pdm_utils: a SEA-PHAGES MySQL phage database management toolkit

Mavrich, Travis N.; Gauthier, C; Abad, Lawrence; Bowman, Charles A.; Cresawn, Steven G.; Hatfull, Graham F.

doi:10.1093/bioinformatics/btaa983

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…MySQL database management system is characterized by small storage volume, fast query speed, and low development cost [ 23 ]. Database and its application system are the core of mobile intelligent medical system [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

He¹,

Zhang²,

Shen³

2022

Journal of Healthcare Engineering

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This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients’ compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group ( P < 0.05 ). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment ( P < 0.05 ), and the scores of the quality of life in the intervention group were higher than those in the control group ( P < 0.05 ). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group ( P < 0.05 ). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group ( P < 0.001 ), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group ( P < 0.05 ).

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Section: Methodsmentioning

confidence: 99%

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

He¹,

Zhang²,

Shen³

2022

Journal of Healthcare Engineering

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“…MMseqs2 and Clustal Omega must be installed separately, using a package manager, such as Anaconda or compiled manually; PhaMMseqs installation and usage instructions are available in the GitHub repository. PhaMMseqs has also been incorporated into the pdm_utils package ( Mavrich et al 2021 ) for creating and maintaining phage genome databases, replacing its “phamerate” pipeline (i.e. the prior system for pham assembly).…”

Section: Methodsmentioning

confidence: 99%

“…Phage gene sequences are first translated into amino acid sequences, and PhaMMseqs uses MMseqs2 to first derive sequence profiles, followed by profile-sequence clustering to merge phams containing more remote homologs; Clustal Omega ( Sievers et al 2011 ) is used to construct pham multiple sequence alignments (MSAs). PhaMMseqs can readily assemble Phams from large genome datasets (>500,000 genes) on modest hardware and can be used in combination with database management utilities like pdm_utils ( Mavrich et al 2021 ) for efficiently updating phams as the genome annotation landscape changes.…”

Section: Introductionmentioning

confidence: 99%

PhaMMseqs: a new pipeline for constructing phage gene phamilies using MMseqs2

Gauthier

Cresawn

Hatfull

2022

G3 Genes|Genomes|Genetics

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The diversity and mosaic architecture of phage genomes present challenges for whole-genome phylogenies and comparative genomics. There are no universally conserved core genes, ∼70% of phage genes are of unknown function, and phage genomes are replete with small (<500 bp) open reading frames. Assembling sequence-related genes into ‘phamilies’ (‘phams’) based on amino acid sequence similarity simplifies comparative phage genomics and facilitates representations of phage genome mosaicism. With the rapid and substantial increase in the numbers of sequenced phage genomes, computationally efficient pham assembly is needed, together with strategies for including newly sequenced phage genomes. Here, we describe the Python package PhaMMseqs, which uses MMseqs2 for pham assembly, and we evaluate the key parameters for optimal pham assembly of sequence- and functionally-related proteins. PhaMMseqs runs efficiently with only modest hardware requirements and integrates with the pdm_utils package for simple genome entry and export of datasets for evolutionary analyses and phage genome map construction.

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“…The PhagesDB database [ 21 ] is also linked to a second database used by the Phamerator package [ 23 ], which provides several key functionalities, including genome comparisons. A software package ‘pdm_utils’ provides the tools for coordinating data between these and GenBank, and for extracting data [ 24 ].…”

Section: Advances In Understanding Phage Diversitymentioning

confidence: 99%

Wildy Prize Lecture, 2020–2021: Who wouldn’t want to discover a new virus?

Hatfull

2021

Microbiology

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Innovations in science education are desperately needed to find ways to engage and interest students early in their undergraduate careers. Exposing students to authentic research experiences is highly beneficial, but finding ways to include all types of students and to do this at large scale is especially challenging. An attractive solution is the concept of an inclusive research education community (iREC) in which centralized research leadership and administration supports multiple institutions, including diverse groups of schools and universities, faculty and students. The Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Sciences (SEA-PHAGES) programme is an excellent example of an iREC, in which students explore viral diversity and evolution through discovery and genomic analysis of novel bacteriophages. The SEA-PHAGES programme has proven to be sustainable, to be implemented at large scale, and to enhance student persistence in science, as well as to produce substantial research advances. Discovering a new virus with the potential for new biological insights and clinical applications is inherently exciting. Who wouldn’t want to discover a new virus?

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pdm_utils: a SEA-PHAGES MySQL phage database management toolkit

Cited by 12 publications

References 20 publications

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

PhaMMseqs: a new pipeline for constructing phage gene phamilies using MMseqs2

Wildy Prize Lecture, 2020–2021: Who wouldn’t want to discover a new virus?

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