Bioinformatics advances最新文献_第5页

Investigating alignment-free machine learning methods for HIV-1 subtype classification. 研究用于 HIV-1 亚型分类的无对齐机器学习方法。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-29 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae108

Kaitlyn E Wade, Lianghong Chen, Chutong Deng, Gen Zhou, Pingzhao Hu

{"title":"Investigating alignment-free machine learning methods for HIV-1 subtype classification.","authors":"Kaitlyn E Wade, Lianghong Chen, Chutong Deng, Gen Zhou, Pingzhao Hu","doi":"10.1093/bioadv/vbae108","DOIUrl":"10.1093/bioadv/vbae108","url":null,"abstract":"Motivation: Many viruses are organized into taxonomies of subtypes based on their genetic similarities. For human immunodeficiency virus 1 (HIV-1), subtype classification plays a crucial role in infection management. Sequence alignment-based methods for subtype classification are impractical for large datasets because they are costly and time-consuming. Alignment-free methods involve creating numerical representations for genetic sequences and applying statistical or machine learning methods. Despite their high overall accuracy, existing models perform poorly on less common subtypes. Furthermore, there is limited work investigating the impact of sequence vectorization methods, in particular natural language-inspired embedding methods, on HIV-1 subtype classification.Results: We present a comprehensive analysis of sequence vectorization methods across machine learning methods. We report a k-mer-based XGBoost model with a balanced accuracy of 0.84, indicating that it has good overall performance for both common and uncommon HIV-1 subtypes. We also report a Word2Vec-based support vector machine that achieves promising results on precision and balanced accuracy. Our study sheds light on the effect of sequence vectorization methods on HIV-1 subtype classification and suggests that natural language-inspired encoding methods show promise. Our results could help to develop improved HIV-1 subtype classification methods, leading to improved individual patient outcomes, and the development of subtype-specific treatments.Availability and implementation: Source code is available at https://www.github.com/kwade4/HIV_Subtypes.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127524","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

survivalContour: visualizing predicted survival via colored contour plots. survivalContour：通过彩色等高线图直观显示预测存活率。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-25 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae105

Yushu Shi, Liangliang Zhang, Kim-Anh Do, Robert R Jenq, Christine B Peterson

引用次数: 0

Genotype imputation in F2 crosses of inbred lines. 近交系 F2 杂交中的基因型估算。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-23 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae107

Saul Pierotti, Bettina Welz, Mireia Osuna-López, Tomas Fitzgerald, Joachim Wittbrodt, Ewan Birney

{"title":"Genotype imputation in F2 crosses of inbred lines.","authors":"Saul Pierotti, Bettina Welz, Mireia Osuna-López, Tomas Fitzgerald, Joachim Wittbrodt, Ewan Birney","doi":"10.1093/bioadv/vbae107","DOIUrl":"10.1093/bioadv/vbae107","url":null,"abstract":"Motivation: Crosses among inbred lines are a fundamental tool for the discovery of genetic loci associated with phenotypes of interest. In organisms for which large reference panels or SNP chips are not available, imputation from low-pass whole-genome sequencing is an effective method for obtaining genotype data from a large number of individuals. To date, a structured analysis of the conditions required for optimal genotype imputation has not been performed.Results: We report a systematic exploration of the effect of several design variables on imputation performance in F2 crosses of inbred medaka lines using the imputation software STITCH. We determined that, depending on the number of samples, imputation performance reaches a plateau when increasing the per-sample sequencing coverage. We also systematically explored the trade-offs between cost, imputation accuracy, and sample numbers. We developed a computational pipeline to streamline the process, enabling other researchers to perform a similar cost-benefit analysis on their population of interest.Availability and implementation: The source code for the pipeline is available at https://github.com/birneylab/stitchimpute. While our pipeline has been developed and tested for an F2 population, the software can also be used to analyse populations with a different structure.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794100","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

Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model. 利用基于化学和基因描述的集合变换器模型从生物医学文献中挖掘药物与靶点的相互作用。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-22 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae106

