Dependencies | Installation and execution | Usage | Notes | References | Citations

fq2dna

fq2dna (FASTQ files to de novo assembly) is a command line tool written in Bash to ease the de novo assembly of archaea, bacteria or virus genomes from raw high-throughput sequencing (HTS) paired-end (PE) reads.

Every data pre- and post-processing step is managed by fq2dna (e.g. HTS read filtering and enhancing, well-tuned de novo assemblies, scaffold sequence accuracy assessment). The main purpose of fq2dna is to efficiently use different methods, programs and tools to quickly infer accurate genome assemblies (e.g. from 5 to 20 minutes to deal with a bacteria HTS sample using 12 threads). This mini-workflow can therefore be very useful to deal with large batches of whole-genome shotgun sequencing data.

fq2dna runs on UNIX, Linux and most OS X operating systems.

Dependencies

Installation and execution

A. Clone this repository with the following command line:

git clone https://gitlab.pasteur.fr/GIPhy/fq2dna.git

B. Go to the created directory and give the execute permission to the file fq2dna.sh:

cd fq2dna/
chmod +x fq2dna.sh

C. Check the dependencies (and their version) using the following command line:

./fq2dna.sh  -d

Note that depending on the installation of some required programs, the corresponding variable can be assigned with complex commands. For example, as FASTA2AGP is a Java tool that can be run using a Java virtual machine, the executable jar file FASTA2AGP.jar can be used by fq2dna by editing the corresponding variable assignment instruction as follows: FASTA2AGP_BIN="java -jar FASTA2AGP.jar".

E. Execute fq2dna with the following command line model:

./fq2dna.sh  [options]

Usage

Run fq2dna without option to read the following documentation:

 USAGE:  fq2dna.sh  [options]

 Processing and assembling high-throughput sequencing (HTS) paired-end (PE) reads:
  + processing HTS reads  using different steps:  deduplicating [D], trimming/clipping [T], error
    correction [E], contaminant removal [C], merging [M], and/or digital normalization [N] 
  + de novo assembly [dna] of  the whole genome from  processed HTS reads,  followed by polishing
    and annotation steps (depending on the specified strategy)

 OPTIONS:
  -1 <infile>   fwd (R1) FASTQ input file name from PE library 1 || input files can be compressed
  -2 <infile>   rev (R2) FASTQ input file name from PE library 1 || using   either   gzip   (file
  -3 <infile>   fwd (R1) FASTQ input file name from PE library 2 || extension .gz), bzip2 (.bz or
  -4 <infile>   rev (R2) FASTQ input file name from PE library 2 || .bz2),   or  DSRC  (.dsrc  or
  -5 <infile>   fwd (R1) FASTQ input file name from PE library 3 || .dsrc2), or uncompressed (.fq
  -6 <infile>   rev (R2) FASTQ input file name from PE library 3 || or .fastq)
  -o <outdir>   path and name of the output directory (mandatory option)
  -b <string>   base name for output files (mandatory option)
  -s <char>     to set a predefined strategy for processing HTS reads among the following ones:
                  A   Archaea:     DT(C)E + [N|MN] + dna + polishing + annotation
                  B   Bacteria:    DT(C)E + [N|MN] + dna + polishing + annotation
                  E   Eukaryote:   DT(C)  + [N|MN] + dna
                  P   Prokaryote:  DT(C)E + [N|MN] + dna + polishing
                  S   Standard:    DT(C)  + [N|MN] + dna + polishing
                  V   Virus:       DT(C)E + [N|MN] + dna + polishing + annotation
                (default: S)
  -L <int>      minimum required length for a contig (default: 300)
  -T <"G S I">  Genus (G), Species (S) and Isolate (I)  names to be used  during annotation step;
                should be set between quotation  marks and separated by a blank space;  only with
                options -s A, -s B or -s V (default: "Genus sp. STRAIN")
  -q <int>      quality score threshold;  all bases  with Phred  score below  this threshold  are 
                considered as non-confident during step [T] (default: 20)
  -l <int>      minimum required length for a read (default: half the average read length)
  -p <int>      maximum allowed percentage  of non-confident bases  (as ruled  by option -q)  per 
                read (default: 50)
  -c <int>      minimum allowed coverage depth during step [N] (default: 3)
  -C <int>      maximum allowed coverage depth during step [N] (default: 60)
  -a <infile>   to set a file containing every  alien oligonucleotide sequence  (one per line) to
                be clipped during step [T] (see below)
  -a <string>   one or several key words (separated with commas),  each corresponding to a set of
                alien oligonucleotide sequences to be clipped during step [T]:
                  POLY                nucleotide homopolymers
                  NEXTERA             Illumina Nextera index Kits
                  IUDI                Illumina Unique Dual index Kits
                  AMPLISEQ            AmpliSeq for Illumina Panels
                  TRUSIGHT_PANCANCER  Illumina TruSight RNA Pan-Cancer Kits
                  TRUSEQ_UD           Illumina TruSeq Unique Dual index Kits
                  TRUSEQ_CD           Illumina TruSeq Combinatorial Dual index Kits
                  TRUSEQ_SINGLE       Illumina TruSeq Single index Kits
                  TRUSEQ_SMALLRNA     Illumina TruSeq Small RNA Kits
                Note that these sets of alien sequences are not exhaustive and will never replace
                the exact oligos used for library preparation (default: "POLY")
  -a AUTO       to (try to)  infer 3' alien  oligonucleotide  sequence(s) for step [T];  inferred
                oligo(s) are completed with those from "POLYS" (see above)
  -A <infile>   to set  sequence or  k-mer model  file(s) to carry  out contaminant  read removal
                during step [C];  several comma-separated  file names  can be specified;  allowed
                file extensions: .fa, .fasta, .fna, .kmr or .kmz
  -t <int>      number of threads (default: 12)
  -w <dir>      path to tmp directory (default: $TMPDIR, otherwise /tmp)
  -x            to not remove (after completing) the tmp directory inside the one set with option
                -w (default: not set)
  -d            checks dependencies and exit
  -h            prints this help and exit

