INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

MOTIVATION: Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers). RESULTS: We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios. AVAILABILITY AND IMPLEMENTATION: The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Recent comparative studies have brought to our attention how somatic mutation detection from next-generation sequencing data is still an open issue in bioinformatics, because different pipelines result in a low consensus. In this context, it is suggested to integrate results from multiple calling tools, but this operation is not trivial and the burden of merging, comparing, filtering and explaining the results demands appropriate software. RESULTS: We developed isma (integrative somatic mutation analysis), an R package for the integrative analysis of somatic mutations detected by multiple pipelines for matched tumor-normal samples. The package provides a series of functions to quantify the consensus, estimate the variability, underline outliers, integrate evidences from publicly available mutation catalogues and filter sites. We illustrate the capabilities of isma analysing breast cancer somatic mutations generated by The Cancer Genome Atlas (TCGA) using four pipelines. CONCLUSIONS: Comparing different "points of view" on the same data, isma generates a unique mutation catalogue and a series of reports that underline common patterns, variability, as well as sites already catalogued by other studies (e.g. TCGA), so as to design and apply filtering strategies to screen more reliable sites. The package is available for non-commercial users at the URL https://www.itb.cnr.it/isma .

BACKGROUND: Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed. RESULTS: In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses. CONCLUSIONS: The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

BACKGROUND: During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis. RESULTS: In this work we propose GPU-DupRemoval, a new implementation of our method able to (i) cluster reads without constraints on the maximum length of the prefixes, (ii) support both single- and paired-end read libraries, and (iii) analyze large clusters of potential duplicates. CONCLUSIONS: Due to the massive parallelization obtained by exploiting graphics cards, GPU-DupRemoval removes duplicate reads faster than other cutting-edge solutions, while outperforming most of them in terms of amount of duplicates reads.