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Concatenating Original Duplex for Error Correction (CODEC) is a method that concatenates both strands of each DNA duplex to enable highly sensitive mutation detection in a range of analytes with fewer reads and lower error rates than current methods.
A single-cell massively parallel reporter assay is used to compare cis-regulatory sequence activities in cell line models and mouse retinal tissue ex vivo, identifying cell state- and cell-type-specific effects of sequence variation.
MetaSTAAR enables functionally informed rare variant association analysis in biobank-scale cohorts using an efficient, sparse matrix approach for summary statistic storage.
Sort-assisted single-cell chromatin immunocleavage (sortChIC) combines single-cell histone modification profiling with fluorescence-activated cell sorting (FACS), enabling the study of rare cell populations. H3K4me1/H3K4me3, H3K9me3 and H3K27me3 profiling of blood suggest a model of lineage-shared repressive and cell type-specific active chromatin.
CRISPR-Select is a quantitative assay for the functional impact of genetic variants, including pathogenicity, drug response, oncogenicity, cell motility and other cell states.
CRISPR-CATCH is used to isolate extrachromosomal DNA (ecDNA) molecules containing oncogenes from human cancer cells. CRISPR-CATCH followed by nanopore sequencing allows for methylation profiling, highlighting differences from the native chromosomal loci.
Single-cell DNA sequencing data are generated from human neurons using primary template-directed amplification and analyzed using SCAN2, an improved genotyping tool. Indels are enriched in neuronal regulatory elements and may be deleterious.
scDRS associates individual cells in scRNA-seq with disease by scoring single-cell transcriptomes using GWAS gene signatures. Applied to 74 GWAS and 1.3 million single-cell profiles, scDRS identifies specific cellular subpopulations associated with these diseases.
snipar is a software package for imputing missing parental genotypes and estimating direct genetic effects. Application to UK Biobank data shows that effects estimated by standard genome-wide association study designs have confounding bias for some phenotypes.
Orca is a sequence-based deep-learning algorithm that predicts 3D genome architecture from kilobase to whole-chromosome scale, including the impact of structural variants. In silico modeling identifies a putative sequence basis for chromatin compartment formation.
GestaltMatcher uses a deep convolutional neural network to improve recognition of rare disorders based on facial morphology. The framework detects similarities among patients with previously unseen syndromes, aiding discovery of new disease genes.
FastGWA-GLMM is a fast, scalable generalized linear mixed model method for genetic association testing for binary traits in large cohorts that is robust to variant frequency and case–control imbalance.
QUILT is a method for rapid genotype imputation and phasing from low-coverage whole-genome sequence data using a large haplotype reference panel. QUILT enables highly accurate imputation across a range of coverages and data types.
REGENIE is a whole-genome regression method based on ridge regression that enables highly parallelized analysis of quantitative and binary traits in biobank-scale data with reduced computational requirements.
GLIMPSE is a new method for haplotype phasing and genotype imputation of low-coverage sequencing datasets from large reference panels. GLIMPSE shows remarkable performance across different coverages and human populations.
PolyGembler, a method for grouping and ordering contigs into complete pseudomolecules by combining long-read sequencing and genotype information from an outbred mapping population, improves the accuracy for assembly of polyploidy plant genomes.
CHESS is an algorithm that compares chromatin contact maps and identifies differential features. It can analyze interspecies syntenic regions and three-dimensional changes caused by genetic perturbation.
MR-JTI, a unified framework for joint-tissue imputation and Mendelian randomization, improves prediction performance in a tissue-dependent manner when applied to large-scale biobanks and meta-analysis data.