scholarly journals An Integrated BAC and Genome Sequence Physical Map of Phytophthora sojae

2006 ◽  
Vol 19 (12) ◽  
pp. 1302-1310 ◽  
Author(s):  
Xuemin Zhang ◽  
Chantel Scheuring ◽  
Sucheta Tripathy ◽  
Zhanyou Xu ◽  
Chengcang Wu ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Physical Map ◽  
Draft Genome ◽  
Restriction Enzymes ◽  
Artificial Chromosome ◽  
Phytophthora Sojae ◽  
Data Sets ◽  
Contig Assembly ◽  
Data Set ◽  
Phytophthora Spp

Phytophthora spp. are serious pathogens that threaten numerous cultivated crops, trees, and natural vegetation worldwide. The soybean pathogen P. sojae has been developed as a model oomycete. Here, we report a bacterial artificial chromosome (BAC)-based, integrated physical map of the P. sojae genome. We constructed two BAC libraries, digested 8,681 BACs with seven restriction enzymes, end labeled the digested fragments with four dyes, and analyzed them with capillary electrophoresis. Fifteen data sets were constructed from the fingerprints, using individual dyes and all possible combinations, and were evaluated for contig assembly. In all, 257 contigs were assembled from the XhoI data set, collectively spanning approximately 132 Mb in physical length. The BAC contigs were integrated with the draft genome sequence of P. sojae by end sequencing a total of 1,440 BACs that formed a minimal tiling path. This enabled the 257 contigs of the BAC map to be merged with 207 sequence scaffolds to form an integrated map consisting of 79 superscaffolds. The map represents the first genome-wide physical map of a Phytophthora sp. and provides a valuable resource for genomics and molecular biology research in P. sojae and other Phytophthora spp. In one illustration of this value, we have placed the 350 members of a superfamily of putative pathogenicity effector genes onto the map, revealing extensive clustering of these genes.

Bacterial artificial chromosome–based physical map of Gibberella zeae (Fusarium graminearum)

Genome ◽  
2007 ◽  
Vol 50 (10) ◽  
pp. 954-962 ◽  
Author(s):  
Yueh-Long Chang ◽  
Seungho Cho ◽  
H. Corby Kistler ◽  
Chun-Sheng Hsieh ◽  
Gary J. Muehlbauer
Keyword(s):  
Bacterial Artificial Chromosome ◽  
Fusarium Graminearum ◽  
Genetic Map ◽  
Genome Sequence ◽  
Restriction Enzyme ◽  
Physical Map ◽  
Artificial Chromosome ◽  
Gibberella Zeae ◽  
Restriction Enzyme Site ◽  
Insert Size

Fusarium graminearum is the primary causal pathogen of Fusarium head blight of wheat and barley. To accelerate genomic analysis of F. graminearum, we developed a bacterial artificial chromosome (BAC)–based physical map and integrated it with the genome sequence and genetic map. One BAC library, developed in the HindIII restriction enzyme site, consists of 4608 clones with an insert size of approximately 107 kb and covers about 13.5 genome equivalents. The other library, developed in the BamHI restriction enzyme site, consists of 3072 clones with an insert size of approximately 95 kb and covers about 8.0 genome equivalents. We fingerprinted 2688 clones from the HindIII library and 1536 clones from the BamHI library and developed a physical map of F. graminearum consisting of 26 contigs covering 39.2 Mb. Comparison of our map with the F. graminearum genome sequence showed that the size of our physical map is equivalent to the 36.1 Mb of the genome sequence. We used 31 sequence-based genetic markers, randomly spaced throughout the genome, to integrate the physical map with the genetic map. We also end-sequenced 17 BamHI BAC clones and identified 4 clones that spanned gaps in the genome sequence. Our new integrated map is highly reliable and useful for a variety of genomics studies.

scholarly journals Development of a sequence-based reference physical map of pea (Pisum sativum L.)

2019 ◽  
Author(s):  
Krishna Kishore Gali ◽  
Bunyamin Tar’an ◽  
Mohammed-Amin Madoui ◽  
Edwin van der Vossen ◽  
Jan van Oeveren ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Positional Cloning ◽  
Physical Map ◽  
Repetitive Sequences ◽  
Bac Library ◽  
Artificial Chromosome ◽  
Bac Clones ◽  
Pooled Dna ◽  
Mapping Technology ◽  
Generation Sequencing