Jehad Aldahdooh, Ziaurrehman Tanoli, Jing Tang

{"title":"Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model.","authors":"Jehad Aldahdooh, Ziaurrehman Tanoli, Jing Tang","doi":"10.1093/bioadv/vbae106","DOIUrl":"10.1093/bioadv/vbae106","url":null,"abstract":"Motivation: Drug-target interactions (DTIs) play a pivotal role in drug discovery, as it aims to identify potential drug targets and elucidate their mechanism of action. In recent years, the application of natural language processing (NLP), particularly when combined with pre-trained language models, has gained considerable momentum in the biomedical domain, with the potential to mine vast amounts of texts to facilitate the efficient extraction of DTIs from the literature.Results: In this article, we approach the task of DTIs as an entity-relationship extraction problem, utilizing different pre-trained transformer language models, such as BERT, to extract DTIs. Our results indicate that an ensemble approach, by combining gene descriptions from the Entrez Gene database with chemical descriptions from the Comparative Toxicogenomics Database (CTD), is critical for achieving optimal performance. The proposed model achieves an F1 score of 80.6 on the hidden DrugProt test set, which is the top-ranked performance among all the submitted models in the official evaluation. Furthermore, we conduct a comparative analysis to evaluate the effectiveness of various gene textual descriptions sourced from Entrez Gene and UniProt databases to gain insights into their impact on the performance. Our findings highlight the potential of NLP-based text mining using gene and chemical descriptions to improve drug-target extraction tasks.Availability and implementation: Datasets utilized in this study are accessible at https://dtis.drugtargetcommons.org/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876854","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

Correction to: Quantitative transcriptomic and epigenomic data analysis: a primer. 更正：定量转录组学和表观基因组学数据分析：入门。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-18 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae091

引用次数: 0

ScyNet: Visualizing interactions in community metabolic models. ScyNet：可视化群落代谢模型中的相互作用

IF 2.4

Bioinformatics advances Pub Date : 2024-07-17 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae104

Michael Predl, Kilian Gandolf, Michael Hofer, Thomas Rattei

引用次数: 0

TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms. TemBERTure：利用深度学习和注意力机制推进蛋白质热稳定性预测。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-13 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae103

Chiara Rodella, Symela Lazaridi, Thomas Lemmin

{"title":"TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms.","authors":"Chiara Rodella, Symela Lazaridi, Thomas Lemmin","doi":"10.1093/bioadv/vbae103","DOIUrl":"10.1093/bioadv/vbae103","url":null,"abstract":"Motivation: Understanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar.Results: In this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Learning models to gain deeper insights into protein thermostability. Analyzing these scores in conjunction with the 3D protein structure can enhance understanding of the complex interactions among amino acid properties, their positioning, and the surrounding microenvironment. By addressing the limitations of current prediction methods and introducing new exploration avenues, this research paves the way for more accurate and informative protein thermostability predictions, ultimately accelerating advancements in protein engineering.Availability and implementation: TemBERTure model and the data are available at: https://github.com/ibmm-unibe-ch/TemBERTure.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11262459/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749771","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

loco-pipe: an automated pipeline for population genomics with low-coverage whole-genome sequencing. loco-pipe：利用低覆盖率全基因组测序进行群体基因组学研究的自动化管道。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-11 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae098

Zehua T Zhou, Gregory L Owens, Wesley A Larson, Runyang Nicolas Lou, Peter H Sudmant

引用次数: 0

INCAWrapper: a Python wrapper for INCA for seamless data import, -export, and -processing. INCAWrapper：INCA 的 Python 封装器，用于无缝导入、导出和处理数据。

IF 2.4

Bioinformatics advances Pub Date : 2024-07-04 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae100

Matthias Mattanovich, Viktor Hesselberg-Thomsen, Annette Lien, Dovydas Vaitkus, Victoria Sara Saad, Douglas McCloskey

引用次数: 0

A customizable secure DIY web application for accessing, sharing, and browsing aggregate experimental results and metadata. 可定制的安全 DIY 网络应用程序，用于访问、共享和浏览综合实验结果和元数据。

IF 2.4

Bioinformatics advances Pub Date : 2024-06-28 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae087

Jaewoo Lee, Mehita Achuthan, Lucas Chen, Paulina Carmona-Mora

引用次数: 0