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Dive into the research topics where Dennis P. Wall is active.

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Featured researches published by Dennis P. Wall.


Genetics | 2005

The Role of Selection in the Evolution of Human Mitochondrial Genomes

Toomas Kivisild; Peidong Shen; Dennis P. Wall; Bao H. Do; Raphael Sung; Karen Davis; Giuseppe Passarino; Peter A. Underhill; Curt Scharfe; Antonio Torroni; Rosaria Scozzari; David Modiano; Alfredo Coppa; Peter de Knijff; Marcus W. Feldman; Luca Cavalli-Sforza; Peter J. Oefner

High mutation rate in mammalian mitochondrial DNA generates a highly divergent pool of alleles even within species that have dispersed and expanded in size recently. Phylogenetic analysis of 277 human mitochondrial genomes revealed a significant (P < 0.01) excess of rRNA and nonsynonymous base substitutions among hotspots of recurrent mutation. Most hotspots involved transitions from guanine to adenine that, with thymine-to-cytosine transitions, illustrate the asymmetric bias in codon usage at synonymous sites on the heavy-strand DNA. The mitochondrion-encoded tRNAThr varied significantly more than any other tRNA gene. Threonine and valine codons were involved in 259 of the 414 amino acid replacements observed. The ratio of nonsynonymous changes from and to threonine and valine differed significantly (P = 0.003) between populations with neutral (22/58) and populations with significantly negative Tajimas D values (70/76), independent of their geographic location. In contrast to a recent suggestion that the excess of nonsilent mutations is characteristic of Arctic populations, implying their role in cold adaptation, we demonstrate that the surplus of nonsynonymous mutations is a general feature of the young branches of the phylogenetic tree, affecting also those that are found only in Africa. We introduce a new calibration method of the mutation rate of synonymous transitions to estimate the coalescent times of mtDNA haplogroups.


Nature Genetics | 2014

A framework for the interpretation of de novo mutation in human disease

Kaitlin E. Samocha; Elise B. Robinson; Stephan J. Sanders; Christine Stevens; Aniko Sabo; Lauren M. McGrath; Jack A. Kosmicki; Karola Rehnström; Swapan Mallick; Andrew Kirby; Dennis P. Wall; Daniel G. MacArthur; Stacey Gabriel; Mark A. DePristo; Shaun Purcell; Aarno Palotie; Eric Boerwinkle; Joseph D. Buxbaum; Edwin H. Cook; Richard A. Gibbs; Gerard D. Schellenberg; James S. Sutcliffe; Bernie Devlin; Kathryn Roeder; Benjamin M. Neale; Mark J. Daly

Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.


Bioinformatics | 2003

Detecting putative orthologs

Dennis P. Wall; Hunter B. Fraser; Aaron E. Hirsh

We developed an algorithm that improves upon the common procedure of taking reciprocal best blast hits(rbh) in the identification of orthologs. The method-reciprocal smallest distance algorithm (rsd)-relies on global sequence alignment and maximum likelihood estimation of evolutionary distances to detect orthologs between two genomes. rsd finds many putative orthologs missed by rbh because it is less likely than rbh to be misled by the presence of a close paralog.


BMC Evolutionary Biology | 2003

A simple dependence between protein evolution rate and the number of protein-protein interactions

Hunter B. Fraser; Dennis P. Wall; Aaron E. Hirsh

BackgroundIt has been shown for an evolutionarily distant genomic comparison that the number of protein-protein interactions a protein has correlates negatively with their rates of evolution. However, the generality of this observation has recently been challenged. Here we examine the problem using protein-protein interaction data from the yeast Saccharomyces cerevisiae and genome sequences from two other yeast species.ResultsIn contrast to a previous study that used an incomplete set of protein-protein interactions, we observed a highly significant correlation between number of interactions and evolutionary distance to either Candida albicans or Schizosaccharomyces pombe. This study differs from the previous one in that it includes all known protein interactions from S. cerevisiae, and a larger set of protein evolutionary rates. In both evolutionary comparisons, a simple monotonic relationship was found across the entire range of the number of protein-protein interactions. In agreement with our earlier findings, this relationship cannot be explained by the fact that proteins with many interactions tend to be important to yeast. The generality of these correlations in other kingdoms of life unfortunately cannot be addressed at this time, due to the incompleteness of protein-protein interaction data from organisms other than S. cerevisiae.ConclusionsProtein-protein interactions tend to slow the rate at which proteins evolve. This may be due to structural constraints that must be met to maintain interactions, but more work is needed to definitively establish the mechanism(s) behind the correlations we have observed.


Current Opinion in Neurobiology | 2006

Heparan sulfate proteoglycans and the emergence of neuronal connectivity

David Van Vactor; Dennis P. Wall; Karl G. Johnson

With the identification of the molecular determinants of neuronal connectivity, our understanding of the extracellular information that controls axon guidance and synapse formation has evolved from single factors towards the complexity that neurons face in a living organism. As we move in this direction - ready to see the forest for the trees - attention is returning to one of the most ancient regulators of cell-cell interaction: the extracellular matrix. Among many matrix components that influence neuronal connectivity, recent studies of the heparan sulfate proteoglycans suggest that these ancient molecules function as versatile extracellular scaffolds that both sculpt the landscape of extracellular cues and modulate the way that neurons perceive the world around them.


Bioinformatics | 2006

Roundup: a multi-genome repository of orthologs and evolutionary distances

Todd DeLuca; I-Hsien Wu; Jian Pu; Thomas Monaghan; Leonid Peshkin; Saurav Singh; Dennis P. Wall

SUMMARY We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data. AVAILABILITY http://rodeo.med.harvard.edu/tools/roundup.


BMC Bioinformatics | 2010

Cloud computing for comparative genomics

Dennis P. Wall; Parul Kudtarkar; Vincent A. Fusaro; Rimma Pivovarov; Prasad Patil; Peter J. Tonellato

BackgroundLarge comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazons Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes.ResultsWe ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of


PLOS Computational Biology | 2011

Biomedical Cloud Computing With Amazon Web Services

Vincent A. Fusaro; Prasad Patil; Erik Gafni; Dennis P. Wall; Peter J. Tonellato

6,302 USD.ConclusionsThe effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems.


Science of The Total Environment | 2012

The seasonality of phosphorus transfers from land to water: Implications for trophic impacts and policy evaluation

Philip Jordan; Alice R. Melland; Per-Erik Mellander; G. Shortle; Dennis P. Wall

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the clouds vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately


Nature Genetics | 2017

Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples

Jack A. Kosmicki; Kaitlin E. Samocha; Daniel P. Howrigan; Stephan J. Sanders; Kamil Slowikowski; Monkol Lek; Konrad J. Karczewski; David J. Cutler; Bernie Devlin; Kathryn Roeder; Joseph D. Buxbaum; Benjamin M. Neale; Daniel G. MacArthur; Dennis P. Wall; Elise B. Robinson; Mark J. Daly

48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.

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Peter J. Tonellato

University of Wisconsin–Milwaukee

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Alice R. Melland

University of Southern Queensland

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P.N.C. Murphy

University College Dublin

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