Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST.

With the advent of next-generation whole-genome sequencing (WGS), the need for good-quality and well-characterised Salmonella genomes has increased over the past years. Good-quality complete genomes are often required for assembly reference mapping or phylogenetic single nucleotide polymorphism (SNP) analysis. Complete genomes or contigs from specific sources or serovars are also searched for clustering analysis or source attribution studies. Therefore, new bioinformatics tools are needed for the extraction of good-quality and well-characterised genomes from public databases. Here, we developed SalmoDEST, an open-source Python tool capable of extracting Salmonella genomes with a coverage higher than 50x and genome length over 4Mb from the GenBank database in the form of complete genomes or contigs, with verification of the serovar to which they belong and identification of the corresponding multi locus sequence type (MLST) profile. To validate the ability to SalmoDEST to screen for and retrieve genomes of good quality, we compared our results for S. Typhi complete genome with those available in the literature and extracted Salmonella genomes from bovine sources strains isolated worldwide. Finally, we provide in this study a list of 239 complete genomes for 123 serovars of Salmonella of high quality. SalmoDEST is a handy and easy-to-use open-source tool to extract complete genomes or contigs that can be routinely used in public health, food safety and research laboratories. SalmoDEST (SALMOnella Download gEnome Serotype sT) is available at https://github.com/I-Guy/SalmoDEST.

Introduction

The investigation of genetic markers or genome relationships between different pathogens and microorganisms requires good-quality genomes. A large panel of good-quality genomes makes it possible to study chromosome rearrangements in more detail, identify sequences of interest and improve the identification of genetic clustering. Among the most frequently consulted sequence databases for collecting genomes is the open-access GenBank database, housed by the National Centre for Biotechnology Information (NCBI). GenBank annotates a collection of all publicly available nucleotide sequences generated by laboratories throughout the world from more than 100,000 distinct organisms. Release 242.0, produced in February 2021, contained over 12 trillion nucleotide bases in more than 2 billion sequences. To facilitate the retrieval of genomes of interest from the GenBank database, we designed a workflow (called SalmoDEST) to search and download genomes with a coverage greater than 50x. The options of this tool make it possible to download either complete genomes or contigs. It is possible to choose to download protein fasta files, if desired, and an output directory where all the selected fasta files are kept. The SalmoDEST tool was developed for Salmonella, a well-known and widely distributed foodborne pathogen. Salmonella enterica is regulated in the European Union (EU) and monitored in the United States (US) and many other countries. In the US, the economic burden due to salmonellosis is estimated to be US$3.66 billion per year. In 2016, the incidences of culture-confirmed cases of salmonellosis were 14.51 and 20.4 cases per 100,000 population in the US and the EU, respectively.[2,3] The economic, social and public health importance of diseases caused by Salmonella has brought many developing and developed countries to implement their monitoring systems with whole-genome sequencing (WGS) of the isolated strains, clustering by single nucleotide polymorphism (SNP) core-genome analysis for outbreaks and source attribution investigations. For countries that can carry out WGS, it is necessary to have access to Salmonella genomes from different regions of the world and for which the serovar has been verified and the multi-locus sequence type (MLST) profile identified. For countries in which WGS is still not readily available, carrying out studies based on good-quality and well-identified open-access Salmonella genomes can prove to be an essential asset.

Materials and Methods

Workflow description

SalmoDEST is implemented as an open-source Python tool (https://github.com/I-Guy/SalmoDEST). It is based on a succession of two Python scripts and a Bash process (Figure 1).

Figure 1.

SALMOnella Download gEnome Serotype sT (SalmoDEST) pipeline.

ST, sequence type.

SalmoDEST is a workflow designed to search and download Salmonella genomes from the NCBI GenBank database using either the ncbi-acc-download tool for complete genomes or ncbi-genome-download for contigs. Using these tools, the first Python script ‘Get_HQ_Genome_1.py’ in SalmoDEST automatically downloads the genome fasta files of the strains for which accession numbers are present in the input text file. Then, the serovar and MLST profile predictions of the downloaded genomes is carried out with a Bash process using SeqSero, MLSTseeman tool and Quast, respectively. The second Python script ‘Get_HQ_Genome2.py’ renames the downloaded fasta files, adding the accession number, the serovar and the MLST profile predictions as follows: antigenic formula or serovar name_ST_ID_Accession number (eg, Montevideo_81_42N_CP037893.1). The Python script ‘Get_HQ_Genome2.py’ also downloads the gff and gbk files and checks the quality of each genome. It retains only those with coverage greater than 50x and a genome length longer than 4 Mb, and removes the others. Finally, this Python script compresses (zips) all files.

