The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI.

Deep learning has already revolutionised the way a wide range of data is processed in many areas of daily life. The ability to learn abstractions and relationships from heterogeneous data has provided impressively accurate prediction and classification tools to handle increasingly big datasets. This has a significant impact on the growing wealth of omics datasets, with the unprecedented opportunity for a better understanding of the complexity of living organisms.

hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.

Horizontal gene transfer (HGT) is well described in prokaryotes: it plays a crucial role in evolution, and has functional consequences in insects and plants. However, less is known about HGT in humans. Studies have reported bacterial integrations in cancer patients, and microbial sequences have been detected in data from well-known human sequencing projects.

TGS1 mediates 2,2,7-trimethyl guanosine capping of the human telomerase RNA to direct telomerase dependent telomere maintenance.

Pathways that direct the selection of the telomerase-dependent or recombination-based, alternative lengthening of telomere (ALT) maintenance pathway in cancer cells are poorly understood. Using human lung cancer cells and tumor organoids we show that formation of the 2,2,7-trimethylguanosine (TMG) cap structure at the human telomerase RNA 5' end by the Trimethylguanosine Synthase 1 (TGS1) is central for recruiting telomerase to telomeres and engaging Cajal bodies in telomere maintenance. TGS1 depletion or inhibition by the natural nucleoside sinefungin impairs telomerase recruitment to telomeres leading to Exonuclease 1 mediated generation of telomere 3' end protrusions that engage in RAD51-dependent, homology directed recombination and the activation of key features of the ALT pathway.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles.

The prolyl-isomerase PIN1 is essential for nuclear Lamin-B structure and function and protects heterochromatin under mechanical stress.

Chromatin organization plays a crucial role in tissue homeostasis. Heterochromatin relaxation and consequent unscheduled mobilization of transposable elements (TEs) are emerging as key contributors of aging and aging-related pathologies, including Alzheimer's disease (AD) and cancer. However, the mechanisms governing heterochromatin maintenance or its relaxation in pathological conditions remain poorly understood.

Breast Cancer Organoids Model Patient-Specific Response to Drug Treatment.

Tumor organoids are tridimensional cell culture systems that are generated in vitro from surgically resected patients' tumors. They can be propagated in culture maintaining several features of the tumor of origin, including cellular and genetic heterogeneity, thus representing a promising tool for precision cancer medicine. Here, we established patient-derived tumor organoids (PDOs) from different breast cancer subtypes (luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple negative).

HmtDB 2016: data update, a better performing query system and human mitochondrial DNA haplogroup predictor.

The HmtDB resource hosts a database of human mitochondrial genome sequences from individuals with healthy and disease phenotypes. The database is intended to support both population geneticists as well as clinicians undertaking the task to assess the pathogenicity of specific mtDNA mutations. The wide application of next-generation sequencing (NGS) has provided an enormous volume of high-resolution data at a low price, increasing the availability of human mitochondrial sequencing data, which called for a cogent and significant expansion of HmtDB data content that has more than tripled in the current release.

A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest.

Assigning a pathogenic role to mitochondrial DNA (mtDNA) variants and unveiling the potential involvement of the mitochondrial genome in diseases are challenging tasks in human medicine. Assuming that rare variants are more likely to be damaging, we designed a phylogeny-based prioritization workflow to obtain a reliable pool of candidate variants for further investigations. The prioritization workflow relies on an exhaustive functional annotation through the mtDNA extraction pipeline MToolBox and includes Macro Haplogroup Consensus Sequences to filter out fixed evolutionary variants and report rare or private variants, the nucleotide variability as reported in HmtDB and the disease score based on several predictors of pathogenicity for non-synonymous variants.

A comprehensive characterization of mitochondrial DNA mutations in glioblastoma multiforme.

Glioblastoma multiforme (GBM) is the most malignant brain cancer in adults, with a poor prognosis, whose molecular stratification still represents a challenge in pathology and clinics. On the other hand, mitochondrial DNA (mtDNA) mutations have been found in most tumors as modifiers of the bioenergetics state, albeit in GBM a characterization of the mtDNA status is lacking to date. Here, a characterization of the burden of mtDNA mutations in GBM samples was performed.

Extraction and annotation of human mitochondrial genomes from 1000 Genomes Whole Exome Sequencing data.

Background: Whole Exome Sequencing (WES) is one of the most used and cost-effective next generation technologies that allows sequencing of all nuclear exons. Off-target regions may be captured if they present high sequence similarity with baits. Bioinformatics tools have been optimized to retrieve a large amount of WES off-target mitochondrial DNA (mtDNA), by exploiting the aspecificity of probes, partially overlapping to Nuclear mitochondrial Sequences (NumtS).

MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing.

Motivation: The increasing availability of mitochondria-targeted and off-target sequencing data in whole-exome and whole-genome sequencing studies (WXS and WGS) has risen the demand of effective pipelines to accurately measure heteroplasmy and to easily recognize the most functionally important mitochondrial variants among a huge number of candidates. To this purpose, we developed MToolBox, a highly automated pipeline to reconstruct and analyze human mitochondrial DNA from high-throughput sequencing data.

Results: MToolBox implements an effective computational strategy for mitochondrial genomes assembling and haplogroup assignment also including a prioritization analysis of detected variants.