Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.

Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes.

Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations.

Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank.

Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.

Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis.

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression.

MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data.

Motivation: Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

Results: We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes.

Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells.

Neuroblastoma is a childhood tumour that is responsible for approximately 15% of all childhood cancer deaths. Neuroblastoma tumours with amplification of the oncogene MYCN are aggressive, however, another aggressive subgroup without MYCN amplification also exists; rather, they have a deleted region at chromosome arm 11q. Twenty-six miRNAs are located within the breakpoint region of chromosome 11q and have been checked for a possible involvement in development of neuroblastoma due to the genomic alteration.

TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes.

Background: Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes.

A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis.

Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules.

Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis.

ComHub: Community predictions of hubs in gene regulatory networks.

Background: Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.

Results: We have developed ComHub, a tool to predict hubs in GRNs.

Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients.

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression.

MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks.

Motivation: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules.

miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer.

Background: MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed.

Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes.

Motivation: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts.

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes.

Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body's inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder.

Using expression profiling to understand the effects of chronic cadmium exposure on MCF-7 breast cancer cells.

Cadmium is a metalloestrogen known to activate the estrogen receptor and promote breast cancer cell growth. Previous studies have implicated cadmium in the development of more malignant tumors; however the molecular mechanisms behind this cadmium-induced malignancy remain elusive. Using clonal cell lines derived from exposing breast cancer cells to cadmium for over 6 months (MCF-7-Cd4, -Cd6, -Cd7, -Cd8 and -Cd12), this study aims to identify gene expression signatures associated with chronic cadmium exposure.