Optionally, it is possible to choose to download fasta protein files, if desired, and, in addition, choose an output directory in which all the selected fasta files are stored.

Get_HQ_Genome_1.py script

The input file of SalmoDEST and the ‘Get_HQ_Genome1.py’ script is a text file, obtained from an NCBI Nucleotide database query (https://www.ncbi.nlm.nih.gov/nuccore) or compiled by the user, listing the accession numbers of the complete genomes or contigs to download.

If an NCBI Nucleotide database query is used, the ‘Complete Record’ must be exported into a destination ‘File’ in the ‘Accession List’ format sorted by ‘Default order’.

In the ‘Get_HQ_Genome_1.py’ script, the function named ‘getFastafromNuccore’ downloads fasta files and transcribes the accession number of the downloaded fasta files in a tsv file. The function named ‘Renamer’ renames every fasta file as ”ID_Accession.fasta” and creates a folder with the same name to which it moves the fasta files. The function named “Filter1Genome” works only if the user chooses the “complete genome mode”. The function named “Filter1Contig” works only if the user chooses the “contigs mode”. These two functions copy the accession numbers of the fasta files in a tsv file named “Genome_HQ.tsv”. Then, they count the number of contigs in every fasta file and report it in a second tsv file named “Genome_HQ_Filter1.tsv”. If the “complete genome mode” is selected, it discards all fasta files with more than one contig.

Get_HQ_Genome2.py

The ‘Get_HQ_Genome2.py’ script runs after the Bash process queries the SeqSero, MLSTseeman and Quast tools. The function named ‘ReadSeqSero’ reads the results from the SeqSero2 tool and retrieves the accession numbers of the genomes and the serovar predictions, with the associated probabilities. Similarly, the function named ‘ReadMLST’ reads results from the MLSTseeman tool and stores accession numbers and MLST profiles. The function named ‘ReadQuast’ reads results from the Quast tool and retrieves length, the N50 value and the number of contigs of genomes. The function named ‘MergeResult’ merges all the information from the previous functions (ie, serovar predictions, MLST profiles, number of contigs, length, N50 and genome size) along with information from ‘Genome_HQ_Filter1.tsv’ (ie, produced by the ‘Get_HQ_Genome_1.py’ script) in a third tsv file named ‘TableMerge.tsv’. The function named ‘GetGBK’ downloads the gbk (GenBank) files associated with fasta files. The function named ‘Renamer2’ moves the gbk files to the folder containing fasta files and renames them according to the fasta file names. The function named ‘Filter2’ generates a fourth tsv file called ‘TableMergeFilter2.tsv’ with the keys (ie, accession numbers) of all genomes that have a coverage higher than 50x (> 50x) based on gbk files and a length longer than 4 Mb (> 4 Mb). It also adds information on the sequencing technology used to this tsv file. The function named ‘GetGFF’ downloads gff files.

The function named ‘RenamerGFF_FASTAprot’ renames gff files and protein fasta files. It moves them to the folder containing the fasta files. The function named ‘FinalRenamer’ renames every file and directories as described above (ie, antigenic formula/serovar name_ST_Accession). The ‘Renamer’ functions can be easily modified at the user’s convenience. The function named ‘zipfiles’ will compress (zip) all the folders containing the downloaded files.

Workflow application

In this study, we report two application examples for SalmoDEST. In the first example, we evaluate the ability of SalmoDEST tool to download complete Salmonella genomes from the NCBI GenBank database and, in the second, its ability to download Salmonella genome contigs for strains isolated from bovine sources.

