Algorithms for Molecular Biology最新文献

The tree of blobs of a species network shows only the tree-like aspects of relationships of taxa on a network, omitting information on network substructures where hybridization or other types of lateral transfer of genetic information occur. By isolating such regions of a network, inference of the tree of blobs can serve as a starting point for a more detailed investigation, or indicate the limit of what may be inferrable without additional assumptions. Building on our theoretical work on the identifiability of the tree of blobs from gene quartet distributions under the Network Multispecies Coalescent model, we develop an algorithm, TINNiK, for statistically consistent tree of blobs inference. We provide examples of its application to both simulated and empirical datasets, utilizing an implementation in the MSCquartets 2.0 R package.

The B cell lineage tree encapsulates the successive phases of B cell differentiation and maturation, transitioning from hematopoietic stem cells to mature, antibody-secreting cells within the immune system. Mathematically, this lineage can be conceptualized as an evolutionary tree, where each node represents a distinct stage in B cell development, and the edges reflect the differentiation pathways. To compare these lineage trees, a rigorous mathematical metric is essential. Analyzing B cell lineage trees mathematically and quantifying changes in lineage attributes over time necessitates a comparison methodology capable of accurately assessing and measuring these changes. Addressing the intricacies of multiple B cell lineage tree comparisons, this study introduces a novel metric that enhances the precision of comparative analysis. This metric is formulated on principles of metric theory and evolutionary biology, quantifying the dissimilarities between lineage trees by measuring branch length distance and weight. By providing a framework for systematically classifying lineage trees, this metric facilitates the development of predictive models that are crucial for the creation of targeted immunotherapy and vaccines. To validate the effectiveness of this new metric, synthetic datasets that mimic the complexity and variability of real B cell lineage structures are employed. We demonstrated the ability of the new metric method to accurately capture the evolutionary nuances of B cell lineages.

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a simple neural network-based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead. The software is available at http://github.com/medvedevgroup/ESSColor .