Bioinformatics advances最新文献

Correction to: PIPLOM: prediction of exogenous peptide loading on major histocompatibility complex class I molecules. 修正：PIPLOM：预测外源肽在主要组织相容性复合体I类分子上的负载。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf138

引用次数: 0

NRGSuite-Qt: a PyMOL plugin for high-throughput virtual screening, molecular docking, normal-mode analysis, the study of molecular interactions, and the detection of binding-site similarities. NRGSuite-Qt：一个PyMOL插件，用于高通量虚拟筛选、分子对接、正常模式分析、分子相互作用研究和结合位点相似性检测。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-16 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf129

Gabriel Tiago Galdino, Thomas DesCôteaux, Natalia Teruel, Rafael Najmanovich

{"title":"NRGSuite-Qt: a PyMOL plugin for high-throughput virtual screening, molecular docking, normal-mode analysis, the study of molecular interactions, and the detection of binding-site similarities.","authors":"Gabriel Tiago Galdino, Thomas DesCôteaux, Natalia Teruel, Rafael Najmanovich","doi":"10.1093/bioadv/vbaf129","DOIUrl":"10.1093/bioadv/vbaf129","url":null,"abstract":"Summary: We introduce NRGSuite-Qt, a PyMOL plugin, that provides a comprehensive toolkit for macromolecular cavity detection, virtual screening, small-molecule docking, normal mode analysis, analyses of molecular interactions, and detection of binding-site similarities. This complete redesign of the original NRGSuite (restricted to cavity detection and small-molecule docking) integrates five new functionalities: protein-protein and protein-ligand interaction analysis using Surfaces, ultra-massive virtual screening with NRGRank, binding-site similarity detection with IsoMIF, normal mode analysis using NRGTEN, and mutational studies through integration with the Modeler Suite. By merging these advanced tools into a cohesive platform, NRGSuite-Qt simplifies visualization and streamlines complex workflows within a single interface. Additionally, we benchmark a newer version of the Elastic Network Contact Model (ENCoM) for normal mode analysis method, utilizing the same 40 atom-type pairwise interaction matrix that is used in all other software. This version outperforms the default model in multiple benchmarking tests.Avalilability and implementation: The Installation guide and tutorial is available at https://nrg-qt.readthedocs.io/en/latest/index.html. The NRGSuite-Qt is implement in Python.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf129"},"PeriodicalIF":2.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12177131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334505","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

GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition. GRU-SCANET：释放基于gru的正弦捕获网络的力量，用于精确驱动的命名实体识别。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-16 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf096

Bill Gates Happi Happi, Geraud Fokou Pelap, Danai Symeonidou, Pierre Larmande

{"title":"GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.","authors":"Bill Gates Happi Happi, Geraud Fokou Pelap, Danai Symeonidou, Pierre Larmande","doi":"10.1093/bioadv/vbaf096","DOIUrl":"10.1093/bioadv/vbaf096","url":null,"abstract":"Motivation: Pre-trained Language Models (PLMs) have achieved remarkable performance across various natural language processing tasks. However, they encounter challenges in biomedical named entity recognition (NER), such as high computational costs and the need for complex fine-tuning. These limitations hinder the efficient recognition of biological entities, especially within specialized corpora. To address these issues, we introduce GRU-SCANET (Gated Recurrent Unit-based Sinusoidal Capture Network), a novel architecture that directly models the relationship between input tokens and entity classes. Our approach offers a computationally efficient alternative for extracting biological entities by capturing contextual dependencies within biomedical texts.Results: GRU-SCANET combines positional encoding, bidirectional GRUs (BiGRUs), an attention-based encoder, and a conditional random field (CRF) decoder to achieve high precision in entity labeling. This design effectively mitigates the challenges posed by unbalanced data across multiple corpora. Our model consistently outperforms leading benchmarks, achieving better performance than BioBERT (8/8 evaluations), PubMedBERT (5/5 evaluations), and the previous state-of-the-art (SOTA) models (8/8 evaluations), including Bern2 (5/5 evaluations). These results highlight the strength of our approach in capturing token-entity relationships more effectively than existing methods, advancing the state of biomedicalNER.Availability and implementation: https://github.com/ANR-DIG-AI/GRU-SCANET.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf096"},"PeriodicalIF":2.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12198495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509791","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

Next-generation sequencing-based tools or nanopore-based tools: which is more suitable for short tandem repeats genotyping of nanopore sequencing? 下一代基于测序的工具还是基于纳米孔的工具：哪个更适合于纳米孔测序的短串联重复序列基因分型？

IF 2.4

Bioinformatics advances Pub Date : 2025-06-12 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf119