Selection of complete genomes from a public database

Complete reference genomes are often required for assembly reference mapping or phylogenetic SNP analysis for the mapping step and the calculation of pairwise distance between genomes. Nevertheless, for a single laboratory it may be difficult to have a complete set of reference genomes, particularly considering that the genus Salmonella is separated into six subspecies and over 2000 serovars. The SalmoDEST tool was tested to search, download and select all complete Salmonella reference genomes available in the GenBank database. SalmoDEST applies a coverage filter set to a minimum of 50x. A second manual filter is based on serovar identification. SalmoDEST was used to compare the listed serovars with the serovars predicted by Seqsero2 in the TableMergeFilter2 tsv file. In this study, SalmoDEST was tested using the list of accession numbers obtained using the NCBI ‘All Databases’ query: ‘Salmonella[title] AND Genome[title] AND Salmonella enterica[title] AND Genome Assembly and Annotation report[title]’ (https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/152/) with the filter ‘Complete’ (on 24 June 2021). A list with 1648 accession numbers was retrieved, and after eliminating duplicates, 1048 unique accession numbers were found (Supplementary Table S1). The SalmoDEST option for complete genome mode ‘-m g’ was used. Finally, after serovar prediction and genome length verifications, 1040 genomes were retained and downloaded. Four tsv output files were produced, including the final TableMergeFilter2 tsv file (Supplementary Table S2).

Selection of contig genomes from public database

Microbiologists need to access to Salmonella serovar genomes from specific sources for many types of analyses such as clustering analyses, source attribution studies or when screening for molecular markers.[10 -13] Obtaining genomes from laboratories around the world is therefore a major advantage. Here, we tested the ability of the SalmoDEST tool to obtain Salmonella genomes from strains isolated from bovine sources worldwide. The SalmoDEST tool was tested using the list of assembly accession numbers obtained using the NCBI ‘All Databases’ query: ‘Salmonella[title] AND Genome[title] AND Salmonella enterica[title] AND Genome Assembly and Annotation report[title]’ (https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/152/) with the following filters: ‘Contig’ AND ‘Bovine’ AND ‘bovine’ (on 24 June 2021), 89 unique accession numbers were found (Supplementary Table S3). The SalmoDEST option for contig genome mode ‘-m c’ was used and, after the filtering process, 88 genomes were downloaded. Four tsv output files were created, including the final TableMergeFilter2 tsv file (Supplementary Table S4).

Results and Discussion

The NCBI Nucleotide query carried out on 7 June 2021 resulted in 1648 accessions. After deduplication, 1048 unique accessions were included in the input txt file and downloaded by the SalmoDEST tool that we developed here. All these complete genomes were checked for 50x coverage, genome length and predicted serovar matching. Finally, 1040 complete genomes with good quality were downloaded and the MLST profile was determined. From the initial list of 1048 complete genomes in the input txt file, SalmoDEST excluded one genome (CP060132.1) for incorrect serovar prediction and seven others (OU015718.1, OU015719.1, OU015720.1, OU015717.1, LR792437.1, LR792391.1 and LN868943.1) due to low genome length (genome lengths of < 4 Mb, comprised between 277 503 and 3 746 274 bases). We obtained 16 genomes of S. enterica subsp. salamae, 10 S. enterica subsp. arizonae, 13 S. enterica subsp. diarizonae, 10 S. enterica subsp. houtenae and 991 S. enterica subsp. enterica, representing 135 serovars with different antigenic formulas. No S. enterica subsp. indica genomes with a coverage higher than 50x were found. Four serovars were overrepresented (ie, more than 50 complete genomes) in the GenBank database and in our results: S. Typhi (ie, responsible for human typhoid fever with 124 genomes/1040), S. Enteritidis, S. Typhimurium and S. 4,[5],12: i:-, with 114/1040, 141/1040 and 56/1040 genomes, respectively. These latter three serovars are the non-typhoid Salmonella serovars the most frequently isolated worldwide. These serovars were followed by S. Heidelberg (40/1040), S. Newport (38/1040), S. Anatum (32/1040), S. Bareilly (30/1040), S. Indiana (22/1040), S. Montevideo (21/1040) and S. Senftenberg (20/1040) (Figure 2). Our results are consistent with CDC and EFSA reports.[14 -18] Since 2016, these 11 serovars have belonged to the top 30 most frequently isolated serovars in the EU and the US.[14 -18]

Figure 2.

Histogram of serovar diversity among the 1040 complete Salmonella genomes downloaded from the NCBI GenBank database using the SalmoDEST tool developed in this study. Only serovars with more than five complete genomes and complete antigenic formula are shown, with the exception of S. 4,[5],12: i:- and S. 1,3,19:g, s,t:-.

