AIMC Topic: Whole Genome Sequencing

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Whole-genome sequencing and comparative genomics reveal antimicrobial potential and adaptive traits of Bacillus velezensis AM12.

Functional & integrative genomics
The global rise of antimicrobial resistance has intensified the demand for novel antimicrobial agents with broad-spectrum efficacy and unique mechanisms of action. Herein, a marine-derived strain, Bacillus velezensis (B. velezensis) AM12, exhibiting ...

Exploring DNA methylation profiles in blood samples of canine gastrointestinal lymphoma.

PloS one
Blood-based testing represents a valuable tool for the detection and monitoring of patient conditions in both human and veterinary medicine. When conventional tissue-based diagnosis is challenging, blood-derived measurements allow for minimally invas...

Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.

PLoS biology
Antimicrobial resistance (AMR) poses a growing threat to human health. Increasingly, genome sequencing is being applied for the surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR...

The best from both disciplines: integrating human and microbial signatures from whole genome sequencing to advance cancer diagnostics.

mSystems
Liquid biopsies are transforming oncology, enabling earlier diagnosis, dynamic treatment guidance, and personalized precision medicine, yet current approaches focusing mainly on circulating host cell-free DNA (cfDNA) neglect crucial information withi...

Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology.

Nature communications
The adoption of whole genome sequencing (WGS) in clinical oncology is challenged by low data quality and increased artifacts in standard-of-care formalin-fixed paraffin-embedded (FFPE) samples. Analysis of 56 fresh frozen (FF) and FFPE matched pairs ...

Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.

BMC medical genomics
BACKGROUND: Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether i...

CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion tests.

Nature communications
Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carb...

Whole-genome sequencing reveals individual and cohort level insights into chromosome 9p syndromes.

Genome medicine
BACKGROUND: Previous genomic efforts on chromosome 9p deletion and duplication syndromes have utilized low-resolution strategies (i.e., karyotypes, chromosome microarrays). These studies have provided important initial insights into these syndromes. ...

Learning the cellular origins across cancers using single-cell chromatin landscapes.

Nature communications
Deciphering the pre-malignant cell of origin (COO) of different cancers is critical for understanding tumor development and improving diagnostic and therapeutic strategies in oncology. Prior work demonstrates that somatic mutations preferentially acc...

Prediction of reduction behavior by heating and strain variability of Campylobacter jejuni using amino acid phylogenetics from whole genome sequencing data.

International journal of food microbiology
Predicting bacterial behavior and strain variability is essential for quantitative microbial risk assessments in food safety. The growing availability of whole-genome sequencing (WGS) data enables deeper insights into microbial thermotolerance. Howev...