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Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors

Bioinformatics Oxford Journals - Sat, 12/11/2022 - 5:30am
AbstractSummaryThe development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by improving our understanding of antibody-antigen binding. We present Paragraph, a structure-based paratope prediction tool that outperforms current state-of-the-art tools using simpler feature vectors and no antigen information.AvailabilitySource code is freely available at www.github.com/oxpig/ParagraphSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

SBMLDiagrams: A python package to process and visualize SBML layout and render

Bioinformatics Oxford Journals - Sat, 12/11/2022 - 5:30am
AbstractSummaryThe Systems Biology Markup Language (SBML) is an extensible standard format for exchanging biochemical models. One of the extensions for SBML is the SBML Layout and Render package. This allows modelers to describe a biochemical model as a pathway diagram. However, up to now there has been little support to help users easily add and retrieve such information from SBML. In this application note, we describe a new Python package called SBMLDiagrams. This package allows a user to add layout and render information or retrieve it using a straightforward Python API. The package uses skia-python to support the rendering of the diagrams, allowing export to commons formats such as PNG or PDF.AvailabilitySBMLDiagrams is publicly available and licensed under the liberal MIT open-source license. The package is available for all major platforms. The source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams). Users can install the package using the standard pip installation mechanism: pip install SBMLDiagrams.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

CAPG: Comprehensive Allopolyploid Genotyper

Bioinformatics Oxford Journals - Fri, 11/11/2022 - 5:30am
AbstractMotivationGenotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls.ResultsWe introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole genome resequencing (WGS) data. We demonstrate CAPG in allotetraploids, where it performs better than GATK’s HaplotypeCaller applied to reads aligned to the combined subgenomic references.AvailabilityCode and tutorials are available at https://github.com/Kkulkarni1/CAPG.git.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

CoMutDB: The Landscape of Somatic Mutation Co-occurrence in Cancers

Bioinformatics Oxford Journals - Thu, 10/11/2022 - 5:30am
AbstractMotivationSomatic mutation co-occurrence has been proven to have a profound effect on tumorigenesis. While some studies have been conducted on co-mutations, a centralized resource dedicated to co-mutations in cancer is still lacking.ResultsUsing multi-omics data from over 30,000 subjects and 1,747 cancer cell lines, we present the Cancer co-mutation database (CoMutDB), the most comprehensive resource devoted to describing cancer co-mutations and their characteristics.Availabilityhttp://www.innovebioinfo.com/Database/CoMutDB/Home.php.Supplementary informationSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

3DPolyS-LE: an accessible simulation framework to model the interplay between chromatin and loop extrusion

Bioinformatics Oxford Journals - Thu, 10/11/2022 - 5:30am
AbstractSummaryRecent studies suggest that the loop extrusion activity of Structural Maintenance of Chromosomes complexes is central to proper organization of genomes in vivo. Polymer physics-based modeling of chromosome structure has been instrumental to assess which structures such extrusion can create. Only few laboratories however have the technical and computational expertise to create in silico models combining dynamic features of chromatin and loop extruders. Here we present 3DPolyS-LE, a self-contained, easy to use modeling and simulation framework allowing non-specialists to ask how specific properties of loop extruders and boundary elements impact on 3D chromosome structure. 3DPolyS-LE also provides algorithms to compare predictions with experimental Hi-C data.AvailabilitySoftware available at https://gitlab.com/togop/3DPolyS-LE; implemented in Python and Fortran 2003 and supported on any Unix-based operating system (Linux, Mac OS).Supplementary informationSupplementary informationSupplementary information are available at Bioinformatics online.
Categories: Bioinformatics Trends

Improving protein structure prediction using templates and sequence embedding

Bioinformatics Oxford Journals - Thu, 10/11/2022 - 5:30am
AbstractMotivationProtein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This paper studies the impacts of two kinds of information for structure prediction: template and MSA embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch which may not perform very well on some targets. In addition, sequence embedding generated by pretrained protein language models has not been fully explored for structure prediction. In this paper, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction.ResultsWe have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 out of 23 CASP14 FM targets and 14 out of 18 CAMEO targets, outperforming RoseTTAFold by 5% and 7%, respectively.Availabilityavailable at https://github.com/xluo233/RaptorXFoldSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