To validate the ability to SalmoDEST to screen for and retrieve complete genomes of good quality, we compared our results for S. Typhi with those available in the literature. As expected, in accordance with the study published by Yap and Thong in 2017, SalmoDEST was able to recover 124 S. Typhi. The SalmoDEST tool developed in this study succeeded in screening for and downloading good-quality reference genomes for S. Typhi, confirming its ability to make good-quality genomes available quickly.

Finally, due to the need for complete genomes for sequence assembly and for SNP phylogenetic analyses (ie, for mapping analyses and to calculate the pairwise distance between genomes), we constituted a panel of complete reference genomes for Salmonella from the SalmoDEST output obtained in this study. We selected 239 complete genomes from the initial 1040 genomes, with 10 S. enterica subsp. salamae, 8 S. enterica subsp. arizonae, 7 S. enterica subsp. diarizonae, 8 S. enterica subsp. houtenae and 206 S. enterica subsp. enterica, representing 123 serovars and 185 MLST profiles (Table 1 and Supplementary Table S5). When possible, the sequencing technology used for complete genome assembly (ie, both short and long reads) and coverage were taken in account for the selection of the final panel. This panel of complete genomes can be used by microbiologists in food poisoning and typhoid investigations involving Salmonella spp.

Table 1.

List of good-quality complete Salmonella genomes (ID, serovar and MLST profile predictions) downloaded from the NCBI GenBank database on 28 June 2021.

