Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Combining Experimental Data and Computational Methods for the Non-Computer Specialist.

ABSTRACT: Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

SUBMITTER: Cardenas R

PROVIDER: S-EPMC7594097 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Combining Experimental Data and Computational Methods for the Non-Computer Specialist.

Cárdenas Reinier R Martínez-Seoane Javier J Amero Carlos C

Molecules (Basel, Switzerland) 20201018 20

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both ...[more]

PMID: 33081072

Similar Datasets

Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides.

Project description:Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosynthetic growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species. IMPORTANCE Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival.

| S-EPMC5513736 | biostudies-literature

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Project description:Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

| S-EPMC11343128 | biostudies-literature

Discovery of Amoebicidal Compounds by Combining Computational and Experimental Approaches.

Project description:Pathogenic and opportunistic free-living amoebae such as Acanthamoeba spp. can cause keratitis (Acanthamoeba keratitis [AK]), which may ultimately lead to permanent visual impairment or blindness. Acanthamoeba can also cause rare but usually fatal granulomatous amoebic encephalitis (GAE). Current therapeutic options for AK require a lengthy treatment with nonspecific drugs that are often associated with adverse effects. Recent developments in the field led us to target cAMP pathways, specifically phosphodiesterase. Guided by computational tools, we targeted the Acanthamoeba phosphodiesterase RegA. Computational studies led to the construction and validation of a homology model followed by a virtual screening protocol guided by induced-fit docking and chemical scaffold analysis using our medicinal and biological chemistry (MBC) chemical library. Subsequently, 18 virtual screening hits were prioritized for further testing in vitro against Acanthamoeba castellanii, identifying amoebicidal hits containing piperidine and urea imidazole cores. Promising activities were confirmed in the resistant cyst form of the amoeba and in additional clinical Acanthamoeba strains, increasing their therapeutic potential. Mechanism-of-action studies revealed that these compounds produce apoptosis through reactive oxygen species (ROS)-mediated mitochondrial damage. These chemical families show promise for further optimization to produce effective antiacanthamoebal drugs.

| S-EPMC7849001 | biostudies-literature

Exploring protein phosphorylation by combining computational approaches and biochemical methods.

Project description:Post-translational modifications of proteins expand their functional diversity, regulating the response of cells to a variety of stimuli. Among these modifications, phosphorylation is the most ubiquitous and plays a prominent role in cell signaling. The addition of a phosphate often affects the function of a protein by altering its structure and dynamics. However, these alterations are often difficult to study and the functional and structural implications remain unresolved. New approaches are emerging to overcome common obstacles related to the production and manipulation of these samples. Here, we summarize the available methods for phosphoprotein purification and phosphomimetic engineering, highlighting the advantages and disadvantages of each. We propose a general workflow for protein phosphorylation analysis combining computational and biochemical approaches, building on recent advances that enable user-friendly and easy-to-access Molecular Dynamics simulations. We hope this innovative workflow will inform the best experimental approach to explore such post-translational modifications. We have applied this workflow to two different human protein models: the hemeprotein cytochrome c and the RNA binding protein HuR. Our results illustrate the usefulness of Molecular Dynamics as a decision-making tool to design the most appropriate phosphomimetic variant.

| S-EPMC7369424 | biostudies-literature

Dynamics of different mGBP as revealed by computational and experimental methods

Project description:Guanylate binding proteins (GBP) belong to the superfamily of dynamin proteins. Those might assemble into larger structures upon initial dimerization. We analyzed monomeric and dimerization of mGBP7 via molecular dynamic simulations and compared the developed models with data from crosslinking mass spectrometry experiments.

2024-01-26 | PXD026979 | Pride

Computational Methods for CLIP-seq Data Processing.

Project description:RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.

| S-EPMC4196881 | biostudies-literature

Optimizing the experimental and computational methods for Infinium DNA methylation BeadChip on low input and single cells

Project description:In this study, the Infinium BeadChip DNA methylome profiling technology was optimized for suboptimal input DNA conditions, including ultra-low input down to single cells. We acquired knowledge on the boundaries and limits of the Infinium DNA methylation BeadChips with suboptimal input DNA. These findings offer comprehensive solutions to expand the application of this technology to cases with limited DNA.

2024-02-27 | GSE239290 | GEO

Storing, combining and analysing turkey experimental data in the Big Data era.

Project description:With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an 'extract, transform and load' (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the 'wall time' (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.

| S-EPMC7538337 | biostudies-literature

Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.

Project description:Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested.A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate.In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models.Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.

| S-EPMC3946937 | biostudies-literature

Discovery of a Potent Allosteric Kinase Modulator by Combining Computational and Synthetic Methods.

Project description:The rational design of allosteric kinase modulators is challenging but rewarding. The protein kinase PDK1, which lies at the center of the growth-factor signaling pathway, possesses an allosteric regulatory site previously validated both in vitro and in cells. ANCHOR.QUERY software was used to discover a potent allosteric PDK1 kinase modulator. Using a recently published PDK1 compound as a template, several new scaffolds that bind to the allosteric target site were generated and one example was validated. The inhibitor can be synthesized in one step by multicomponent reaction (MCR) chemistry when using the ANCHOR.QUERY approach. Our results are significant because the outlined approach allows rapid and efficient scaffold hopping from known molecules into new easily accessible and biologically active ones. Based on increasing interest in allosteric-site drug discovery, we foresee many potential applications for this approach.

| S-EPMC4721676 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data