18 research outputs found

    Numbers and frequencies of moth sequences inserted in AcMNPV baculovirus genome populations.

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    <p><i>T</i>. <i>ni</i> G0: initial AcMNPV genome population sequenced after amplification of the virus in larvae of the cabbage looper (<i>Trichoplusia ni</i>). <i>T</i>. <i>ni</i> G10 and <i>S</i>. <i>exigua</i> G10: AcMNPV populations obtained after 10 infection cycles of the virus on 10 lines of <i>T</i>. <i>ni</i> and 10 lines of the beet armyworm (<i>Spodoptera exigua</i>). G10 populations were obtained by sequencing viral genomes produced during the 10<sup>th</sup> infection cycle of each of the 10 <i>T</i>.<i>ni</i> and <i>S</i>. <i>exigua</i> lines. TE: transposable element.</p

    Patterns of moth DNA sequence integration along the circular AcMNPV baculovirus genome.

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    <p>A. Distribution of independent moth sequence integrations through transposition (black bars) and microhomology-mediated recombination (red bars) in 500-bp contiguous windows. The blue and grey bar plots respectively illustrate the number of the most frequent transposon target motifs (TTAA, TAA, TTA, TA) and the average sequencing depth in these windows. Beige arrows represent AcMNPV genes. B. Correlation between the numbers of <i>T</i>. <i>ni</i> and <i>S</i>. <i>exigua</i> sequences integrated by transposition in 1500-bp contiguous windows of the AcMNPV genome. Each point on the plot represents a window.</p

    Length distribution of microhomology motifs found at 434 junctions between moth and AcMNPV baculovirus sequences.

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    <p>The observed distribution is shown in red and the distribution expected by chance is shown in grey. An example of a five base-pair microhomology motif between an integrated moth sequence and the AcMNPV genome is shown at the top right corner of the graph. Negative microhomology lengths correspond to junctions characterized by the presence of 1 to 2 nucleotides that did not originate from either the host or viral genomes.</p

    Timetree of various insect species in which we found evidence for horizontal transfer of <i>Spodoptera exigua</i> (A) or/and <i>Trichoplusia ni</i> (B) transposable elements (TEs) found integrated in one or more AcMNPV populations.

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    <p>Names of contig containing TEs correspond to those in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s004" target="_blank">S4</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s005" target="_blank">S5</a> Tables. Black dots indicate that we have found a Blastn hit aligning with at least 85% nucleotide identity over at least 100 bp to a <i>S</i>. <i>exigua</i> or <i>T</i>. <i>ni</i> TE. For example, the figure shows that the <i>S</i>. <i>exigua</i> contig called Spodo_Contig_23 (which is a <i>piggybac</i> TE according to <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s004" target="_blank">S4 Table</a>) was horizontally transferred between <i>S</i>. <i>exigua</i>, <i>Danaus plexipus</i> and <i>Glossina fusciceps</i>. Numbers on top of contig names indicate the level (or range) of nucleotide identity between each <i>S</i>. <i>exigua</i> or <i>T</i>. <i>ni</i> TE and their Blastn hit(s) in other species (in percentages). Numbers between brackets at the right of taxa names are the average percent similarities for 11 conserved genes between <i>S</i>. <i>exigua</i> or <i>T</i>. <i>ni</i> and the other species. These percent similarities are derived from synonymous distances (dS) calculated for each gene and are equal to (1 –dS) × 100. All distances are provided in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s004" target="_blank">S4</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s005" target="_blank">S5</a> Tables. Divergence times were taken from refs [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.ref044" target="_blank">44</a>–<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.ref046" target="_blank">46</a>]. Divergence times between Nymphalidae species are unknown and were set arbitrarily at 50 million years for illustrative purposes. *Species known to be susceptible to AcMNPV [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.ref029" target="_blank">29</a>].</p

    Sequencing depth (number of reads covering a position) along four host TEs found inserted into genomes of AcMNPV populations infecting <i>T</i>. <i>ni</i> or <i>S</i>. <i>exigua</i> moths.

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    <p>Grey curves represent sequencing depth by reads composed of host TE sequences only. Red curves represent chimeric reads whose right parts are composed of TE sequences (the left part being viral sequences) and green curves represent reads whose left parts are composed of TE sequences. Right and green curves thus respectively represent sequencing depths at junctions involving the left and right ends of a TE. The junctions at each end result from transposition at many viral sites, for which a sequence conservation logo is shown. Conserved bases correspond to known target sites of TE families (which are specified next to the host TE names). Black arrows indicate the locations and orientations of putative transposase genes along TEs. Sequencing depth of other moth contigs and sequence conservation logos of other host-virus junctions are provided in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s008" target="_blank">S3</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005838#pgen.1005838.s009" target="_blank">S4</a> Figs.</p

    Additional file 3 of Accuracy of RNAseq based SNP discovery and genotyping in Populusnigra

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    Figure S2: Distribution of genotyping accuracy of RNAseq data computed from a comparison with genotyping from a previously available SNP array [1] for the 12 individuals used in the study. Figure S3: Variation of the total SNP number and identical positions found with the chip data using 7 calling modalities times 3 options for missing values. Figure S4: Positions of SNPs discovered and genotyped with RNAseq across 12 Populus nigra individuals and along two genes. Figure S5: Graphical representation of the enrichment in GO terms (biological process) for the genes covered by at least 5 SNPs. (PDF 434 kb

    The repertoire of mutational signatures in human cancer

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    Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer

    Divergent mutational processes distinguish hypoxic and normoxic tumours

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    Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer

    A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

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    In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA
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