Bacteria Completed Genome Analysis Report
1 Project Overview
1.1 Project Information
1.2 Project Background
Sequencing performed using both the Nanopore third-generation and second-generation sequencing technology platforms; analysis of the Bacteria genome completed map accomplished, with sequencing depth ≥100X and assembly results showing 0 gaps (except for exceptional cases, see contract) ,plasmid presence is not guaranteed.
2 Analysis Overview
The comprehensive bacterial genome sequencing workflow comprises nucleic acid detection, library construction, sequencing, data quality control, and data analysis.Initially, DNA quality control and library preparation are performed to generate the sample library for sequencing, which is then subjected to next-generation or third-generation sequencing to obtain both short-read and long-read data. Subsequently, sequencing data is processed through quality control steps to remove low-quality sequences and adapters, ensuring data integrity. Thereafter, genome sequences are assembled and error-corrected using second- and third-generation sequencing data, followed by genomic structural prediction and functional annotation.
3 Technical Workflow
The overall process of the Bacteria genome includes DNA quality control, library preparation, sequencing, data quality control, genome assembly, and genome annotation.Initially, DNA quality control is performed to ensure nucleic acid integrity, followed by library preparation to convert DNA samples into sequencing-compatible libraries. Subsequently, sequencing is carried out.Subsequently, sequencing data is processed through quality control steps to remove low-quality sequences and adapters, ensuring data integrity.Thereafter, genome sequences are obtained through assembly and error correction, followed by structural prediction and functional annotation of the genome sequences.
Figure 1 Technical Workflow Diagram
4 Analysis Pipeline
The information analysis primarily comprises the following steps:
1.Raw data quality control, filtering out low-quality and excessively short reads;
2.Genome assembly, performing de novo assembly on the filtered reads followed by error correction of the assembled genome;
3.Genome structural analysis, including the characterization of repetitive sequences, coding genes, non-coding RNAs, genomic islands, CRISPR arrays, and others;
4.Functional annotation, primarily utilizing eight major comprehensive databases: Pfam, RefSeq, UniProt, Nr, TIGRFAMs, COG, KEGG, and GO;Proprietary databases: ARDB, CAZy, CARD, CYP450, VFDB, TCDB, and signal peptide prediction;
5.Personalized analyses, including comparative genomics and discrepancies between sequencing samples and the reference genome, such as gene family construction, collinearity analysis, shared and unique gene analysis, species evolution analysis, and selection pressure analysis.
Figure 2 Information analysis workflow diagram
5 Results presentation
5.1 Conclusion preview
After filtering adapters, short fragments, and low-quality data, a total of 391,778,934 bp of clean data were obtained from second-generation sequencing.Third-generation Nanopore sequencing generated a total of 319,864,552 bp of clean data;The assembled genome size is 3.90 M, meeting the contractual requirements.
5.2 Sequencing data statistics
The sequencing data generated post-run undergoes stringent data quality control to obtain high-quality sequencing data.
5.2.1 Second-Generation Data Statistics
The quality assessment of sample sequencing output data is as follows:
Table1. Second-Generation Data Quality Statistics Table
Note: Sample refers to the sample name;Raw reads refer to the number of reads in the original unfiltered data; Raw bases refer to the number of bases in the original unfiltered data; Clean reads refer to the number of reads remaining after filtering; Clean bases refer to the number of bases remaining after filtering; Q20_rate represents the proportion of bases with a quality score greater than 20 (error rate less than 1%) within the total sequence after filtering. A higher value indicates better sequencing quality; Q30_rate represents the proportion of bases with a quality score greater than 30 (error rate less than 0.1%) within the total sequence after filtering. A higher value indicates better sequencing quality.
5.2.2 Third-Generation Data Statistics
The effective data statistics for third-generation sequencing are as follows:
Table2. Nanopore Effective Data Statistics Table
Note: Sample: sample; num_seqs: number of reads; sum_len: total length in base pairs (bp); min_len: length of the shortest read; avg_len: average read length; max_len: length of the longest read;N50: reads are ordered from longest to shortest and cumulatively summed; the read length at which the cumulative length reaches half of the total is defined as N50.
5.3 Genome assembly
5.3.1 Assembly result statistics
This project employed unicycler for assembly; the assembly results are summarized in the table below:
Table 3. Genome Assembly Result Statistics
Note: Contig: Sequence ID;Contig Length (bp): Length of the contig; %%GC: GC content;Topology: Circularization status; circular indicates circularized, linear indicates non-circularized.
5.3.2 Plasmid Identification and Annotation
Plasmer software was employed to identify plasmids within the assembly results. Sequences identified as plasmids were further annotated using BLAST software and the PLSDB database.
Table 4. Plasmid Identification Result Statistics
Note: Contig: Sequence ID; Predict: Sequence classification result, where chromosome denotes identification as chromosome, plasmid denotes identification as plasmid, and unclassified denotes no classification.
Table 5. Plasmid Annotation Result Statistics
Note: Contig: Sequence ID; Accession: Accession number matched to the reference database; Identity (%): Sequence identity, representing the percentage of identical regions between two sequences; E-value: Assessment of the reliability of the Identity value, where smaller values indicate greater confidence; Taxon: Taxonomic classification information of the plasmid; Plasmid_name: Name of the plasmid in the reference database.
5.3.3 Sequencing depth statistics
Align the second- and third-generation sequencing data to the assembled genome and calculate the mapping rate and average coverage depth for both sequencing datasets. The statistical results are as follows:
Table6. Second- and Third-Generation Sequencing Depth Statistics Table
Note: Sample: Sample name; MapRate2: Mapping rate of second-generation data;AverageDepth2: Average coverage depth of second-generation sequencing; MapRate3: Mapping rate of third-generation data;AverageDepth3: Average coverage depth of third-generation sequencing.
The average coverage depth of each sequence in the genome is summarized as follows:
Table7. Sequence Average Coverage Depth Statistics Table
Note: Contig: Sequence identifier;AverageDepth2: Average coverage depth of second-generation sequencing;AverageDepth3: Average coverage depth of third-generation sequencing.
5.3.4 Multilocus sequence typing
Multilocus sequence typing (MLST) is a conventional gene typing technique that employs a set of approximately seven housekeeping genes to classify strains. MLST analysis was conducted using the mlst software. The statistical results are summarized as follows:
Table 8. MLST statistics table
Note: Sample: Sample name; Species: Species name;
ST: Sequence Type, where “-” indicates the sequence type was not found in the database; The following columns represent allele names; the corresponding numbers denote allele identifiers, with numeric values indicating both identity and coverage are 100%; “~” indicates coverage is 100% but identity is below 100%; “?” indicates both identity and coverage are below 100%; “-” indicates both identity and coverage are below the threshold, and no allele match was found; “,” denotes the presence of multiple allele matches, with allele identifiers separated by commas.