Bioinformatics Oxford Journals - Thu, 10/11/2022 - 5:30am
AbstractMotivationMultiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for.ResultsHere, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood.AvailabilityOur code and examples are available at: https://github.com/spetti/SMURF.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

DREAMM: A web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design

Bioinformatics Oxford Journals - Thu, 10/11/2022 - 5:30am
AbstractSummaryThe allosteric modulation of peripheral membrane proteins by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information, and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web-server. DREAMM works in the back-end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of peripheral membrane proteins. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM.Availability and implementationDREAMM web-server is accessible via https://dreamm.ni4os.eu.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

ViMRT: a text-mining tool and search engine for automated virus mutation recognition

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationVirus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations.ResultsWe developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1,662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases.AvailabilityViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.
Categories: Bioinformatics Trends

A framework for group-wise summarization and comparison of chromatin state annotations

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationGenome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution.ResultsWe developed CSREP, which takes as input chromatin state annotations for a group of samples. CSREP then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers that predict the chromatin state assignment of each sample given the state maps from all other samples. The difference of CSREP’s probability assignments for two groups can be used to identify genomic locations with differential chromatin state assignments. Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution.AvailabilityThe CSREP source code and generated data is available at http://github.com/ernstlab/csrep.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

sAMPpred-GAT: Prediction of Antimicrobial Peptide by Graph Attention Network and Predicted Peptide Structure

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationAntimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance.ResultsIn this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction.AvailabilityA user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Vaeda computationally annotates doublets in single-cell RNA sequencing data

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology’s utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation computational methods to annotate and remove doublets form scRNA-seq datasets are needed.ResultsWe introduce vaeda, a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to sixteen benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. All together, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows.AvailabilityVaeda is available at https://github.com/kostkalab/vaeda and the version used for results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

An Approach of Gene Regulatory Network Construction Using Mixed Entropy Optimizing Context-Related Likelihood Mutual Information

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationThe question of how to construct gene regulatory networks has long been a focus of biological research. Mutual information can be used to measure nonlinear relationships, and it has been widely used in the construction of gene regulatory networks. However, this method cannot measure indirect regulatory relationships under the influence of multiple genes, which reduces the accuracy of inferring gene regulatory networks.ApproachThis work proposes a method for constructing gene regulatory networks based on mixed entropy optimizing context-related likelihood mutual information (MEOMI). First, two entropy estimators were combined to calculate the mutual information between genes. Then, distribution optimization was performed using a context-related likelihood algorithm to eliminate some indirect regulatory relationships and obtain the initial gene regulatory network. To obtain the complex interaction between genes and eliminate redundant edges in the network, the initial gene regulatory network was further optimized by calculating the conditional mutual inclusive information (CMI2) between gene pairs under the influence of multiple genes. The network was iteratively updated to reduce the impact of mutual information on the overestimation of the direct regulatory intensity.ResultsThe experimental results show that the MEOMI method performed better than several other kinds of gene network construction methods on DREAM challenge simulated datasets (DREAM3 and DREAM5), three real Escherichia coli datasets (E. coli SOS pathway network, E. coli SOS DNA repair network, and E. coli community network) and two human datasets.Availability and implementationSource code and dataset are available at https://github.com/Dalei-Dalei/MEOMI/ and http://122.205.95.139/MEOMI/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Global FDR control across multiple RNAseq experiments

Bioinformatics Oxford Journals - Thu, 03/11/2022 - 5:30am
AbstractMotivationWhile classical approaches for controlling the false discovery rate (FDR) of RNAseq experiments have been well described, modern research workflows and growing databases enable a new paradigm of controlling the FDR globally across RNAseq experiments in the past, present, and future. The simplest analysis strategy that analyses each RNAseq experiment separately and applies an FDR correction method can lead to inflation of the overall FDR. We propose applying recently developed methodology for online multiple hypothesis testing to control the global FDR in a principled way across multiple RNAseq experiments.ResultsWe show that repeated application of classical repeated offline approaches has variable control of global FDR of RNAseq experiments over time. We demonstrate that the online FDR algorithms are a principled way to control FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to repeated offline approaches.Availability and ImplementationThe onlineFDR package is freely available at http://www.bioconductor.org/packages/onlineFDR. Additional code used for the simulation studies can be found at https://github.com/latlio/onlinefdr_rnaseq_simulationSupplementary InformationSupplementary AppendixSupplementary Appendix is available in Bioinformatics online.
Categories: Bioinformatics Trends

Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities

Bioinformatics Oxford Journals - Thu, 03/11/2022 - 5:30am
AbstractMotivationAntimicrobial peptides (AMP) have the potential to inhibit multiple types of pathogens and to heal infections. Computational strategies can assist in characterizing novel AMPs from proteome or collections of synthetic sequences and discovering their functional abilities towards different microbial targets without intensive labor.ResultsHere, we present a deep learning-based method for computer-aided novel AMP discovery that utilizes the transformer neural network architecture with knowledge from natural language processing to extract peptide sequence information. We implemented the method for two AMP-related tasks: the first is to discriminate AMPs from other peptides, and the second task is identifying AMPs functional activities related to seven different targets (gram-negative bacteria, gram-positive bacteria, fungi, viruses, cancer cells, parasites, and mammalian cell inhibition), which is a multi-label problem. In addition, asymmetric loss was adopted to resolve the intrinsic imbalance of dataset, particularly for the multi-label scenarios. The evaluation showed that our proposed scheme achieves the best performance for the first task (96.85% balanced accuracy) and has a more unbiased prediction for the second task (79.83% balanced accuracy averaged across all functional activities) when compared to that of strategies without imbalanced learning or deep learning.AvailabilityThe source code of this study is available at https://github.com/BiOmicsLab/TransImbAMP.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

PEMT: a patent enrichment tool for drug discovery

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationDrug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources.ResultsHere, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification (IPC) codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes.Availability and implementationPEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/ respectively.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

MIDAS2: Metagenomic Intra-species Diversity Analysis System

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractSummaryThe Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants (CNVs) in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped.AvailabilityThe source code is available at https://github.com/czbiohub/MIDAS2.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

GAVISUNK: Genome assembly validation via inter-SUNK distances in Oxford Nanopore reads

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationHighly contiguous de novo phased diploid genome assemblies are now feasible for large numbers of species and individuals. Methods are needed to validate assembly accuracy and detect misassemblies with orthologous sequencing data to allow for confident downstream analyses.ResultsWe developed GAVISUNK, an open-source pipeline that detects misassemblies and produces a set of reliable regions genome-wide by assessing concordance of distances between unique k-mers in Pacific Biosciences high-fidelity (HiFi) assemblies and raw Oxford Nanopore Technologies reads.AvailabilityGAVISUNK is available at https://github.com/pdishuck/GAVISUNK.
Categories: Bioinformatics Trends

BraneMF: Integration of Biological Networks for Functional Analysis of Proteins

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationThe cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. The increase of omics technologies has favored the generation of large-scale disparate data and the consequent demand for simultaneously using molecular and functional interaction networks: gene co-expression, protein-protein interaction (PPI), genetic interaction, and metabolic networks. They are rich sources of information at different molecular levels, and their effective integration is essential to understand cell functioning and their building blocks (proteins). Therefore, it is necessary to obtain informative representations of proteins and their proximity, that are not fully captured by features extracted directly from a single informational level. We propose BraneMF, a novel random walk-based matrix factorization method for learning node representation in a multilayer network, with application to omics data integration.ResultsWe test BraneMF with PPI networks of Saccharomyces cerevisiae, a well-studied yeast model organism. We demonstrate the applicability of the learned features for essential multi-omics inference tasks: clustering, function and PPI prediction. We compare it to state-of-the-art integration methods for multilayer network. BraneMF outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks. The robustness of results is assessed by an extensive parameter sensitivity analysis.AvailabilityBraneMF’s code is freely available at: https://github.com/Surabhivj/BraneMF, along with datasets, embeddings, and result files.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

An in silico genome-wide screen for circadian clock strength in human samples

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationYears of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how ‘non-clock’ pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function.ResultsWe developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples are correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues.AvailabilityLTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

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