Bioinformatics advances最新文献_第9页

ICoN: integration using co-attention across biological networks. ICoN：利用生物网络的共同关注进行整合。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-22 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbae182

Nure Tasnina, T M Murali

{"title":"ICoN: integration using co-attention across biological networks.","authors":"Nure Tasnina, T M Murali","doi":"10.1093/bioadv/vbae182","DOIUrl":"10.1093/bioadv/vbae182","url":null,"abstract":"Motivation: Molecular interaction networks are powerful tools for studying cellular functions. Integrating diverse types of networks enhances performance in downstream tasks such as gene module detection and protein function prediction. The challenge lies in extracting meaningful protein feature representations due to varying levels of sparsity and noise across these heterogeneous networks.Results: We propose ICoN, a novel unsupervised graph neural network model that takes multiple protein-protein association networks as inputs and generates a feature representation for each protein that integrates the topological information from all the networks. A key contribution of ICoN is exploiting a mechanism called \"co-attention\" that enables cross-network communication during training. The model also incorporates a denoising training technique, introducing perturbations to each input network and training the model to reconstruct the original network from its corrupted version. Our experimental results demonstrate that ICoN surpasses individual networks across three downstream tasks: gene module detection, gene coannotation prediction, and protein function prediction. Compared to existing unsupervised network integration models, ICoN exhibits superior performance across the majority of downstream tasks and shows enhanced robustness against noise. This work introduces a promising approach for effectively integrating diverse protein-protein association networks, aiming to achieve a biologically meaningful representation of proteins.Availability and implementation: The ICoN software is available under the GNU Public License v3 at https://github.com/Murali-group/ICoN.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbae182"},"PeriodicalIF":2.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11723530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The combined focal loss and dice loss function improves the segmentation of beta-sheets in medium-resolution cryo-electron-microscopy density maps. 结合焦点损失和骰子损失函数可改善中分辨率冷冻电子显微镜密度图中贝塔片的分割。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-22 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae169

Yongcheng Mu, Thu Nguyen, Bryan Hawickhorst, Willy Wriggers, Jiangwen Sun, Jing He

{"title":"The combined focal loss and dice loss function improves the segmentation of beta-sheets in medium-resolution cryo-electron-microscopy density maps.","authors":"Yongcheng Mu, Thu Nguyen, Bryan Hawickhorst, Willy Wriggers, Jiangwen Sun, Jing He","doi":"10.1093/bioadv/vbae169","DOIUrl":"10.1093/bioadv/vbae169","url":null,"abstract":"Summary: Although multiple neural networks have been proposed for detecting secondary structures from medium-resolution (5-10 Å) cryo-electron microscopy (cryo-EM) maps, the loss functions used in the existing deep learning networks are primarily based on cross-entropy loss, which is known to be sensitive to class imbalances. We investigated five loss functions: cross-entropy, Focal loss, Dice loss, and two combined loss functions. Using a U-Net architecture in our DeepSSETracer method and a dataset composed of 1355 box-cropped atomic-structure/density-map pairs, we found that a newly designed loss function that combines Focal loss and Dice loss provides the best overall detection accuracy for secondary structures. For β-sheet voxels, which are generally much harder to detect than helix voxels, the combined loss function achieved a significant improvement (an 8.8% increase in the F1 score) compared to the cross-entropy loss function and a noticeable improvement from the Dice loss function. This study demonstrates the potential for designing more effective loss functions for hard cases in the segmentation of secondary structures. The newly trained model was incorporated into DeepSSETracer 1.1 for the segmentation of protein secondary structures in medium-resolution cryo-EM map components. DeepSSETracer can be integrated into ChimeraX, a popular molecular visualization software.Availability and implementation: https://www.cs.odu.edu/∼bioinfo/B2I_Tools/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae169"},"PeriodicalIF":2.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142735054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

blast2galaxy: a CLI and Python API for BLAST+ and DIAMOND searches on Galaxy servers. blast2galaxy：用于在Galaxy服务器上进行BLAST+和DIAMOND搜索的CLI和Python API。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-22 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae185