5.3.5 Genome Evaluation and Annotation
Genome completeness and contamination were assessed using CheckM2 software, and genome classification annotation was performed using the GTDB-Tk tool, with the results as follows:
Table 9. Genome assessment and annotation statistics
Note: Sample: Sample name; Completeness: Completeness; Contamination: Contamination; Taxonomy: Taxonomic classification.
5.4 Structural and nucleic acid level prediction
5.4.1 Gene prediction
Gene prediction on the assembled genome was performed using Prokka software. The specific results of the predicted coding genes are as follows:
Table 10. Gene prediction result statistics
Note: Type: Predicted gene type; Number: Predicted gene count; Total_len: Total length of predicted genes (bp); Average_len: Predicted average gene length (bp); Percentage of genome (%): Percentage of genome length.
5.4.2 Pseudogene Prediction
Pseudogenes, also known as pseudo-genes, are generally not transcribed and lack definite physiological function. Pseudofinder was used to detect candidate pseudogene sequences from annotated GenBank files of bacterial and archaeal genomes.
Table 11. Summary of pseudogene prediction results
Note: Sample Name: sample name; Pseudogene size: total predicted length of pseudogene sequences; Pseudogene number: predicted number of pseudogenes; Average Length: The average length of predicted pseudogenes.
5.5 Functional Element Analysis
5.5.1 CRISPR Sequence Prediction
CRISPRs typically consist of repetitive sequences 25–48 bp in length interspersed with unique spacer sequences. Short fragments of exogenous DNA (spacer sequences) act as memory elements integrated into the host genome within the intervals between CRISPR repeat regions. CRISPR prediction was conducted on the genome using MinCED software, with the results summarized in the table below:
Table 12. Statistics of CRISPR Prediction Results
Note: seqid: sequence ID; type: annotation type; start: start position; end: end position; score: score; crispr_id: CRISPR ID; rpt_unit_seq: repeat unit sequence.
5.5.2 Genomic Island Prediction
Genomic islands present in the genome were predicted using islandpath; the prediction results are shown in the table below:
Table 13. Genomic Island Prediction Results
Note: The first column (GI_ID) represents the predicted genomic island ID; the second column (Seq_ID) denotes the sequence ID where the genomic island is located; the third (start) and fourth (end) columns indicate the start and end positions of the genomic island within the genome; the fifth column (GI_Length) represents the length of the genomic island sequence; GeneNumber indicates the number of genes contained within the genomic island.
5.5.3 Prophage Analysis
Nucleic acids of temperate bacteriophages integrated into the host genome are referred to as Prophage.Prophages within the genome were predicted using the PhiSpy web tool. The prediction results are presented in the table below:
Table 14. Prophage Prediction Results
Note: The first column (PP_ID) indicates the Prophage ID; the second column (Contig_ID) indicates the ID of the contig containing the prophage; the third column (PP_start) indicates the start position of the phage on the contig; the fourth column (PP_end) indicates the end position of the phage on the contig;Column 5 (attL_start): start position of attL; Column 6 (attL_end): end position of attL; Column 7 (attR_start): start position of attR; Column 8 (attR_end): end position of attR; Column 9 (attL_sequence): sequence of attL; Column 10 (attR_sequence): sequence of attR; Column 11 (Reason): rationale for selecting the att site; att: attachment site; attL: locus on the left side of the original phage; attR: locus on the right side of the original phage.
5.5.4 Repeat Sequence Analysis
Repeat sequence prediction of the bacterial genome was performed using RepeatMasker software. The detailed results of the repeat sequence prediction are as follows:
Table 15. Statistical summary of repeat sequence prediction results
Note: SINE denotes Short Interspersed Nuclear Elements; LINE denotes Long Interspersed Nuclear Elements; LTR denotes Long Terminal Repeats, primarily including two types, Gypsy and Copia; DNA denotes Transposons; Satellite denotes satellite repeat sequences; Simple_repeat denotes simple repeats; Low_complexity denotes low complexity repeats; Other denotes other types of repeats; Unknown denotes unknown repeat sequences; Total denotes the overall repeat sequences.
5.5.5 Analysis of Secondary Metabolite Gene Clusters
Typically, biosynthetic enzyme genes involved in secondary metabolic pathways are organized in clusters within the genome. Utilizing specified HMM profiles, the antiSMASH database can accurately identify all known secondary metabolite clusters (natural product biosynthetic gene clusters). The antiSMASH tool is employed to predict these clusters in the genome.
Table 16. Statistics of Secondary Metabolite Gene Cluster Prediction
Note: Location: the position of secondary metabolites on the genome (sequence identifier: start position–end position); Synthetase Type: type of secondary metabolite; Size (Kb): size of the gene cluster; Known Cluster: name of the known secondary metabolite; Known Cluster Type: type of the known secondary metabolite; Similarity: similarity to known secondary metabolites; Genes: including core biosynthetic genes and additional biosynthetic genes.
5.5.6 Insert Sequence Analysis
Insertion sequence (IS) is a transposon that encodes enzymes required for transposition and constitutes a normal component of bacterial chromosomes and plasmids. Insertion sequences were predicted using BLAST software and the ISfinder database.
Table 17. Statistics of insertion sequence analysis
Note: IS_ID: insertion sequence identifier; qseqid: sequence identifier; IS name: ISfinder sequence ID; IS family: ISfinder classification; pident: confidence; length: alignment length; mismatch: number of mismatches within the aligned region; gapopen: number of gaps; qstart: sequence alignment start position; qend: sequence alignment end position; sstart: reference sequence alignment start position; send: reference sequence alignment end position; evalue: E-value (metric assessing match significance; the lower the E-value, the higher the significance); bitscore: alignment score.
5.5.7 Promoter Prediction
A promoter is a DNA sequence segment recognized and bound by RNA polymerase, where transcription is initiated. PromPredict software was employed to predict promoter sequences within the genome.
Table 18. Statistical Summary of Promoter Prediction Results
Note: ID: Chromosome Number; pstart: Start Position; pend: End Position; length of the promoter: Promoter Length; promoter seq: Promoter Sequence; lsp: Base Position of the Least Stable Window; lspe: Free Energy of the Least Stable Window; Dmax pos: Base Position with Maximum D value; Dmax: Maximum D value; Dave: Average D value; Davereliability level: Confidence level; E value: Average free energy over promoter sequences of 100 nt length (spanning the region from -80 to +20 relative to known TSSs); D value: The difference between E and the average free energy (REav) over downstream (100 to 500 relative to TSS) shuffled sequences; Prediction reliability: level1 -> Low; level2 -> Medium; level3 -> High; level4 -> Very high; level5 -> Highest。
5.6 Genome Functional Annotation
5.6.1 General Database Annotation
Using the protein sequences of coding genes obtained from annotation, gene functional annotation information is derived by mapping to functional databases based on sequence similarity and protein domains. Annotation statistics are summarized in the table below:
Table 19. Summary statistics of coding gene annotation results
Note: In the first column, ‘All’ refers to all genes;
Annotation refers to genes with at least one type of annotation; KEGG refers to genes annotated in the KEGG database; Pathway refers to genes annotated in the KEGG Pathway database; Nr refers to genes annotated in the Nr database; UniProt refers to genes annotated in the UniProt database; GO refers to genes annotated in the GO database; COG refers to genes annotated in the COG database; Pfam refers to genes annotated in the Pfam database; Refseq refers to genes annotated in the Refseq database; Tigrfam refers to genes annotated in the Tigrfams database; the second column indicates the count for each item; the third column indicates the proportion of each item.
