BackgroundRecent years have seen a substantial increase in RNA-seq data production, with this technique becoming the primary approach for gene expression studies across a wide range of non-model organisms. The majority of these organisms lack a well-annotated reference genome to serve as a basis for studying differentially expressed genes (DEGs). As an alternative cost-effective protocol to using a reference genome, the assembly of RNA-seq raw reads is performed to produce what is referred to as a ‘de novo transcriptome,’ serving as a reference for subsequent DEGs’ analysis. This assembly step for conventional DEGs analysis pipelines for non-model organisms is a computationally expensive task. Furthermore, the complexity of the de novo transcriptome assembly workflows poses a challenge for researchers in implementing best-practice techniques and the most recent software versions, particularly when applied to various organisms of interest.ResultsTo address computational challenges in transcriptomic analyses of non-model organisms, we present HPC-T-Assembly, a tool for de novo transcriptome assembly from RNA-seq data on high-performance computing (HPC) infrastructures. It is designed for straightforward setup via a Web-oriented interface, allowing analysis configuration for several species. Once configuration data is provided, the entire parallel computing software for assembly is automatically generated and can be launched on a supercomputer with a simple command line. Intermediate and final outputs of the assembly pipeline include additional post-processing steps, such as assembly quality control, ORF prediction, and transcript count matrix construction.ConclusionHPC-T-Assembly allows users, through a user-friendly Web-oriented interface, to configure a run for simultaneous assemblies of RNA-seq data from multiple species. The parallel pipeline, launched on HPC infrastructures, significantly reduces computational load and execution times, enabling large-scale transcriptomic and meta-transcriptomics analysis projects.

BackgroundMetabolomics describes the metabolic profile of an organism at a given time by the concentrations of its constituent metabolites. When studied over time, metabolite concentrations can help understand the dynamical evolution of a biological process. However, metabolites are involved into sequences of chemical reactions, called metabolic pathways, related to a given biological function. Accounting for these pathways into statistical methods for metabolomic data is thus a relevant way to directly express results in terms of biological functions and to increase their interpretability.MethodsWe propose a new method, phoenics, to perform differential analysis for longitudinal metabolomic data at the pathway level. In short, phoenics proceeds in two steps: First, the matrix of metabolite quantifications is transformed by a dimension reduction approach accounting for pathway information. Then, a mixed linear model is fitted on the transformed data.ResultsThis method was applied to semi-synthetic NMR data and two real NMR datasets assessing the effects of antibiotics and irritable bowel syndrome on feces. Results showed that phoenics properly controls the Type I error rate and has a better ability to detect differential metabolic pathways and to extract new impacted biological functions than alternative methods. The method is implemented in the R package phoenics available on CRAN

BackgroundGene set analysis methods have played a major role in generating biological interpretations of omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression while methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.ResultsWe used ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson–Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterized the detection power and Type I error rate of these methods in addition to two methods developed earlier using simulation results with different parameters. We applied the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to within phenotype variability and to heterogeneous differences between two compared phenotypes. Our results show that methods designed to detect differential sample variance provide meaningful biological interpretations by detecting specific hallmark gene sets associated with the two compared phenotypes as documented in available literature.ConclusionsThe results of this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. Our model transforms methylation Beta values to M values for Gaussian distributed data optimization, dynamically adjusts the output channels according to input dimension, and implements residual blocks to mitigate the problem of gradient vanishing when training very deep networks. Benchmarking against the state-of-the-art geneEXPLORE model (R2 = 0.449), DeepMethyGene (R2 = 0.640) demonstrated superior predictive performance. Further analysis revealed that the number of methylation sites and the average distance between these sites and gene transcription start sites (TSS) significantly affected the prediction accuracy. By exploring the complex relationship between methylation and gene expression, this study provides theoretical support for disease progression prediction and clinical intervention. Relevant data and code are available at https://github.com/yaoyao-11/DeepMethyGene.

