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Search Results: 1 - 10 of 224169 matches for " C. Titus Brown "
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Evolutionary Learning in the 2D Artificial Life System "Avida"
Chris Adami,C. Titus Brown
Physics , 1994,
Abstract: We present a new tierra-inspired artificial life system with local interactions and two-dimensional geometry, based on an update mechanism akin to that of 2D cellular automata. We find that the spatial geometry is conducive to the development of diversity and thus improves adaptive capabilities. We also demonstrate the adaptive strength of the system by breeding cells with simple computational abilities, and study the dependence of this adaptability on mutation rate and population size.
khmer: Working with Big Data in Bioinformatics
Eric McDonald,C. Titus Brown
Computer Science , 2013,
Abstract: We introduce design and optimization considerations for the 'khmer' package.
Evaluating a lightweight transcriptome assembly pipeline on two closely related ascidian species
Elijah K Lowe,Billie J Swalla,C. Titus Brown
PeerJ , 2015, DOI: 10.7287/peerj.preprints.505v1
Abstract: De novo transcriptome sequencing and assembly for non-model organisms has become prevalent in the past decade. However, most assembly approaches are computationally expensive, and little in-depth evaluation has been done to compare de novo approaches. We sequenced several developmental stages of two free-spawning marine species—Molgula occulta and Molgula oculata—assembled their transcriptomes using four different combinations of preprocessing and assembly approaches, and evaluated the quality of the assembly. We present a straightforward and reproducible mRNAseq assembly protocol that combines quality filtering, digital normalization, and assembly, together with several metrics to evaluate our de novo assemblies. The use of digital normalization in the protocol reduces the time and memory needed to complete the assembly and makes this pipeline available to labs without large computing infrastructure. Despite varying widely in basic assembly statistics, all of the assembled transcriptomes evaluate well in metrics such as gene recovery and estimated completeness.
RNA-Seq Mapping Errors When Using Incomplete Reference Transcriptomes of Vertebrates
Alexis Black Pyrkosz,Hans Cheng,C. Titus Brown
Quantitative Biology , 2013,
Abstract: Whole transcriptome sequencing is increasingly being used as a functional genomics tool to study non- model organisms. However, when the reference transcriptome used to calculate differential expression is incomplete, significant error in the inferred expression levels can result. In this study, we use simulated reads generated from real transcriptomes to determine the accuracy of read mapping, and measure the error resulting from using an incomplete transcriptome. We show that the two primary sources of count- ing error are 1) alternative splice variants that share reads and 2) missing transcripts from the reference. Alternative splice variants increase the false positive rate of mapping while incomplete reference tran- scriptomes decrease the true positive rate, leading to inaccurate transcript expression levels. Grouping transcripts by gene or read sharing (similar to mapping to a reference genome) significantly decreases false positives, but only by improving the reference transcriptome itself can the missing transcript problem be addressed. We also demonstrate that employing different mapping software does not yield substantial increases in accuracy on simulated data. Finally, we show that read lengths or insert sizes must increase past 1kb to resolve mapping ambiguity.
Paircomp, FamilyRelationsII and Cartwheel: tools for interspecific sequence comparison
C Titus Brown, Yuan Xie, Eric H Davidson, R Andrew Cameron
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-70
Abstract: We describe three tools for comparative analysis of pairs of BAC-sized genomic regions. Paircomp is a tool that does windowed (ungapped) comparisons of two sequences and reports all matches above a set threshold. FamilyRelationsII is a graphical viewer for comparisons that enables interactive exploration of several different kinds of comparisons. Cartwheel is a Web site and compute-cluster management system used to execute and store comparisons for display by FamilyRelationsII. These tools are specialized for the discovery of cis-regulatory regions in animal genomes. All tools and their source code are freely available at http://family.caltech.edu/ webcite.These tools have been shown to effectively identify regulatory regions in echinoderms, mammals, and nematodes.Comparative sequence analysis is fast becoming a standard method for discovering cis-regulatory modules [1]. The technique relies on the signatures of conservation left by functional genomic regions as the background sequence evolves. It is often the only way to computationally discover cis-regulatory modules in animal genomes when definite knowledge of upstream regulators is lacking, and it can serve as an excellent complement to experimental techniques.Paircomp, FamilyRelationsII (FRII), and Cartwheel are an integrated system for comparing two BAC-sized (~100 kb) genomic sequences, viewing the comparison, manipulating thresholds and views, and extracting the results. These tools and their predecessors, seqcomp and FamilyRelations, have been used extensively in the years since we first made them available [2]. However, the addition of Cartwheel, a Web server system for performing, storing, and revisiting analyses, makes this combined toolkit considerably more useful to the experimental biologist.The first analysis done with FamilyRelations was a comparison of the otx region between two sea urchins; 11 of the 17 conserved blocks were shown to drive expression of a reporter [3]. Kirouac and Sternberg [4] sh
Scaling metagenome sequence assembly with probabilistic de Bruijn graphs
Jason Pell,Arend Hintze,Rosangela Canino-Koning,Adina Howe,James M. Tiedje,C. Titus Brown
Quantitative Biology , 2011, DOI: 10.1073/pnas.1121464109
Abstract: Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for {\em de novo} assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for {\em de novo} assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly.