  EXAMPLES:
    fq2dna.sh  -1 r1.fastq -2 r2.fastq  -o out -b ek12  -s P -T "Escherichia coli K12" -a AUTO
    fq2dna.sh  -1 rA.1.fq -2 rA.2.fq -3 rB.1.fq.gz -4 rB.2.fq.gz  -o out -b name  -a NEXTERA

Notes

• fq2dna is able to consider up to three paired-ends libraries (PE; options -1 to -6). Input files should be in FASTQ format and can be compressed using gzip, bzip2 or DSRC (Roguski and Deorowicz 2014).

• In brief, fq2dna first performs initial basic HTS read preprocessing (i.e. step I) using fqCleanER, i.e. deduplication (D), trimming/clipping (T; as ruled by options -a, -q, -l and -p) and error correction (E); when specified (option -A), a contaminating HTS read removal step (C ) can also be performed.
  Next, fq2dna creates two distinct datasets from the preprocessed HTS reads: a first one (i.e. step N) obtained using a digital normalization procedure (N; as ruled by options -C and -c), and a second one (i.e. step M) by merging the PE HTS reads with short insert size (M) followed by a digital normalization procedure. Each of these two HTS datasets is used to infer a de novo genome assembly (dnaN and dnaM, respectively) using SPAdes (Bankevich et al. 2012).
  Among the two genome assemblies, the less fragmented one is retained, and next polished (i.e. correcting putative local assembly errors such as mismatches and short indels) using samtools on the aligned step I HTS reads. Polished scaffolds are then used together with their generating (step N or M) HTS reads to infer a genome coverage profile (e.g. Lindner et al. 2013); based on this coverage profile, sufficiently long and well-covered scaffold sequences are finally selected.
  Depending on the specified strategy (option -s), selected scaffold sequences are classified as chromosome/plasmid/undetermined (i.e. CPU) using Platon (Schwengers et al. 2020) and/or annotated using Prokka (Seemann 2014).

• Output files are all defined by the same specified prefix (mandatory option -b) and written into a specified output directory (mandatory option -o). Each output file content is determined by its file extension:

• Temporary files are written into a dedicated directory created into the $TMPDIR directory (when defined, otherwise /tmp/). When possible, it is highly recommended to set a temp directory with large capacity (option -w).

• Default options lead to a de novo assembly inferred from a subset of well-suited HTS read (digital normalization) corresponding to 60x coverage depth (option -C), which is a good tradeoff to observe accurate results with fast running times. It is therefore expected that (i) the genome coverage profile (output file <prefix>._cov.info.txt_) is a unimodal distribution with both mean and mode close to 60x, and (ii) most of the selected scaffold sequences (<prefix>._scf.fasta_) are supported by 60x coverage depth on average (covr values in FASTA headers). Strong deviation from the specified coverage depth (option -C) can be indicative of a sequencing problem, e.g. low coverage depth (small mode value), locally insufficient coverage depth (very negative skewness), sample contamination (bimodal distribution). To ease the identification of contaminated samples, an original mono-modality (momo) index is available in the output file <prefix>._cov.info.txt_, e.g. non-monomodal distributions generally lead to momo < 0.99.

