scholarly journals Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing

2015 ◽  
Author(s):  
Minh Duc Cao ◽  
Devika Ganesamoorthy ◽  
Alysha G. Elliott ◽  
Huihui Zhang ◽  
Matthew A. Cooper ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Antibiotic Resistance ◽  
Real Time ◽  
Sequence Data ◽  
Bacterial Species ◽  
Strain Typing ◽  
Streaming Algorithms ◽  
Sequencing Data ◽  
Desktop Computer ◽  
Antibiotic Resistance Profile

AbstractThe recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This opens immense potential to shorten the sample-to-results time and is likely to lead to enormous benefits in rapid diagnosis of bacterial infection and identification of drug resistance. However, there are very few tools available for streaming analysis of real-time sequencing data. Here, we present a framework for streaming analysis of MinION real-time sequence data, together with probabilistic streaming algorithms for species typing, multi-locus strain typing, gene presence strain-typing and antibiotic resistance profile identification. Using three culture isolate samples as well as a mixed-species sample, we demonstrate that bacterial species and strain information can be obtained within 30 minutes of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 hours. Multi-locus strain typing required more than 15x coverage to generate confident assignments, whereas gene-presence typing could detect the presence of a known strain with 0.5x coverage. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.

scholarly journals MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5895 ◽  
Author(s):  
Thomas Andreas Kohl ◽  
Christian Utpatel ◽  
Viola Schleusener ◽  
Maria Rosaria De Filippo ◽  
Patrick Beckert ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Mycobacterium Tuberculosis ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Phylogenomic Analysis ◽  
Whole Genome ◽  
Sequencing Data ◽  
Desktop Computer

Analyzing whole-genome sequencing data of Mycobacterium tuberculosis complex (MTBC) isolates in a standardized workflow enables both comprehensive antibiotic resistance profiling and outbreak surveillance with highest resolution up to the identification of recent transmission chains. Here, we present MTBseq, a bioinformatics pipeline for next-generation genome sequence data analysis of MTBC isolates. Employing a reference mapping based workflow, MTBseq reports detected variant positions annotated with known association to antibiotic resistance and performs a lineage classification based on phylogenetic single nucleotide polymorphisms (SNPs). When comparing multiple datasets, MTBseq provides a joint list of variants and a FASTA alignment of SNP positions for use in phylogenomic analysis, and identifies groups of related isolates. The pipeline is customizable, expandable and can be used on a desktop computer or laptop without any internet connection, ensuring mobile usage and data security. MTBseq and accompanying documentation is available from https://github.com/ngs-fzb/MTBseq_source.

scholarly journals Isolation, Molecular Identification and Multidrug Resistance Profiling of Bacteria Causing Clinical Mastitis in Cows

2020 ◽  
Author(s):  
D.J. Vatalia ◽  
B.B. Bhanderi ◽  
V.R. Nimavat ◽  
M.K. Jhala
Keyword(s):  
Drug Resistance ◽  
Antibiotic Resistance ◽  
Multiple Drug Resistance ◽  
Bacterial Species ◽  
Research Work ◽  
Resistance Index ◽  
Diffusion Method ◽  
Multiple Drug ◽  
Milk Samples ◽  
Sensitivity Pattern

Background: Mastitis, the inflammation of parenchyma of mammary gland is frequently considered to be costliest and complex disease prevalent in India. Mastitis is caused by pathogens like Staphylococcus spp., Streptococcus spp., Mycoplasma bovis, E. coli, Klebsiella spp., Citrobacter spp., Enterobacter spp. and Entercoccus. The treatment of mastitis in animals is carried out using antibiotics. Treatment failure in mastitis is due to increased antibiotic resistance of mastitis pathogens and also due to indiscriminate use of antibiotics without testing in vitro antibiotic sensitivity test against causal organisms. In comparison to cultural method, PCR assays takes less time for detection of bacteria from the mastitis milk samples. Present research work was carried out regarding isolation, identification and multiple drug resistance profile of clinical bovine mastitis associated pathogens using conventional as well as molecular approach. Methods: In the present study, 73 mastitis milk samples were collected from Anand and Panchmahal district of Gujarat. The milk samples were subjected for cultural isolation and DNA extraction for identification of bacteria by cultural and PCR method. Antimicrobial sensitivity pattern of the isolates were carried by disc diffusion method and isolates were categorized in multiple drug resistant. Result: In the present study, Out of 73 mastitis milk samples collected from cows 48 (65.75%) cows were positive for bacterial isolation and S. aureus was the most predominant bacterial species. PCR from the mastitis milk additionally detected bacteria in culturally negative milk samples. Most sensitive drug was gentamicin and most of the isolates (90.19%) showed the multiple drug resistance for the two to nine drugs with 0.1 to 0.6 multiple antibiotic resistance index.

