An introduction to computational tools for differential binding analysis with ChIP‐seq data

Bioinformatics workflows frequently access various distributed biological data sources and computational analysis tools for data analysis and knowledge discovery. They move large volumes of data from biological data sources to computational analysis tools and follow the traditional data migration approach for workflow execution. However, in the advent of big-data in bioinformatics, moving large volumes of data to computation during workflow execution is no longer feasible. Considering the fact that the size of biological data is continuously growing and is much larger than the computational analysis tool size, moving computation to data in a workflow is a better solution to handle the growing data. In this paper, we therefore propose a computation migration approach to execute bioinformatics workflows. We move computational analysis tools to data sources during workflow execution and demonstrate with workflow patterns that moving computation instead of data yields high performance gains in terms of data-flow and execution time.

Docking allows to predict ligand binding to proteins, since the 3D-structure for the target is available. Several docking studies have been carried out to identify potential ligands for drug targets. Many of these studies resulted in the leads that were later developed as drugs. Our goal here is to describe the development of an integrated computational tool to assess docking accuracy and build new scoring functions to predict ligandbinding affinity. We carried out docking simulations using MVD program for a data set available on CSAR 2014 database (coagulation factor Xa) for which ligand-binding information and structures are available. These docking results were analyzed using SAnDReS available at www.sandres.net. Machine learning methods were applied to build new scoring functions and our results were compared with previously published benchmarks. Our integrated docking strategy generated poses with docking accuracy higher than previously published benchmarks. In addition, the new scoring function developed using SAnDReS shows better performance than well-established scoring functions such the ones available in Autodock, Autodock- Vina, Gold, Glide, and MVD. The big data generated during docking lacked an integrated computational tool for statistical analysis of the influence of structural parameters on docking and scoring function performance. Here we describe methods to evaluate docking results using SAnDReS, a computational environment for statistical analysis of docking results and development of scoring functions. We believe that SAnDReS is a computational tool with potential to improve accuracy in docking projects.

Myc and Bet Proteins Orchestrate the Early Regulatory Genome Changes Required for Osteoclast Lineage Commitment

Chapter 1 - Genome to phenome: bioinformatics of crop plants

This work presents a computational tool for preliminary analysis of hypersonic vehicles, based on local surface inclination methods: the HipeX. This program was developed for reading standard triangulation language (STL) geometry files and calculating pressure coefficient and temperature distributions over vehicle’s surface using the Newtonian, modified Newtonian or tangent-wedge methods. Validations were made with a cylinder and a sphere, where only the Newtonian method was applied, and with experimental data from Apollo capsule at Mach 10, where the Newtonian and the modified Newtonian methods were applied. These validations presented the code capability to read geometries as well as to estimate aerodynamic force coefficients. A preliminary application was to predict the aerodynamic force coefficients of a generic hypersonic vehicle over constant dynamic pressure trajectories of 23,940, 60,000 and 95,760 N/m2 with zero angle of attack. With a fixed dynamic pressure of 60,000 N/m2, this vehicle was tested over several Mach numbers and with angle of attack variation from -10 to 10 deg. Zero angle of attack investigation showed minor changes on the force coefficients with altitude, while the variation of angle of attack produced more pronounced variations on these parameters. Maximum flow temperatures over vehicle’s surface were estimated ranging from 850 to 5,315 K.

Computational tools for design, analysis, and management of residential energy systems

Systems biology is a multidisciplinary field that weaves together all the basic sciences through the use of computational and bioinformatics tools, to provide a more integrative view of the complex molecular interactions taking place within and among cells. The successes in the development and improvement of techniques for high throughput –omics has rapidly increased the amount of available data. The complexity of the underlying biological system it describes requires the development of tools to properly process it and analyze it. Computational models mathematically describe the systems interactions, allowing intrinsic properties of the data to emerge that would otherwise be overlooked. These models provide context to the data and are used to make predictions about the behavior of the system and to simulate a broader landscape of hypothesis, saving the time and cost of performing numerous experiments. However, the number of required parameters to mathematically formulate the system increases with the model complexity to integrate the available data. Thus, the development of estimation procedures and workflows to retrieve these values from literature and databases become crucial. In this work, we developed a set of computational models, analysis tools, and pipelines to support the study of two biological systems crucial to the cell survival: metabolism and protein synthesis. Metabolism is responsible for the production of most cell biomass, including proteins. Together, these two systems balance the renewal of protein in the cell, where metabolism provides the amino acids obtained from protein breakdown to the mRNA translation machinery. Deregulation of these systems is known to cause multiple disorders, such as neurodegenerative diseases and cancer. In the study of protein synthesis, we employ a combination of deterministic and stochastic modeling approaches to understand its intrinsic mechanistic properties and its rate-limiting steps. A better understanding of the system properties can have a profound impact on the development of drug targets and, in particular, in the optimization of heterologous protein production. Our studies revealed that more than one factor plays a role in the speed of translation: competition for tRNA resources and the type of cognate binding interaction between tRNA and the mRNA-ribosome complex. We also derived an equation that, given the knowledge about certain intracellular parameters pertaining to the host organism of interest, can assist in the design of transcripts for optimizing heterologous protein production. For the study of human metabolism, we established a pipeline to generate tissue-specific reduced metabolic models that can be used to study the metabolic reprogramming of different cancers and compare it with the metabolic phenotype of a healthy cell type. Despite being presented herein for human models, this pipeline is general and can be applied to the models of any organism. Starting from a human genome scale metabolic model, the pipeline improves compound annotation, identification, thermodynamics parameter retrieval, and facilitates data integration through the connection of several compound databases in a semi-automatized fashion. This work sets a standard for metabolic model assessment and curation and improves on existing tools to generate the first thermodynamically feasible reduced model of human metabolism, which is specifically tailored to the physiology and conditions under study.

