Gene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.
 
  Therefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.
 
  The ESCO implementation is available as R package ESCO. Users can either download the development version via github (https//github.com/JINJINT/ESCO) or the archived version via Zenodo (https//zenodo.org/record/4455890).
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.
  Durbin's positional Burrows-Wheeler transform (PBWT) is a scalable data structure for haplotype matching.  https://www.selleckchem.com/products/sulfopin.html  It has been successfully applied to identical by descent (IBD) segment identification and genotype imputation. Once the PBWT of a haplotype panel is constructed, it supports efficient retrieval of all shared long segments among all individuals (long matches) and efficient query between an external haplotype and the panel. However, the standard PBWT is an array-based static data structure and does not support dynamic updates of the panel.
 
  Here, we generalize the static PBWT to a dynamic data structure, d-PBWT, where the reverse prefix sorting at each position is stored with linked lists.We also developed efficient algorithms for insertion and deletion of individual haplotypes. In addition, we verified that d-PBWT can support all algorithms of PBWT. In doing so, we systematically investigated variations of set maximal match and long match query algorithms while they all have average case time complexity independent of database size, they have different worst case complexities and dependencies on additional data structures.
 
  The benchmarking code is available at genome.ucf.edu/d-PBWT.
 
  Supplementary Materials are available at Bioinformatics online.
 Supplementary Materials are available at Bioinformatics online.
  Hutchinson-Gilford progeria syndrome (HGPS) is an ultrarare laminopathy caused by expression of progerin, a lamin A variant, also present at low levels in non-HGPS individuals. HGPS patients age and die prematurely, predominantly from cardiovascular complications. Progerin-induced cardiac repolarization defects have been described previously, although the underlying mechanisms are unknown.
 
  We conducted studies in heart tissue from progerin-expressing LmnaG609G/G609G (G609G) mice, including microscopy, intracellular calcium dynamics, patch-clamping, in vivo magnetic resonance imaging, and electrocardiography. Mouse G609G cardiomyocytes showed tubulin-cytoskeleton disorganization, t-tubular system disruption, sarcomere shortening, altered excitation-contraction coupling, and reductions in ventricular thickening and cardiac index. G609G mice exhibited severe bradycardia, and significant alterations of atrio-ventricular conduction and repolarization. Most importantly, 50% of G609G mice had altered heart rate tment with low-dose of paclitaxel.
  The Probabilistic Identification of Causal SNPs (PICS) algorithm and web application was developed as a fine-mapping tool to determine the likelihood that each single nucleotide polymorphism (SNP) in LD with a reported index SNP is a true causal polymorphism. PICS is notable for its ability to identify candidate causal SNPs within a locus using only the index SNP, which are widely available from published GWAS, whereas other methods require full summary statistics or full genotype data. However, the original PICS web application operates on a single SNP at a time, with slow performance, severely limiting its usability. We have developed a next-generation PICS tool, PICS2, which enables performance of PICS analyses of large batches of index SNPs with much faster performance. Additional updates and extensions include use of LD reference data generated from 1000 Genomes phase 3; annotation of variant consequences; annotation of GTEx eQTL genes and downloadable PICS SNPs from GTEx eQTLs; the option of generating PICS probabilities from experimental summary statistics; and generation of PICS SNPs from all SNPs of the GWAS catalog, automatically updated weekly. These free and easy-to-use resources will enable efficient determination of candidate loci for biological studies to investigate the true causal variants underlying disease processes.
 
  PICS2 is available at https//pics2.ucsf.edu.
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.
  Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information.
 
  We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e., the proportion of variation explained by the eQTLs) was set at 1%, 2%, and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.