Patrick König, Anne Fiebig, Thomas Münch, Björn Grüning, Uwe Scholz

{"title":"blast2galaxy: a CLI and Python API for BLAST+ and DIAMOND searches on Galaxy servers.","authors":"Patrick König, Anne Fiebig, Thomas Münch, Björn Grüning, Uwe Scholz","doi":"10.1093/bioadv/vbae185","DOIUrl":"10.1093/bioadv/vbae185","url":null,"abstract":"Motivation: The Galaxy workflow system is an open-source platform supporting data-intensive research in life sciences, featuring a user-friendly web interface for complex analyses without extensive programming. It also offers a representational state transfer based API, enabling remote execution of specific tools. Galaxy supports similarity searches for nucleotide and amino acid sequences, with integrated tools like NCBI BLAST+ and DIAMOND. However, no specialized software currently exists for convenient use of NCBI BLAST+ and DIAMOND via the Galaxy API.Results: blast2galaxy is a Python package that uses the Galaxy API to run sequence alignments with NCBI BLAST+ and DIAMOND as Galaxy-wrapped tools on compatible servers. It includes a command-line interface that mirrors the CLI of BLAST+ and DIAMOND and a high-level Python API for direct alignments from Python applications. The package relies on bioblend for communication with the Galaxy API.Availability and implementation: blast2galaxy is available as open-source software under the MIT license. The source code is available on Github: https://github.com/IPK-BIT/blast2galaxy. It can be installed from the Python Package Index using \"pip install blast2galaxy\" or from the Bioconda channel using \"conda install -c bioconda blast2galaxy\". Docker and Apptainer images are available and referenced in the documentation which is available under https://blast2galaxy.readthedocs.io.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae185"},"PeriodicalIF":2.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11629687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LncLSTA: a versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention. LncLSTA：一个多功能的预测器，通过长期、短期的关注揭示lncrna的亚细胞定位。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-22 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbae173

Kai Wang, Yueming Hu, Sida Li, Ming Chen, Zhong Li

{"title":"LncLSTA: a versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention.","authors":"Kai Wang, Yueming Hu, Sida Li, Ming Chen, Zhong Li","doi":"10.1093/bioadv/vbae173","DOIUrl":"https://doi.org/10.1093/bioadv/vbae173","url":null,"abstract":"Motivation: Much evidence suggests that the subcellular localization of long-stranded noncoding RNAs (LncRNAs) provides key insights for the study of their biological function.Results: This study proposes a novel deep learning framework, LncLSTA, designed for predicting the subcellular localization of LncRNAs. It firstly exploits LncRNA sequence, electron-ion interaction pseudopotentials, and nucleotide chemical property as feature inputs. Departing from conventional k-mer approaches, this model uses a set of 1D convolutional and maxpooling operations for dynamical feature aggregation. Furthermore, LncLSTA integrates a long-short term attention module with a bidirectional long and short term memory network to comprehensively extract sequence information. In addition, it incorporates a TextCNN module to enhance accuracy and robustness in subcellular localization tasks. Experimental results demonstrate the efficacy of LncLSTA, showcasing its superior performance compared to other state-of-the-art methods. Notably, LncLSTA exhibits the transfer learning capability, extending its utility to predict the subcellular localization prediction of mRNAs, while maintaining consistently satisfactory prediction results. This research contributes valuable insights into understanding the biological functions of LncRNAs through subcellular localization, emphasizing the potential of deep learning approaches in advancing RNA-related studies.Availability and implementation: The source code is publicly available at https://bis.zju.edu.cn/LncLSTA.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbae173"},"PeriodicalIF":2.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11700581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SeqLengthPlot v2.0: an all-in-one, easy-to-use tool for visualizing and retrieving sequence lengths from FASTA files. SeqLengthPlot v2.0：一款一体化的易用工具，用于从 FASTA 文件中可视化和检索序列长度。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-20 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbae183

Dany Domínguez-Pérez, Guillermin Agüero-Chapin, Serena Leone, Maria Vittoria Modica