Cells respond to environmental stress and proinflammatory cytokines by stimulating the Jun N-terminal kinase/stress-activated protein kinase (JNK/SAPK) and the p38 mitogen-activated protein kinase cascades. Infection of eukaryotic cells with herpes simplex virus type 1 (HSV-1) resulted in stimulation of both JNK/SAPK and p38 mitogen-activated protein kinase after 3 h of infection, and activation reached a maximum of 4-fold by 9 h post-infection. By using a series of mutant viruses, we showed that the virion transactivator protein VP16 stimulates p38/JNK, whereas no immediate-early, early, or late viral expressed gene is involved. We identified the stress-activated protein kinase kinase 1 as an upstream activator of p38/JNK, and we demonstrated that activation of AP-1 binding proceeded p38/JNK stimulation. During infection, the activated AP-1 consisted mainly of JunB and JunD with a simultaneous decrease in the cellular levels of Jun protein. We suggest that activation of the stress pathways by HSV-1 infection either represents a cascade triggered by the virus to facilitate the lytic cycle or a defense mechanism of the host cell against virus invasion.

A flexible transcription initiation architecture and a uniform regulatory grammar buffer expression variability and the effects of genetic variants on essential genes while a rigid transcription initiation architecture and a diverse regulatory grammar ensure condition-specific responsiveness of other genes.

Diabetes Mellitus is a chronic health condition (long-lasting) due to inadequate control of blood levels of glucose. This study presents a prediction of Type 2 Diabetes Mellitus among women using various Machine Learning Algorithms deployed to predict the diabetic condition. A University of California Irvine Diabetes Mellitus Dataset posted in Kaggle was used for analysis. The dataset included eight risk factors for Type 2 Diabetes Mellitus prediction, including Age, Systolic Blood Pressure, Glucose, Body Mass Index, Insulin, Skin Thickness, Diabetic Pedigree Function, and Pregnancy. R language was used for the data visualization, while the algorithms considered for the study are Logistic Regression, Support Vector Machines, Decision Trees and Extreme Gradient Boost. The performance analysis of these algorithms on various classification metrics is also presented here, considering the Area Under the Curve and Receiver Operating Characteristics score is the best for Extreme Gradient Boost with 85%, followed by Support Vector Machines and Decision Trees. The Logistic Regression is showing low performance. But the Decision Trees and Extreme Gradient Boost show promising performance against all the classification metrics. But the Support Vector Machines offers a lower support value; hence it cannot be claimed to be a good classifier. The model showed that the most significant predictors of Type 2 Diabetes Mellitus were strongly correlated with Glucose Levels and mediumly correlated with Body Mass Index, whereas Age, Skin Thickness, Systolic Blood Pressure, Insulin, Pregnancy, and Pedigree Function were less significant. This type of real-time analysis has proved that the symptoms of Type 2 Diabetes Mellitus in women fall entirely different compared to men, which highlights the importance of Glucose Levels and Body Mass Index in women. The prediction of Type 2 Diabetes Mellitus helps public health professionals to help people by suggesting proper food intake and adjusting lifestyle activities with good fitness management in women to make glucose levels and body mass index controlled. Therefore, the healthcare systems should give special attention to diabetic conditions in women to reduce exacerbations of the disease and other associated symptoms. This work attempts to predict the occurrence of Type 2 Diabetes Mellitus among women on their behavioral and biological conditions.

Driven by anticipated increase in satellite deployments and corresponding increase on space-launch demand, numerous domestic and international government programs as well as several start-up private ventures have been formed to develop launch vehicles that will provide less expensive alternatives to the current fleet, including numerous reusable launch vehicle concepts. The different launch-vehicle concepts are expected to have different operational characteristics, and thus different impact and requirements on the National Airspace System (NAS). This paper describes a set of computer analysis capabilities being developed for the study of air traffic management issues related to the space operations. NASA already has the Future ATM Concepts Evaluation Tool (FACET) for evaluation of advanced technologies on the air traffic over the continental U.S. The computer tool described in this paper builds on FACET to include the capability to model and analyze space-flight operations within the NAS. Specifically, it includes an extensible model database for inclusion of future launch and return vehicle models for simulation and analysis; modeling of new reserved airspace definitions; functionality for analyzing launch/return operations and potential debris fallout; and more powerful visualization capabilities. The computer tool will be instrumental for studying the interaction of air and space traffic with regard to such issues as air traffic control, spaceport operations, airspace environments, automation and decision-support tools, and communication, navigation, and surveillance.

Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) provides insights into both the genomic location occupied by the protein of interest and the difference in DNA occupancy between experimental states. Given that ChIP-seq data are collected experimentally, an important step for determining regions with differential DNA occupancy between states is between-sample normalization. While between-sample normalization is crucial for downstream differential binding analysis, the technical conditions underlying between-sample normalization methods have yet to be examined for ChIP-seq. We identify three important technical conditions underlying ChIP-seq between-sample normalization methods: balanced differential DNA occupancy, equal total DNA occupancy, and equal background binding across states. To illustrate the importance of satisfying the selected normalization method's technical conditions for downstream differential binding analysis, we simulate ChIP-seq read count data where different combinations of the technical conditions are violated. We then externally verify our simulation results using experimental data. Based on our findings, we suggest that researchers use their understanding of the ChIP-seq experiment at hand to guide their choice of between-sample normalization method. Alternatively, researchers can use a high-confidence peakset, which is the intersection of the differentially bound peaksets obtained from using different between-sample normalization methods. In our two experimental analyses, roughly half of the called peaks were called as differentially bound for every normalization method. High-confidence peaks are less sensitive to one's choice of between-sample normalization method, and thus could be a more robust basis for identifying genomic regions with differential DNA occupancy between experimental states when there is uncertainty about which technical conditions are satisfied.

The spatial organization of the genome plays a vital role in maintaining the cell type-specific chromatin structures and regulating gene expression. The three-dimensional (3D) genome structures can be measured by sequencing-based techniques such as Hi-C on the ensemble level or by imaging-based techniques such as chromatin tracing on the single-molecule level. Chromatin tracing is a multiplexed DNA fluorescence in situ hybridization (FISH) technique that can directly trace the 3D positions of genomic loci along individual chromosomes in single cells. By designing the primary probes targeting specific genomic regions and dye-labeled secondary probes to indicate their positions, chromatin tracing can construct the 3D trace of chromatin at high resolution using microscopy. However, few computational tools have been developed for differential analysis of chromatin tracing data. Major challenges include the high variability of chromatin traces and missing values in the 3D coordinates of some loci. Here, we present Dory, a statistical method for identifying differential patterns from two groups of chromatin traces without imputation of missing data. Dory measures the spatial distance between each pair of regions in each chromatin trace and employs multi-level statistical tests to quantify the distance difference between the two groups of chromatin traces. The output of Dory includes the differential score for each region pair comparing chromatin traces between two biological conditions. We applied Dory to chromatin tracing data from six cell types in mouse fetal liver and from a mouse lung cancer progression model. We found that the changes in chromatin structure are associated with A-B compartment alterations and promoter-enhancer interaction alterations that cause differential gene expression. Dory is a user-friendly computational tool that can be applied to multiple types of imaging-based 3D genome data analysis for studying genome functions and gene regulation. Citation Format: Zhaoxia Ma, Miao Liu, Mengwei Hu, Shengyuan Wang, Siyuan Wang, Chongzhi Zang. Dory: a computational method for differential chromatin tracing analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6308.

BackgroundThe analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but their performance is strongly dependent on the parameters of the biological system under investigation. Thus, a systematic assessment of available computational tools for differential ChIP-seq analysis is required to guide the optimal selection of analysis tools based on the present biological scenario.ResultsWe created standardized reference datasets by in silico simulation and sub-sampling of genuine ChIP-seq data to represent different biological scenarios and binding profiles. Using these data, we evaluated the performance of 33 computational tools and approaches for differential ChIP-seq analysis. Tool performance was strongly dependent on peak size and shape as well as on the scenario of biological regulation.ConclusionsOur analysis provides unbiased guidelines for the optimized choice of software tools in differential ChIP-seq analysis.

With the completion of various whole genomes, one of the fundamental bioinformatics tasks is the identification of functional regulatory regions, such as promoters, and the computational discovery of genes from the produced DNA sequences. Confronted with huge amounts of DNA sequences, the utilization of automated computational sequence analysis methods and tools is more than demanding. In this article, we present an efficient feature selection to the promoter recognition, prediction, and localization problem. The whole approach is implemented in a system called MineProm. The basic idea underlying our approach is that each position-nucleotide pair in a DNA sequence is represented by a distinct binary-valued feature—the binary position base value (BPBV). A hybrid filter-wrapper, featuredeletion (or addition) algorithmic process is called for in order to select those BPBVs that best discriminate between two DNA sequences target classes (i.e., promoter vs. nonpromoter). MineProm is tested on two widely used benchmark data sets. Assessment of results demonstrates the reliability of the approach.