抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

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Scalable molecular dynamics with NAMD

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

Scalable Molecular Dynamics with NAMD

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

https://cdr.lib.unc.edu/downloads/v692t8920

A global reference for human genetic variation.

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

A striking finding from recent large-scale sequencing efforts is that the vast majority of variants in the human genome are rare and found within single populations or lineages. These observations hold important implications for the design of the next round of disease variant discovery efforts—if genetic variants that influence disease risk follow the same trend, then we expect to see population-specific disease associations that require large samples sizes for detection. To address this challenge, and due to the still prohibitive cost of sequencing large cohorts, researchers have developed a new generation of low-cost genotyping arrays that assay rare variation previously identified from large exome sequencing studies. Genotyping approaches rely not only on directly observing variants, but also on phasing and imputation methods that use publicly available reference panels to infer unobserved variants in a study cohort. Rare variant exome arrays are intentionally enriched for variants likely to be disease causing, and here we assay the ability of the first commercially available rare exome variant array (the Illumina Infinium HumanExome BeadChip) to also tag other potentially damaging variants not molecularly assayed. Using full sequence data from chromosome 22 from the phase I 1000 Genomes Project, we evaluate three methods for imputation (BEAGLE, MaCH-Admix, and SHAPEIT2/IMPUTE2) with the rare exome variant array under varied study panel sizes, reference panel sizes, and LD structures via population differences. We find that imputation is more accurate across both the genome and exome for common variant arrays than the next generation array for all allele frequencies, including rare alleles. We also find that imputation is the least accurate in African populations, and accuracy is substantially improved for rare variants when the same population is included in the reference panel. Depending on the goals of GWAS researchers, our results will aid budget decisions by helping determine whether money is best spent sequencing the genomes of smaller sample sizes, genotyping larger sample sizes with rare and/or common variant arrays and imputing SNPs, or some combination of the two.

/pdf/imputation-based-assessment-of-next-generation-rare-exome-16pa7fdc5y.pdf

Imputation-based assessment of next generation rare exome variant arrays

Nucleosome DNA unwrapping and its disassembly into hexasomes and tetrasomes is necessary for genomic access and plays an important role in transcription regulation. Previous single-molecule mechanical nucleosome unwrapping revealed a low- and a high-force transitions, and force-FRET pulling experiments showed that DNA unwrapping is asymmetrical occurring always first from one side before the other. However, the assignment of DNA segments involved in these transitions remains controversial. Here, using high-resolution optical tweezers with simultaneous single-molecule FRET detection we show that the low-force transition corresponds to the undoing of the outer-wrap of one side of the nucleosome (~27 bp), a process that can occur either cooperatively or non-cooperatively, whereas the high-force transition corresponds to the simultaneous unwrapping of ~76 bp from both sides. This process may give rise stochastically to the disassembly of nucleosomes into hexasomes and tetrasomes whose unwrapping/rewrapping trajectories we establish. In contrast, nucleosome rewrapping does not exhibit asymmetry. To rationalize all previous nucleosome unwrapping experiments, it is necessary to invoke that mechanical unwrapping involves two nucleosome reorientations: one that contributes to the change in extension at the low-force transition, and another that coincides but does not contribute to the high-force transition. Significance statement Nucleosomes, the fundamental structural unit of chromatin, consists of ~147 DNA base pairs wrapped around a histone protein octamer. Determining the forces required to unwrap the DNA from the core particle and the stepwise transitions involved in the process are essential to characterize the strength of the nucleosomal barrier and its contribution as a mechanism of control of gene expression. Here, we performed combined optical tweezers and single-molecule fluorescence measurements to annotate the specific DNA segments unwrapping during the force transitions observed in mechanical unwrapping of nucleosomes. Furthermore, we characterize the mechanical signatures of subnucleosomal particles: hexasomes and tetrasomes. The characterization performed in this work is essential for the interpretation of ongoing studies of chromatin remodelers, polymerases, and histone chaperones.

Assignment of structural transitions during mechanical unwrapping of nucleosomes and their disassembly products

Analysis of ancient nuclear DNA, recovered from 40,000-year-old remains in the Denisova Cave, Siberia, hints at the multifaceted interaction of human populations following their migration out of Africa. See Article p.1053
 Anatomically modern humans were in Africa from some point after 200,000 years ago and reached Eurasia rather later. Meanwhile, archaic hominins — including the Neanderthals — had been in Eurasia from at least 230,000 years ago and disappear from the fossil record only about 30,000 years ago. The genome of a female archaic hominin from Denisova Cave in southern Siberia has now been sequenced from DNA extracted from a finger bone. The group to which this 'Denisovan' individual belonged shares a common origin with Neanderthals and, although it was not involved in the putative gene flow from Neanderthals into Eurasians, it contributed 4–6% of the genomes of present-day Melanesians. In addition, the morphology of a tooth with a mitochondrial genome very similar to that of the finger bone suggests that these hominins are evolutionarily distinct from both Neanderthals and modern humans.

Human origins: Shadows of early migrations.

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of the technology used to generate them as well as inherent variation within samples. The presence of high levels of noise can hamper discovery of patterns and dynamics encapsulated by these networks. Here we propose Network Enhancement (NE), a novel method for improving the signal-to-noise ratio of undirected, weighted networks, and thereby improving the performance of downstream analysis. NE applies a novel operator that induces sparsity and leverages higher-order network structures to remove weak edges and enhance real connections. This iterative approach has a closed-form solution at convergence with desirable performance properties. We demonstrate the effectiveness of NE in denoising biological networks for several challenging yet important problems. Our experiments show that NE improves gene function prediction by denoising interaction networks from 22 human tissues. Further, we use NE to interpret noisy Hi-C contact maps from the human genome and demonstrate its utility across varying degrees of data quality. Finally, when applied to fine-grained species identification, NE outperforms alternative approaches by a significant margin. Taken together, our results indicate that NE is widely applicable for denoising weighted biological networks, especially when they contain high levels of noise.

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Carlos Bustamante

Papers

Imputation-based assessment of next generation rare exome variant arrays

Assignment of structural transitions during mechanical unwrapping of nucleosomes and their disassembly products

Human origins: Shadows of early migrations.

Network Enhancement: a general method to denoise weighted biological networks

Probing biological surfaces.