Figure 3. Statistical chart of shared and unique annotations of coding genes across universal databases
Note: The left side presents gene annotation statistics from general databases; The right side displays shared and unique statistics. For example, the first column shows shared statistics across all general databases, with counts above and labels indicating shared or unique databases below.
5.6.1.1 Pfam Domain Classification
Based on Pfam domain annotations, genes annotated by each domain were statistically summarized, and the top 20 domains with the highest annotation counts were graphically presented. The results are shown in the figure below:
Figure 4. Pfam Domain Statistics
Note: The Pfam database is a collection of protein families, each represented by multiple sequence alignments and hidden Markov models.
The horizontal axis in the figure represents the names of protein families, and the vertical axis represents the number of genes mapped to each protein family.Table 20. PFAM domain annotation
Note: SeqID: Sequence identifier (Gene identifier assigned during annotation by Prokka software); Accession: PF number, a stable and unique identifier for each family; HMMProfile: domain; Description: descriptive information.
5.6.1.2 Gene Ontology classification
GO (Gene Ontology) annotation information was simplified into GOslim classifications. After summarizing gene functions from three aspects: cellular component, molecular function, and biological process, the top 20 GOslim secondary classifications with the highest number of annotations in each category were selected for plotting. The results are shown in the figure below:
Figure 5. Classification chart of GO annotation results
Note: The horizontal axis represents the GO category content, and the vertical axis represents the number of genes. This figure illustrates the gene enrichment of each secondary GO function within the entire gene background, reflecting the relevance of each secondary function in this context.
Table 21. GO database annotation
Note: SeqID: Sequence identifier (the gene identifier assigned by Prokka software during annotation); Accession: GO accession number; GOterm: Subcategories under the three main GO classifications (biological process, cellular component, molecular function); NameSpace: The three components of Gene Ontology information, including Molecular function, Biological process, and Cellular component.
5.6.1.3 KEGG Annotation
Following KEGG annotation of genes, classification was performed according to the KEGG metabolic pathways in which they participate. The results are presented in the figure below:
Figure 6. KEGG pathway classification result chart
Note: The horizontal axis represents the number of genes annotated under each Pathway classification;
The vertical axis indicates Pathway classifications, with different colors representing various major categories.
Table 22. KEGG database annotation
Note: SeqID: Sequence identifier (Gene identifier assigned by Prokka software during annotation); A and B: Secondary classifications of KO pathways; C: Annotation information corresponding to the KO number; PathwayID: KO number.
5.6.1.4 COG Classification
COG is divided into 26 groups; genes annotated by COG are classified according to these groups. The results are presented in the figure below:
Figure 7. COG Classification Results
Note: The x-axis represents the various COG categories, and the y-axis represents the number of genes. The proportion of genes in different functional categories reflects metabolic or physiological trends under specific temporal and environmental contexts; scientific interpretations can be drawn by analyzing the distribution of the subject within each functional category.
Table 23. COG Database Annotation
Note: SeqID: Sequence identifier (gene identifier assigned during annotation by Prokka software); CogID: COG identifier; CogName: COG protein function; CogClassName: Functional classification;
CogClassCode: representative letters of the COG functional classification.5.6.1.5 UniProt Annotation
The coding genes were mapped to the UniProt database, with the following statistics:
Table 24. UniProt Database Annotation
Note: SeqID: sequence identifier (gene identifier assigned during Prokka software annotation); accession: UniProt database accession number; annotation: annotation information.
5.6.1.6 RefSeq Annotation
The coding genes were mapped to the RefSeq database, with the following statistics:
Table 25. RefSeq Database Annotation
Note: SeqID: sequence identifier (gene identifier assigned during Prokka software annotation); Accession: RefSeq database accession number; Annotation: annotation information; Species: species information.
5.6.1.7 NR Annotation
The coding genes were aligned to the NR database; the top 10 species by proportion are shown in the figure below:
Fig 8. NR Species Distribution
Note: Different colors represent different species.
Table26. NR Database Annotation
Note: SeqID—Sequence identifier (gene identifier assigned during annotation with Prokka software); Accession—NR database accession number; Annotation—Annotation information; Species—Species information.
5.6.1.8 TIGRFAMs Annotation
The coding genes were aligned to the TIGRFAMs database; the statistical results are as follows:
Table27. TIGRFAMs Database Annotation
Note: SeqID: Sequence identifier (Gene identifier assigned during annotation by Prokka software); Accession: PF number, a stable and unique identifier for each family; HMMProfile: domain; Description: descriptive information.
5.6.2 Specialized Database Annotation
5.6.2.1 ARDB Database Annotation
Align coding genes to the ARDB database,statistical results are as follows:
Table 28. ARDB Database Annotation
Note: ResistanceType: type of resistance gene; Description: description of this type of antibiotic resistance gene; ResistanceProfile: name of the resistance gene; Require: dependent resistance genes; SeqList: gene identifiers annotated to the resistance gene; Num: number of annotations to this resistance gene.
5.6.2.2 Carbohydrate-active Enzyme Annotation
Align coding genes to the carbohydrate-active enzyme database,statistical results are as follows:
Table 29. Correspondence Table of Functional Classification in the Carbohydrate-active Enzyme Database
| Function | ID |
|---|---|
| Glycoside Hydrolases | GHs |
| Glycosyl Transferases | GTs |
| Polysaccharide Lyases | PLs |
| Carbohydrate Esterases | CEs |
| Auxiliary Activities | AAs |
| Carbohydrate-Binding Modules | CBMs |
Table 30. CAZy Annotation
Note: Class: functional classification names in the Carbohydrate-Active enZYmes database; Num: number of genes annotated to this classification; Description: description of this classification.
5.6.2.3 Pathogen-Host Interaction Analysis
Coding genes were aligned to the PHI database, and the statistical results are as follows:
Table 31. PHI Annotation
Note: SeqID: Sequence identifier; Pident: pident value from blastp alignment results, indicating the percentage of exact matches; Qcovs: qcovs value from blastp alignment results, representing the percentage of the Query alignment length relative to the total length; ProteinID: protein ID in the database; PhiID: PHI identifier; PathogenGene: pathogen gene; PathogenSpecies: pathogen species; MutantPhenotype: mutant phenotype.
5.6.2.4 Antibiotic Resistance Gene Prediction
The coding genes were aligned against the CARD database, and the statistical results are as follows:
Table 32. Antibiotic resistance gene prediction
Note: ORF_ID: Predicted gene ID; Cut_Off: Annotation threshold; Best_Hit_ARO: Best matching ARO; Best_Identities: Highest alignment identity;ARO: ARO ID; Drug Class: Drug resistance type; Resistance Mechanism: Mechanism of resistance; AMR Gene Family: Resistance gene family affiliation.