Predicted_serovar	MLST Profile	Accession number	Predicted_serovar	MLST Profile	Accession number	Predicted_serovar	MLST Profile	Accession number
1,3,19:g, s,t:-	217	CP038604.1	II 56: b:z6	5324	CP029995.1	Oranienburg	3613	CP033344.1
Abaetetuba	2041	CP007532.1	II 56: z10:e, n,x,z15	2403	CP029992.1	Orion	684	CP030235.1
Aberdeen	426	LS483453.1	II 58: d:z6	3379	CP070222.1	Oslo	1370	CP030231.1
Abony	1483	CP007534.1	II 58:l, z13,z28:-	1141	LS483477.1	Ouakam	1610	CP022116.1
Adjame	3929	CP049881.1	IIIa -: z4, z23:-	106	CP053584.1	Panama	48	CP012346.1
Adjame	4023	CP054827.1	IIIa 40: z4, z23:-	6216	CP041011.1	Paratyphi A	85	CP000026.1
Agona	13	CP025452.1	IIIa 41: z4, z23:-	2131	CP000880.1	Paratyphi A	129	CP009049.1
Albany or Duesseldorf	292	CP019177.1	IIIa 48: z36:-	3711	LR134150.1	Paratyphi B	28	CP020492.1
Albert	19	CP044188.1	IIIa 53: z4, z23,z32:-	2127	CP022504.1	Paratyphi B var. L(+) tartrate +	307	CP000886.1
Anatum	64	CP029800.1	IIIa 53: z4, z23:-	874	LR133910.1	Paratyphi C or Choleraesuis or Typhisuis	66	AE017220.1
Anatum	2167	CP014620.1	IIIa 62: z36:-	2402	CP006693.1	Paratyphi C or Choleraesuis or Typhisuis	68	CP007639.1
Antsalova	4407	CP019116.1	IIIa 63:g, z51:-	1425	CP029991.1	Paratyphi C or Choleraesuis or Typhisuis	90	CP043773.1
Apapa		CP019403.1	IIIb 47: k:z35	1195	CP053583.1	Paratyphi C or Choleraesuis or Typhisuis	114	CP000857.1
Bareilly	203	CP063684.2	IIIb 48: i:z	574	CP029989.1	Paratyphi C or Choleraesuis or Typhisuis	139	CP012344.2
Bareilly	909	CP006053.1	IIIb 50: k:z	430	CP059886.1	Paratyphi C or Choleraesuis or Typhisuis	145	CP051366.1
Bareilly	5146	CP034721.1	IIIb 60: r:z	3457	CP011289.1	Pomona	451	CP019186.1
Bergen	1356	CP019405.1	IIIb 60: z52:z53	2830	CP030180.1	Poona	308	CP046279.1
Berta	435	CP030005.1	IIIb 61: i:z	57	LS483474.1	Poona	447	CP037891.1
Birkenhead	424	CP045958.1	IIIb 65: c:z	1260	CP022135.1	Poona	812	LS483489.1
Bispebjerg	251	CP043027.1	Indiana	17	CP028131.1	Poona	964	CP019189.1
Blockley	52	CP043662.1	Infantis	32	CP047881.1	Quebec	4409	CP022019.1
Blukwa	367	LR134148.1	Inverness	1384	CP019181.1	Reading	1628	CP051307.1
Bovismorbificans	142	CP060517.1	Irumu		LR134144.1	Rissen	469	CP030190.1
Bovismorbificans	1499	CP069297.1	Isangi	216	CP030225.1	Rubislaw	94	CP019192.1
Braenderup	22	CP022490.1	IV -: z4, z23:-	963	LS483478.1	Saintpaul	27	CP017723.1
Brancaster	2133	CP036166.1	IV -: z4, z23:-	3942	CP051368.1	Saintpaul	49	CP053055.1
Brandenburg	65	CP025280.1	IV [1],40:g, z51:-	2265	CP053582.1	Saintpaul	50	CP045954.1
Bredeney	241	CP043222.1	IV 16: z4, z32:-	596	CP045761.1	Saintpaul	95	CP023512.1
Bredeney	897	CP007533.1	IV 41: z52:-	3924	CP054715.1	Saintpaul	680	CP022491.1
Butantan	600	CP046278.1	IV 45:g, z51:-	107	CP030194.1	Saintpaul	3602	CP023166.1
Carmel	2123	LS483455.1	IV 50:g, z51:-	2882	LR134159.1	Sanjuan	785	LR134142.1
Cerro	367	CP008925.1	IV 50: z4, z23:-	2053	CP053579.1	Schoeneberg		LR134153.1
Chester		CP019178.1	Javiana	24	CP004027.1	Schwarzengrund	96	CP045447.1
Coeln		LR134190.1	Johannesburg	471	CP019411.1	Schwarzengrund	322	CP001127.1
Concord	534	CP044177.1	Kentucky	152	CP022500.1	Senftenberg	14	CP038591.1
Concord	599	CP028196.1	Kentucky	198	CP043667.1	Senftenberg	185	CP016837.1
Corvallis	1541	CP027677.1	Kisarawe	906	CP030203.1	Senftenberg	210	AP020332.1
Cubana	286	CP006055.1	Kottbus	212	CP062220.1	Senftenberg	290	CP034233.1
Dakar	5734	CP046280.1	Kottbus	808	CP030211.1	Sloterdijk	3179	CP012349.1
Daytona		LR133909.1	Krefeld	1799	CP019413.1	Stanley	29	CP036167.1
Derby	40	CP028900.1	Litchfield	214	CP030202.1	Stanley	1027	LS483434.1
Derby	71	CP026609.1	Litchfield	491	CP019414.1	Stanleyville	97	CP017727.1
Derby	72	CP022494.1	Livingstone	2247	CP030233.1	Stanleyville	1986	CP034716.1
Djakarta		CP019409.1	Llandoff		CP060585.1	Stanleyville	4762	CP034700.1
Dublin	10	CP032393.1	London	155	CP061159.1	Sundsvall	5323	LS483457.1
Dublin	4406	CP019179.1	London	504	CP064709.1	Taksony	2204	LR134146.1
Enteritidis	11	CP063700.1	Lubbock	413	CP032814.1	Telelkebir	450	CP030217.1
Enteritidis	3175	CP008928.1	Macclesfield	4976	CP022117.1	Tennessee	319	CP014994.1
Florida	931	LS483454.1	Manhattan	18	CP019418.1	Thompson	26	CP012514.1
Fresno	649	CP032444.1	Mbandaka	413	CP022489.1	Typhi	1	CP003278.1
Gallinarum or Enteritidis	78	CP019035.1	Mbandaka	3016	CP019183.1	Typhi	2	AL513382.1
Gallinarum or Enteritidis	92	CP022963.1	Menston		LS483490.1	Typhi	8	LT904887.1
Gallinarum or Enteritidis	136	CP018633.1	Miami	85	CP023470.1	Typhi	2138	LT905088.1
Gallinarum or Enteritidis	331	AM933173.1	Miami	129	CP009559.1	Typhi	2209	CP029918.1
Gallinarum or Enteritidis	1972	CP045955.1	Miami	140	CP023468.1	Typhimurium	19	AE006468.2
Gallinarum or Enteritidis	3304	CP045956.1	Mikawasima	5372	CP034713.1	Typhimurium	34	CP045952.1
Gaminara	2439	CP024165.1	Milwaukee	1245	CP030175.1	Typhimurium	36	CP036168.1
Gaminara	2440	CP030288.1	Minnesota	548	CP060508.1	Typhimurium	99	CP020922.1
Gateshead	6131	CP046291.1	Montevideo	4	CP069518.1	Typhimurium	128	HG326213.1
Give	516	CP046277.1	Montevideo	81	CP037893.1	Typhimurium	213	CP035547.1
Give	654	CP019174.1	Montevideo	138	CP040380.1	Typhimurium	302	CP014356.1
Goldcoast or Brikama	358	CP062223.1	Montevideo	316	CP029336.1	Typhimurium	313	CP060169.1
Goldcoast or Brikama	2529	LR134158.1	Muenchen	83	CP016014.1	Typhimurium	328	CP025736.1
Grumpensis	751	CP030223.1	Muenchen	112	CP045056.1	Typhimurium	568	CP064919.1
Hadar	33	CP022069.2	Muenchen	112	CP045063.1	Typhimurium	568	LR862421.1
Havana	1237	LR134187.1	Muenster	321	CP019198.1	Typhimurium	2066	CP009102.1
Heidelberg	15	CP005995.1	Muenster		CP045038.1	Typhimurium	2210	CP040562.1
Hidalgo or Cocody		CP022663.1	Napoli	2095	CP063140.1	Typhimurium	3631	CP039854.1
Hillingdon		CP019410.1	Newport	5	CP015923.1	Typhimurium	5036	CP029840.1
Hvittingfoss	434	CP045831.1	Newport	31	CP007559.2	Typhimurium	5401	CP033226.2
Hvittingfoss	446	CP022503.1	Newport	45	CP012598.1	Uganda	684	CP051398.1
I 4,[5],12: i:-	2379	CP039610.1	Newport	118	CP015924.1	Virchow	16	CP045945.1
I 9: g, m,q:-	2912	CP019406.1	Newport	132	CP025232.1	Wandsworth	1498	CP019417.1
I 9: g, p,s:-	10	CP030207.1	Newport	166	CP012144.1	Waycross	2460	CP034707.1
II -: z:e, n,x,z15	3706	LS483495.1	Newport	350	CP016010.1	Weltevreden	365	CP014996.1
II 40: z4, z24: z39	4415	LS483456.1	Newport	4157	CP039436.1	Weltevreden	2384	LN890524.1
II 42: r:-	1208	CP034717.1	Newport	4166	CP039437.1	Weslaco	1088	LR134143.1
II 47: b:e, n,x,z15	3910	CP053585.1	Ohio	329	CP030181.1	Worthington	592	CP029041.1
II 50: z:e, n,x	1110	LS483475.1	Onderstepoort	3102	CP022034.1	Yoruba	1316	CP030209.1
II 55: z39:k	1121	CP022139.1	Oranienburg	23	CP019197.1