{"title":"SeqLengthPlot v2.0: an all-in-one, easy-to-use tool for visualizing and retrieving sequence lengths from FASTA files.","authors":"Dany Domínguez-Pérez, Guillermin Agüero-Chapin, Serena Leone, Maria Vittoria Modica","doi":"10.1093/bioadv/vbae183","DOIUrl":"10.1093/bioadv/vbae183","url":null,"abstract":"Motivation: Accurate sequence length profiling is essential in bioinformatics, particularly in genomics and proteomics. Existing tools like SeqKit and the Trinity toolkit provide basic sequence statistics but often fall short in offering comprehensive analytics and plotting options. For instance, SeqKit is a very complete and fast tool for sequence analysis, delivering useful metrics (e.g. number of sequences, average, minimum, and maximum lengths) and can return sequences either shorter or longer (but not both at once) for a given length. Similarly, Trinity's Perl-based scripts provide detailed contig length distributions (e.g. N50, median, and average lengths) but do not include the total number of sequences or offer graphical representations of the data.Results: Given that key sequence analysis tasks are often distributed across multiple tools, we introduce SeqLengthPlot v2.0, an all-in-one, easy-to-use Python-based tool. Through a simple command-line interface, this straightforward tool enables users to split input FASTA files (nucleotide and protein) into two distinct files based on a customizable sequence length cutoff. It also automatically retrieves the resulting FASTA files, generates length distribution plots, and provides comprehensive statistical summaries.Availability and implementation: SeqLengthPlot_v2.0.2 can be accessed at https://github.com/danydguezperez/SeqLengthPlot/releases/tag/v2.0.2.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbae183"},"PeriodicalIF":2.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KTED: a comprehensive web-based database for transposable elements in the Korean genome. 韩国基因组转座因子的综合网络数据库。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-19 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae179

Jin-Ok Lee, Sejoon Lee, Dongyoon Lee, Taeyeon Hwang, Soobok Joe, Jin Ok Yang, Jibin Jeong, Jung Hun Ohn, Jee Hyun Kim

{"title":"KTED: a comprehensive web-based database for transposable elements in the Korean genome.","authors":"Jin-Ok Lee, Sejoon Lee, Dongyoon Lee, Taeyeon Hwang, Soobok Joe, Jin Ok Yang, Jibin Jeong, Jung Hun Ohn, Jee Hyun Kim","doi":"10.1093/bioadv/vbae179","DOIUrl":"10.1093/bioadv/vbae179","url":null,"abstract":"Summary: Transposable elements (TEs), commonly referred to as \"mobile elements,\" constitute DNA segments capable of relocating within a genome. Initially disregarded as \"junk DNA\" devoid of specific functionality, it has become evident that TEs have diverse influences on an organism's biology and health. The impact of these elements varies according to their location, classification, and their effects on specific genes or regulatory components. Despite their significant roles, a paucity of resources concerning TEs in population-scale genome sequencing remains. Herein, we analyze whole-genome sequencing data sourced from the Korean Genome and Epidemiology Study, encompassing 2500 Korean individuals. To facilitate convenient data access and observation, we developed a web-based database, KTED. Additionally, we scrutinized the differential distributions of TEs across five distinct common disease groups: dyslipidemia, hypertension, diabetes, thyroid disease, and cancer.Availability and implementation: https://snubh.shinyapps.io/KTED.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae179"},"PeriodicalIF":2.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11652267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training. ISCB能力框架v. 3：修订和扩展的生物信息学教育和培训标准。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-18 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae166

Cath Brooksbank, Michelle D Brazas, Nicola Mulder, Russell Schwartz, Verena Ras, Sarah L Morgan, Marta Lloret Llinares, Patricia Carvajal López, Lee Larcombe, Amel Ghouila, Tom Hancocks, Venkata Satagopam, Javier De Las Rivas, Gaston Mazandu, Bruno Gaeta

{"title":"The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training.","authors":"Cath Brooksbank, Michelle D Brazas, Nicola Mulder, Russell Schwartz, Verena Ras, Sarah L Morgan, Marta Lloret Llinares, Patricia Carvajal López, Lee Larcombe, Amel Ghouila, Tom Hancocks, Venkata Satagopam, Javier De Las Rivas, Gaston Mazandu, Bruno Gaeta","doi":"10.1093/bioadv/vbae166","DOIUrl":"10.1093/bioadv/vbae166","url":null,"abstract":"Motivation: Developing competency in the broad area of bioinformatics is challenging globally, owing to the breadth of the field and the diversity of its audiences for education and training. Course design can be facilitated by the use of a competency framework-a set of competency requirements that define the knowledge, skills and attitudes needed by individuals in (or aspiring to be in) a particular profession or role. These competency requirements can help to define curricula as they can inform both the content and level to which competency needs to be developed. The International Society for Computational Biology (ISCB) developed a list of bioinformatics competencies in 2014, and these have undergone several rounds of improvement. In consultation with a broad bioinformatics training community, these have now been further refined and extended to include knowledge skills and attitudes, and mappings to previous and other existing competency frameworks.Results: Here, we present version 3 of the ISCB competency framework. We describe how it was developed and how to access it, as well as providing some examples of how it has been used.Availability and implementation: The framework is openly accessible at https://competency.ebi.ac.uk/framework/iscb/3.0/competencies.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae166"},"PeriodicalIF":2.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11646570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical affirmation.

