NAR Genomics and Bioinformatics最新文献

Epigenetic deregulation through altered DNA methylation is a fundamental feature of tumorigenesis, but tumor data from bulk tissue samples contain different proportions of malignant and non-malignant cells that may confound the interpretation of DNA methylation values. The adjustment of DNA methylation data based on tumor purity has been proposed to render both genome-wide and gene-specific analyses more precise, but it requires sample purity estimates. Here we present PureBeta, a single-sample statistical framework that uses genome-wide DNA methylation data to first estimate sample purity and then adjust methylation values of individual CpGs to correct for sample impurity. Purity values estimated with the algorithm have high correlation (>0.8) to reference values obtained from DNA sequencing when applied to samples from breast carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Methylation beta values adjusted based on purity estimates have a more binary distribution that better reflects theoretical methylation states, thus facilitating improved biological inference as shown for BRCA1 in breast cancer. PureBeta is a versatile tool that can be used for different Illumina DNA methylation arrays and can be applied to individual samples of different cancer types to enhance biological interpretability of methylation data.

To address the lack of intronic reads in secondary structure probing data for the human MYC pre-mRNA, we developed a method that combines spliceosomal inhibition with RNA probing and sequencing. Here, the SIRP-seq method was applied to study the secondary structure of human MYC RNAs by chemically probing HeLa cells with dimethyl sulfate in the presence of the small molecule spliceosome inhibitor pladienolide B. Pladienolide B binds to the SF3B complex of the spliceosome to inhibit intron removal during splicing, resulting in retained intronic sequences. This method was used to increase the read coverage over intronic regions of MYC. The purpose for increasing coverage across introns was to generate complete reactivity profiles for intronic sequences via the DMS-MaPseq approach. Notably, depth was sufficient for analysis by the program DRACO, which was able to deduce distinct reactivity profiles and predict multiple secondary structural conformations as well as their suggested stoichiometric abundances. The results presented here provide a new method for intronic RNA secondary structural analyses, as well as specific structural insights relevant to MYC RNA splicing regulation and therapeutic targeting.

The development of next-generation sequencing (NGS) technologies paved the way for studying the spatiotemporal coordination of cellular processes along the genome. However, data sets are commonly limited to a few time points, and missing information needs to be interpolated. Most models assume that the studied dynamics are similar between individual cells, so that a homogeneous cell culture can be represented by a population-wide average. Here, we demonstrate that this understanding can be inappropriate. We developed a thought experiment-which we call the NGS chess problem-in which we compare the temporal sequencing data analysis to observing a superimposed picture of many independent games of chess at a time. The analysis of the spatiotemporal kinetics advocates for a new methodology that considers DNA-particle interactions in each cell independently even for a homogeneous cell population.

The recent growth of microbial sequence data allows comparisons at unprecedented scales, enabling the tracking of strains, mobile genetic elements, or genes. Querying a genome against a large reference database can easily yield thousands of matches that are tedious to interpret and pose computational challenges. We developed Graphite that uses a colored de Bruijn graph (cDBG) to paint query genomes, selecting the local best matches along the full query length. By focusing on the best genomic match of each query region, Graphite reduces the number of matches while providing the most promising leads for sequence tracking or genomic forensics. When applied to hundreds of Campylobacter genomes we found extensive gene sharing, including a previously undetected C. coli plasmid that matched a C. jejuni chromosome. Together, genome painting using cDBGs as enabled by Graphite, can reveal new biological phenomena by mitigating computational hurdles.

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.

Technological advances in high-throughput technologies improve our ability to explore the molecular mechanisms of life. Computational infrastructures for scientific applications fulfil a critical role in harnessing this potential. However, there is an ongoing need to improve accessibility and implement robust data security technologies to allow the processing of sensitive data, particularly human genetic data. Scientific clouds have emerged as a promising solution to meet these needs. We present three components of the Laniakea software stack, initially developed to support the provision of private on-demand Galaxy instances. These components can be adopted by providers of scientific cloud services built on the INDIGO PaaS layer. The Dashboard translates configuration template files into user-friendly web interfaces, enabling the easy configuration and launch of on-demand applications. The secret management and the encryption components, integrated within the Dashboard, support the secure handling of passphrases and credentials and the deployment of block-level encrypted storage volumes for managing sensitive data in the cloud environment. By adopting these software components, scientific cloud providers can develop convenient, secure and efficient on-demand services for their users.

Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of 'marker regions' which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat.

Differentiation of multipotential progenitor cells is a key process in the development of any multi-cellular organism and often continues throughout its life. It is often assumed that a bi-potential progenitor develops along a (relatively) straight trajectory until it reaches a decision point where the trajectory bifurcates. At this point one of two directions is chosen, each direction representing the unfolding of a new transcriptional programme. However, we have lacked quantitative means for testing this model. Accordingly, we have developed the R package TrajectoryGeometry. Applying this to published data we find several examples where, rather than bifurcate, developmental pathways branch. That is, the bipotential progenitor develops along a relatively straight trajectory leading to one of its potential fates. A second relatively straight trajectory branches off from this towards the other potential fate. In this sense only cells that branch off to follow the second trajectory make a 'decision'. Our methods give precise descriptions of the genes and cellular pathways involved in these trajectories. We speculate that branching may be the more common behaviour and may have advantages from a control-theoretic viewpoint.

We can now analyze 3D physical interactions of chromatin regions with chromatin conformation capture technologies, in addition to the 1D chromatin state annotations, but methods to integrate this information are lacking. We propose a method to integrate the chromatin state of interacting regions into a vector representation through the contact-weighted sum of chromatin states. Unsupervised clustering on integrated chromatin states and Micro-C contacts reveals common patterns of chromatin interaction signatures. This provides an integrated view of the complex dynamics of concurrent change occurring in chromatin state and in chromatin interaction, adding another layer of annotation beyond chromatin state or Hi-C contact separately.