Antibiotic Resistance in the Veterinary Perspective

2019 ◽  
pp. 142-157
Author(s):  
Sophia Inbaraj ◽  
Vamshi Krishna Sriram ◽  
Prasad Thomas ◽  
Abhishek Verma ◽  
Pallab Chaudhuri
Keyword(s):  
Drug Resistance ◽  
Antibiotic Resistance ◽  
Food Chain ◽  
Bacterial Species ◽  
Animal Husbandry ◽  
Drug Resistant ◽  
Antimicrobial Drug Resistance ◽  
Antibiotic Drug ◽  
Antibiotics Use ◽  
Genetic Mechanisms

Antibiotic resistance is an emerging threat to achieving one health all over the globe. The phenomenon leads to the emergence of drug-resistant microbes previously susceptible to an antibiotic. Drug-resistant microbes are the major reasons for medical complications like patient mortality and treatment failure. Unregulated use of antibiotics in animal husbandry is one of the major reasons for the emergence of antibiotic resistance. The resistance enters the human population mainly through the food chain. The genetic markers associated with drug resistance spread among different bacterial species by horizontal gene transfer mechanisms. Therefore, regulation of antibiotics use in animal husbandry and proper safety measures at farm level are necessary to check drug-resistant microbes entering the food chain. This chapter discusses the antibiotics, antibiotic resistance, genetic mechanisms involved, the spread of resistance, and also the available strategies to combat antimicrobial drug resistance.

scholarly journals Fast and accurate HLA typing from short-read next-generation sequence data with xHLA

2017 ◽  
Vol 114 (30) ◽  
pp. 8059-8064 ◽  
Author(s):  
Chao Xie ◽  
Zhen Xuan Yeo ◽  
Marie Wong ◽  
Jason Piper ◽  
Tao Long ◽  
...  
Keyword(s):  
Sequence Data ◽  
Sequence Similarity ◽  
Amino Acid Level ◽  
Hla Typing ◽  
Sequencing Data ◽  
Desktop Computer ◽  
Short Read ◽  
Short Read Sequencing ◽  
Hla Genes ◽  
Human Chromosome 6

The HLA gene complex on human chromosome 6 is one of the most polymorphic regions in the human genome and contributes in large part to the diversity of the immune system. Accurate typing of HLA genes with short-read sequencing data has historically been difficult due to the sequence similarity between the polymorphic alleles. Here, we introduce an algorithm, xHLA, that iteratively refines the mapping results at the amino acid level to achieve 99–100% four-digit typing accuracy for both class I and II HLA genes, taking only∼3 min to process a 30× whole-genome BAM file on a desktop computer.

scholarly journals Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing

GigaScience ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Minh Duc Cao ◽  
Devika Ganesamoorthy ◽  
Alysha G. Elliott ◽  
Huihui Zhang ◽  
Matthew A. Cooper ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Real Time ◽  
Streaming Algorithms

scholarly journals Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ravi Ranjan ◽  
Asha Rani ◽  
Patricia W. Finn ◽  
David L. Perkins
Keyword(s):  
Sequence Data ◽  
Bacterial Species ◽  
Abundant Species ◽  
Shotgun Sequencing ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Illumina Platform ◽  
Shotgun Metagenomics ◽  
Functional Aspects ◽  
Detectable Bias

It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of this study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced, and to determine if the data are comparable on different platforms. In this study, we perform ultradeep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We performed sequencing analysis using different Illumina platforms, with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others, and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.

scholarly journals MALT: Fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman

2016 ◽  
Author(s):  
Alexander Herbig ◽  
Frank Maixner ◽  
Kirsten I. Bos ◽  
Albert Zink ◽  
Johannes Krause ◽  
...  
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Bacterial Species ◽  
Human Microbiome ◽  
Metagenomic Analysis ◽  
Sequencing Data ◽  
Metagenomic Dna ◽  
Alignment Procedure ◽  
Taxonomic Profile ◽  
Tyrolean Iceman

AbstractModern next generation sequencing technologies produce vast amounts of data in the context of large-scale metagenomic studies, in which complex microbial communities can be reconstructed to an unprecedented level of detail. Most prominent examples are human microbiome studies that correlate the bacterial taxonomic profile with specific physiological conditions or diseases.In order to perform these analyses high-throughput computational tools are needed that are able to process these data within a short time while preserving a high level of sensitivity and specificity.Here we present MALT (MEGAN ALignment Tool) a program for the ultrafast alignment and analysis of metagenomic DNA sequencing data. MALT processes hundreds of millions of sequencing reads within only a few hours. In addition to the alignment procedure MALT implements a taxonomic binning algorithm that is able to specifically assign reads to bacterial species. Its tight integration with the interactive metagenomic analysis software MEGAN allows for visualization and further analyses of results.We demonstrate MALT by its application to the metagenomic analysis of two ancient microbiomes from oral cavity and lung samples of the 5,300-year-old Tyrolean Iceman. Despite the strong environmental background, MALT is able to pick up the weak signal of the original microbiomes and identifies multiple species that are typical representatives of the respective host environment.

