Using Relational Databases for Improved Sequence Similarity Searching and Large‐Scale Genomic Analyses

Author(s):  
Aaron J. Mackey ◽  
William R. Pearson
2019 ◽  
Vol 14 (7) ◽  
pp. 628-639 ◽  
Author(s):  
Bizhi Wu ◽  
Hangxiao Zhang ◽  
Limei Lin ◽  
Huiyuan Wang ◽  
Yubang Gao ◽  
...  

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.


Genome ◽  
2004 ◽  
Vol 47 (1) ◽  
pp. 141-155 ◽  
Author(s):  
H H Yan ◽  
J Mudge ◽  
D-J Kim ◽  
R C Shoemaker ◽  
D R Cook ◽  
...  

To gain insight into genomic relationships between soybean (Glycine max) and Medicago truncatula, eight groups of bacterial artificial chromosome (BAC) contigs, together spanning 2.60 million base pairs (Mb) in G. max and 1.56 Mb in M. truncatula, were compared through high-resolution physical mapping combined with sequence and hybridization analysis of low-copy BAC ends. Cross-hybridization among G. max and M. truncatula contigs uncovered microsynteny in six of the contig groups and extensive microsynteny in three. Between G. max homoeologous (within genome duplicate) contigs, 85% of coding and 75% of noncoding sequences were conserved at the level of cross-hybridization. By contrast, only 29% of sequences were conserved between G. max and M. truncatula, and some kilobase-scale rearrangements were also observed. Detailed restriction maps were constructed for 11 contigs from the three highly microsyntenic groups, and these maps suggested that sequence order was highly conserved between G. max duplicates and generally conserved between G. max and M. truncatula. One instance of homoeologous BAC contigs in M. truncatula was also observed and examined in detail. A sequence similarity search against the Arabidopsis thaliana genome sequence identified up to three microsyntenic regions in A. thaliana for each of two of the legume BAC contig groups. Together, these results confirm previous predictions of one recent genome-wide duplication in G. max and suggest that M. truncatula also experienced ancient large-scale genome duplications.Key words: Glycine max, Medicago truncatula, Arabidopsis thaliana, conserved microsynteny, genome duplication.


2016 ◽  
Vol 45 (7) ◽  
pp. e46-e46 ◽  
Author(s):  
William R. Pearson ◽  
Weizhong Li ◽  
Rodrigo Lopez

2018 ◽  
Vol 95 (1) ◽  
pp. e71 ◽  
Author(s):  
Gang Hu ◽  
Lukasz Kurgan

Author(s):  
Berkay Aydin ◽  
Vijay Akkineni ◽  
Rafal A Angryk

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.


1999 ◽  
Vol 46 (1) ◽  
pp. 19.3.1-19.3.29 ◽  
Author(s):  
Tyra G. Wolfsberg ◽  
Thomas L. Madden

2020 ◽  
Vol 36 (18) ◽  
pp. 4699-4705
Author(s):  
Hamid Bagheri ◽  
Andrew J Severin ◽  
Hridesh Rajan

Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability and implementation Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online.


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