Search

Proteomic data from natural isolates of Saccharomyces cerevisiae provide insight into how these cells tolerate aneuploidy (an imbalance in the number of chromosomes), and reveal differences between lab-engineered aneuploids and diverse natural yeasts.

The authors use data on the entire Finnish population to develop a machine learning model for predicting COVID-19 vaccination uptake. Important predictors are proxies of socio-economic status, and those at high risk for COVID-19 consequences are less likely to get vaccinated.

HippoMaps provides an open-source resource for studying the human hippocampus at different scales and with different modalities such as histology, fMRI, structural MRI and EEG.

Analysis of soundscape data from 139 globally distributed sites reveals that sounds of biological origin exhibit predictable rhythms depending on location and season, whereas sounds of anthropogenic origin are less predictable. Comparisons between paired urban–rural sites show that urban green spaces are noisier and dominated by sounds of technological origin.

A global network of researchers was formed to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity; this paper reports 13 genome-wide significant loci and potentially actionable mechanisms in response to infection.

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

The molecular mechanisms underlying drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML) remain to be explored. Here, the use of bulk and single cell multi-omics and ex vivo drug profiling for 21 rrAML patients reveals mechanisms of resistance to the Bcl-2 inhibitor venetoclax and treatment vulnerabilities.

To improve annotation in non-targeted metabolomics studies, authors develop a Multiplexed Chemical Metabolomics (MCheM) platform, combining post-column derivatization with integrated data processing. MCheM enables feature-resolved chemical tagging across complex mixtures, which provides orthorgonal chemical information about unknown metabolites.

This study examines socio-demographic and genetic factors for adherence and persistence to five medication classes, identifying lower socio-economic and immigration status as major predictors of lower adherence, while genetic factors show minimal impact.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

In the Tumor Profiler proof-of-concept observational study, a multiomics approach for profiling tumors from patients with melanoma was feasible, returning data within 4 weeks and informing treatment recommendations in 75% of cases.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

HistoPlexer, a deep learning model, generates multiplexed protein expression maps from H&E images, capturing tumour–immune cell interactions. It outperforms baselines, enhances immune subtyping and survival prediction and offers a cost-effective tool for precision oncology.

Using multi-ancestry genome-wide association study and fine-mapping, 298 loci and 36 credible genes are identified in the aetiology of bipolar disorder.

A pulse of ocean acidification is reconstructed from the boron isotope composition of fossilized oysters at the Triassic-Jurassic boundary, implicating ocean acidification from volcanic outgassing as a kill mechanism during the extinction event.

To better understand the etiology of frailty, the authors perform a large genetic study. They identified 45 additional variants and implicated MET, CHST9, ILRUN, APOE, CGREF1 and PPP6C as potential causal genes, linking frailty to immune regulation, metabolism and cellular signaling.

Here, the authors compared measurements between 34 laboratories from 19 countries, to quantify by mass spectrometry four ceramides of clinical relevance in human blood plasma Standard Reference Materials. The main goals were to evaluate concordance obtained in a large inter-laboratory trial and to report absolute concentrations of four circulating lipids in a publicly available standard.

Exome sequencing within a structured diagnostic process for rare diseases in Germany shows how facial image analysis and machine learning can guide variant prioritization and uncover many ultrarare diseases.

A genome-wide study by the Long COVID Host Genetics Initiative identifies an association between the FOXP4 locus and long COVID, implicating altered lung function in its pathophysiology.

A consensus cell type atlas for the fly brain provides both an intellectual framework and open-source toolchains for brain-scale comparative connectomics.

Here the authors apply machine learning approaches to Alzheimer’s genetics, confirm known associations and suggest novel risk loci. These methods demonstrate predictive power comparable to traditional approaches, while also offering potential new insights beyond standard genetic analyses.

Analysis of whole-genome sequencing data across 2,658 tumors spanning 38 cancer types shows that chromothripsis is pervasive, with a frequency of more than 50% in several cancer types, contributing to oncogene amplification, gene inactivation and cancer genome evolution.

Analysis of mitochondrial genomes (mtDNA) by using whole-genome sequencing data from 2,658 cancer samples across 38 cancer types identifies hypermutated mtDNA cases, frequent somatic nuclear transfer of mtDNA and high variability of mtDNA copy number in many cancers.

Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities.

Whole-genome sequencing, transcriptome-wide association and fine-mapping analyses in over 7,000 individuals with critical COVID-19 are used to identify 16 independent variants that are associated with severe illness in COVID-19.