Wei Han, Xuemei Zhang, Qingzhen Zhang, Zhe Zhou

{"title":"Next-generation sequencing-based tools or nanopore-based tools: which is more suitable for short tandem repeats genotyping of nanopore sequencing?","authors":"Wei Han, Xuemei Zhang, Qingzhen Zhang, Zhe Zhou","doi":"10.1093/bioadv/vbaf119","DOIUrl":"10.1093/bioadv/vbaf119","url":null,"abstract":"Motivation: Short tandem repeats (STRs) are widely recognized as critical genetic markers for individual identification. Nanopore sequencing technology holds promise as an effective tool for onsite STR detection owing to its portability. Initially, low sequencing quality led to the development of various genotyping tools specifically tailored for nanopore data. However, recent advancements in nanopore sequencing quality suggest that tools designed for next-generation sequencing (NGS) may be more suitable for analyzing nanopore data than those specifically developed for nanopore sequencing.Results: We selected two sequencing platforms, MinION Mk1C, and PolySeqOne, to generate sequencing data from 61 unrelated individual samples. Samples were amplified using a custom NanoSTR panel that included 31 autosomal STRs (A-STRs) and 31 Y chromosomal STRs (Y-STRs). Sequencing data were analyzed using four distinct tools: NASTRA, STRspy, STRinNGS, and STRait Razor. Our findings indicated that STRinNGS showed greater accuracy for both A-STRs and Y-STRs, enabling the accurate detection of a broad range of STRs. Compared with STRinNGS, NASTRA exhibited greater STR depth and featured more non-integer stutters. Therefore, in practical applications, STRinNGS demonstrates high reliability in genotyping.Availability and implementation: NASTRA, STRspy, STRinNGS and STRait Razor, which can be accessed via the following links: https://github.com/renzilin/NASTRA, https://github.com/unique379r/strspy, https://bitbucket.org/rirgabiss/strinngs/src/master, and https://github.com/Ahhgust/STRaitRazor, respectively. The commands during process are provided as requested by the corresponding author.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf119"},"PeriodicalIF":2.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303789","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

Harpy: a pipeline for processing haplotagging linked-read data. Harpy：处理单倍标记链接读数据的管道。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf133

Pavel V Dimens, Ryan P Franckowiak, Azwad Iqbal, Jennifer K Grenier, Paul R Munn, Nina Overgaard Therkildsen

引用次数: 0

SynDRep: a synergistic partner prediction tool based on knowledge graph for drug repurposing. SynDRep：基于知识图谱的药物再利用协同伙伴预测工具。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf092

Karim S Shalaby, Sathvik Guru Rao, Bruce Schultz, Martin Hofmann-Apitius, Alpha Tom Kodamullil, Vinay Srinivas Bharadhwaj

{"title":"SynDRep: a synergistic partner prediction tool based on knowledge graph for drug repurposing.","authors":"Karim S Shalaby, Sathvik Guru Rao, Bruce Schultz, Martin Hofmann-Apitius, Alpha Tom Kodamullil, Vinay Srinivas Bharadhwaj","doi":"10.1093/bioadv/vbaf092","DOIUrl":"10.1093/bioadv/vbaf092","url":null,"abstract":"Motivation: Drug repurposing is gaining interest due to its high cost-effectiveness, low risks, and improved patient outcomes. However, most drug repurposing methods depend on drug-disease-target semantic connections of a single drug rather than insights from drug combination data. In this study, we propose SynDRep, a novel drug repurposing tool based on enriching knowledge graphs (KG) with drug combination effects. It predicts the synergistic drug partner with a commonly prescribed drug for the target disease, leveraging graph embedding and machine learning (ML) techniques. This partner drug is then repurposed as a single agent for this disease by exploring pathways between them in the KG.Results: HolE was the best-performing embedding model (with 84.58% of true predictions for all relations), and random forest emerged as the best ML model with an area under the receiver operating characteristic curve (ROC-AUC) value of 0.796. Some of our selected candidates, such as miconazole and albendazole for Alzheimer's disease, have been validated through literature, while others lack either a clear pathway or literature evidence for their use for the disease of interest. Therefore, complementing SynDRep with more specialized KGs, and additional training data, would enhance its efficacy and offer cost-effective and timely solutions for patients.Availability and implementation: SynDRep is available as an open-source Python package at https://github.com/SynDRep/SynDRep under the Apache 2.0 License.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf092"},"PeriodicalIF":2.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12148216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259500","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

OReO: optimizing read order for practical compression. OReO：为实际压缩优化读顺序。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-03 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf128