• Of important note is that the genome coverage profile is also used to fit a theoretical distribution using SAM2MAP (i.e. Negative Binomial or Generalized Poisson, depending on the variance). Such a theoretical distribution enables to determine a coverage depth cutoff, under which any observed coverage depth is considered as significantly low (p-value ≤ 0.000001). Every assembled base with coverage depth lower than the estimated coverage cutoff (specified in FASTA headers) is written in lowercase (into both files <prefix>._all.fasta_ and <prefix>._scf.fasta_) and should be considered with caution. If the selected scaffold sequences (i.e. with average coverage depth greater than the coverage cutoff; output file <prefix>._scf.fasta_) do not seems to represent the whole genome, try to consider the entire set of assembled scaffolds (output file <prefix>._all.fasta_).

• An ambiguously assembled base is defined as a (uppercase) character state N that is sufficiently covered by its generating HTS reads (e.g. non-monoallelic position). Every ambiguously assembled base is listed in the tab-delimited output file <prefix>._scf.amb.txt_ (one per line) together with an estimate of the sequenced content corresponding to this assembled position (i.e. proportions of A, C, G, T and gaps, respectively, estimated from the alignment of the generating HTS reads).

• For more details about the different assembly and scaffold sequence statistics (output files <prefix>._dna.info.txt_ and <prefix>._scf.info.txt_, respectively), see the documentation of contig_info.

• For more details about the chromosome/plasmid/undetermined classification of each scaffold sequence (i.e. column CPU in <prefix>._scf.info.txt_ when using option -s B), see the documentation of Platon.

• For more details about the annotation procedure when using strategies A, B or V (option -s; output files <prefix>._gbk_ and <prefix>._gbk.info.txt_), see the documentation of Prokka.

Example

In order to illustrate the usefulness of fq2dna and to better describe its output files, the following use case example describes its usage for (re)assembling the draft genome of Listeria monocytogenes 2HF33 (Duru et al. 2020).

All output files are available in the directory example/ (the four large sequence files are compressed using gzip), as well as the version of every used tool and program (see program.versions.txt).

Downloading input files

Paired-end sequencing of this genome was performed using Illumina Miseq, and the resulting pair of (compressed) FASTQ files (112 Mb and 128 Mb, respectively) can be downloaded using the following command lines:

wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR403/006/ERR4032786/ERR4032786_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR403/006/ERR4032786/ERR4032786_2.fastq.gz

As phiX genome was used as spike-in during Illumina sequencing (Duru et al. 2020), this putative contaminating sequence (5.4 kb) can be downloded using the following command line:

wget -O phiX.fasta "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nuccore&rettype=fasta&id=NC_001422.1"

Running fq2dna

Below is a typical command line to run fq2dna on PE FASTQ files to deal with bacteria HTS data:

./fq2dna.sh -1 ERR4032786_1.fastq.gz -2 ERR4032786_2.fastq.gz -a NEXTERA -A phiX.fasta   \\
            -s B -T "Listeria monocytogenes 2HF33" -o 2HF33 -b Lm.2HF33 -t 12 -w /tmp

Of note, option -a NEXTERA was set to clip the Nextera XT technical oligonucleotides used during library preparation (see Duru et al. 2020), the putative contaminating sequence file phiX.fasta was set using option -A, and the strategy ‘Bacteria’ was set (option -s B).

Different log outputs can be observed depending on the OS and/or installed program versions (see Dependencies). Below is the one observed when using fq2dna together with program and tool versions listed into example/program.versions.txt:

# fq2dna v25.03
# Copyright (C) 2016-2025 Institut Pasteur
+ https://gitlab.pasteur.fr/GIPhy/fq2dna
> System: x86_64-redhat-linux-gnu
> Bash:   4.4.20(1)-release
# input file(s):
> PE lib 1
+ FQ11: [111.9 Mb]  ERR4032786_1.fastq.gz
+ FQ12: [127.8 Mb]  ERR4032786_2.fastq.gz
# output directory
+ OUTDIR=2HF33
# tmp directory
+ TMP_DIR=/tmp/fq2dna.Lm.2HF33.Iqb7XNY1Q
# STRATEGY B: Bacteria
[00:00] Deduplicating, Trimming, Clipping, Decontaminating and Correcting reads ... [ok]
[03:00] Merging and/or Normalizing reads ... [ok]
[04:07] Approximating genome size ... [ok]
> 3112890 bps (N=3112026 M=3113754)
[04:12] Assembling genome with/without PE read merging (dnaM/dnaN) ... [ok]
> [dnaN]  covr=67   arl=278   k=21,33,55,77,99,121
> [dnaM]  covr=60   arl=279   k=21,33,55,77,99,121
[06:31] Comparing genome assemblies ... [ok]
> [dnaN]  Nseq=32   Nres=3099402   NG50=361897   auGN=462403
> [dnaM]  Nseq=33   Nres=3099184   NG50=452690   auGN=346675
> selecting dnaN
> [dna]   Nseq=32   Nres=3099402    N50=361897    auN=464415
[06:33] Aligning PE reads against scaffolds .... [ok]
[06:51] Polishing scaffolds ...
> [dna]   Nseq=32   Nres=3099547   NG50=361909   auGN=462419
> [dna]   Nseq=31   Nres=3099603   NG50=361915   auGN=462429
> [dna]   Nseq=31   Nres=3099603    N50=361915    auN=464406
[07:38] Processing scaffolds ... [ok]
> expect. cov.        60
> avg. cov.           65.3006
> cov. mode           65
> momo index          0.999494
> accuracy            0.994893
> min. cov. cutoff    40
> ambiguous pos.      19
> [dna]   Nseq=26   Nres=3097515    N50=361915    auN=464718
[08:10] Annotating scaffold sequences ... [ok]
> taxon:     Listeria monocytogenes 2HF33
> locus tag: LISMON2HF33
# output files:
+ de novo assembly (all scaffolds):         2HF33/Lm.2HF33.all.fasta
+ coverage profile summary:                 2HF33/Lm.2HF33.cov.info.txt
+ de novo assembly (selected scaffolds):    2HF33/Lm.2HF33.scf.fasta
+ sequence stats (selected scaffolds):      2HF33/Lm.2HF33.scf.info.txt
+ ambiguous positions (selected scaffolds): 2HF33/Lm.2HF33.scf.amb.txt
+ de novo assembly (selected contigs):      2HF33/Lm.2HF33.agp.fasta
+ scaffolding info:                         2HF33/Lm.2HF33.agp
+ descriptive statistics:                   2HF33/Lm.2HF33.dna.info.txt
+ insert size statistics:                   2HF33/Lm.2HF33.isd.txt
+ annotation (selected scaffolds):          2HF33/Lm.2HF33.scf.gbk
+ annotation info:                          2HF33/Lm.2HF33.scf.gbk.info.txt
[12:17] exit

This log output shows that the complete analysis was performed on 12 threads in less than 15 minutes (HTS read processing in < 5 minutes, de novo assemblies in ~2 minutes, and scaffold sequence post-processing in ~4 minutes). One can also observe that the selected de novo assembly was dnaN (i.e. greater NG50 and auGN metrics), therefore showing that the PE HTS read merging step (M) does not always enable to obtain better genome assemblies.

Output files

The genome coverage profile summary file (Lm.2HF33.cov.info.txt; see excerpt below) shows an average coverage depth of ~65x, corresponding to a symmetrical coverage depth distribution with mode 65×, close to the expected value (default option -C 60). The momo index is 0.999245 (above the mono-modality cutoff, e.g. 0.99), therefore suggesting that no contamination occurred in the HTS reads selected for assembly. The overall accuracy is assessed by the following formula: momo × min( ocov/ecov , mode/ocov ), where ocov and ecov are the observed and expected coverage depths, respectively. As ocov = 65.3006, ecov = 60 (option -C) and mode = 65, this leads to accuracy = 0.994893, therefore suggesting that the sequencing data are of good quality (i.e. accuracy ≥ 0.99).

=  observed coverage distribution:  no.pos=3100533
   avg=65.3006  mode=65  momo=0.999494  accuracy=0.994893

#  Poisson(l) coverage tail distribution:  l=0.17783505  w=0.00012514
*  GP(l',r) coverage distribution:  l'=96.67031669  r=-0.47633017  1-w=0.99987486
   min.cov.cutoff=40 (p-value<=0.000001)

These different statistics therefore assess that the assembled sequences are accurate and well-supported by sufficient data. Finally, a generalized Poisson (GP) distribution (that fits the observed genome coverage profile) enables to determine a minimum coverage cutoff (i.e. 40×) under which any assembled base is considered as putatively not trustworthy (lowercase characters in the scaffold sequence file Lm.2HF33.scf.fasta).

