The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation Analysis.

Calling differential methylation at a cell-type level from tissue-level bulk data is a fundamental challenge in genomics that has recently received more attention. These studies most often aim at identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention has been given to the fact that this directionality assumption may not hold and can consequently affect statistical power and control for false positives.

Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences.

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia causing irreversible brain damage to the elderly and presents a major public health challenge. Clinical research and genome-wide association studies have suggested a potential contribution of the endocytic pathway to AD, with an emphasis on common loci. However, the contribution of rare variants in this pathway to AD has not been thoroughly investigated.

xGAP: a python based efficient, modular, extensible and fault tolerant genomic analysis pipeline for variant discovery.

Motivation: Since the first human genome was sequenced in 2001, there has been a rapid growth in the number of bioinformatic methods to process and analyze next-generation sequencing (NGS) data for research and clinical studies that aim to identify genetic variants influencing diseases and traits. To achieve this goal, one first needs to call genetic variants from NGS data, which requires multiple computationally intensive analysis steps. Unfortunately, there is a lack of an open-source pipeline that can perform all these steps on NGS data in a manner, which is fully automated, efficient, rapid, scalable, modular, user-friendly and fault tolerant.

A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable.

The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance.

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM.

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM).

Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression.

ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings.

Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus.

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

Background: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart.

Methods: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery.

Variant calling and quality control of large-scale human genome sequencing data.

Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases.

An efficient linear mixed model framework for meta-analytic association studies across multiple contexts.

Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model.