Automated bioinformatics for microbiology labs.
- Analysis Name
- Food safety and infectious microbes – 96 plex
- Sample Name
- m64004_210929_143746.bc2002.fastq
- Sample Type
- Bacterial isolate
- Isolate Taxon
- Bacillus cereus
- Run ID
- ba944665-5a5f-4a03-9311-eb74d0b7e9ca
- BugSeq Pipeline Version
- Latest
- Metagenomic Database
- BugSeq Default
- Contact E-mail
- support@bugseq.com
Report generated on 2023-02-28, 21:49 UTC
General Statistics
Showing 1/1 rows and 4/4 columns.| Sample Name | N50 (Mbp) | Assembly Length (Mbp) | ≥ 20X | Median |
|---|---|---|---|---|
| Bacillus cereus | 5.4Mbp | 5.4Mbp | 100.0% | 28.0X |
Plasmid Detection
Cluster IDs reflect unique taxonomic identifiers for plasmids and are stable across time. Cluster IDs are generated separately from bacterial host identification and therefore may be used to track plasmid spread across species. Novel plasmids not found in the BugSeq database are labelled "Novel_
| Cluster ID | Circular | Length (bp) | Number of Contigs | Coverage | Predicted Host Range | Detected Resistance Markers | Nearest NCBI Accession | Replicon Type(s) | Relaxase Type(s) | MPF Type | oriT Type(s) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| AG428 | No | 14069bp | 1 | 127.0 | Bacillus | CP034552 | - | - | None | - |
Detection of Genotypic Markers Predicting Antimicrobial Resistance
Note: Genotype does not necessarily predict phenotypic antimicrobial resistance. Laboratory and/or clinical correlation are required.
Confidence explanation:
- Very high confidence reflects a 100% identity match across 100% of the reference gene sequence.
- High confidence reflects a 100% identity match across less than 100% of the reference gene sequence.
- Moderate confidence reflects less than 100% identity match across less than 100% of the reference gene sequence.
Bacillus cereus
| Antimicrobial | Class | Genotypic Predictor of Resistance | Confidence | Genotypic Determinant |
|---|---|---|---|---|
| Fosfomycin | Fosfomycin | Present | High | fosB1 |
| Spectinomycin | Aminocyclitol | Not Detected | ||
| Amikacin | Aminoglycoside | Not Detected | ||
| Bleomycin | Aminoglycoside | Not Detected | ||
| Gentamicin | Aminoglycoside | Not Detected | ||
| Kanamycin | Aminoglycoside | Not Detected | ||
| Paromomycin | Aminoglycoside | Not Detected | ||
| Streptomycin | Aminoglycoside | Not Detected | ||
| Tobramycin | Aminoglycoside | Not Detected | ||
| Unknown aminoglycoside | Aminoglycoside | Not Detected | ||
| Chloramphenicol | Amphenicol | Not Detected | ||
| Amoxicillin | Beta-Lactam | Not Detected | ||
| Amoxicillin clavulanic acid | Beta-Lactam | Not Detected | ||
| Ampicillin | Beta-Lactam | Not Detected | ||
| Aztreonam | Beta-Lactam | Not Detected | ||
| Cefazolin | Beta-Lactam | Not Detected | ||
| Cefepime | Beta-Lactam | Not Detected | ||
| Cefixime | Beta-Lactam | Not Detected | ||
| Cefotaxime | Beta-Lactam | Not Detected | ||
| Cefotaxime clavulanic acid | Beta-Lactam | Not Detected | ||
| Cefoxitin | Beta-Lactam | Not Detected | ||
| Ceftaroline | Beta-Lactam | Not Detected | ||
| Ceftazidime | Beta-Lactam | Not Detected | ||
| Ceftazidime avibactam | Beta-Lactam | Not Detected | ||
| Ceftriaxone | Beta-Lactam | Not Detected | ||
| Cefuroxime | Beta-Lactam | Not Detected | ||
| Ertapenem | Beta-Lactam | Not Detected | ||
| Imipenem | Beta-Lactam | Not Detected | ||
| Meropenem | Beta-Lactam | Not Detected | ||
| Penicillin | Beta-Lactam | Not Detected | ||
| Piperacillin | Beta-Lactam | Not Detected | ||