BackgroundAdaptive Banded Event Alignment (ABEA) stands as a critical algorithmic component in sequence polishing and DNA methylation detection, employing dynamic programming to align raw Nanopore signal with reference reads. Motivated by the observation that, compared to CPUs and GPUs, cutting-edge FPGAs demonstrate—in certain cases—superior performance at a reduced cost and energy consumption, this paper presents an efficient FPGA-based accelerator for ABEA, leveraging the inherent high parallelism and sequential access pattern within ABEA.ResultOur proposed FPGA-based ABEA accelerator significantly enhances ABEA performance compared to the original CPU-based implementation in Nanopolish as well as the state-of-art acceleration on GPU and FPGA platforms. Specifically, targeting Xilinx VU9P, our accelerator achieves an average throughput speedup of 10.05× over the CPU-only implementation, an average 1.81× speedup over the state-of-art GPU acceleration with only 7.2% of the energy, and a speedup of 10.11× compared to an existing FPGA accelerator.ConclusionOur work demonstrates that intensive genome analysis can benefit significantly from cutting-edge FPGAs, offering improvements in both performance and energy consumption.

BackgroundPredicting and studying essential proteins not only helps to understand the fundamental requirements for cell survival and growth regulation mechanisms but also deepens our understanding of disease mechanisms and drives drug development. Existing methods for identifying essential proteins primarily focus on PPI networks within a single species, without fully exploiting interspecies homologous relationships. These homologous relationships connect proteins from different species, forming multilayer PPI networks. Some methods only construct interlayer edges based on homologous relationships between two species, without incorporating appropriate biological attributes to assess the biological significance of these edges. Furthermore, homologous proteins are often highly conserved across multiple species, and expanding homologous relationships to more species allows for a more accurate assessment of interlayer edge importance.ResultsTo address these issues, we propose a novel model, MLPR, which constructs a multilayer PPI network based on homologous proteins and integrates multiple PageRank algorithms to identify essential proteins. This study combines homologous protein data from three species to construct interlayer transition matrices and assigns weights to interlayer edges by integrating the biological attributes of homologous proteins and cross-species GO annotations. The MLPR model uses multiple PageRank methods to comprehensively consider homologous relationships across species and designs three key parameters to find the optimal combination that balances random walks within layers, global jumps, interlayer biases, and interspecies homologous relationships.ConclusionsExperimental results show that MLPR outperforms other state-of-the-art methods in terms of performance. Ablation experiments further validate that integrating homologous relationships across three species effectively enhances the overall performance of MLPR and demonstrates the advantages of the multiple PageRank model in identifying essential proteins.

BackgroundThe scarcity of available structural data makes characterizing the binding of T-cell receptors (TCRs) to peptide-Major Histocompatibility Complexes (pMHCs) very challenging. The recent surge in sequencing data makes TCRs an ideal target for protein structure modeling. Through these 3D models, researchers can potentially identify key motifs on the TCR’s binding regions. Furthermore, computational methods can be employed to pair a TCR structure with a pMHC, leading to predictions of docked TCRpMHC structures. However, going from sequence to predicted 3D TCRpMHC complexes requires a non-trivial amount of steps and specialized immunoinformatics expertise.ResultsWe developed a Python tool named TRain (T-cell Receptor Automated ImmunoiNformatics) to streamline this process by: (1) converting single-cell sequencing data into full TCR amino acid sequences; (2) efficiently submitting TCR amino acid sequences to existing TCR-specific modeling pipelines; (3) pairing modeled TCR structures with existing crystal structures of pMHC complexes in a non-biased manner before docking; (3) automating the preparation and submission process of TCRs and pMHCs for docking using the RosettaDock tool; and (4) providing scripts to analyze the predicted TCRpMHC interface. We illustrate the basic functionality of TRain with a case study, while further information can be found in a dedicated manual.ConclusionsWe introduced an open-source tool that streamlines going from full TCR sequence information to predicted 3D TCRpMHC complexes, using well-established tools. Analyzing these predicted complexes can provide deeper insights into the binding properties of TCRs, and can help shed light on one of the key steps in adaptive immune responses.