IF 2.4

Bioinformatics advances Pub Date : 2024-11-18 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbae181

Maytha Alshammari, Jing He, Willy Wriggers

{"title":"Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical affirmation.","authors":"Maytha Alshammari, Jing He, Willy Wriggers","doi":"10.1093/bioadv/vbae181","DOIUrl":"10.1093/bioadv/vbae181","url":null,"abstract":"Motivation: This study investigates the flexible refinement of AlphaFold2 models against corresponding cryo-electron microscopy (cryo-EM) maps using normal modes derived from elastic network models (ENMs) as basis functions for displacement. AlphaFold2 generally predicts highly accurate structures, but 18 of the 137 models of isolated chains exhibit a TM-score below 0.80. We achieved a significant improvement in four of these deviating structures and used them to systematically optimize the parameters of the ENM motion model.Results: We successfully refined four AlphaFold2 models with notable discrepancies: lipid-preserved respiratory supercomplex (TM-score increased from 0.52 to 0.69), flagellar L-ring protein (TM-score increased from 0.53 to 0.64), cation diffusion facilitator YiiP (TM-score increased from 0.76 to 0.83), and Sulfolobus islandicus pilus (TM-score increased from 0.77 to 0.85). We explored the effect of three different mode ranges (modes 1-9, 7-9, and 1-12), masked or box-cropped density maps, numerical optimization methods, and two similarity measures (Pearson correlation and inner product). The best results were achieved for the widest mode range (modes 1-12), masked maps, inner product, and local Powell optimization. These optimal parameters were implemented in the flexible fitting utility elforge.py in version 1.4 of our Python-based ModeHunter package.Availability and implementation: https://modehunter.biomachina.org.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbae181"},"PeriodicalIF":2.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MeTEor: an R Shiny app for exploring longitudinal metabolomics data. MeTEor：一个用于探索纵向代谢组学数据的R Shiny应用程序。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-14 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae178

Gordon Grabert, Daniel Dehncke, Tushar More, Markus List, Anke R M Kraft, Markus Cornberg, Karsten Hiller, Tim Kacprowski

{"title":"MeTEor: an R Shiny app for exploring longitudinal metabolomics data.","authors":"Gordon Grabert, Daniel Dehncke, Tushar More, Markus List, Anke R M Kraft, Markus Cornberg, Karsten Hiller, Tim Kacprowski","doi":"10.1093/bioadv/vbae178","DOIUrl":"10.1093/bioadv/vbae178","url":null,"abstract":"Motivation: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.Results: To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients.Availability and implementation: MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae178"},"PeriodicalIF":2.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MultiOmicsIntegrator: a nextflow pipeline for integrated omics analyses. MultiOmicsIntegrator：一个用于综合 omics 分析的 nextflow 管道。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-14 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae175

Bianka Alexandra Pasat, Eleftherios Pilalis, Katarzyna Mnich, Afshin Samali, Aristotelis Chatziioannou, Adrienne M Gorman

{"title":"MultiOmicsIntegrator: a nextflow pipeline for integrated omics analyses.","authors":"Bianka Alexandra Pasat, Eleftherios Pilalis, Katarzyna Mnich, Afshin Samali, Aristotelis Chatziioannou, Adrienne M Gorman","doi":"10.1093/bioadv/vbae175","DOIUrl":"10.1093/bioadv/vbae175","url":null,"abstract":"Motivation: Analysis of gene and isoform expression levels is becoming critical for the detailed understanding of biochemical mechanisms. In addition, integrating RNA-seq data with other omics data types, such as proteomics and metabolomics, provides a strong approach for consolidating our understanding of biological processes across various organizational tiers, thus promoting the identification of potential therapeutic targets.Results: We present our pipeline, called MultiOmicsIntegrator (MOI), an inclusive pipeline for comprehensive omics analyses. MOI represents a unified approach that performs in-depth individual analyses of diverse omics. Specifically, exhaustive analysis of RNA-seq data at the level of genes, isoforms of genes, as well as miRNA is offered, coupled with functional annotation and structure prediction of these transcripts. Additionally, proteomics and metabolomics data are supported providing a holistic view of biological systems. Finally, MOI has tools to integrate simultaneously multiple and diverse omics datasets, with both data- and function-driven approaches, fostering a deeper understanding of intricate biological interactions.Availability and implementation: MOI and ReadTheDocs.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae175"},"PeriodicalIF":2.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0