A community effort to create standards for evaluating tumor subclonal reconstruction.

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Detecting DNA cytosine methylation using nanopore sequencing.

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.

Nanocall: an open source basecaller for Oxford Nanopore sequencing data.

MOTIVATION: The 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. RESULTS: To 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 IMPLEMENTATION: Nanocall is available at github.com/mateidavid/nanocall, released under the MIT license. CONTACT: matei.david@oicr.on.caSupplementary information: Supplementary data are available at Bioinformatics online.

Assembly and characterization of novel Alu inserts detected from next-generation sequencing data.

Repetitive elements generally, and Alu inserts specifically are a large contributor to the recent evolution of the human genome. By assembling the sequences of novel Alu inserts using their respective subfamily consensus sequences as references, we found an exponential decay in the Alu subfamily call enrichment with increased number of sequence variants (Pearson correlation [Formula: see text], [Formula: see text]). By mapping the sequences of these inserts to a human reference genome, we infer the reference Alu sources of a subset of the novel Alus, of which 85% were previously shown to be active. We also evaluate relationships between the loci of the novel inserts and their inferred sources.

Detecting Alu insertions from high-throughput sequencing data.

High-throughput sequencing technologies have allowed for the cataloguing of variation in personal human genomes. In this manuscript, we present alu-detect, a tool that combines read-pair and split-read information to detect novel Alus and their precise breakpoints directly from either whole-genome or whole-exome sequencing data while also identifying insertions directly in the vicinity of existing Alus. To set the parameters of our method, we use simulation of a faux reference, which allows us to compute the precision and recall of various parameter settings using real sequencing data. Applying our method to 100 bp paired Illumina data from seven individuals, including two trios, we detected on average 1519 novel Alus per sample. Based on the faux-reference simulation, we estimate that our method has 97% precision and 85% recall. We identify 808 novel Alus not previously described in other studies. We also demonstrate the use of alu-detect to study the local sequence and global location preferences for novel Alu insertions.

Deep-sequencing of endothelial cells exposed to hypoxia reveals the complexity of known and novel microRNAs.

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% O2 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 5' variations, potentially affecting target recognition. High-stringency 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 hypoxia-inducible 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.

SHRiMP2: sensitive yet practical SHort Read Mapping.

UNLABELLED: We report on a major update (version 2) of the original SHort Read Mapping Program (SHRiMP). SHRiMP2 primarily targets mapping sensitivity, and is able to achieve high accuracy at a very reasonable speed. SHRiMP2 supports both letter space and color space (AB/SOLiD) reads, enables for direct alignment of paired reads and uses parallel computation to fully utilize multi-core architectures. AVAILABILITY: SHRiMP2 executables and source code are freely available at: http://compbio.cs.toronto.edu/shrimp/.