AbstractWhole genome profiling (WGP) is a sequence-based physical mapping technology and uses sequence tags generated by next generation sequencing for construction of bacterial artificial chromosome (BAC) contigs of complex genomes. The physical map provides a framework for assembly of genome sequence and information for localization of genes that are difficult to find through positional cloning. To address the challenges of accurate assembly of the pea genome (~4.2 GB of which approximately 85% is repetitive sequences), we have adopted the WGP technology for assembly of a pea BAC library. Multi-dimensional pooling of 295,680 BAC clones and sequencing the ends of restriction fragments of pooled DNA generated 1,814 million high quality reads, of which 825 million were deconvolutable to 1.11 million unique WGP sequence tags. These WGP tags were used to assemble 220,013 BACs into contigs. Assembly of the BAC clones using the modified Fingerprinted Contigs (FPC) program has resulted in 13,040 contigs, consisting of 213,719 BACs, and 6,294 singleton BACs. The average contig size is 0.33 Mbp and the N50 contig size is 0.62 Mbp. WGPTM technology has proved to provide a robust physical map of the pea genome, which would have been difficult to assemble using traditional restriction digestion based methods. This sequence-based physical map will be useful to assemble the genome sequence of pea. Additionally, the 1.1 million WGP tags will support efficient assignment of sequence scaffolds to the BAC clones, and thus an efficient sequencing of BAC pools with targeted genome regions of interest.

scholarly journals Draft Genome Sequence of the Predatory Marine Bacterium Halobacteriovorax sp. Strain JY17

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Jonathan M. Young ◽  
Timofey Skvortsov ◽  
Ksenia Arkhipova ◽  
Christopher C. R. Allen
Keyword(s):  
Genome Sequence ◽  
Marine Bacterium ◽  
Draft Genome ◽  
Draft Genome Sequence ◽  
Rrna Genes ◽  
Metagenomic Data ◽  
Data Set ◽  
Protein Coding ◽  
The Third ◽  
5S Rrna Genes

ABSTRACT A draft genome sequence of Halobacteriovorax sp. strain JY17 was assembled from a metagenomic data set. The 3.47-Mbp genome of this unusual predatory bacterium contains 3,263 protein-coding sequences, 33 tRNAs, and 2 copies each of the 16S, 23S, and 5S rRNA genes. This is only the third sequenced representative of this genus.

Bacterial Artificial Chromosome-Based Physical Map of the Rice Genome Constructed by Restriction Fingerprint Analysis

Genetics ◽  
2001 ◽  
Vol 158 (4) ◽  
pp. 1711-1724 ◽  
Author(s):  
Quanzhou Tao ◽  
Yueh-Long Chang ◽  
Jingzhao Wang ◽  
Huaming Chen ◽  
M Nurul Islam-Faridi ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Physical Map ◽  
Rice Genome ◽  
Restriction Enzymes ◽  
Grass Species ◽  
Fingerprint Analysis ◽  
Contig Assembly ◽  
Electrophoretic Method ◽  
Genome Wide ◽  
A Genome

Abstract Genome-wide physical mapping with bacteria-based large-insert clones (e.g., BACs, PACs, and PBCs) promises to revolutionize genomics of large, complex genomes. To accelerate rice and other grass species genome research, we developed a genome-wide BAC-based map of the rice genome. The map consists of 298 BAC contigs and covers 419 Mb of the 430-Mb rice genome. Subsequent analysis indicated that the contigs constituting the map are accurate and reliable. Particularly important to proficiency were (1) a high-resolution, high-throughput DNA sequencing gel-based electrophoretic method for BAC fingerprinting, (2) the use of several complementary large-insert BAC libraries, and (3) computer-aided contig assembly. It has been demonstrated that the fingerprinting method is not significantly influenced by repeated sequences, genome size, and genome complexity. Use of several complementary libraries developed with different restriction enzymes minimized the “gaps” in the physical map. In contrast to previous estimates, a clonal coverage of 6.0–8.0 genome equivalents seems to be sufficient for development of a genome-wide physical map of ~95% genome coverage. This study indicates that genome-wide BAC-based physical maps can be developed quickly and economically for a variety of plant and animal species by restriction fingerprint analysis via DNA sequencing gel-based electrophoresis.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Draft Genome Sequence of Trebouxiophyceae sp. Strain KSI-1, Isolated from an Island Hot Spring

2018 ◽  
Vol 7 (16) ◽  
Author(s):  
Hiro Takahashi ◽  
Satoshi Tanaka ◽  
Shuhei Hayashi ◽  
Shido Miyaki ◽  
Anna Takahashi ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Green Alga ◽  
Genome Sequence ◽  
Hot Spring ◽  
Draft Genome ◽  
Draft Genome Sequence

Trebouxiophyceae sp. KSI-1 is a green alga isolated from a seashore hot spring on Satsuma Iōjima in Kagoshima, Japan, and is highly tolerant to oxidative stress.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

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