A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data
C. Titus Brown,Adina Howe,Qingpeng Zhang,Alexis B. Pyrkosz,Timothy H. Brom
Quantitative Biology , 2012,
Abstract: Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes has enabled investigation of a wide range of organisms and ecosystems. However, sampling variation in short-read data sets and high sequencing error rates of modern sequencers present many new computational challenges in data interpretation. These challenges have led to the development of new classes of mapping tools and {\em de novo} assemblers. These algorithms are challenged by the continued improvement in sequencing throughput. We here describe digital normalization, a single-pass computational algorithm that systematizes coverage in shotgun sequencing data sets, thereby decreasing sampling variation, discarding redundant data, and removing the majority of errors. Digital normalization substantially reduces the size of shotgun data sets and decreases the memory and time requirements for {\em de novo} sequence assembly, all without significantly impacting content of the generated contigs. We apply digital normalization to the assembly of microbial genomic data, amplified single-cell genomic data, and transcriptomic data. Our implementation is freely available for use and modification.
These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure
Qingpeng Zhang,Jason Pell,Rosangela Canino-Koning,Adina Chuang Howe,C. Titus Brown
Quantitative Biology , 2013, DOI: 10.1371/journal.pone.0101271
Abstract: K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We present the khmer software package for fast and memory efficient online counting of k-mers in sequencing data sets. Unlike previous methods based on data structures such as hash tables, suffix arrays, and trie structures, khmer relies entirely on a simple probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits online updating and retrieval of k-mer counts in memory which is necessary to support online k-mer analysis algorithms. On sparse data sets this data structure is considerably more memory efficient than any exact data structure. In exchange, the use of a Count-Min Sketch introduces a systematic overcount for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we analyze the speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-mer counts for individual k-mers. We also compare the performance of khmer to several other k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC, Turtle and KAnalyze. Finally, we examine the effectiveness of profiling sequencing error, k-mer abundance trimming, and digital normalization of reads in the context of high khmer false positive rates. khmer is implemented in C++ wrapped in a Python interface, offers a tested and robust API, and is freely available under the BSD license at github.com/ged-lab/khmer.
Phylogeny and phylogeography of functional genes shared among seven terrestrial subsurface metagenomes reveal N-cycling and microbial evolutionary relationships
Maggie C. Y. Lau,Connor Cameron,Cara Magnabosco,C. Titus Brown,Sarah Hendrickson,Thomas L. Kieft,Tullis C. Onstott
Frontiers in Microbiology , 2014, DOI: 10.3389/fmicb.2014.00531
Abstract: Comparative studies on community phylogenetics and phylogeography of microorganisms living in extreme environments are rare. Terrestrial subsurface habitats are valuable for studying microbial biogeographical patterns due to their isolation and the restricted dispersal mechanisms. Since the taxonomic identity of a microorganism does not always correspond well with its functional role in a particular community, the use of taxonomic assignments or patterns may give limited inference on how microbial functions are affected by historical, geographical and environmental factors. With seven metagenomic libraries generated from fracture water samples collected from five South African mines, this study was carried out to (1) screen for ubiquitous functions or pathways of biogeochemical cycling of CH4, S, and N; (2) to characterize the biodiversity represented by the common functional genes; (3) to investigate the subsurface biogeography as revealed by this subset of genes; and (4) to explore the possibility of using metagenomic data for evolutionary study. The ubiquitous functional genes are NarV, NPD, PAPS reductase, NifH, NifD, NifK, NifE, and NifN genes. Although these eight common functional genes were taxonomically and phylogenetically diverse and distinct from each other, the dissimilarity between samples did not correlate strongly with geographical or environmental parameters or residence time of the water. Por genes homologous to those of Thermodesulfovibrio yellowstonii detected in all metagenomes were deep lineages of Nitrospirae, suggesting that subsurface habitats have preserved ancestral genetic signatures that inform the study of the origin and evolution of prokaryotes.
Standing Genetic Variation in Contingency Loci Drives the Rapid Adaptation of Campylobacter jejuni to a Novel Host
John P. Jerome,Julia A. Bell,Anne E. Plovanich-Jones,Jeffrey E. Barrick,C. Titus Brown,Linda S. Mansfield
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0016399
Abstract: The genome of the food-borne pathogen Campylobacter jejuni contains multiple highly mutable sites, or contingency loci. It has been suggested that standing variation at these loci is a mechanism for rapid adaptation to a novel environment, but this phenomenon has not been shown experimentally. In previous work we showed that the virulence of C. jejuni NCTC11168 increased after serial passage through a C57BL/6 IL-10-/- mouse model of campylobacteriosis. Here we sought to determine the genetic basis of this adaptation during passage. Re-sequencing of the 1.64Mb genome to 200-500X coverage allowed us to define variation in 23 contingency loci to an unprecedented depth both before and after in vivo adaptation. Mutations in the mouse-adapted C. jejuni were largely restricted to the homopolymeric tracts of thirteen contingency loci. These changes cause significant alterations in open reading frames of genes in surface structure biosynthesis loci and in genes with only putative functions. Several loci with open reading frame changes also had altered transcript abundance. The increase in specific phases of contingency loci during in vivo passage of C. jejuni, coupled with the observed virulence increase and the lack of other types of genetic changes, is the first experimental evidence that these variable regions play a significant role in C. jejuni adaptation and virulence in a novel host.
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