Bioinformatics Advances

Abstract Motivation Although machine learning models are commonly used in medical research, many analyses implement a simple partition into training data and hold-out test data, with cross-validation (CV) for tuning of model hyperparameters. Nested CV with embedded feature selection is especially suited to biomedical data where the sample size is frequently limited, but the number of predictors may be significantly larger (P ≫ n). Results The nestedcv R package implements fully nested k × l-fold CV for lasso and elastic-net regularized linear models via the glmnet package and supports a large array of other machine learning models via the caret framework. Inner CV is used to tune models and outer CV is used to determine model performance without bias. Fast filter functions for feature selection are provided and the package ensures that filters are nested within the outer CV loop to avoid information leakage from performance test sets. Measurement of performance by outer CV is also used to implement Bayesian linear and logistic regression models using the horseshoe prior over parameters to encourage a sparse model and determine unbiased model accuracy. Availability and implementation The R package nestedcv is available from CRAN: https://CRAN.R-project.org/package=nestedcv.

Myles J Lewis

Athina Spiliopoulou

Katriona Goldmann

Costantino Pitzalis

Paul McKeigue

Michael R Barnes

nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data

nestedcv：一种用于转录组学和高维数据嵌入特征选择快速实现嵌套交叉验证的R包

Abstract Motivation Direct RNA-seq (dRNA-seq) using Oxford Nanopore Technology (ONT) has revolutionized transcript mapping by offering enhanced precision due to its long-read length. Unlike traditional techniques, dRNA-seq eliminates the need for PCR amplification, reducing the impact of GC bias, and preserving valuable base physical information, such as RNA modification and poly(A) length estimation. However, the rapid advancement of ONT devices has set higher standards for analytical software, resulting in potential challenges of software incompatibility and reduced efficiency. Results We present a novel workflow, called FASTdRNA, to manipulate dRNA-seq data efficiently. This workflow comprises two modules: a data preprocessing module and a data analysis module. The preprocessing data module, dRNAmain, encompasses basecalling, mapping, and transcript counting, which are essential for subsequent analyses. The data analysis module consists of a range of downstream analyses that facilitate the estimation of poly(A) length, prediction of RNA modifications, and assessment of alternative splicing events across different conditions with duplication. The FASTdRNA workflow is designed for the Snakemake framework and can be efficiently executed locally or in the cloud. Comparative experiments have demonstrated its superior performance compared to previous methods. This innovative workflow enhances the research capabilities of dRNA-seq data analysis pipelines by optimizing existing processes and expanding the scope of analysis. Availability and implementation The workflow is freely available at https://github.com/Tomcxf/FASTdRNA under an MIT license. Detailed install and usage guidance can be found in the GitHub repository.

Xiaofeng Chen

Yongqi Liu

Kaiwen Lv

Meiling Wang

Xiaoqin Liu

Bosheng Li

FASTdRNA: a workflow for the analysis of ONT direct RNA sequencing

FASTdRNA：一种用于ONT直接RNA测序分析的工作流程

Abstract Motivation Genomic biotechnology has rapidly advanced, allowing for the inference and modification of genetic and epigenetic information at the single-cell level. While these tools hold enormous potential for basic and clinical research, they also raise difficult issues of how to design studies to deploy them most effectively. In designing a genomic study, a modern researcher might combine many sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. This is especially relevant for studies of somatic variation, which may involve highly heterogeneous cell populations whose differences can be probed via an extensive set of biotechnological tools. Efficiently deploying genomic technologies in this space will require principled ways to create study designs that recover desired genomic information while minimizing various measures of cost. Results The central problem this paper attempts to address is how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation that occur most often with application to cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem. We introduce a Bayesian optimization framework that iteratively optimizes for an objective function using surrogate modeling combined with pattern and gradient search. We demonstrate our procedure on several test cases to derive resource and study design allocations optimized for various goals and criteria, demonstrating its ability to optimize study designs efficiently across diverse scenarios. Availability and implementation https://github.com/CMUSchwartzLab/StudyDesignOptimization

Arjun Srivatsa

Russell Schwartz

Optimizing design of genomics studies for clonal evolution analysis

克隆进化分析的基因组学研究优化设计

Abstract Motivation Joint extraction of entity and relation is an important research direction in Information Extraction. The number of scientific and technological biomedical literature is rapidly increasing, so automatically extracting entities and their relations from these literatures are key tasks to promote the progress of biomedical research. Results The joint extraction of entity and relation model achieves both intra-sentence extraction and cross-sentence extraction, alleviating the problem of long-distance information dependence in long literature. Joint extraction of entity and relation model incorporates a variety of advanced deep learning techniques in this paper: (1) a fine-tuning BERT text classification pre-training model, (2) Graph Convolutional Network learning method, (3) Robust Learning Against Textual Label Noise with Self-Mixup Training, (4) Local regularization Conditional Random Fields. The model implements the following functions: identifying entities from complex biomedical literature effectively, extracting triples within and across sentences, reducing the effect of noisy data during training, and improving the robustness and accuracy of the model. The experiment results prove that the model performs well on the self-built BM_GBD dataset and public datasets, enabling precise large language model enhanced knowledge graph construction for biomedical tasks. Availability and implementation The model and partial code are available on GitHub at https://github.com/zhaix922/Joint-extraction-of-entity-and-relation.