5.6.2.5 Cytochrome P450 annotation
The coding genes were aligned against the CYPED database, and the statistical results are as follows:
Table 33. Cytochrome P450 annotation
Note: ID: Sequence identifier;Pident: the pident value from DIAMOND blastp alignment results, representing the percentage of exact matches; Qcovhsp: the qcovhsp value from DIAMOND blastp alignment results, indicating the percentage of the query aligned in high-scoring segment pairs (HSPs); SequenceID: nucleic acid sequence identifier in the database; ProteinID: protein sequence identifier in the database; HomologousFamilies: homologous families; Superfamilies: superfamilies; GI: NCBI GI number; TaxonID: NCBI species taxonomy identifier; Species: species information.
5.6.2.6 VFDB Annotation
The coding genes were aligned against the VFDB database; the statistics are as follows:
Table 34. Virulence Factor Annotation Table
Note: SeqID: Sequence identifier; Pident: pident value from the blastp alignment results; Qcovs: qcovs value from the blastp alignment results; Accession: VFDB sequence identifier; Gene: matched gene ID; VF_Name: virulence factor name; VFcategory: virulence factor category.
5.6.2.7 Signal Peptide Prediction
Using the software SignalP, all predicted protein sequences of genes were analyzed to identify proteins containing signal peptides. The prediction results are as follows:
Table 35. Signal Peptide Prediction Statistics
5.6.2.7.1 Transmembrane Protein Prediction
All predicted protein sequences of genes were analyzed using the software TMHMM to identify proteins containing transmembrane helices, defined as transmembrane proteins. The prediction results are as follows:
Table 36. Transmembrane Protein Prediction Statistics
5.6.2.7.2 Secreted Protein Prediction
Proteins containing transmembrane helices were excluded from the previously predicted proteins with signal peptides. The remaining proteins are classified as secreted proteins. The prediction results are as follows:
Table 37. Secreted Protein Prediction Statistics
5.6.2.8 Annotation of Type III Secretion System Effector Proteins
EffectiveT3 software was utilized to predict Type III secretion system effector proteins; the prediction results are as follows:
Table 38. Statistical Table of Type III Secretion System Effector Protein Annotations
Note: Sample: Sample name; T3SS: Number of sequences annotated as effector proteins.
5.6.2.9 TCDB Transport Proteins
Coding genes were aligned against the TCDB database, and the statistical results are as follows:
Figure 9. Classification Chart of TCDB Results
Note: The x-axis represents the categories within the TCDB database, and the y-axis represents the number of genes.
5.7 Genome Circos Plot
The R package circlize was employed to integrate predicted genomic information, including genome structural annotations, GC content distribution, and genome COG functional annotations, to construct a genome circos plot.The circlize package facilitates genome visualization, enabling a clearer investigation of relationships among genomic components or loci. The figure below depicts a circos plot of the assembled genome:
Figure 10. Genome Circos Plot
Note:
From outermost to innermost, the layers are as follows:
First layer: genome coordinates;
Second layer: genes located on the positive strand of the genome sequence, with different colors representing various COG functional categories;
Third layer: genes located on the negative strand of the genome sequence, with distinct colors corresponding to different COG functional categories;
The fourth track depicts rRNA and tRNA regions on the genome sequence, with rRNA represented in blue and tRNA in red;
The fifth track illustrates the GC content profile of the genome sequence, calculated using a 2000 bp sliding window to determine the average GC content;
The sixth track presents the GC skew profile of the genome sequence, computed via a 2000 bp sliding window to assess GC skew;Green denotes regions where guanine content exceeds cytosine content, whereas purple indicates regions where guanine content is lower than cytosine content;
5.8 Structure and Description of Result Files
6 Bioinformatics methods
6.1 Introduction to sequencing data formats
The raw files obtained from high-throughput sequencing instruments are processed through base calling to generate FASTQ format (abbreviated as fq) raw sequencing reads, referred to as raw data or raw reads.Data in FASTQ format contains the sequence information of sequencing reads as well as their corresponding quality scores. Each read comprises four lines, as demonstrated below:
| @ST-E00600:415:HFV5LALXX:1:1101:21486:1031 1:N:0:TGACAGGT |
| GATCTGGTCCTCTCGGACCGCGATTCTTGCCTTCTGTGAACCTCGACAGATCGATCTTGAACCTCGACAGATCGATCTTGAACCTCGA |
| + |
| AAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAFFFAAF |
The first line is the sequence identifier (read ID) along with associated descriptive information, beginning with the ‘@’ character;
The second line contains the nucleotide sequence, whose length is determined by the sequencing strategy;
The third line begins with a ‘+’ character, followed by the sequence identifier, descriptive information, or may be left blank;
The fourth line encodes the sequencing quality scores (Phred scores), corresponding one-to-one with the bases in the second line. Phred scores are represented as ASCII characters; subtracting 33 from each ASCII value yields the quality score for the respective base.
6.2 Data Filtering
As raw sequencing data may contain low-quality sequences and adapter sequences, to ensure the reliability of downstream analyses, raw sequencing data must be filtered to obtain clean reads, which are retained in FASTQ format (this sequence file can be used directly for publication, submission to public databases, etc., with all subsequent analyses based on these data).
6.2.1 Second-Generation Data Filtering Criteria
1.If the proportion of ambiguous bases (N) in any sequencing read exceeds 10% of the total bases of that read, both reads of the pair are removed;
2.If the number of low-quality bases (Q ≤ 5) in any sequencing read exceeds 50% of the total bases of that read, both reads of the pair are removed.
6.2.2 Third-generation Data Filtering
Nanopore sequencing raw output data are fast5 files, which are converted to FASTQ format through base calling.Subsequently, raw sequencing data are subjected to quality filtering (Q≥7) and length filtering (length ≥1600 bp) to obtain valid data for downstream assembly analysis.
PacBio sequencing raw output data are BAM files, which are converted to FASTQ format followed by length filtering (length ≥1600 bp) to obtain valid data for downstream assembly analysis.
6.3 Assembly Principles
Unicycler is a tool capable of effectively utilizing both short-read and long-read sequences to assemble bacterial genomes rich in plasmids.The assembly is based on the De Bruijn graph and incorporates tools such as miniasm and Racon to ensure that, even with long-read sequences of low depth and accuracy, larger contigs can be assembled with fewer errors. Unicycler initially performs a comprehensive k-mer size scan using SPAdes to guarantee the absence of linear ends that cannot circularize. Employing a greedy algorithm alongside sequencing depth and graph connectivity, the copy number of overlapping sequences is determined to generate an Illumina assembly scaffold. The graph is simplified by consolidating repeat sequences between single-copy contigs into a single sequence. Subsequently, bridging of long-read sequences is conducted (miniasm & Racon), utilizing long-read fragments to span short-read fragments, thereby enabling the assembly of longer repetitive regions. These bridges can be aligned to two or more single-copy contigs, thereby connecting the contigs. Bridging fragments derived from the path on the map between two contigs exhibit higher accuracy than long-read fragments. When multiple potential bridging paths exist, the optimal path is selected based on the consistency of conserved long-read sequences; moreover, each bridge is assigned a quality score based on the available evidence and displayed in descending order of score on the graph. When bridging methods conflict, the bridge with the highest quality score is selected, and the bridge along with adjacent sequences are merged into a longer contig. Subsequently, precise short-read sequences are aligned to the assembled contigs using Bowtie2 software, and Pilon software is employed to correct the contigs, thereby reducing mismatches, insertions, and deletions. Finally, BLAST is used to determine whether the contig is circular and to correct its start position.