BackgroundMosaic loss of the Y chromosome (mLOY) in circulating leukocytes is the most frequently detected age-related chromosomal mosaic event in men. Current mLOY detection approaches use genotyping arrays and employ a phase-based approach that identifies B allele frequency (BAF) deviations in the pseudo-autosomal region (PAR) shared between the X and Y chromosome. As some widely used genotyping arrays lack sufficient probe coverage of the PAR, methods for accurately measuring mLOY utilizing the median log2 R ratio across the male-specific region of Y chromosome (mLRR_Y) are needed for detecting mLOY on these platforms.ResultsWe derived a formula from mLRR_Y to estimate the cellular fraction (CF) of cells with Y loss and validated the approach, finding high alignment with the CF estimation from female data and lab-generated qPCR data (R2 = 0.98). Additionally, we compared the correlation between phase-based BAF and mLRR_Y methods for CF estimation, achieving a high correlation with R2 > 0.80.ConclusionAlthough mLRR_Y is a noisier metric for mosaic chromosomal alteration detection relative to BAF, we demonstrate mLRR_Y across non-PAR variants can accurately estimate mLOY CF, especially for high CF mLOY.

BackgroundGene-environment (G × E) interactions play a critical role in understanding the etiology of diseases and exploring the factors that affect disease prognosis. There are several challenges in detecting G × E interactions for censored survival outcomes, such as the high dimensionality, complexity of environmental effects, and specificity of survival analysis. The effect heredity, which incorporates the dependence of the main effects and interactions in the analysis, has been widely applied in the study of interaction detection. However, it has not yet been applied to Bayesian Cox proportional hazards models for detecting interactions for censored survival outcomes.ResultsIn this study, we propose Bayesian heredity-constrained Cox proportional hazards (BHCox) models with novel spike-and-slab and regularized horseshoe priors that incorporate effect heredity to identify and estimate the main and interaction effects. The no-U-turn sampler (NUTS) algorithm, which has been implemented in the R package brms, was used to fit the proposed model. Extensive simulations were performed to evaluate and compare our proposed approaches with other alternative models. The simulation studies illustrated that BHCox models outperform other alternative models. We applied the proposed method to real data of non-small-cell lung cancer (NSCLC) and identified biologically plausible G × smoking interactions associated with the prognosis of patients with NSCLC.ConclusionsIn summary, BHCox can be used to detect the main effects and interactions and thus have significant implications for the discovery of high-dimensional interactions in censored survival outcome data.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in drug discovery. Multiple heterogeneous biological data provide abundant features for DTI prediction. Many computational methods have been proposed based on these data. However, most of these methods either extract features from sequences or from networks, utilizing only one aspect of the characteristics of drugs and targets, neglecting the complementary information between these two types of features. In fact, integrating different types of features will provide more valuable information for DTI prediction. In this article, we propose a novel method to improve the predictive capability for DTIs, named MFCADTI, by integrating multi-source feature through cross-attention mechanisms. The method extracts network topological features from the heterogeneous network and attribute features from sequences of drugs and targets. Considering the complementarity and heterogeneity between network and attribute features, cross-attention mechanisms are used to integrate the network and attribute features of drugs and targets. To capture the correlations between drugs and targets, cross-attention is used to learn the interaction features of each drug-target pair. We evaluate MFCADTI on two datasets and experimental results demonstrate a significant improvement in the performance of MFCADTI compared to state-of-the-art methods. Finally, case studies illustrate that MFCADTI is an effective DTI prediction way that provides valuable guidance for drug development. The data and source code used in this study are available at: https://github.com/Dejavun/MFCADTI.