Functional Bias and Spatial Organization of Genes in Mutational Hot and Cold Regions in the Human Genome

The neutral mutation rate is known to vary widely along human chromosomes, leading to mutational hot and cold regions. We provide evidence that categories of functionally-related genes reside preferentially in mutationally hot or cold regions, the size of which we have measured. Genes in hot regions are biased toward extra-cellular communication (surface receptors, cell adhesion, immune response, etc.) while those in cold regions are biased toward essential cellular processes (gene regulation, RNA processing, protein modification, etc.). From a selective perspective, this organization of genes could minimize the mutational load on genes that need to be conserved and allow fast evolution for genes that must frequently adapt. We also analyze the effect of gene duplication and chromosomal recombination, which contribute significantly to these biases for certain categories of hot genes. Overall, our results show that genes are located non-randomly with respect to hot and cold regions, offering the possibility that selection acts at the level of gene location in the human genome.Comment: 17 pages, 6 figures, 2 tables. accepted to PLOS Biology, Feb. 2004 issu

Deterministic and cascadable conditional phase gate for photonic qubits

Previous analyses of conditional \phi-phase gates for photonic qubits that treat cross-phase modulation (XPM) in a causal, multimode, quantum field setting suggest that a large (~\pi rad) nonlinear phase shift is always accompanied by fidelity-degrading noise [J. H. Shapiro, Phys. Rev. A 73, 062305 (2006); J. Gea-Banacloche, Phys. Rev. A 81, 043823 (2010)]. Using an atomic V-system to model an XPM medium, we present a conditional phase gate that, for sufficiently small nonzero \phi, has high fidelity. The gate is made cascadable by using using a special measurement, principal mode projection, to exploit the quantum Zeno effect and preclude the accumulation of fidelity-degrading departures from the principal-mode Hilbert space when both control and target photons illuminate the gate

Qudit-Basis Universal Quantum Computation using χ(2)\chi^{(2)} Interactions

We prove that universal quantum computation can be realized---using only linear optics and $\chi^{(2)}$ (three-wave mixing) interactions---in any $(n+1)$-dimensional qudit basis of the $n$-pump-photon subspace. First, we exhibit a strictly universal gate set for the qubit basis in the one-pump-photon subspace. Next, we demonstrate qutrit-basis universality by proving that $\chi^{(2)}$ Hamiltonians and photon-number operators generate the full $\mathfrak{u}(3)$ Lie algebra in the two-pump-photon subspace, and showing how the qutrit controlled-$Z$ gate can be implemented with only linear optics and $\chi^{(2)}$ interactions. We then use proof by induction to obtain our general qudit result. Our induction proof relies on coherent photon injection/subtraction, a technique enabled by $\chi^{(2)}$ interaction between the encoding modes and ancillary modes. Finally, we show that coherent photon injection is more than a conceptual tool in that it offers a route to preparing high-photon-number Fock states from single-photon Fock states.Comment: 9 pages, 3 figure

Ghrelin's Roles in Stress, Mood, and Anxiety Regulation

Several studies suggest that the peptide hormone ghrelin mediates some of the usual behavioral responses to acute and chronic stress. Circulating ghrelin levels have been found to rise following stress. It has been proposed that this elevated ghrelin helps animals cope with stress by generating antidepressant-like behavioral adaptations, although another study suggests that decreasing CNS ghrelin expression has antidepressant-like effects. Ghrelin also seems to have effects on anxiety, although these have been shown to be alternatively anxiogenic or anxiolytic. The current review discusses our current understanding of ghrelin's roles in stress, mood, and anxiety

Whole-exome sequencing capture kit biases yield false negative mutation calls in TCGA cohorts.

The Cancer Genome Atlas (TCGA) provides a genetic characterization of more than ten thousand tumors, enabling the discovery of novel driver mutations, molecular subtypes, and enticing drug targets across many histologies. Here we investigated why some mutations are common in particular cancer types but absent in others. As an example, we observed that the gene CCDC168 has no mutations in the stomach adenocarcinoma (STAD) cohort despite its common presence in other tumor types. Surprisingly, we found that the lack of called mutations was due to a systematic insufficiency in the number of sequencing reads in the STAD and other cohorts, as opposed to differential driver biology. Using strict filtering criteria, we found similar behavior in four other genes across TCGA cohorts, with each gene exhibiting systematic sequencing depth issues affecting the ability to call mutations. We identified the culprit as the choice of exome capture kit, as kit choice was highly associated with the set of genes that have insufficient reads to call a mutation. Overall, we found that thousands of samples across all cohorts are subject to some capture kit problems. For example, for the 6353 samples using the Broad Institute\u27s Custom capture kit there are undercalling biases for at least 4833 genes. False negative mutation calls at these genes may obscure biological similarities between tumor types and other important cancer driver effects in TCGA datasets

