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Feature embedding methods have been proposed in the literature to represent sequences as numeric vectors to be used in some bioinformatics investigations, such as family classification and protein structure prediction. Recent theoretical results showed that the well-known Lyndon factorization preserves common factors in overlapping strings [1]. Surprisingly, the fingerprint of a sequencing read, which is the sequence of lengths of consecutive factors in variants of the Lyndon factorization of the read, is effective in capturing sequence similarities, suggesting it as basis for the definition of novel representations of sequencing reads. We propose a novel feature embedding method for Next-Generation Sequencing (NGS) data using the notion of fingerprint. We provide a theoretical and experimental framework to estimate the behaviour of fingerprints and of the k-mers extracted from it, called k-fingers, as possible feature embeddings for sequencing reads. As a case study to assess the effectiveness of such embeddings, we use fingerprints to represent RNA-Seq reads in order to assign them to the most likely gene from which they originated as fragments of transcripts of the gene. We provide an implementation of the proposed method in the tool lyn2vec, which produces Lyndon-based feature embeddings of sequencing reads.

Chimeric RNAs are transcripts generated by gene fusion and intergenic splicing events, thus comprising nucleotide sequences from different genes. Recent studies have shown that some chimeric RNAs can play a role in cancer development, and so can be used as diagnostics biomarkers when specifically expressed in cancerous cells and tissues. Most gene fusion prediction tools rely on an initial alignment step. However, alignments might be biased, especially for chimeric reads, creating many false positives. Therefore, developing alignment-free prediction methods of fusion genes would be helpful and may provide new insights into the genomic breakage phenomenon in the cell. In this direction, machine learning could pave the way for new solutions, due to their success in predicting genomic regulatory elements and alternative junction events from the genomic context. To date, however, these techniques have had a marginal supporting role, and, furthermore, manually-curated data sets, that are crucial for model training, are often expensive, unreliable or simply unavailable. Here we propose a novel ML-based method that learn to recognize the hidden patterns that allow to identify chimeric RNAs deriving from oncogenic gene fusions. Preliminary comparison with another state-ofthe- art method shows promising results.

Computing maximal perfect blocks of a given panel of haplotypes is a crucial task for efficiently solving problems such as polyploid haplotype reconstruction and finding identical-by-descent segments shared among individuals of a population. Unfortunately, the presence of missing data in the haplotype panel limits the usefulness of the notion of perfect blocks. We propose a novel algorithm for computing maximal blocks in a panel with missing data (represented as wildcards). The algorithm is based on the Positional Burrows-Wheeler Transform (PBWT) and has been implemented in the tool Wild-pBWT, available at https://github.com/AlgoLab/Wild-pBWT/. Experimental comparison showed that Wild-pBWT is 10–15 times faster than another state-of-the-art approach, while using a negligible amount of memory.

Astrocytes are essential players in development and functions, being particularly relevant as regulators of brain energy metabolism, ionic homeostasis and synaptic transmission. They are also the major source of l-serine in the brain, which is synthesized from the glycolytic intermediate 3-phosphoglycerate through the phosphorylated pathway. l-Serine is the precursor of the two main co-agonists of the N-methyl-d-aspartate receptor, glycine and d-serine. Strikingly, dysfunctions in both l- and d-serine metabolism are associated with neurological and psychiatric disorders. Here, we exploited a differentiation protocol, based on the generation of human mature astrocytes from neural stem cells, and investigated the modification of the proteomic and metabolomic profile during the differentiation process. We show that differentiated astrocytes are more similar to mature rather than to reactive ones, and that axogenesis and pyrimidine metabolism increase up to 30 days along with the folate cycle and sphingolipid metabolism. Consistent with the proliferation and cellular maturation processes that are taking place, also the intracellular levels of l-serine, glycine, threonine, l- and d-aspartate (which level is unexpectedly higher than that of d-serine) show the same biosynthetic time course. A significant utilization of l-serine from the medium is apparent while glycine is first consumed and then released with a peak at 30 days, parallel to its intracellular level. These results underline how metabolism changes during astrocyte differentiation, highlight that d-serine synthesis is restricted in differentiated astrocytes and provide a valuable model for developing potential novel therapeutic approaches to address brain diseases, especially the ones related to serine metabolism alterations.

The Cdkn2a locus is one of the most studied tumor suppressor loci in the context of several cancer types. However, in the last years, its expression has also been linked to terminal differentiation and the activation of the senescence program in different cellular subtypes. Knock-out (KO) of the entire locus enhances the capability of stem cells to proliferate in some tissues and respond to severe physiological and non-physiological damages in different organs, including the heart. Emery-Dreifuss muscular dystrophy (EDMD) is characterized by severe contractures and muscle loss at the level of skeletal muscles of the elbows, ankles and neck, and by dilated cardiomyopathy. We have recently demonstrated, using the LMNA Delta 8-11 murine model of Emery-Dreifuss muscular dystrophy (EDMD), that dystrophic muscle stem cells prematurely express non-lineage-specific genes early on during postnatal growth, leading to rapid exhaustion of the muscle stem cell pool. Knock-out of the Cdkn2a locus in EDMD dystrophic mice partially restores muscle stem cell properties. In the present study, we describe the cardiac phenotype of the LMNA Delta 8-11 mouse model and functionally characterize the effects of KO of the Cdkn2a locus on heart functions and life expectancy.