scholarly journals Real-Time, Direct Classification of Nanopore Signals with SquiggleNet

2021 ◽  
Author(s):  
Yuwei Bao ◽  
Jack Wadden ◽  
John R. Erb-Downward ◽  
Piyush Ranjan ◽  
Robert P. Dickson ◽  
...  
Keyword(s):  
Real Time ◽  
Single Molecule ◽  
Bacterial Species ◽  
Sequencing Data ◽  
Base Calling ◽  
Oxford Nanopore ◽  
Order Of Magnitude ◽  
Unseen Species ◽  
Deep Learning Model

AbstractSingle-molecule sequencers made by Oxford Nanopore provide results in real time as DNA passes through a nanopore and can eject a molecule after it has been partly sequenced. However, the computational challenge of deciding whether to keep or reject a molecule in real time has limited the application of this capability. We present SquiggleNet, the first deep learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than the DNA passes through the pore, allowing real-time classification and read ejection. When given the amount of sequencing data generated in one second, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than approaches based on alignment. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy across test datasets from different flowcells and sample preparations, generalized to unseen species, and identified bacterial species in a human respiratory meta genome sample.

scholarly journals Detection of aerobic bacterial pathogens associated with early embryonic death in pregnant New Zealand female Rabbits in Egypt

2021 ◽  
Vol 14 (4) ◽  
pp. 986-995
Author(s):  
Heba Roshdy ◽  
Azhar G. Shalaby ◽  
Ahmed Abd Elhalem Mohamed ◽  
Heba Badr
Keyword(s):  
Antibiotic Resistance ◽  
Pasteurella Multocida ◽  
Bacterial Pathogens ◽  
Bacterial Species ◽  
Bacterial Pathogen ◽  
Conventional Polymerase Chain Reaction ◽  
Bacterial Strains ◽  
Antibiotic Resistance Profile ◽  
Stx1 Gene ◽  
Embryonic Death

Background and Aim: Rabbits are a highly sensitive species and susceptible to various bacterial pathogens that may be causative agents for early embryonic death. This study aimed to explore the administration of different bacterial agents in does suffering from early embryonic death. Furthermore, identification of genes associated with virulence was performed to identify the phenotypic and genotypic antimicrobial resistance patterns that may increase the virulence of pathogens and lead to early embryonic death. Materials and Methods: We isolated and identified bacterial agents in 106 samples from live and dead female rabbits that had undergone early embryonic death, including liver and intestine tissue, aborted fetuses, discharges, and vaginal swabs. Conventional polymerase chain reaction (PCR) was conducted to confirm the identity of the isolated bacterial strains and their virulence. Moreover, antibiotic resistance was studied phenotypically and genotypically. Results: We isolated Escherichia coli, Salmonella, Staphylococcus aureus, Pasteurella multocida, and Listeria monocytogenes. PCR confirmed typical identification except in P. multocida, which was confirmed as Gallibacterium spp. in some cases. The final percentage of detection was 34%, 30.2%, 16.9%, 13.2%, and 11.3%, respectively. Virulence properties were investigated using different designated genes. All Salmonella strains harbored invA, stn, avrA, and ompf genes, while the sopE gene was identified in 31.25%. E. coli strains harboring the iss gene lacked the shiga toxin (stx1) gene. L. monocytogenes and S. aureus strains harbored the hemolysin gene (66.7% and 33.4%, respectively). Multidrug resistance was detected phenotypically and genotypically in most strains. Each bacterial pathogen had a different antibiotic resistance profile. Conclusion: Multiple bacterial species may contribute to early embryonic death in does. Furthermore, the combined infection could be the main cause of early embryonic death. Thus, monitoring programs should bear this in mind and focus on the early detection of these bacterial agents in female rabbits to avoid embryonic death.

scholarly journals Evaluating bacterial and functional diversity of human gut microbiota by complementary metagenomics and metatranscriptomics

2018 ◽  
Author(s):  
Ravi Ranjan ◽  
Asha Rani ◽  
Patricia W. Finn ◽  
David L. Perkins
Keyword(s):  
Gut Microbiota ◽  
Sequence Data ◽  
Bacterial Species ◽  
Abundant Species ◽  
Shotgun Sequencing ◽  
Sequencing Data ◽  
Illumina Platform ◽  
Shotgun Metagenomics ◽  
Differential Gene ◽  
Detectable Bias

ABSTRACTIt is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of the study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced and determine if the data are comparable on different platforms. In this study, we perform ultra-deep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We validated the sequencing and analysis methods using different Illumina platform, and with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are more highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome, and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.

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