Weak preservation of local neutral substitution rates across mammalian genomes

<p>Abstract</p> <p>Background</p> <p>The rate at which neutral (non-functional) bases undergo substitution is highly dependent on their location within a genome. However, it is not clear how fast these location-dependent rates change, or to what extent the substitution rate <it>patterns </it>are conserved between lineages. To address this question, which is critical not only for understanding the substitution process but also for evaluating phylogenetic footprinting algorithms, we examine ancestral repeats: a predominantly neutral dataset with a significantly higher genomic density than other datasets commonly used to study substitution rate variation. Using this repeat data, we measure the extent to which orthologous ancestral repeat sequences exhibit similar substitution patterns in separate mammalian lineages, allowing us to ascertain how well local substitution rates have been preserved across species.</p> <p>Results</p> <p>We calculated substitution rates for each ancestral repeat in each of three independent mammalian lineages (primate – from human/macaque alignments, rodent – from mouse/rat alignments, and laurasiatheria – from dog/cow alignments). We then measured the correlation of local substitution rates among these lineages. Overall we found the correlations between lineages to be statistically significant, but too weak to have much predictive power (<it>r</it>²<<it>5%</it>). These correlations were found to be primarily driven by regional effects at the scale of several hundred kb or larger. A few repeat classes (e.g. 7SK, Charlie8, and MER121) also exhibited stronger conservation of rate patterns, likely due to the effect of repeat-specific purifying selection. These classes should be excluded when estimating local neutral substitution rates.</p> <p>Conclusion</p> <p>Although local neutral substitution rates have some correlations among mammalian species, these correlations have little predictive power on the scale of individual repeats. This indicates that local substitution rates have changed significantly among the lineages we have studied, and are likely to have changed even more for more diverged lineages. The correlations that do persist are too weak to be responsible for many of the highly conserved elements found by phylogenetic footprinting algorithms, leading us to conclude that such elements must be conserved due to selective forces.</p

CodingMotif: exact determination of overrepresented nucleotide motifs in coding sequences

<p>Abstract</p> <p>Background</p> <p>It has been increasingly appreciated that coding sequences harbor regulatory sequence motifs in addition to encoding for protein. These sequence motifs are expected to be overrepresented in nucleotide sequences bound by a common protein or small RNA. However, detecting overrepresented motifs has been difficult because of interference by constraints at the protein level. Sampling-based approaches to solve this problem based on codon-shuffling have been limited to exploring only an infinitesimal fraction of the sequence space and by their use of parametric approximations.</p> <p>Results</p> <p>We present a novel <it>O</it>(<it>N</it>(log <it>N</it>)²)-time algorithm, CodingMotif, to identify nucleotide-level motifs of unusual copy number in protein-coding regions. Using a new dynamic programming algorithm we are able to exhaustively calculate the distribution of the number of occurrences of a motif over all possible coding sequences that encode the same amino acid sequence, given a background model for codon usage and dinucleotide biases. Our method takes advantage of the sparseness of loci where a given motif can occur, greatly speeding up the required convolution calculations. Knowledge of the distribution allows one to assess the exact non-parametric p-value of whether a given motif is over- or under- represented. We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP.</p> <p>Conclusions</p> <p>CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences. We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions. A software implementation is available at <url>http://bioinformatics.bc.edu/chuanglab/codingmotif.tar</url></p

pyBedGraph: a python package for fast operations on 1D genomic signal tracks.

MOTIVATION: Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed. RESULTS: We developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph or a bigWig file. When tested on 12 ChIP-seq, ATAC-seq, RNA-seq and ChIA-PET datasets, pyBedGraph is on average 260 times faster than the existing program pyBigWig. On average, pyBedGraph can look up the exact mean signal of 1 million regions in ∼0.26 s and can compute their approximate means in AVAILABILITY AND IMPLEMENTATION: pyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online