Salmonella contig genomes from bovine sources

Among the recognised pathogens causing human disease, almost 60% are of animal origin and cattle bred for meat and for milk are common reservoirs of Salmonella spp. Almost 40% of a herd can be infected, and the risk of infection increases with the size of the herd.[22,23] Salmonellosis in cattle puts producers at risk for direct economic losses associated with mortality or body weight loss, and also indirect losses caused by reduced feed conversion or veterinary care costs. Genomes from strains isolated from cattle can be used in source attribution studies, as well as in searches for specific host marker sequences. Our test successfully downloaded Salmonella genomes of strains isolated from bovine animals. The SalmoDEST tool was able to download 88 contig genomes of Salmonella isolated from bovine sources with a coverage of > 50x, lengths of > 4 Mb and correct serovar prediction from the initial input list file of 89 genomes. One genome (GCA_004744895,1) was excluded due to a genome length of < 4 Mb (Supplementary Tables 3 and 4). Fifty-two entries in the TableMergeFilter2.tsv file showed missing information on coverage and sequence type in the gbk files of the corresponding genomes. Interestingly, among the 88 contig genomes downloaded, the most represented serovars were S. Typhimurium (28 contig genomes/88), S. Newport (14/88) and S. Dublin (11/88). These three serovars are well known for contaminating bovine animals in the EU and the US.[18,20,22]

