Analyzing and Biasing Simulations with PLUMED

This chapter discusses how the PLUMED plugin for molecular dynamics can be used to analyze and bias molecular dynamics trajectories. The chapter begins by introducing the notion of a collective variable and by then explaining how the free energy can be computed as a function of one or more collective variables. A number of practical issues mostly around periodic boundary conditions that arise when these types of calculations are performed using PLUMED are then discussed. Later parts of the chapter discuss how PLUMED can be used to perform enhanced sampling simulations that introduce simulation biases or multiple replicas of the system and Monte Carlo exchanges between these replicas. This section is then followed by a discussion on how free-energy surfaces and associated error bars can be extracted from such simulations by using weighted histogram and block averaging techniques

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Sequence analysis of the D2/D3 region of the large subunit RDNA from different Meloidogyne isolates.

The phylogenetic relationships of eight Meloidogyne species and twelve isolates from Brazil and other countries were investigated using sequence data of the D2/D3 expansion segments of the large subunit of ribosomal DNA. The phylogenetic procedures used were maximum parsimony, maximum likelihood and neighbor-joining, using different mathematical alignment algorithms as implemented in TreeAlign and ClustalX, and different tree construction methods of TreeAlign and PAUP*. The results obtained with ClustalX alignments are robust and supported by high bootstrap values, suggesting a strong phylogenetic signal, as also supported by the obtained values of skewness parameter g1. Although the consensus topology of trees derived from TreeAlign alignments is more poorly resolved, the topologies obtained with different algorithms and software are congruent in dividing the species into two clusters: a heterogeneous grouping of M. chitwoodi, M. exigua (three isolates), M. graminicola and M. trifoliophila; and a much less divergent cluster with M. arenaria (race 2), M. incognita, M. konaensis and M. paranaensis (three isolates). The phylogenetic usefulness of the D2/D3 region clearly depends greatly on the evolutionary rates within the investigated lineages. For the Meloidogyne isolate study presented here, the D2/D3 region seems to be a more appropriate marker for relationships between species groups than between individual specie