Figure 11. Schematic representation of the Unicycler genome assembly principle.
Flye software is a novel assembler designed for single-molecule sequencing reads, such as those generated by PacBio and Oxford Nanopore Technologies. It can process long-read datasets spanning small bacterial genomes to large mammalian genomes and incorporates specialized modules for metagenomic applications. The Flye algorithm first determines the optimal K-mer value for performance, which is then set as the fixed K-mer value during the assembly process. Subsequently, this fixed K-mer value is used to detect repeat sequences and assemble them into longer contigs. It employs a repeat graph as the core data structure; unlike the De Bruijn graph, which requires exact k-mer matches, the repeat graph is constructed using approximate sequence matching and can tolerate the higher noise levels characteristic of SMS reads. Edges in the repeat graph represent genome sequences, while nodes define connection points. Each edge is classified as either unique or repetitive. The genome is traversed through the graph (by an unknown method), ensuring each unique edge appears exactly once during traversal. The repeat graph elucidates the repetitive structure of the genome, facilitating the reconstruction of an optimal assembly.
Figure 12. Schematic diagram of the Flye genome assembly principle
Following assembly completion with the Flye software, Pilon is applied for error correction of the assembly using Illumina second-generation sequencing data, ultimately yielding a high-quality assembled genome.
6.4 Plasmid identification and annotation
Plasmids are intrinsic nucleic acid molecules within biological cells that replicate independently of the host chromosome and are stably inherited.Plasmer software is employed to identify plasmids within the bacterial genome assembly results; sequences identified as plasmids are annotated by alignment to the PLSDB database using BLAST (parameters: -evalue 1e-5 -max_target_seqs 1).
6.5 Sequencing Depth Statistics
Sequencing depth statistics for second-generation sequencing were performed using BWA software to align Illumina short reads to the assembled genome, while third-generation sequencing depth statistics employed Minimap2 to align long reads to the assembled genome.
Subsequently, the Samtools depth tool was applied, using a sliding window of 2,000 bp across the genome to calculate the average sequencing depth of both second- and third-generation sequencing data, thereby reflecting the read coverage across different genomic regions.
6.6 Multilocus Sequence Typing
Multilocus sequence typing (MLST) is a traditional genotyping technique that utilizes a set of approximately seven housekeeping genes to classify bacterial strains.The MLST method typically determines nucleotide sequences of approximately 400–600 bp within about seven housekeeping genes. Each sequence at a locus is assigned an allele identifier based on the chronological order of its discovery. The arrangement of allele identifiers for each strain in a specific order constitutes its allele profile, known as the strain’s sequence type (ST). Relevant information based on allele profiles or sequence types can be obtained from the PubMLST (https://pubmlst.org/) database. The mlst software is employed for multilocus sequence typing (identity: 95%, coverage: 80%).
6.7 Genome Evaluation and Annotation
CheckM2 is a tool for evaluating microbial genome quality, employing deep neural networks and gradient boosting tree models to predict genome completeness and contamination levels; GTDB-Tk (Genome Taxonomy Database Toolkit) is a tool for taxonomic annotation of microbial genomes based on the Genome Taxonomy Database (GTDB). It uses pretrained models to align input genomes with the GTDB reference tree, thereby enabling accurate species classification.
6.8 Gene Prediction
Protein-coding gene prediction on the assembled genome was conducted using Prokka (Version: 1.14.6) software. Prokka is a suite of gene element prediction tools; it employs Prodigal for protein-coding gene prediction, Aragorn for tRNA prediction, RNAmmer for rRNA prediction, and Infernal for miscRNA prediction, ultimately consolidating all predicted gene elements to generate preliminary annotation.
6.9 Pseudogene Prediction
Pseudogenes are nonfunctional remnants formed during the evolution of gene families. They are DNA sequences similar to functional genes but have lost their normal function and can be regarded as nonfunctional genomic DNA copies highly similar to coding gene sequences. Typically, they are not transcribed and have no clear physiological significance. According to their origin, pseudogenes are classified into duplicated pseudogenes retaining spacer sequences (e.g., the globin pseudogene family) and processed pseudogenes lacking spacer sequences. Pseudogene candidate sequences in annotated GenBank files from bacterial and archaeal genomes were identified using Pseudofinder (Version: v1.1.0).
6.10 CRISPR sequence prediction
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) represent a distinct family of DNA direct repeat sequences broadly present within prokaryotic genomes. CRISPRs typically comprise repeat sequences of 25 to 48 bp in length, separated by unique spacer sequences. Current research indicates that CRISPRs confer a form of adaptive immunity to the host. Short fragments of exogenous DNA (spacer sequences) act as memory elements integrated into the host genome within the intervals between CRISPR repeat regions. The functional mechanism of CRISPRs is analogous to eukaryotic RNA interference (RNAi), utilizing spacer regions to identify and silence foreign genetic material. CRISPR prediction of the genome was conducted using MinCED (Version: 0.4.2).
6.11 Genomic Island Prediction
Genomic islands (GIs) are segments of the genome found in certain bacteria, phages, or plasmids that bear evidence of horizontal acquisition. Numerous genomic islands have been identified across various microbial genomes, including those of Salmonella, Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, and Klebsiella pneumoniae. Genomic islands typically range in size from 10 to 500 Kbp and exhibit GC content and codon usage patterns that differ from the remainder of the genome. A genomic island can be associated with various biological functions, such as symbiosis, pathogenic mechanisms, or organismal adaptation, and has therefore long been a research focus. Many subcategories of genomic islands are defined based on their functional characteristics, such as pathogenicity islands (PAIs), which harbor virulence genes associated with pathogenic mechanisms; antibiotic resistance islands, which contain numerous antibiotic resistance genes. Genomic islands (GIs) are generally considered to be acquired via horizontal gene transfer. These regions frequently contain genes related to antibiotic resistance and virulence. Currently, commonly used genomic island prediction methods include sequence composition-based prediction and comparative genomics approaches. Genomic islands present in the genome were predicted using islandpath (Version 1.0.6).
