Identification of Plasma Protein Biomarkers for Predicting Lung Cancer.

Background/aim: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating the development of effective early diagnostic strategies. Despite advancements in imaging and screening technologies, late-stage diagnoses remain common, limiting treatment options and reducing survival rates. Thus, there is a critical need for reliable, minimally invasive biomarkers to improve early detection and patient outcomes.

Integrating Vascular Phenotypic and Proteomic Analysis in an Open Microfluidic Platform.

This research introduces a vascular phenotypic and proteomic analysis (VPT) platform designed to perform high-throughput experiments on vascular development. The VPT platform utilizes an open-channel configuration that facilitates angiogenesis by precise alignment of endothelial cells, allowing for a 3D morphological examination and protein analysis. We study the effects of antiangiogenic agents─bevacizumab, ramucirumab, cabozantinib, regorafenib, wortmannin, chloroquine, and paclitaxel─on cytoskeletal integrity and angiogenic sprouting, observing an approximately 50% reduction in sprouting at higher drug concentrations.

Landmarks of human embryonic development inscribed in somatic mutations.

Although cell lineage information is fundamental to understanding organismal development, very little direct information is available for humans. We performed high-depth (250×) whole-genome sequencing of multiple tissues from three individuals to identify hundreds of somatic single-nucleotide variants (sSNVs). Using these variants as "endogenous barcodes" in single cells, we reconstructed early embryonic cell divisions.

The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing.

We characterize the landscape of somatic mutations-mutations occurring after fertilization-in the human brain using ultra-deep (~250×) whole-genome sequencing of prefrontal cortex from 59 donors with autism spectrum disorder (ASD) and 15 control donors. We observe a mean of 26 somatic single-nucleotide variants per brain present in ≥4% of cells, with enrichment of mutations in coding and putative regulatory regions. Our analysis reveals that the first cell division after fertilization produces ~3.

BamSnap: a lightweight viewer for sequencing reads in BAM files.

Summary: Despite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout.

Accurate detection of mosaic variants in sequencing data without matched controls.

Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants and indels, achieving a multifold increase in specificity compared with existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80-90% of the mosaic single-nucleotide variants and 60-80% of indels detected in human brain whole-genome sequencing data.

Linked-read analysis identifies mutations in single-cell DNA-sequencing data.

Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.

Background: With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants.

Correction: Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel.

[This corrects the article DOI: 10.18632/oncotarget.21861.

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.

Aging and neurodegeneration are associated with increased mutations in single human neurons.

It has long been hypothesized that aging and neurodegeneration are associated with somatic mutation in neurons; however, methodological hurdles have prevented testing this hypothesis directly. We used single-cell whole-genome sequencing to perform genome-wide somatic single-nucleotide variant (sSNV) identification on DNA from 161 single neurons from the prefrontal cortex and hippocampus of 15 normal individuals (aged 4 months to 82 years), as well as 9 individuals affected by early-onset neurodegeneration due to genetic disorders of DNA repair (Cockayne syndrome and xeroderma pigmentosum). sSNVs increased approximately linearly with age in both areas (with a higher rate in hippocampus) and were more abundant in neurodegenerative disease.

Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel.

Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers.

CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery.

The recent creation of enormous, cancer-related "Big Data" public depositories represents a powerful means for understanding tumorigenesis. However, a consistently accurate system for clinically evaluating single/multi-biomarkers remains lacking, and it has been asserted that oft-failed clinical advancement of biomarkers occurs within the very early stages of biomarker assessment. To address these challenges, we developed a clinically testable, web-based tool, CANcer-specific single/multi-biomarker Evaluation System (CANES), to evaluate biomarker effectiveness, across 2,134 whole transcriptome datasets, from 94,147 biological samples (from 18 tumor types).

Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network.

Neuropsychiatric disorders have a complex genetic architecture. Human genetic population-based studies have identified numerous heritable sequence and structural genomic variants associated with susceptibility to neuropsychiatric disease. However, these germline variants do not fully account for disease risk.

The genetic architecture of type 2 diabetes.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups.

Temporal pattern of acoustic imaging noise asymmetrically modulates activation in the auditory cortex.

This study investigated the hemisphere-specific effects of the temporal pattern of imaging related acoustic noise on auditory cortex activation. Hemodynamic responses (HDRs) to five temporal patterns of imaging noise corresponding to noise generated by unique combinations of imaging volume and effective repetition time (TR), were obtained using a stroboscopic event-related paradigm with extra-long (≥27.5 s) TR to minimize inter-acquisition effects.

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits.

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS.

A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype.

Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs.

Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure.

A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization.

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer.

Background: microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies.

Methods: In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues.

Multivariate Quantitative Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions.

Objectives: To determine gene-gene interactions and missing heritability of complex diseases is a challenging topic in genome-wide association studies. The multifactor dimensionality reduction (MDR) method is one of the most commonly used methods for identifying gene-gene interactions with dichotomous phenotypes. For quantitative phenotypes, the generalized MDR or quantitative MDR (QMDR) methods have been proposed.