PGAP-X: extension on pan-genome analysis pipeline

Zhao, Yongbing; Sun, Chen; Zhao, Dongyu; Zhang, Yadong; You, Yunxiang; Jia, Xiaoyun; Yang, Junhui; Wang, Lingping; Wang, Jinyue; Fu, Haohuan; Kang, Yu; Chen, Fei; Yu, Jun; Wu, Jiayan; Xiao, Jingfa

doi:10.1186/s12864-017-4337-7

Cited by 30 publications

(20 citation statements)

References 30 publications

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“…This immense array of data has enabledt he development of genome minings trategies for the detection, structure analysis, and exploitation of yet-unknown bioactive compounds. [1][2][3][4][5][6][7][8][9][10][11] Particularly successful procedures that combine bioinformatic genome screening with modernm ass spectrometry by utilizing the unsurpassed sensitivitya nd accuracyo f such techniques for the detection of novel compounds and for structure elucidation have been revealed. [5] Paenibacilli are ar ich source of attractive secondary metabolites, and among them, nonribosomal peptides (NRPs), polyketides (PKs), NRP-PK hybrids, and bacteriocins are of great interest for both agro-biotechnological and medicalu ses.…”

Section: Introductionmentioning

confidence: 99%

“…[5] Paenibacilli are ar ich source of attractive secondary metabolites, and among them, nonribosomal peptides (NRPs), polyketides (PKs), NRP-PK hybrids, and bacteriocins are of great interest for both agro-biotechnological and medicalu ses. [6][7][8]12] The genomes of numerous Paenibacillus strains have been completely sequenced, [13][14][15][16] and all of them are equipped with gene clusters encoding the biosynthesis of such compounds and revealing the molecular organization of the cell factories producing them. Genomem ining studies have been initiated with several of theses trains to detecta nd evaluate their biosynthetic capacity.…”

Section: Introductionmentioning

confidence: 99%

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Genome Mining of the Lipopeptide Biosynthesis of Paenibacillus polymyxa E681 in Combination with Mass Spectrometry: Discovery of the Lipoheptapeptide Paenilipoheptin

et al. 2018

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Paenibacillus polymyxa strains are qualified for agro-biotechnological uses such as plant growth promotion and for biocontrol strategies against deleterious phytopathogenic competitors in the soil depending on their attractive arsenal of bioactive compounds. Moreover, they are potent producers of antibiotics for medical applications. To identify new products of such organisms, genome mining strategies in combination with mass spectrometry are the methods of choice. Herein, we performed such studies with the Paenibacillus strain E681. Bioinformatic evaluation of its genome sequence revealed four gene clusters A-D encoding nonribosomal peptide synthetases (NRPSs). Accordingly, four lipopeptide families were detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Clusters A and D codify the well known fusaricidins and polymyxins. A yet-unknown lipoheptapeptide was discovered and structurally characterized by de novo sequencing by using MALDI-LIFT-TOF/TOF MS. It was designated as paenilipoheptin. From structure predictions we infer that the production of this agent is encoded by gene cluster C. Gene cluster B encodes the synthesis of tridecaptins, a family of open-chain lipotridecapeptides. Strain E681 produces new subspecies of such compounds (tridecaptins E) showing variations both in their fatty-acid part as well as in their peptide part.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genome Mining of the Lipopeptide Biosynthesis of Paenibacillus polymyxa E681 in Combination with Mass Spectrometry: Discovery of the Lipoheptapeptide Paenilipoheptin

et al. 2018

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“… GT-WGS is a distributed whole-genome computing system based on Amazon Web Service cloud computing platform [ 53 ]. Pan-genome tool PGAP-X is a cross-platform software to analyze and visualize genome structure dynamics and and gene content [ 54 ]. Pará rubber ( H. brasiliensis ) genome transcriptome database [ 55 ].…”

Section: Manuscript Submission and Reviewmentioning

confidence: 99%

A bioinformatics potpourri

Schönbach

et al. 2018

BMC Genomics

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The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26–28, 2018.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4326-x) contains supplementary material, which is available to authorized users.

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“…However, few software tools are currently available to describe characteristics of panresistomes. Existing pan-genome analysis tools such as PanOTC [15], ClustAGE [16] and PGAP-X [17] were not specifically developed for ARGs. Other tools such as ARG-ANNOT [18] and KmerResistance [19] focus only on ARGs identification.…”

Section: Introductionmentioning

confidence: 99%

PRAP: Pan Resistome analysis pipeline

Zhou

Chen

et al. 2020

BMC Bioinformatics

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Background: Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs distribution within particular pathogen populations. Software tools are readily available for either ARGs identification or pan-genome analysis, but few exist to combine the two functions. Results: We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiotic resistance genes from various formats of whole genome sequences based on the CARD or ResFinder databases. Detailed annotations were used to analyze pan-resistome features and characterize distributions of ARGs. The contribution of different alleles to antibiotic resistance was predicted by a random forest classifier. Results of analysis were presented in browsable files along with a variety of visualization options. We demonstrated the performance of PRAP by analyzing the genomes of 26 Salmonella enterica isolates from Shanghai, China. Conclusions: PRAP was effective for identifying ARGs and visualizing pan-resistome features, therefore facilitating pan-genomic investigation of ARGs. This tool has the ability to further excavate potential relationships between antibiotic resistance genes and their phenotypic traits.

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PGAP-X: extension on pan-genome analysis pipeline

Cited by 30 publications

References 30 publications

Genome Mining of the Lipopeptide Biosynthesis of Paenibacillus polymyxa E681 in Combination with Mass Spectrometry: Discovery of the Lipoheptapeptide Paenilipoheptin

Genome Mining of the Lipopeptide Biosynthesis of Paenibacillus polymyxa E681 in Combination with Mass Spectrometry: Discovery of the Lipoheptapeptide Paenilipoheptin

A bioinformatics potpourri

PRAP: Pan Resistome analysis pipeline

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