Solving genomic puzzles: computational methods for metagenomic binning.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Discovery of thermophilic Bacillales using reduced-representation genotyping for identification.

BACKGROUND: This study demonstrates the use of reduced-representation genotyping to provide preliminary identifications for thermophilic bacterial isolates. The approach combines restriction enzyme digestion and PCR with next-generation sequencing to provide thousands of short-read sequences from across the bacterial genomes. Isolates were obtained from compost, hot water systems, and artesian bores of the Great Artesian Basin. Genomic DNA was double-digested with two combinations of restriction enzymes followed by PCR amplification, using a commercial provider of DArTseq™, Diversity Arrays Technology Pty Ltd. (Canberra, Australia). The resulting fragments which formed a reduced-representation of approximately 2.3% of the genome were sequenced. The sequence tags obtained were aligned against all available RefSeq bacterial genome assemblies by BLASTn to identify the nearest reference genome. RESULTS: Based on the preliminary identifications, a total of 99 bacterial isolates were identified to species level, from which 8 isolates were selected for whole-genome sequencing to assess the identification results. Novel species and strains were discovered within this set of isolates. The preliminary identifications obtained by reduced-representation genotyping, as well as identifications obtained by BLASTn alignment of the 16S rRNA gene sequence, were compared with those derived from the whole-genome sequence data, using the same RefSeq sequence database for the three methods. Identifications obtained with reduced-representation sequencing agreed with the identifications provided by whole-genome sequencing in 100% of cases. The identifications produced by BLASTn alignment of 16S rRNA gene sequence to the same database differed from those provided by whole-genome sequencing in 37.5% of cases, and produced ambiguous identifications in 50% of cases. CONCLUSIONS: Previously, this method has been successfully demonstrated for use in bacterial identification for medical microbiology. This study demonstrates the first successful use of DArTseq™ for preliminary identification of thermophilic bacterial isolates, providing results in complete agreement with those obtained from whole-genome sequencing of the same isolates. The growing database of bacterial genome sequences provides an excellent resource for alignment of reduced-representation sequence data for identification purposes, and as the available sequenced genomes continue to grow, the technique will become more effective.

Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment of infections caused by various microorganisms, including the gram-negative bacterium Escherichia coli. Traditional culture-based methods for detecting AMR are slow and imprecise, hindering timely clinical decision-making. In contrast, whole genome sequencing offers a faster, more accurate alternative for AMR detection. A novel machine learning study leveraging whole genomic sequencing data to predict the phenotypic susceptibility of Escherichia coli to ciprofloxacin is presented. Using a novel dataset of 256 bacterial genomes and related susceptibility data, features were generated based on AMRFinderPlus findings and k-mer frequencies. The machine learning models, Random Forest and XGBoost, were evaluated using a five-fold cross-validation approach. Results showed that combining AMRFinderPlus and k-mer frequency features could achieve more than 90% accuracy using the XGBoost gradient boosting model. These findings suggest that the best results may be achieved using reference-free features combined with known gene markers.

Identification of bacterial isolates from a public hospital in Australia using complexity-reduced genotyping.

Bacterial identification methods used in routine identification of pathogens in medical microbiology include a combination approach of biochemical tests, mass spectrometry or molecular biology techniques. Extensive publicly-available databases of DNA sequence data from pathogenic bacteria have been amassed in recent years; this provides an opportunity for using bacterial genome sequencing for identification purposes. Whole genome sequencing is increasing in popularity, although at present it remains a relatively expensive approach to bacterial identification and typing. Complexity-reduced bacterial genome sequencing provides an alternative. We evaluate genomic complexity-reduction using restriction enzymes and sequencing to identify bacterial isolates. A total of 165 bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015 were used in this study. They were identified and typed by the Microbiology Department of Canberra Public Hospital, and represented 14 bacterial species. DNA extractions from these samples were processed using a combination of the restriction enzymes PstI with MseI, PstI with HpaII and MseI with HpaII. The resulting sequences (length 30-69 bp) were aligned against publicly available bacterial genome and plasmid sequences. Results of the alignment were processed using a bioinformatics pipeline developed for this project, Currito3.1 DNA Fragment Analysis Software. All 165 samples were correctly identified to genus and species by each of the three combinations of restriction enzymes. A further 35 samples typed to the level of strain identified and compared for consistency with MLST typing data and in silico MLST data derived from the nearest sequenced candidate reference. The high level of agreement between bacterial identification using complexity-reduced genome sequencing and standard hospital identifications indicating that this new approach is a viable alternative for identification of bacterial isolates derived from pathology specimens. The effectiveness of species identification and in particular, strain typing, depends on access to a comprehensive and taxonomically accurate bacterial genome sequence database containing relevant bacterial species and strains.

Short-read fastA files dataset from complexity-reduced genotyping by sequencing data of bacterial isolates from a public hospital in Australia.

This data article contains short-read sequences (length 30-69 bp) obtained from complexity-reduced genotyping by sequencing (GBS) of 165 samples bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015. These samples represented 14 bacterial species. Data format is shown as filtered fastA files obtained from an Illumina HiSeq2500 sequencer. The experimental factors of this research used three complexity reduction methods with three combinations of restriction enzymes: PstI with MseI, PstI with HpaII and MseI with HpaII.