Ting Gao

Xue Zhai

Chuan Yang

Linlin Lv

Han Wang

Joint extraction of entity and relation based on Fine-tuning BERT for Long Biomedical literatures

基于BERT微调的长篇生物医学文献实体与关系联合提取

Abstract Motivation Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how MSA (multiple sequence alignment) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states. Results We verified this approach by predicting the conformational landscapes of chemokine (CXCR4) and glucagon (GCGR) receptors in the presence of their agonists and antagonists. In our experiments AF-Multimer predicted the structures of CXCR4 and GCGR predominantly in active state in the presence of agonists and in inactive state in the presence of antagonists. Moreover, we tested our approach with proteins known to switch between monomeric and dimeric states, such as lymphotactin, SH3 and thermonuclease. AFcluster-Multimer accurately predicted conformational states during oligomerization, which AFcluster with AlphaFold2 alone fails to achieve. In conclusion, MSA clustering enhances AF-Multimer's ability to predict protein conformational landscapes and mechanistic effects of ligand binding, offering a robust tool for understanding protein-ligand interactions. Availability and Implementation Code for running AFcluster-Multimer is available at https://github.com/KhondamirRustamov/AF-Multimer-cluster Supplementary information Supplementary figures are available at Bioinformatics Advances online.

Khondamir R Rustamov

Artyom Y Baev

MSA clustering enhances AF-Multimer's ability to predict conformational landscapes of protein-protein interactions

MSA聚类提升AF-Multimer预测蛋白质-蛋白质相互作用构象景观的能力

Abstract Motivation Proteins at the cell surface connect signaling networks and largely determine a cell’s capacity to communicate and interact with its environment. In particular, variations in transcriptomic profiles are often observed between healthy and diseased cells, leading to distinct sets of cell-surface proteins. For these reasons, cell-surface proteins may act as biomarkers for the detection of cells of interest in tissues or body fluids, are often the target of pharmaceutical agents, and hold significant promise in the clinical practice for diagnosis, prognosis, treatment development, and evaluation of therapy response. Therefore, implementing robust methods to identify condition-specific cell-surface proteins is of pivotal importance to advance biomedical research. Results We developed SurfR, an R/Bioconductor package providing a streamlined end-to-end workflow for computationally identifying surface protein coding genes from expression data. Our user-friendly, comprehensive workflow performs systematic expression data retrieval from public databases, differential gene expression across conditions, integration of datasets, enrichment analysis, identification of targetable proteins on a condition of interest, and data visualization Availability and implementation SurfR is released under GNU-GPL-v3.0 License. Source code, documentation, examples, and tutorials are available through Bioconductor (http://www.bioconductor.org/packages/SurfR). RMD notebooks with the use cases code described in the manuscript can be found on GitHub (https://github.com/auroramaurizio/SurfR_UseCases).

Aurora Maurizio

Anna Sofia Tascini

Marco J Morelli

SurfR: Riding the wave of RNA-Seq data with a comprehensive Bioconductor package to identify Surface Protein Coding Genes

SurfR：借助全面的Bioconductor包，在RNA-Seq数据浪潮中识别表面蛋白编码基因

Abstract Motivation Rab GTPases (Rabs) are crucial for membrane trafficking within mammalian cells and their dysfunction is implicated in many diseases. This gene family plays a role in several crucial cellular processes. Network analyses can uncover the complete repertoire of interaction patterns across the Rab network, informing disease research, opening new opportunities for therapeutic interventions. Results We examined Rabs and their interactors in the context of Epithelial-to-Mesenchymal Transition (EMT), an indicator of cancer metastasising to distant organs. A Rab network was first established from analysis of literature, and was gradually expanded. Our Python module, resnet, assessed its network resilience and selected an optimally sized, resilient Rab network for further analyses. Pathway enrichment confirmed its role in EMT. We then identified 73 candidate genes showing a strong up/down regulation, across 10 cancer types, in patients with metastasised tumours compared to only primary-site tumours. We suggest that their encoded proteins might play a critical role in EMT, and further in vitro studies are needed to confirm their role as predictive markers of cancer metastasis. The use of resnet within the systematic analysis approach described here can be easily applied to assess other gene families and their role in biological events of interest. Availability and implementation Source code for resnet is freely available at https://github.com/Unmani199/resnet Supplementary information Supplementary data are available on Figshare at https://figshare.com/s/1e8c1c43a73f083cbe92

