Bioinformatics and Genomic Networks

Illuminating dark genes and dark metabolites: calibrated functional association across kingdoms with autoencoders and correlation

VaLPAS pairs a bi-directional autoencoder with correlation for omics guilt-by-association with calibrated confidence; in L. starkeyi its autoencoder linked 176 unidentified metabolites to known-function proteins at ≥50% confidence.

HiMaLAYAS: enrichment-based annotation and visualization of hierarchically clustered matrices

HiMaLAYAS, a Python package, cuts dendrograms, tests clusters for categorical annotation enrichment (hypergeometric, Benjamini-Hochberg FDR), and renders significant annotations beside clustered heatmaps in one workflow.

Towards reconstruction of the human interactome from positive and negative experimental evidence

Inferring tested-but-undetected pairs from prey lists of 5,530 published interaction screens yields 97 million tested human protein pairs and 18,982,508 non-interactions (≥3 tests) for calibrating screens and predictors.

Inferring disruption of directed graphs using LIKA reveals altered protein phosphorylation networks in schizophrenia

LIKA, a likelihood model over many-to-many kinase-substrate networks, beats KSEA in simulations with shared substrates, recovers EGFR and ABL1 in cell lines, and ranks MAPK3 first in schizophrenia cortex.

clusterProfiler: An universal enrichment tool for functional and comparative study

clusterProfiler performs over-representation (Fisher's exact test) and gene set enrichment analyses on Gene Ontology, KEGG, and user-supplied annotations, supporting non-model organisms, multi-condition comparisons, and ggplot2-based visualization.

Fast gene set enrichment analysis

FGSEA estimates preranked GSEA P-values down to 10^-100 with predictable error in seconds to minutes, recovering more FDR-significant pathways across 605 GEO datasets than the Broad implementation.

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

Signaling Pathways Project maps over 10,000 transcriptomic and ChIP-Seq experiments to signaling nodes and ranks node targets by meta-analysis (consensomes), validated against literature and bench assays.

Pathway enrichment analysis of -omics data

Step-by-step protocol for interpreting omics gene lists via g:Profiler or GSEA enrichment testing, then grouping redundant enriched pathways into themes with Cytoscape Enrichment Map.

Cytoscape stringApp: Network analysis and visualization of proteomics data

stringApp, a Cytoscape app, imports STRING and STITCH networks with disease, tissue, localization and drug-target annotations, enabling GUI-based enrichment, clustering and visualization of proteomics data.

Multi-Omics factor analysis - a framework for unsupervised integration of multi-omic data sets

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.

MAGMA: Generalized Gene-Set Analysis of GWAS Data

MAGMA couples principal-components regression gene tests with gene-level regression for gene sets, detecting more Crohn's Disease genes and pathways than PLINK while running far faster.