A comparative study of structural variant calling in WGS from Alzheimer's disease families.

Detecting structural variants (SVs) in whole-genome sequencing poses significant challenges. We present a protocol for variant calling, merging, genotyping, sensitivity analysis, and laboratory validation for generating a high-quality SV call set in whole-genome sequencing from the Alzheimer's Disease Sequencing Project comprising 578 individuals from 111 families. Employing two complementary pipelines, Scalpel and Parliament, for SV/indel calling, we assessed sensitivity through sample replicates (N = 9) with in silico variant spike-ins.

: A high-throughput tool for alignment-free phylogenetics.

Phylogenetic tools are fundamental to the studies of evolutionary relationships. In this paper, we present , a novel high-throughput tool for alignment-free phylogenetic analysis. computes the pairwise distance matrix between molecular sequences, using seven widely accepted -mer based distance measures.

SVEngine: an efficient and versatile simulator of genome structural variations with features of cancer clonal evolution.

Background: Simulating genome sequence data with variant features facilitates the development and benchmarking of structural variant analysis programs. However, there are only a few data simulators that provide structural variants in silico and even fewer that provide variants with different allelic fraction and haplotypes.

Findings: We developed SVEngine, an open-source tool to address this need.

Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis.

Background: Periodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity.

Methods: A total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing.

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts.