Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data.

Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.

Community detection in the human connectome: Method types, differences and their impact on inference.

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from n $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and n $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Vascularization of PEGylated fibrin hydrogels increases the proliferation of human iPSC-cardiomyocytes.

Studies have long sought to develop engineered heart tissue for the surgical correction of structural heart defects, as well as other applications and vascularization of this tissue has presented a challenge. Recent studies suggest that vascular cells and a vascular network may have regenerative effects on implanted cardiomyocytes (CM) and nearby heart tissue separate from perfusion of oxygen and nutrients. The goal of this study was to test whether vascular cells or a formed vascular network in a fibrin-based hydrogel would alter the proliferation of human iPSC-derived CM. First, vascular network formation in a slowly degrading PEGylated fibrin hydrogel was optimized by altering the cell ratio of human umbilical vein endothelial cells to human dermal fibroblasts, the inclusion of growth factors, and the total cell concentration. An endothelial to fibroblast ratio of 5:1 and a total cell concentration of 1.1 × 106 cells/mL without additional growth factors generated robust vascular networks while minimizing the number of cells required. Using this optimized system, human iPSC-derived CM were cultured on hydrogels without vascular cells, hydrogels with unorganized encapsulated vascular cells, or hydrogels with encapsulated vascular cells organized into networks for 7 days. CM proliferation and gene expression were assayed following 7 days of culture on the hydrogels. The presence of vascular cells in the hydrogel, whether unorganized or in vascular networks, significantly increased CM proliferation compared to an acellular hydrogel. Hydrogels with unorganized vascular cells resulted in lower CM maturity evidenced by decreased expression of cardiac troponin t (TNNT2), myosin light chain 7, and phospholamban compared to hydrogels without vascular cells and hydrogels with vascular networks. Altogether, this study details a robust method of forming rudimentary vascular networks in a fibrin-based hydrogel and shows that a hydrogel containing endothelial cells and fibroblasts can induce proliferation in adjacent CM, and these cells do not hinder CM gene expression when organized into a vascular network.