OH Department of Hematology and Medical Oncology Leukemia Program Taussig Cancer Institute Cleveland Clinic Foundation Cleveland OH Leukemia Program Department of Hematology and Medical Oncology Taussig Cancer Institute Cleveland Clinic Cleveland OH Leukemia Program Department of Hematology and Medical Oncology Cleveland Clinic Taussig Cancer Institute Cleveland OH

Mutations in tumor suppressor genes and oncogenes are both potentially therapeutically actionable in acute myeloid leukemia (AML). The Wilms' Tumor 1 (WT1 ) gene is located on 11p13 and encodes a zinc finger transcription factor which has been found to be overexpressed and mutated in AML. In normal development, WT1 is only expressed in a small subset of hematopoietic stem cells. While its overexpression suggests an oncogenic role, the invariable presence of mutations in the cysteine-histidine zinc finger domains indicates a tumor suppressor function, similar to that in WAGR syndrome/11p deletion syndrome in which it was first discovered. Like its unknown function in AML, the clinical significance and genetic associations of WT1 mutations have been also controversial. Although studies of WT1 mutations in AML have been conducted, the lack of solid clinical and molecular characterization of large WT1 -mutant (WT1 MT ) AML cohort has hampered its definition. In this study, we took advantage of a compendia of genomic results from Cleveland Clinic and publicly available data of 2188 AML patients (primary (p)AML, n= 1636; secondary (s)AML, n= 433; therapy-related (t)AML, n= 119, excluding cases with acute promyelocytic leukemia, MLL- rearrangement, and core-binding factor AML). While several reports only focused on cytogenetic normal AML (CN-AML), which represented 61% of our cohort, we additionally included all other cytogenetic risk groups. In total, WT1 mutations were detected in 5% (114/2188) of patients. WT1 mutations were enriched in pAML (85%) compared to sAML (11%) and tAML (4%). Thirty-nine patients (13%) carried more than 1 WT1 mutation. WT1 MT were younger [59 vs 64 years, P =0.0002] and more often females (55% vs 45%, P =0.03) as compared to WT1 wild type (WT1 WT ) patients. Univariate analyses of baseline parameters showed that WT1 MT AML had a more proliferative phenotype with a higher WBC [15.1 vs 9.5 x109 /L, P =0.03] and bone marrow blast percentages [73 v

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Application of Random Forest in Limited Size Human Long Non-coding RNAs Identification with Secondary Structure Features

Application of Random Forest in Limited Size Human Long Non-...

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International Computer Science and Engineering Conference (ICSEC)

作者： Songtham Anuntakarun Warin Wattanapornprom Supatcha Lertampaiporn Bioinformatics and Systems Biology Program King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Mathematics University of Technology Thonburi Bangkok Thailand Biochemical Engineering and Systems Biology Research Group National Center for Genetic Engineering and Biotechnology (BIOTEC) King Mongkut’s University of Technology Thonburi Bangkok Thailand

In this work, preliminary experiments of using diverse machine learning algorithms and testing of multiple relevant features to discriminate between human lncRNAs and coding/partial coding sequences was performed. This research limited the size of human lncRNAs such that they are shorter than 1000 nucleotides. Various significant features in describing RNA sequence including sequenced based features, secondary structure features, base-pair features and structural robustness features were used in this study. Then, the top 20 significant features were selected using Wilcoxon rank-sum test and discovered that the secondary structure features are the unique characteristics for identifying the human lncRNAs which are quite difference with those in the groups of shorter and longer types of ncRNAs. Such features are suitable with the rule-based classifiers like Random Forest. According to 10-folding cross validation, the random forest model has shown the highest accuracy, sensitivity and specificity as well as the lowest false positive rate among all competitors. Furthermore, the model was compared with other state-of -the-art approaches such as CPC, CPAT, RNAcon and achieved the highest accuracy of 84.5% among all the participants.

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A systems biology Approach to Investigate Control Targets of Intracellular Calcium Transients Induced by Clostridium Perfringens Phospholipase C

A Systems Biology Approach to Investigate Control Targets of...

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： Laura Brenes-Guillén Man-Sai Acón-Chan Rodrigo Mora-Rodríguez Laura Monturiol-Gross Celular and Molecular Biology University of Costa Rica (UCR) San José Costa Rica Master Program on Bioinformatics and Systems Biology University of Costa Rica (UCR) San José Costa Rica Faculty of Microbiology University of Costa Rica (UCR) San José Costa Rica

Clostridium perfringens phospholipase C (CpPLC) plays a key role in the pathogenesis of gas gangrene, an acute and life-threatening infection in humans. CpPLC leads to the unregulated production of second messengers, such as calcium, and activates important signaling pathways like PKC, MEK, and NFkB, inducing ROS production, that leads towards cellular damage. We propose a systems biology approach to identify potential microRNAs which could be used as tools to explore the role of calcium channels in the mechanism of action of CpPLC, and eventually as therapeutic agents that render cells less sensitive to this toxin. Results show that miR19B1 and miR449B are interesting candidate targets which perturbations may affect the expression of genes involved in calcium transport. This approach may be useful to elucidate calcium molecular interactions involved in the CpPLC mechanism of action.

关键词： Calcium Mathematical model Gene expression Network topology Data models systems biology Parameter estimation

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