Unmani Jaygude

Graham M Hughes

Jeremy C Simpson

Exploring the role of the Rab network in Epithelial to Mesenchymal Transition

探索Rab网络在上皮间质转化中的作用

Abstract Motivation Microbial signatures in the human microbiome are closely associated with various human diseases, driving the development of machine learning models for microbiome-based disease prediction. Despite progress, challenges remain in enhancing prediction accuracy, generalizability, and interpretability. Confounding factors, such as host’s gender, age and body mass index significantly influence the human microbiome, complicating microbiome-based predictions. Results To address these challenges, we developed MicroKPNN-MT, a unified model for predicting human phenotype based on microbiome data, as well as additional metadata like age and gender. This model builds on our earlier MicroKPNN framework, which incorporates prior knowledge of microbial species into neural networks to enhance prediction accuracy and interpretability. In MicroKPNN-MT, metadata, when available, serves as additional input features for prediction. Otherwise, the model predicts metadata from microbiome data using additional decoders. We applied MicroKPNN-MT to microbiome data collected in mBodyMap, covering healthy individuals and 25 different diseases, and demonstrated its potential as a predictive tool for multiple diseases, which at the same time provided predictions for the missing metadata. Our results showed that incorporating real or predicted metadata helped improve the accuracy of disease predictions, and more importantly, helped improve the generalizability of the predictive models. Availability https://github.com/mgtools/MicroKPNN-MT.

Mahsa Monshizadeh

Yuhui Hong

Yuzhen Ye

Multitask Knowledge-primed Neural Network for Predicting Missing Metadata and Host Phenotype based on Human Microbiome

基于人类微生物组的多任务知识引导神经网络用于预测缺失元数据和宿主表型

Abstract Motivation The scale and scope of comparative trait data are expanding at unprecedented rates, and recent advances in evolutionary modeling and simulation sometimes struggle to match this pace. Well-organized and flexible applications for conducting large-scale simulations of evolution hold promise in this context for understanding models and more so our ability to confidently estimate them with real trait data sampled from nature. Results We introduce TraitTrainR, an R package designed to facilitate efficient, large-scale simulations under complex models of continuous trait evolution. TraitTrainR employs several output formats, supports popular trait data transformations, accommodates multi-trait evolution, and exhibits flexibility in defining input parameter space and model stacking. Moreover, TraitTrainR permits measurement error, allowing for investigation of its potential impacts on evolutionary inference. We envision a wealth of applications of TraitTrainR, and we demonstrate one such example by examining the problem of evolutionary model selection in three empirical phylogenetic case studies. Collectively, these demonstrations of applying TraitTrainR to explore problems in model selection underscores its utility and broader promise for addressing key questions, including those related to experimental design and statistical power, in comparative biology. Availability and implementation TraitTrainR is developed in R 4.4.0 and is freely available at https://github.com/radamsRHA/TraitTrainR/, which includes detailed documentation, quick-start guides, and a step-by-step tutorial.

Jenniffer Roa Lozano

Mataya Duncan

Duane D McKenna

Todd A Castoe

Michael DeGiorgio

Richard Adams

TraitTrainR: Accelerating large-scale simulation under models of continuous trait evolution

TraitTrainR：加速连续性状演化模型下的大规模模拟

短名	Bioinform. Adv.
Journal Impact	2.32
国际分区	MATHEMATICAL & COMPUTATIONAL BIOLOGY(Q2)

涉及主题	bioinformaticscomputational biology
出版信息	出版商: Oxford University Press，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2021，原创研究文献占比: ，自引率:， Gold OA占比:
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平均审稿周期	8 weeks

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nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data

FASTdRNA: a workflow for the analysis of ONT direct RNA sequencing

Optimizing design of genomics studies for clonal evolution analysis

Joint extraction of entity and relation based on Fine-tuning BERT for Long Biomedical literatures

MSA clustering enhances AF-Multimer's ability to predict conformational landscapes of protein-protein interactions

SurfR: Riding the wave of RNA-Seq data with a comprehensive Bioconductor package to identify Surface Protein Coding Genes

Exploring the role of the Rab network in Epithelial to Mesenchymal Transition

Multitask Knowledge-primed Neural Network for Predicting Missing Metadata and Host Phenotype based on Human Microbiome

TraitTrainR: Accelerating large-scale simulation under models of continuous trait evolution

MSA clustering enhances AF-Multimer's ability to predict conformational landscapes of protein-protein interactions

ISSN	2635-0041
h-index	9