GASOLINE: detecting germline and somatic structural variants from long-reads data.

Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30[Formula: see text] sequencing coverage experiments requires 4-5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.

High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias.

Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

Third-Generation Cytogenetic Analysis: Diagnostic Application of Long-Read Sequencing.

Copy number variants (CNVs) play important roles in the pathogenesis of several genetic syndromes. Traditional and molecular karyotyping are considered the first-tier diagnostic tests to detect macroscopic and cryptic deletions/duplications. However, their time-consuming and laborious experimental protocols protract diagnostic times from 3 to 15 days. Nanopore sequencing has the ability to reduce time to results for the detection of CNVs with the same resolution of current state-of-the-art diagnostic tests. Nanopore sequencing was compared to molecular karyotyping for the detection of pathogenic CNVs of seven patients with previously diagnosed causative CNVs of different sizes and cellular fractions. Larger chromosomal anomalies included trisomy 21 and mosaic tetrasomy 12p. Among smaller CNVs, two genomic imbalances of 1.3 Mb, a small deletion of 170 kb, and two mosaic deletions (1.2 Mb and 408 kb) were tested. DNA was sequenced and data generated during runs were analyzed in online mode. All pathogenic CNVs were identified with detection time inversely proportional to size and cellular fraction. Aneuploidies were called after only 30 minutes of sequencing, whereas 30 hours were needed to call small CNVs. These results demonstrate the clinical utility of our approach that allows the molecular diagnosis of genomic disorders within a 30-minute to 30-hour time frame and its easy implementation as a routinary diagnostic tool.

Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients.

In the "precision oncology" era the characterization of tumor genetic features is a pivotal step in cancer patients' management. Liquid biopsy approaches, such as analysis of cell-free DNA from plasma, represent a powerful and noninvasive strategy to obtain information about the genomic status of the tumor. Sequencing-based analyses of cell-free DNA, currently performed with second generation sequencers, are extremely powerful but poorly scalable and not always accessible also due to instrumentation costs. Third generation sequencing platforms, such as Nanopore sequencers, aim at overcoming these obstacles but, unfortunately, are not designed for cell-free DNA analysis.Here we present a customized workflow to exploit low-coverage Nanopore sequencing for the detection of copy number variations from plasma of cancer patients. Whole genome molecular karyotypes of 6 lung cancer patients and 4 healthy subjects were successfully produced with as few as 2 million reads, and common lung-related copy number alterations were readily detected.This is the first successful use of Nanopore sequencing for copy number profiling from plasma DNA. In this context, Nanopore represents a reliable alternative to Illumina sequencing, with the advantages of minute instrumentation costs and extremely short analysis time.The availability of protocols for Nanopore-based cell-free DNA analysis will make this analysis finally accessible, exploiting the full potential of liquid biopsy both for research and clinical purposes.

NanoR: A user-friendly R package to analyze and compare nanopore sequencing data.

MinION and GridION X5 from Oxford Nanopore Technologies are devices for real-time DNA and RNA sequencing. On the one hand, MinION is the only real-time, low cost and portable sequencing device and, thanks to its unique properties, is becoming more and more popular among biologists; on the other, GridION X5, mainly for its costs, is less widespread but highly suitable for researchers with large sequencing projects. Despite the fact that Oxford Nanopore Technologies' devices have been increasingly used in the last few years, there is a lack of high-performing and user-friendly tools to handle the data outputted by both MinION and GridION X5 platforms. Here we present NanoR, a cross-platform R package designed with the purpose to simplify and improve nanopore data visualization. Indeed, NanoR is built on few functions but overcomes the capabilities of existing tools to extract meaningful informations from MinION sequencing data; in addition, as exclusive features, NanoR can deal with GridION X5 sequencing outputs and allows comparison of both MinION and GridION X5 sequencing data in one command. NanoR is released as free package for R at https://github.com/davidebolo1993/NanoR.

Nano-GLADIATOR: real-time detection of copy number alterations from nanopore sequencing data.

MOTIVATION: The past few years have seen the emergence of nanopore-based sequencing technologies which interrogate single molecule of DNA and generate reads sequentially. RESULTS: In this paper, we demonstrate that, thanks to the sequentiality of the nanopore process, the data generated in the first tens of minutes of a typical MinION/GridION run can be exploited to resolve the alterations of a human genome at a karyotype level with a resolution in the order of tens of Mb, while the data produced in the first 6-12 h allow to obtain a resolution comparable to currently available array-based technologies, and thanks to a novel probabilistic approach are capable to predict the allelic fraction of genomic alteration with high accuracy. To exploit the unique characteristics of nanopore sequencing data we developed a novel software tool, Nano-GLADIATOR, that is capable to perform copy number variants/alterations detection and allelic fraction prediction during the sequencing run ('On-line' mode) and after experiment completion ('Off-line' mode). We tested Nano-GLADIATOR on publicly available ('Off-line' mode) and on novel whole genome sequencing dataset generated with MinION device ('On-line' mode) showing that our tool is capable to perform real-time copy number alterations detection obtaining good results with respect to other state-of-the-art tools. AVAILABILITY AND IMPLEMENTATION: Nano-GLADIATOR is freely available at https://sourceforge.net/projects/nanogladiator/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nanopore sequencing data analysis: state of the art, applications and challenges.

The nanopore sequencing process is based on the transit of a DNA molecule through a nanoscopic pore, and since the 90s is considered as one of the most promising approaches to detect polymeric molecules. In 2014, Oxford Nanopore Technologies (ONT) launched a beta-testing program that supplied the scientific community with the first prototype of a nanopore sequencer: the MinION. Thanks to this program, several research groups had the opportunity to evaluate the performance of this novel instrument and develop novel computational approaches for analyzing this new generation of data. Despite the short period of time from the release of the MinION, a large number of algorithms and tools have been developed for base calling, data handling, read mapping, de novo assembly and variant discovery. Here, we face the main computational challenges related to the analysis of nanopore data, and we carry out a comprehensive and up-to-date survey of the algorithmic solutions adopted by the bioinformatic community comparing performance and reporting limits and advantages of using this new generation of sequences for genomic analyses. Our analyses demonstrate that the use of nanopore data dramatically improves the de novo assembly of genomes and allows for the exploration of structural variants with an unprecedented accuracy and resolution. However, despite the impressive improvements reached by ONT in the past 2 years, the use of these data for small-variant calling is still challenging, and at present, it needs to be coupled with complementary short sequences for mitigating the intrinsic biases of nanopore sequencing technology.