Briefings in bioinformatics最新文献

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic factors associated with specific traits. However, ethical constraints prevent the direct exchange of genetic information, prompting the need for privacy preservation solutions. To address these issues, earlier works are based on cryptographic mechanisms such as homomorphic encryption, secure multi-party computing, and differential privacy. Very recently, federated learning has emerged as a promising solution for enabling secure and collaborative GWAS computations. This work provides an extensive overview of existing methods for GWAS privacy preserving, with the main focus on collaborative and distributed approaches. This survey provides a comprehensive analysis of the challenges faced by existing methods, their limitations, and insights into designing efficient solutions.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.

Motivation: Single cell RNA sequencing (scRNA-seq) technique enables the transcriptome profiling of hundreds to ten thousands of cells at the unprecedented individual level and provides new insights to study cell heterogeneity. However, its advantages are hampered by dropout events. To address this problem, we propose a Blockwise Accelerated Non-negative Matrix Factorization framework with Structural network constraints (BANMF-S) to impute those technical zeros.

Results: BANMF-S constructs a gene-gene similarity network to integrate prior information from the external PPI network by the Triadic Closure Principle and a cell-cell similarity network to capture the neighborhood structure and temporal information through a Minimum-Spanning Tree. By collaboratively employing these two networks as regularizations, BANMF-S encourages the coherence of similar gene and cell pairs in the latent space, enhancing the potential to recover the underlying features. Besides, BANMF-S adopts a blocklization strategy to solve the traditional NMF problem through distributed Stochastic Gradient Descent method in a parallel way to accelerate the optimization. Numerical experiments on simulations and real datasets verify that BANMF-S can improve the accuracy of downstream clustering and pseudo-trajectory inference, and its performance is superior to seven state-of-the-art algorithms.

Availability: All data used in this work are downloaded from publicly available data sources, and their corresponding accession numbers or source URLs are provided in Supplementary File Section 5.1 Dataset Information. The source codes are publicly available in Github repository https://github.com/jiayingzhao/BANMF-S.

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer-promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer-promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer-promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types.

Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software.

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.

Systematic investigation of tumor-infiltrating immune (TII) cells is important to the development of immunotherapies, and the clinical response prediction in cancers. There exists complex transcriptional regulation within TII cells, and different immune cell types display specific regulation patterns. To dissect transcriptional regulation in TII cells, we first integrated the gene expression profiles from single-cell datasets, and proposed a computational pipeline to identify TII cell type-specific transcription factor (TF) mediated activity immune modules (TF-AIMs). Our analysis revealed key TFs, such as BACH2 and NFKB1 play important roles in B and NK cells, respectively. We also found some of these TF-AIMs may contribute to tumor pathogenesis. Based on TII cell type-specific TF-AIMs, we identified eight CD8+ T cell subtypes. In particular, we found the PD1 + CD8+ T cell subset and its specific TF-AIMs associated with immunotherapy response. Furthermore, the TII cell type-specific TF-AIMs displayed the potential to be used as predictive markers for immunotherapy response of cancer patients. At the pan-cancer level, we also identified and characterized six molecular subtypes across 9680 samples based on the activation status of TII cell type-specific TF-AIMs. Finally, we constructed a user-friendly web interface CellTF-AIMs (http://bio-bigdata.hrbmu.edu.cn/CellTF-AIMs/) for exploring transcriptional regulatory pattern in various TII cell types. Our study provides valuable implications and a rich resource for understanding the mechanisms involved in cancer microenvironment and immunotherapy.