6.12 Prophage Analysis
Nucleic acids of temperate bacteriophages integrated into the host genome are referred to as prophages (Prophage).Bacteria harboring prophages in their genomes are termed lysogenic bacteria; they possess the genetic capacity to produce phages without external infection, and this capability can be transmitted to their progeny. Under appropriate conditions that disrupt the maintenance of the prophage state, the phage genome becomes replicative, initiating autonomous proliferation and resulting in host cell lysis. The presence of prophage sequences may enable certain bacteria to acquire antibiotic resistance, enhance environmental adaptability, improve adhesion, or become pathogenic. Furthermore, research on prophages may facilitate the identification of specific antibiotics or even innovative cancer therapies. Prophages within the genome were predicted using the PhiSpy (Version: 4.2.21) online tool.
6.13 Repeat Sequence Analysis
Repeat sequences are exceedingly rare in prokaryotic genomes. RepeatMasker (Version: 4.1.2-p1) software was employed to predict repeat sequences in bacterial genomes.
6.14 Secondary Metabolite Gene Cluster Analysis
antiSMASH serves both as a database and as software utilizing this database. Typically, biosynthetic enzyme genes involved in secondary metabolic pathways are organized in clusters within the genome. Using specific types of HMMs, the antiSMASH database can accurately identify all known secondary metabolite gene clusters (natural product biosynthetic gene clusters). At present, the combination of classical isolation and analytical methods with genome mining has facilitated the identification and characterization of genome-based natural product biosynthetic pathways. The antiSMASH software (Version: 6.0.0) was used to perform genome prediction.
6.15 Insertion Sequence Analysis
Insertion sequences (IS) are transposons that encode the enzymes required for transposition; they are flanked by short inverted terminal repeat sequences. The target sequences of transposon insertion are duplicated during the insertion process, resulting in the formation of two short direct repeat sequences at both ends of the transposon. IS elements are normal components of bacterial chromosomes and plasmids. Insertion sequences are predicted using BLAST software (parameters: -evalue 1e-5 -max_target_seqs 1) combined with the ISfinder database.
6.16 Promoter Prediction
A promoter is a DNA sequence recognized, bound, and utilized by RNA polymerase to initiate transcription. It contains conserved sequences required for specific binding by RNA polymerase and transcription initiation, primarily located upstream of the transcription start site of structural genes. The promoter itself is not transcribed, but its presence is essential for the initiation and regulation of gene transcription.Promoter sequences of the genome were predicted and analyzed using PromPredict (Version: V1).
6.17 Genome Functional Annotation
Functional annotation primarily involves sequence alignment to identify similar sequences or structures, thereby inferring the functions of unknown sequences. Three methods were employed for sequence alignment in this project: BLAST, DIAMOND, and HMMER. BLAST determines sequence similarity by identifying local similarity regions (high-scoring segment pairs), offering rapid speed suitable for large-scale similarity searches. BLAST performs approximate matching and may produce errors when aligning highly similar sequences or complex domains. DIAMOND is similar to BLAST, but offers significantly faster speed and lower resource consumption; however, it only supports protein database alignments, thus having a narrower application scope than BLAST. HMMER employs Hidden Markov Models (HMMs) to represent the structural characteristics of sequences, determining similarity by aligning target sequences to the model. HMMER is mainly used for protein sequence alignment and domain annotation. It utilizes a global alignment approach, which enables more precise identification of structural or functional domains within target sequences. Consequently, HMMER exhibits superior performance in complex sequence analysis and domain annotation tasks. However, owing to its use of global alignment methods and complex algorithms, its computational speed is comparatively slower, rendering it more suitable for processing small-scale sequence data or applications demanding high accuracy.
The Pfam database is a homologous protein family repository, comprising a collection of protein domain families that contains more than sixteen thousand protein families. Proteins are generally composed of one or more functional regions, commonly referred to as domains. Domains occur in different combinations across various proteins, generating protein diversity; identifying these domains is essential for understanding protein function. Annotation of target protein sequences was performed using HMMER with reference to the Pfam database.
Gene Ontology (GO) is an internationally standardized gene functional classification system that provides a dynamically updated controlled vocabulary to comprehensively describe the attributes of genes and gene products in organisms. Protein sequences of interest were annotated using blastp based on the GO database.
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a major public database for the systematic analysis of cellular metabolic pathways and functions of gene products. KEGG facilitates further study of genes’ complex biological behaviors. Annotation was performed based on HMMER alignment.
COG (Cluster of Orthologous Groups of proteins) is a database for the orthologous classification of gene products. Each COG protein is presumed to originate from a common ancestral protein. The COG database is constructed based on encoded proteins from bacteria, algae, and eukaryotes with complete genomes and their phylogenetic relationships. Comparing Unigene with the COG database allows prediction of potential gene functions and functional classification, offering a comprehensive understanding of the gene functional distribution within the species. Annotation was conducted using eggNOG-Mapper software, which employs DIAMOND blastp (parameters: -evalue 1e-5 -max_target_seqs 1) for alignment, based on the COG database to annotate the target protein sequences.
UniProt Archive is a comprehensive, non-redundant database comprising protein sequences from all major public databases. Proteins may be present in multiple databases and may have several versions within the same database. Annotation of the target protein sequences was performed using DIAMOND BLASTP (parameters: -evalue 1e-5 -max_target_seqs 1) against the UniProt database.
The RefSeq (Reference Sequences) database is a curated collection of annotated, non-redundant transcript, protein, and genome sequence data. It is maintained by NCBI and other organizations, employs terminology defined by the HUGO Gene Nomenclature Committee, and includes official gene symbols along with optional aliases. RefSeq sequences constitute a non-redundant database curated by NCBI, generally regarded as highly reliable. Annotation of the target protein sequence was performed using diamond blastp (parameters: -evalue 1e-5 -max_target_seqs 1) against the RefSeq database.
The NR database is a non-redundant protein amino acid sequence database that includes non-redundant data from SwissProt, PIR (Protein Information Resource), PRF (Protein Research Foundation), and PDB (Protein Data Bank) protein databases, as well as protein sequences translated from CDS data in GenBank and RefSeq. Annotation of the target protein sequence was performed using diamond blastp (parameters: -evalue 1e-5 -max_target_seqs 1) against the NR database.
TIGRFAMs is a database consisting of multiple sequence alignments, Hidden Markov Models (HMMs), and related information. It is a member of InterPro and is used for protein sequence classification, supporting the automatic annotation of proteins, predominantly from prokaryotes.From version 10.0 onward, TIGRFAMs models employ HMMER 3, which provides superior retrieval speed and sensitivity.
HMMER is applied to annotate target protein sequences based on the TIGRFAMs database.
The Antibiotic Resistance Genes Database (ARDB) is primarily constructed through literature review, database searches, comparative analysis, and data curation. Besides resistance genes, it also contains twelve drug target sites.The target protein sequences were annotated using Diamond blastp (version 2.0.11) against the ARDB database. The target protein sequences were annotated using blastp against the ARDB database.