Mathilde Girard, Léa Vandamme, Bastien Cazaux, Antoine Limasset

{"title":"OReO: optimizing read order for practical compression.","authors":"Mathilde Girard, Léa Vandamme, Bastien Cazaux, Antoine Limasset","doi":"10.1093/bioadv/vbaf128","DOIUrl":"10.1093/bioadv/vbaf128","url":null,"abstract":"Motivation: Recent advances in high-throughput and third-generation sequencing technologies have created significant challenges in storing and managing the rapidly growing volume of read datasets. Although more than 50 specialized compression tools have been developed, employing methods such as reference-based approaches, customized generic compressors, and read reordering, many users still rely on common generic compressors (e.g. gzip, zstd, xz) for convenience, portability, and reliability, despite their low compression ratios. Here, we introduce Optimizing Read Order (OReO), a simple read-reordering framework that achieves high compression performance without requiring specialized software for decompression. By grouping overlapping reads together before applying generic compressors, OReO exploits inherent redundancies in sequencing data and achieves compression ratios on par with state-of-the-art tools. Moreover, because it relies only on standard decompressors, OReO avoids the need for dedicated installations and maintenance, removing a key barrier to practical adoption.Results: We evaluated OReO on both Oxford Nanopore Technologies (ONT) and HiFi genomic and metagenomic datasets of varying sizes and complexities. Our results demonstrate that OReO provides substantial compression gains with comparable resource usage and outperforms dedicated methods in decompression speed. We propose that future compression strategies should focus on reordering as a means to let generic compression tools fully exploit data redundancy, offering an efficient, sustainable, and user-friendly solution to the growing challenges of sequencing data storage.Availability and implementation: The OReO code is open source and available at github.com/girunivlille/oreo.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf128"},"PeriodicalIF":2.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12185860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487289","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

Predicting gene expression using millions of yeast promoters reveals cis-regulatory logic. 利用数百万个酵母启动子预测基因表达揭示了顺式调控逻辑。

IF 2.4

Bioinformatics advances Pub Date : 2025-06-02 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf130

Tirtharaj Dash, Susanne Bornelöv

{"title":"Predicting gene expression using millions of yeast promoters reveals cis-regulatory logic.","authors":"Tirtharaj Dash, Susanne Bornelöv","doi":"10.1093/bioadv/vbaf130","DOIUrl":"10.1093/bioadv/vbaf130","url":null,"abstract":"Motivation: Gene regulation involves complex interactions between transcription factors. While early attempts to predict gene expression were trained using naturally occurring promoters, gigantic parallel reporter assays have vastly expanded potential training data. Despite this, it is still unclear how to best use deep learning to study gene regulation. Here, we investigate the association between promoters and expression using Camformer, a residual convolutional neural network that ranked fourth in the Random Promoter DREAM Challenge 2022. We present the original model trained on 6.7 million sequences and investigate 270 alternative models to find determinants of model performance. Finally, we use explainable AI to uncover regulatory signals.Results: Camformer accurately decodes the association between promoters and gene expression ( <math> <mrow> <mrow> <msup><mrow><mi>r</mi></mrow> <mn>2</mn></msup> </mrow> <mo>=</mo> <mn>0.914</mn> <mo> ± </mo> <mn>0.003</mn></mrow> </math> , <math><mrow><mi>ρ</mi> <mo>=</mo> <mn>0.962</mn> <mo> ± </mo> <mn>0.002</mn></mrow> </math> ) and provides a substantial improvement over previous state of the art. Using Grad-CAM and in silico mutagenesis, we demonstrate that our model learns both individual motifs and their hierarchy. For example, while an IME1 motif on its own increases gene expression, a co-occurring UME6 motif instead strongly reduces gene expression. Thus, deep learning models such as Camformer can provide detailed insights into cis-regulatory logic.Availability and implementation: Data and code are available at: https://github.com/Bornelov-lab/Camformer.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf130"},"PeriodicalIF":2.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12188188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499625","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

Causeway: a pipeline for genome-wide effector gene screening with Mendelian Randomization and colocalization. Causeway：用孟德尔随机化和共定位筛选全基因组效应基因的管道。

IF 2.4

Bioinformatics advances Pub Date : 2025-05-29 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf110

Julia A de Amorim, João Vitor F Cavalcante, Diego Marques-Coelho, Rodrigo J S Dalmolin, Vasiliki Lagou

引用次数: 0

ReSort enhances reference-based cell type deconvolution for spatial transcriptomics through regional information integration. ReSort通过区域信息整合增强了基于参考的细胞类型反褶积的空间转录组学。

IF 2.4

Bioinformatics advances Pub Date : 2025-05-27 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf091

Linhua Wang, Ling Wu, Guantong Qi, Chaozhong Liu, Wanli Wang, Xiang H-F Zhang, Zhandong Liu

引用次数: 0