| Piperacillin tazobactam | Beta-Lactam | Not Detected | ||
| Ticarcillin | Beta-Lactam | Not Detected | ||
| Ticarcillin clavulanic acid | Beta-Lactam | Not Detected | ||
| Unknown beta-lactam | Beta-Lactam | Not Detected | ||
| Sulfamethoxazole | Folate Pathway Antagonist | Not Detected | ||
| Trimethoprim | Folate Pathway Antagonist | Not Detected | ||
| Teicoplanin | Glycopeptide | Not Detected | ||
| Vancomycin | Glycopeptide | Not Detected | ||
| Maduramicin | Ionophores | Not Detected | ||
| Narasin | Ionophores | Not Detected | ||
| Salinomycin | Ionophores | Not Detected | ||
| Clindamycin | Lincosamide | Not Detected | ||
| Azithromycin | Macrolide | Not Detected | ||
| Erythromycin | Macrolide | Not Detected | ||
| Spiramycin | Macrolide | Not Detected | ||
| Metronidazole | Nitroimidazole | Not Detected | ||
| Linezolid | Oxazolidinone | Not Detected | ||
| Colistin | Polymyxin | Not Detected | ||
| Mupirocin | Pseudomonic Acid | Not Detected | ||
| Ciprofloxacin | Quinolone | Not Detected | ||
| Fluoroquinolone | Quinolone | Not Detected | ||
| Levofloxacin | Quinolone | Not Detected | ||
| Nalidixic acid | Quinolone | Not Detected | ||
| Rifampicin | Rifamycin | Not Detected | ||
| Fusidic acid | Steroid Antibacterial | Not Detected | ||
| Dalfopristin | Streptogramin A | Not Detected | ||
| Pristinamycin iia | Streptogramin A | Not Detected | ||
| Quinupristin dalfopristin | Streptogramin A | Not Detected | ||
| Pristinamycin ia | Streptogramin B | Not Detected | ||
| Quinupristin | Streptogramin B | Not Detected | ||
| Doxycycline | Tetracycline | Not Detected | ||
| Minocycline | Tetracycline | Not Detected | ||
| Tetracycline | Tetracycline | Not Detected | ||
| Tigecycline | Tetracycline | Not Detected |
Multilocus Sequence Typing
Schemes available on PubMLST.
| Genome Name | Sequence Type | Scheme | Locus 1 | Locus 2 | Locus 3 | Locus 4 | Locus 5 | Locus 6 | Locus 7 |
|---|---|---|---|---|---|---|---|---|---|
| Bacillus cereus | 4 | bcereus | glp(13) | gmk(8) | ilv(8) | pta(11) | pur(11) | pyc(12) | tpi(7) |
Assembly Statistics
Assembly Statistics reports the length, contiguity and and quality of assemblies.DOI: 10.1093/bioinformatics/btt086.
Assembly Statistics
| Sample Name | N50 (Mbp) | L50 | Largest contig (Mbp) | Length (Mbp) | Genome Fraction |
|---|---|---|---|---|---|
| Bacillus cereus | 5.4Mbp | 1.0 | 5.4Mbp | 5.4Mbp | 100.0% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
Assembly Completeness
Assembly Completeness is assessed using universal single-copy orthologs.DOI: 10.1093/bioinformatics/btv351.
Lineage: bacteria_odb10
Depth of Sequencing
Depth of Sequencing is calculated relative to the reference genome of each species. Reference genomes are designated by NCBI.DOI: 10.1093/bioinformatics/btx699.
Cumulative coverage distribution
Proportion of bases in the reference genome with, at least, a given depth of coverage
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).
Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).
For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.
Coverage distribution
Proportion of bases in the reference genome with a given depth of coverage
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).
Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.
If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).
This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).
Average coverage per contig
Average coverage per contig or chromosome