Carbohydrates have a significant role in numerous biological functions. Investigating carbohydrate-active enzymes yields abundant meaningful biological insights. The CAZy database specializes in the analysis of genomic, structural, and biochemical data of carbohydrate-active enzymes. Protein sequences were annotated using HMMER based on the CAZy database (filtering parameters: E-value < 1e-18; coverage > 0.35). The target protein sequences were annotated using HMMER based on the CAZy database.
PHI refers to Pathogen Host Interactions, a database documenting pathogen-host interactions. This database contains experimentally validated data primarily derived from infections caused by fungal, oomycete, and bacterial pathogens, with hosts including animals, plants, fungi, and insects. The target protein sequences were annotated using blastp (parameter: –evalue 1e-5) based on the PHI database. The target protein sequences were annotated using blastp based on the HI database.
Annotation using the CARD database yielded information regarding antibiotic resistance genes present within each genome. The CARD database is constructed using the Antibiotic Resistance Ontology (ARO) as the classification unit, associating antibiotic modules with their targets, resistance mechanisms, gene variations, and related information. Alignment results can display the locations of each gene annotated in the CARD database, the ARO identifier, and the ARO classification description. The ARO classification provides detailed insight into each gene’s specific functions related to antibiotic resistance. Annotation of the target protein sequence is performed using blastp against the CARD database.
Cytochromes P450 (abbreviated as CYP450) constitute a large family of proteins containing ferrous heme as a prosthetic group. They catalyze the oxidation of a wide variety of substrates. This type of protein is designated P450 because its reduced form binds to CO, exhibiting a maximum absorption peak at 450 nm. It participates in the metabolism of endogenous substances as well as exogenous compounds, including drugs and environmental chemicals. Annotation was conducted using diamond blastp against the CYPED database.
The Virulence Factors Database (VFDB) is a comprehensive, integrated online resource for acquiring information on virulence factors of bacterial pathogens. The VFDB was constructed with two principal aims: to provide detailed coverage of the most distinctive major virulence factors of bacterial pathogens, and to elucidate the structural characteristics, functions, and mechanisms enabling these pathogens to colonize new ecological niches, evade host defense systems, and ultimately cause disease; To provide researchers with the most recent insights into the various mechanisms utilized by bacterial pathogens, aiming to elucidate the pathogenic mechanisms of bacterial diseases that are not yet well characterized and to develop new, rational approaches for the treatment and prevention of infectious diseases. The target protein sequences were annotated using blastp based on the VFDB database.
TCDB is a database for the classification of membrane transport proteins. It has established a Transporter Classification system (TC System), similar to the EC system for enzyme classification, but the TC System provides both functional and evolutionary information; The TCDB provides a TC Number for each transporter protein family. The TC Number consists of five digits or letters separated by periods. Annotation of target protein sequences was performed using diamond blastp against the TCDB database.
SignalP software (Version: 5.0b) was used to predict proteins containing signal peptides, and tmhmm software (Version: 2.0c) was used to predict proteins containing transmembrane helices, i.e., transmembrane proteins. Among the proteins predicted to contain signal peptides, those with transmembrane helices were excluded, with the remaining proteins classified as secreted proteins.
The type III secretion system (T3SS) is a multiprotein complex predominantly found in Gram-negative pathogens, responsible for translocating virulence factors, known as effector proteins, into host cells. EffectiveT3 software (Version 2.0.2) was employed to predict type III secretion system effector proteins by assigning a score to each amino acid sequence based on its internal computational model. Sequences with scores above the designated threshold were identified as type III secretion system effector proteins, reflecting higher confidence levels.
6.18 Software, Databases, and References
Table 39. Summary of Software, Databases, and References
| Analysis Content | Software/Database | Version | Parameter | Reference or URL |
| Nanopore Data Conversion | GUPPY | 5.0.16 | - | Wick RR, Judd LM, Gorrie CL, et al. Completing bacterial genome assemblies with multiplex MinION sequencing. Microbial genomics. 2017;3(10). https://timkahlke.github.io/LongRead_tutorials/BS_G.html |
| Second-Generation Data Filtering | fastp | 0.23.2 | Default | Chen S, Zhou Y, Chen Y, et al. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17). https://github.com/OpenGene/fastp |
| Assembly | Unicycler | 0.5.0 | bold | Wick RR, Judd LM, Gorrie CL, et al. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS computational biology. 2017;13(6). https://github.com/rrwick/Unicycler |
| Assembly | Flye | 2.9 | Default | Kolmogorov M, Yuan J, Lin Y, et al. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol. 2019 May;37(5):540-546. https://github.com/fenderglass/Flye |
| Alignment | BWA | 0.7.17-r1188 | Default | Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754-1760. https://github.com/lh3/bwa |
| Alignment | Minimap2 | 2.24-r1122 | -ax map-ont | Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34(18):3094-3100. https://github.com/lh3/minimap2 |
| Alignment | Samtools | 1.15.1 | Default | Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079. https://github.com/samtools/samtools |
| Plasmid Identification | Plasmer | v0.1 | Default | Zhu Q, Gao S, Xiao B, He Z, Hu S. Plasmer: an Accurate and Sensitive Bacterial Plasmid Prediction Tool Based on Machine Learning of Shared k-mers and Genomic Features. Microbiol Spectr. 2023 Jun 15;11(3):e0464522. https://github.com/nekokoe/plasmer |
| Multilocus Sequence Typing | mlst | 2.23.0 | identity：95%，coverage：80% | https://github.com/tseemann/mlst |
| Genome Evaluation | checkm2 | 1.1.0 | Default | Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023). CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nature methods, 20(8), 1203–1212. https://github.com/chklovski/CheckM2 |
| Species Annotation | gtdb-tk | 2.5.0 | Default | Chaumeil, P. A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2022). GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics (Oxford, England), 38(23), 5315–5316. https://github.com/Ecogenomics/GTDBTk |
| Gene Prediction | Prokka | 1.14.6 | –kingdom Bacteria –gcode 11 –evalue 1e-6 –rnammer –rfam –compliant | Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068-2069. https://github.com/tseemann/prokka |
| Pseudogene Prediction | Pseudofinder | v1.1.0 | -di -skpdb | Machnicka MA, Stawowy P, Toczylowski W, et al. Pseudofinder: detecting pseudogenes in prokaryotic genomes. Bioinformatics. 2019;35(17):3113-3114. https://github.com/filip-husnik/pseudofinder |
