DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data.

Summary: Unsupervised deconvolution algorithms are often used to estimate cell composition from bulk tissue samples. However, applying cell-type deconvolution and interpreting the results remain a challenge, even more without prior training in bioinformatics. Here, we propose a tool for estimating and identifying cell type composition from bulk transcriptomes or methylomes.

Transcriptome and acetylome profiling identify crucial steps of neuronal differentiation in Rubinstein-Taybi syndrome.

Rubinstein-Taybi syndrome (RTS) is a rare and severe genetic developmental disorder characterized by multiple congenital anomalies and intellectual disability. CREBBP and EP300, the two genes known to cause RTS encode transcriptional coactivators with a catalytic lysine acetyltransferase (KAT) activity. Loss of CBP or p300 function results in a deficit in protein acetylation, in particular at histones.

CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.

Motivation: Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates.

Results: In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

Motivation: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.

Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis.

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose.

Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects.

Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations.

A large-scale validation of NOCIt's a posteriori probability of the number of contributors and its integration into forensic interpretation pipelines.

Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signal from forensic stains is often garnered from low-copy, high-contributor-number samples and is frequently obfuscated by allele sharing, allele drop-out, stutter and noise. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous, probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution's and defense's hypotheses have been developed.

Statistical modeling of STR capillary electrophoresis signal.

Background: In order to isolate an individual's genotype from a sample of biological material, most laboratories use PCR and Capillary Electrophoresis (CE) to construct a genetic profile based on polymorphic loci known as Short Tandem Repeats (STRs). The resulting profile consists of CE signal which contains information about the length and number of STR units amplified. For samples collected from the environment, interpretation of the signal can be challenging given that information regarding the quality and quantity of the DNA is often limited.

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution.

In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph.

MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs.

The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications.

Metabolic connectivity as a driver of host and endosymbiont integration.

The origin of oxygenic photosynthesis in the Archaeplastida common ancestor was foundational for the evolution of multicellular life. It is very likely that the primary endosymbiosis that explains plastid origin relied initially on the establishment of a metabolic connection between the host cell and captured cyanobacterium. We posit that these connections were derived primarily from existing host-derived components.

Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms.

Background: High-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches. Here, we introduce the analytical and statistical framework of sequence similarity networks, increasingly used in evolutionary studies and graph theory, into the field of ecology to analyze novel pyrosequenced V4 small subunit rDNA (SSU-rDNA) sequence data sets in the context of previous studies, including SSU-rDNA Sanger sequence data from cultured ciliates and from previous environmental diversity inventories.

Results: Our broadly applicable protocol quantified the progress in the description of genetic diversity of ciliates by environmental SSU-rDNA surveys, detected a fundamental historical bias in the tendency to recover already known groups in these surveys, and revealed substantial amounts of hidden microbial diversity.

Deciphering primordial cyanobacterial genome functions from protein network analysis.

The Great Oxidation Event (GOE) ∼2.4 billion years ago resulted from the accumulation of oxygen by the ancestors of cyanobacteria [1-3]. Cyanobacteria continue to play a significant role in primary production [4] and in regulating the global marine and limnic nitrogen cycles [5, 6].

Exploring biotic interactions within protist cell populations using network methods.

The study of diseased human cells and of cells isolated from the natural environment will likely be revolutionized by single cell genomics (SCG). Here, we used protein similarity networks to explore within- and between-cell DNA differences from SCG data derived from six individual rhizarian cells related to Paulinella ovalis and proteins from the complete genome of another rhizarian, Bigelowiella natans. We identified shared and distinct DNA components within our SCG data and between P.

A multi-level parcellation approach for brain functional connectivity analysis.

Most brain functional connectivity methods in fMRI require a brain parcellation into functionally homogeneous regions. In this work we propose a novel parcellation approach based on a spatial hierarchical clustering, that provides clusters within a multi-level framework. The method has the advantage of producing several brain parcellations rather than a single one from a fixed size-homogeneity criterion.