Graph pangenomics is a new emerging field in computational biology that is changing the traditional view of a reference genome from a linear sequence to a new paradigm: a sequence graph (pangenome graph or simply pangenome) that represents the main similarities and differences in multiple evolutionary related genomes. The speed in producing large amounts of genome data, driven by advances in sequencing technologies, is far from the slow progress in developing new methods for constructing and analyzing a pangenome. Most recent advances in the field are still based on notions rooted in established and quite old literature on combinatorics on words, formal languages and space efficient data structures. In this paper we discuss two novel notions that may help in managing and analyzing multiple genomes by addressing a relevant question: how can we summarize sequence similarities and dissimilarities in large sequence data? The first notion is related to variants of the Lyndon factorization and allows to represent sequence similarities for a sample of reads, while the second one is that of sample specific string as a tool to detect differences in a sample of reads. New perspectives opened by these two notions are discussed.

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

Detecting common regions and overlaps between DNA sequences is crucial in many Bioinformatics tasks. One of them is genome assembly based on the use of the overlap graph which is constructed by detecting the overlap between genomic reads. When dealing with long reads this task is further complicated by the length of the reads and the high sequencing error rate. This paper proposes a novel alignment-free method for detecting the overlaps in a set of long reads which exploits a signature (called fingerprint) of reads built from a factorization of the read based on the notion of Lyndon words. The method has been implemented in the tool KFinger and tested over a simulated and a real PacBio HiFi dataset of genomic reads; its results have been compared with the well-known aligner Minimap2. KFinger is available at https://github.com/AlgoLab/kfinger.

Alterations in the DMD gene, which codes for the protein dystrophin, cause forms of dystrophinopathies such as Duchenne muscular dystrophy, an X-linked disease. Cardiomyopathy linked to DMD mutations is becoming the leading cause of death in patients with dystrophinopathy. Since phenotypic pathophysiological mechanisms are not fully understood, the improvement and development of new disease models, considering their relative advantages and disadvantages, is essential. The application of genetic engineering approaches on induced pluripotent stem cells, such as gene-editing technology, enables the development of physiologically relevant human cell models for in vitro dystrophinopathy studies. The combination of induced pluripotent stem cells-derived cardiovascular cell types and 3D bioprinting technologies hold great promise for the study of dystrophin-linked cardiomyopathy. This combined approach enables the assessment of responses to physical or chemical stimuli, and the influence of pharmaceutical approaches. The critical objective of in vitro microphysiological systems is to more accurately reproduce the microenvironment observed in vivo. Ground-breaking methodology involving the connection of multiple microphysiological systems comprised of different tissues would represent a move toward precision body-on-chip disease modelling could lead to a critical expansion in what is known about inter-organ responses to disease and novel therapies that have the potential to replace animal models. In this review, we will focus on the generation, development, and application of current cellular, animal, and potential for bio-printed models, in the study of the pathophysiological mechanisms underlying dystrophin-linked cardiomyopathy in the direction of personalized medicine.

Lamin A is a component of the inner nuclear membrane that, together with epigenetic factors, organizes the genome in higher order structures required for transcriptional control. Mutations in the lamin A/C gene cause several diseases belonging to the class of laminopathies, including muscular dystrophies. Nevertheless, molecular mechanisms involved in the pathogenesis of lamin A-dependent dystrophies are still largely unknown. The polycomb group (PcG) of proteins are epigenetic repressors and lamin A interactors, primarily involved in the maintenance of cell identity. Using a murine model of Emery-Dreifuss muscular dystrophy (EDMD), we show here that lamin A loss deregulated PcG positioning in muscle satellite stem cells, leading to derepression of non-muscle-specific genes and p16INK4a, a senescence driver encoded in the Cdkn2a locus. This aberrant transcriptional program caused impairment in self-renewal, loss of cell identity, and premature exhaustion of the quiescent satellite cell pool. Genetic ablation of the Cdkn2a locus restored muscle stem cell properties in lamin A/C-null dystrophic mice. Our findings establish a direct link between lamin A and PcG epigenetic silencing and indicate that lamin A-dependent muscular dystrophy can be ascribed to intrinsic epigenetic dysfunctions of muscle stem cells.

We analyzed 160 young Waldenström Macroglobulinemia (WM) patients with a median age of 49 years (range 23-55 years), diagnosed between January 2000 and January 2019 in 14 Italian centers. At diagnosis, 70% of patients were asymptomatic. With a median follow-up of 5.6 years, 57% have been treated. As initial therapy 79% of patients received chemo-immunotherapy, 13% a chemo-free induction and 8% chemotherapy only. At relapse or progression, 6% underwent an autologous stem cell transplantation. Overall, 19% of patients received ibrutinib during the course of the disease. According to IPSSWM, 63% were classified as low risk, 27% as intermediate risk and 10% as high risk. Five-year OS was shorter in high-risk as compared with low or intermediate risk patients (92.9% vs 100% P =.002). According to revised IPSSWM, 92% were classified as very low or low risk and 8% as intermediate risk, with a shorter 5-year OS in the latter group (87.5% vs 100%, P =.028). The OS of young WM patients was not significantly reduced as compared with age-matched, sex-matched and calendar year-matched general population. Early diagnosis, absence of high-risk features in symptomatic patients and high efficacy of modern treatments are the main determinants of the excellent outcome of young WM patients.