| CRISPR Sequence Prediction | MinCED | 0.4.2 | -minNR 1 | Ziebell K, Goll J, Didelot X, et al. MinCED: automated inference of CRISPR loci and their encoding environments from assembled bacterial genomes. Bioinformatics. 2019;35(5):787-791. https://github.com/ctSkennerton/minced/tree/master |
| Genomic Island Prediction | islandpath | 1.0.6 | Default | Hsiao W, Wan I, Jones SJ, et al. IslandPath: aiding detection of genomic islands in prokaryotes. Bioinformatics. 2003;19(3):418-420. https://github.com/brinkmanlab/islandpath |
| Prophage Analysis | PhiSpy | 4.2.21 | Default | Akhter S, Aziz RK, Edwards RA. PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity-and composition-based strategies. Nucleic acids research. 2012;40(16). https://github.com/linsalrob/PhiSpy |
| Repeat Sequence Analysis | RepeatMasker | 4.1.2-p1 | -pa 10 -q -html -gff | Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Current Protocols in Bioinformatics. 2009;25(1):4-10. http://www.repeatmasker.org/ |
| Secondary Metabolite Gene Cluster Analysis | antiSMASH | 6.0.0 | –taxon bacteria –genefinding-tool prodigal –cb-general –cb-knownclusters –cb-subclusters –asf –pfam2go –smcog-trees | Blin K, Shaw S, Kloosterman AM, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic acids research. 2021;49(W1). https://docs.antismash.secondarymetabolites.org/ |
| Signal peptide prediction | SignalP | 5.0b | -batch 50 -gff3 -mature -format long | Petersen TN, Brunak S, von Heijne G, et al. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nature methods. 2011;8(10):785-786. http://www.cbs.dtu.dk/services/SignalP/ |
| Transmembrane protein prediction | TMHMM | 2.0c | -N1 -PrintNumbers -PrintScore -PrintStat -plp | Krogh A, Larsson B, von Heijne G, et al. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of molecular biology. 2001;305(3):567-580. http://www.cbs.dtu.dk/services/TMHMM/ |
| Type III secretion system effector protein prediction | EffectiveT3 | 2.0.2 | Default | Arnold R, Brandmaier S, Kleine F, et al. Sequence-based prediction of type III secreted proteins. PLoS pathogens. 2009;5(4). https://effectors.org/method/effectivet3 |
| Functional annotation | BLAST+ | 2.11.0+ | –evalue 1e-05 | Camacho C, Coulouris G, Avagyan V, et al. BLAST+: architecture and applications. BMC bioinformatics. 2009;10(1):421. https://blast.ncbi.nlm.nih.gov/Blast.cgi |
| Functional annotation | HMMER | 3.3.2 | -E 1e-5 | Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14(9):755-763. http://www.hmmer.org/ |
| Functional annotation | Diamond | 2.0.11 | –evalue 1e-05 | Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nature methods. 2015;12(1):59-60. http://github.com/bbuchfink/diamond/ |
| Functional annotation | Uniport | 2024-05-29 | - | UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic acids research. 2021;49(D1). https://www.uniprot.org/ |
| Functional annotation | Refseq | 2024-05-10 | - | O’Leary NA, Wright MW, Brister JR, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic acids research. 2016;44(D1). https://www.ncbi.nlm.nih.gov/refseq/ |
| Functional annotation | Pfam | 37.0 | - | El-Gebali S, Mistry J, Bateman A, et al. The Pfam protein families database in 2019. Nucleic acids research. 2019;47(D1). https://pfam.xfam.org/ |
| Functional annotation | TIGRFAMs | 15.0 | - | Haft DH, Selengut JD, Richter RA, et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic acids research. 2013;41(D1). http://tigrfams.jcvi.org/cgi-bin/index.cgi |
| Functional annotation | GO | 2024-05-29 | - | Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. Nature genetics. 2000;25(1):25-29. http://geneontology.org/ |
| Functional annotation | KEGG | 2024-05-27 | - | Kanehisa M, Sato Y, Kawashima M, et al. KEGG as a reference resource for gene and protein annotation. Nucleic acids research. 2016;44(D1). https://www.kegg.jp/kegg/ |
| Functional annotation | EGGNOG | 5.0 | - | Huerta-Cepas J, Szklarczyk D, Forslund K, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic acids research. 2016;44(D1). http://eggnog5.embl.de/#/app/home |
| Functional annotation | Nr | 2024-02-07 | - | Pruitt KD, Tatusova T, Klimke W, et al. NCBI Reference Sequences: current status, policy and new initiatives. Nucleic acids research. 2009;37(Database issue). https://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/ |
| Functional annotation | ARDB | 1.1 | - | Liu B, Pop M. ARDB—Antibiotic Resistance Genes Database. Nucleic acids research. 2009;37(suppl_1). Ftp://ftp.cbcb.umd.edu/pub/data/ARDB/ |
| Functional annotation | CAZy | dbCAN2 V12 | - | Lombard V, Golaconda Ramulu H, Drula E, et al. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic acids research. 2014;42(D1). http://www.cazy.org/ |
| Functional annotation | PHI | 4.17 | - | Winnenburg R, Baldwin TK, Urban M, et al. PHI-base: a new database for pathogen host interactions. Nucleic acids research. 2006;34(suppl_1). http://www.phi-base.org/ |
| Functional annotation | CARD | 3.2.9 | - | McArthur AG, Waglechner N, Nizam F, et al. The comprehensive antibiotic resistance database. Antimicrobial agents and chemotherapy. 2013;57(7):3348-3357. http://arpcard.mcmaster.ca |
| Functional annotation | CYPED | 6.0 | - | Mazumder R, Phelps TJ, Krieg NR, et al. Physiological role of the cytochrome P-450 system in environmental microorganisms. FEMS Microbiology Reviews. 1999;23(2):303-319. https://cyped.biocatnet.de |
| Functional annotation | VFDB | 2024-06-28 | - | Chen L, Yang J, Yu J, et al. VFDB: a reference database for bacterial virulence factors. Nucleic acids research. 2005;33(suppl_1). http://www.mgc.ac.cn/VFs/ |
| Functional annotation | TCDB | 2024-06-26 | - | Saier MH, Reddy VS, Tamang DG, et al. The Transporter Classification Database. Nucleic acids research. 2014;42(D1). http://www.tcdb.org/ |
| Functional annotation | PLSDB | v.2021_06_23_v2 | - | Galata V, Fehlmann T, Backes C, et al. PLSDB: a resource of complete bacterial plasmids. Nucleic acids research. 2019;47(D1). https://ccb-microbe.cs.uni-saarland.de/plsdb/ |
7 Frequently Asked Questions and Research Recommendations
7.1 Frequently Asked Questions
1.The uploaded result data files are primarily in text formats (fa files, txt files, detail files, xls files, etc.). For viewing files on the Windows operating system, it is recommended to use Editplus or UltraEdit as text viewers, or PilotEdit Lite to open very large text files. In Unix or Linux systems, larger text files can be easily viewed using commands such as less.
2.The report file contains images in SVG format. SVG is a vector-based image format that can be scaled arbitrarily without loss of resolution. To view SVG files, please install an SVG plugin (for example, software such as Adobe Illustrator).
3.It is recommended to open the result files using Excel or professional text editors such as EditPlus.
7.2 Research Recommendations
After obtaining the genome and gene information of a single bacterial strain, comparative genomic analysis can be performed to investigate the evolutionary relationships and differences among multiple bacterial genomes. Pan-genome analysis may be conducted to construct the gene repository of the strain, thereby laying the groundwork for subsequent studies. Additionally, validation experiments such as gene knockout, heterologous expression, and PCR can be performed on specific functional and structural key gene segments within the genome.
8 Contact Us
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