50x coverage

The value of 50x was chosen for Salmonella in the SalmoDEST tool following the recommendations of the European Centre for Disease Control and Prevention (ECDC). The amount of data generated per Salmonella isolate by a DNA sequencer is substantial (ie, megabytes) and a trade-off must be struck between genome coverage (ie, quality) and the size of the files generated. For example, although a coverage of 30x is typically sufficient for routine surveillance of foodborne pathogens, the appropriate coverage threshold is platform-dependent and may also vary by organism. ECDC has fixed a coverage of 50x for Salmonella, considering this value as reasonable for corresponding file size. Coverage is frequently considered as the main quality metric typically used in WGS. Furthermore, the quality of genome sequences also have an impact on successful in silico serovar prediction. Missing or incomplete MLST and cgMLST loci sequences largely contribute to errors in identification.[6,26] Similarly, partial or missing antigenic data in the rfb region (ie, the O-antigen flippase and polymerase genes) and the fliC and fljB genes influence in silico serovar prediction. Good coverage prevents poor MLST, cgMLST and, antigenic data and contributes to the correct listing of the serovar.[6,26,27]

Errors in serotyping

Salmonella genomes from GenBank have already revealed errors in the serovar listed in their metadata. In 2016, Yoshiba et al carried out in silico serovar prediction on over 4,291 genomes extracted from GenBank, and revealed that 3.5% gave incorrect serovar predictions and that 1.8% had missing or ambiguous metadata, making it impossible to ascertain the listed phenotypic serovar. For this reason, we integrated the Bash process in the SalmoDEST tool to query the SeqSero2 and MLSTseeman tools. SeqSero is a Web-based tool developed by the Centres for Disease Control and Prevention (CDC) in Atlanta, GA (US) for determining Salmonella serotypes using the rfb region and the fliC and fljB alleles.[6,28] SeqSero2 was chosen because it is the only tool that relies on characterising genetic determinants of Salmonella serovars without consulting any markers, such as MLST types; it saves time because it predicts serovars directly from raw sequencing reads and not from assemblies, and finally it is able to detect inter-serovar contaminations. The MLSTseeman is a tool developed by Torsten Seemann in 1991 that scans contig files against traditional PubMLST typing schemes conceived as part of the development of the first MLST scheme in 1998, making it possible to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes.

Information on serovar and MLST type were integrated in SalmoDEST to enable genome verification and because they are integral to surveillance and outbreak investigations.

Conclusion

SalmoDEST is a handy and easy-to-use tool that can be routinely used in public health, food safety and research laboratories to extract complete Salmonella reference genomes of high quality from GenBank. It can also be used to download contig genomes from a list of assembly IDs. A coverage of 50x, as well as correct Salmonella genome size and serovar and MLST type prediction, are used as quality controls for both genome modes (ie, complete and contig genomes search and download). Moreover, SalmoDEST screens downloaded genomes for contamination by using the SeqSero2 tool for serovar prediction.

Click here for additional data file.

Supplemental material, sj-jpg-1-bbi-10.1177_11779322221080264 for Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST by Emeline Cherchame, Guy Ilango and Sabrina Cadel-Six in Bioinformatics and Biology Insights

Click here for additional data file.

Supplemental material, sj-txt-1-bbi-10.1177_11779322221080264 for Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST by Emeline Cherchame, Guy Ilango and Sabrina Cadel-Six in Bioinformatics and Biology Insights

Click here for additional data file.

Supplemental material, sj-txt-2-bbi-10.1177_11779322221080264 for Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST by Emeline Cherchame, Guy Ilango and Sabrina Cadel-Six in Bioinformatics and Biology Insights

Click here for additional data file.

Supplemental material, sj-xls-1-bbi-10.1177_11779322221080264 for Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST by Emeline Cherchame, Guy Ilango and Sabrina Cadel-Six in Bioinformatics and Biology Insights

Click here for additional data file.

Supplemental material, sj-xlsx-1-bbi-10.1177_11779322221080264 for Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST by Emeline Cherchame, Guy Ilango and Sabrina Cadel-Six in Bioinformatics and Biology Insights

Click here for additional data file.

Supplemental material, sj-xlsx-2-bbi-10.1177_11779322221080264 for Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using a Python Tool, SalmoDEST by Emeline Cherchame, Guy Ilango and Sabrina Cadel-Six in Bioinformatics and Biology Insights