In nanopore sequencing devices, electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine (5-mC). Here we quantified the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine. We applied our method to sequence the methylome of human DNA, without requiring special steps for library preparation.

SHRiMP2 executables and source code are freely available at: http://compbio.cs.toronto.edu/shrimp/.

MotivationThe highly portable Oxford Nanopore MinION sequencer has enabled new applications of genome sequencing directly in the field. However, the MinION currently relies on a cloud computing platform, Metrichor (metrichor.com), for translating locally generated sequencing data into basecalls.ResultsTo allow offline and private analysis of MinION data, we created Nanocall. Nanocall is the first freely available, open-source basecaller for Oxford Nanopore sequencing data and does not require an internet connection. Using R7.3 chemistry, on two E.coli and two human samples, with natural as well as PCR-amplified DNA, Nanocall reads have ∼68% identity, directly comparable to Metrichor ‘1D’ data. Further, Nanocall is efficient, processing ∼2500 Kbp of sequence per core hour using the fastest settings, and fully parallelized. Using a 4 core desktop computer, Nanocall could basecall a MinION sequencing run in real time. Metrichor provides the ability to integrate the ‘1D’ sequencing of template and complement strands of a single DNA molecule, and create a ‘2D’ read. Nanocall does not currently integrate this technology, and addition of this capability will be an important future development. In summary, Nanocall is the first open-source, freely available, off-line basecaller for Oxford Nanopore sequencing data.Availability and ImplementationNanocall is available at github.com/mateidavid/nanocall, released under the MIT license.Supplementary information Supplementary data are available at Bioinformatics online.

first draft of paper, designed experiments performed statistical analyses, performed bioinformatics analyses, performed data visualisation. M.T. wrote first draft of paper, designed experiments, generated tools & reagents, performed statistical analyses, performed bioinformatics analyses, performed data visualisation. S.M.G.E. wrote first draft of paper, generated tools & reagents, performed bioinformatics analyses, performed data visualisation. A.G.D. wrote first draft of paper, designed experiments, generated tools & reagents, performed bioinformatics analyses. M.D. generated tools & reagents. S.D. generated tools & reagents. L.Y.L. generated tools & reagents. S.S. generated tools & reagents. H.Z. generated tools & reagents. K.Z. generated tools & reagents, performed bioinformatics analyses. T.O.Y. generated tools & reagents, performed bioinformatics analyses. J.M.C. generated tools & reagents. A.B. generated tools & reagents. C.M.L. generated tools & reagents. I.U. generated tools & reagents. B.L. generated tools & reagents. W.Z. generated tools & reagents. A.D.E. generated tools & reagents, supervised research. NMW performed bioinformatics analyses, performed data visualisation. J.A.W. performed bioinformatics analyses. M.K.H.Z. performed bioinformatics analyses. C.V.A. performed bioinformatics analyses. C.P. performed data visualisation. J.T.S. supervised research. J.M.S. supervised research. D.A. supervised research. Y.G. supervised research. K.E. wrote first draft of paper, supervised research. D.C.W. designed experiments, supervised research. Q.D.M. wrote first draft of paper, designed experiments, generated tools & reagents, supervised research. P.V.L. wrote first draft of paper, designed experiments, supervised research. P.C.B. wrote first draft of paper, designed experiments, supervised research.

In order to understand the role of microRNAs (miRNAs) in vascular physiopathology, we took advantage of deep-sequencing techniques to accurately and comprehensively profile the entire miRNA population expressed by endothelial cells exposed to hypoxia. SOLiD sequencing of small RNAs derived from human umbilical vein endothelial cells (HUVECs) exposed to 1% % O 2 or normoxia for 24 h yielded more than 22 million reads per library. A customized bioinformatic pipeline identified more than 400 annotated microRNA/microRNA* species with a broad abundance range: miR-21 and miR-126 totaled almost 40% % of all miRNAs. A complex repertoire of isomiRs was found, displaying also 59 variations, potentially affecting target recognition. Highstringency bioinformatic analysis identified microRNA candidates, whose predicted pre-miRNAs folded into a stable hairpin. Validation of a subset by qPCR identified 18 high-confidence novel miRNAs as detectable in independent HUVEC cultures and associated to the RISC complex. The expression of two novel miRNAs was significantly down-modulated by hypoxia, while miR-210 was significantly induced. Gene ontology analysis of their predicted targets revealed a significant association to hypoxiainducible factor signaling, cardiovascular diseases, and cancer. Overexpression of the novel miRNAs in hypoxic endothelial cells affected cell growth and confirmed the biological relevance of their down-modulation. In conclusion, deep-sequencing accurately profiled known, variant, and novel microRNAs expressed by endothelial cells in normoxia and hypoxia.