The tab-delimited file Lm.2HF33.dna.info.txt shows that the de novo assembly procedure led to quite few scaffold and contig sequences:

#File               Nseq Nres    A      C      G      T      N   %A     %C     %G     %T     %N    %AT    %GC    Min Q25  Med   Q75    Max     Avg       auN    N50    N75    N90   L50 L75 L90
Lm.2HF33.all.fasta  34   3100965 943191 604057 567949 985468 300 30.41% 19.47% 18.31% 31.77% 0.00% 62.21% 37.79% 237 440  1664  99066  1091349 91204.85  559175 532745 125907 97509 2   6   10
Lm.2HF33.scf.fasta  24   3097730 942310 603413 567209 984585 213 30.41% 19.47% 18.31% 31.78% 0.00% 62.21% 37.79% 407 1251 46823 108604 1091380 129072.08 559792 532745 125913 97524 2   6   10
Lm.2HF33.agp.fasta  26   3097550 942310 603412 567209 984584 35  30.42% 19.48% 18.31% 31.78% 0.00% 62.21% 37.79% 407 1664 46823 125913 935180  119136.53 464718 361915 125913 90129 3   7   11

Of note, 10 small (min. length = 300 bps; option -L) and/or insufficiently covered sequences (cutoff = 40×; see Lm.2HF33.cov.info.txt) were not selected for the final scaffold sequence set (i.e. Lm.2HF33.scf.fasta), therefore leading to a final assembled genome of total size 3,097,550 bps (Nres), represented in 26 contigs (Nseq), with 37.79% GC-content.

The tab-delimited file Lm.2HF33.scf.info.txt (displayed below) shows different residue statistics for each final scaffold sequence. Note that each FASTA header (info files Lm.2HF33.all.fasta and Lm.2HF33.scf.fasta) contains the k-mer coverage depth returned by SPAdes (covk), the base coverage depth estimated by fq2dna (covr), and the (low) coverage cutoff derived from the coverage profile analysis (cutoff).

#Seq      Nres    A      C      G      T      N   %A     %C     %G     %T     %N    %AT    %GC    Pval   CPU
NODE_0001 1091380 330565 212930 195683 352102 100 30.28% 19.51% 17.92% 32.26% 0.00% 62.56% 37.44% 0.0245 C
NODE_0002 532745  154855 110898 91560  175432 0   29.06% 20.81% 17.18% 32.92% 0.00% 62.00% 38.00% 0.5029 C
NODE_0003 361915  113234 65662  74541  108478 0   31.28% 18.14% 20.59% 29.97% 0.00% 61.27% 38.73% 0.0013 C
NODE_0004 206465  68180  35149  42251  60880  5   33.02% 17.02% 20.46% 29.48% 0.00% 62.52% 37.48% 0.0087 C
NODE_0005 126584  37676  26293  21299  41316  0   29.76% 20.77% 16.82% 32.63% 0.00% 62.41% 37.59% 0.2969 C
NODE_0006 125913  41257  22119  25733  36804  0   32.76% 17.56% 20.43% 29.22% 0.00% 62.00% 38.00% 0.9984 C
NODE_0007 108604  32364  22660  17840  35740  0   29.80% 20.86% 16.42% 32.90% 0.00% 62.71% 37.29% 0.0347 C
NODE_0008 103527  29748  21599  16744  35344  92  28.73% 20.86% 16.17% 34.13% 0.08% 62.94% 37.06% 0.0106 C
NODE_0009 99087   32746  17156  20513  28672  0   33.04% 17.31% 20.70% 28.93% 0.00% 61.99% 38.01% 0.8111 C
NODE_0010 97524   28264  20285  17249  31726  0   28.98% 20.80% 17.68% 32.53% 0.00% 61.52% 38.48% 0.0114 C
NODE_0011 64151   21653  11031  13320  18147  0   33.75% 17.19% 20.76% 28.28% 0.00% 62.05% 37.95% 0.9605 C
NODE_0012 61185   17687  12639  9717   21142  0   28.90% 20.65% 15.88% 34.55% 0.00% 63.47% 36.53% 0.0000 P
NODE_0013 46823   14085  9566   7968   15204  0   30.08% 20.43% 17.01% 32.47% 0.00% 62.56% 37.44% 0.1593 C
NODE_0014 35062   10004  7708   6005   11345  0   28.53% 21.98% 17.12% 32.35% 0.00% 60.89% 39.11% 0.0134 C
NODE_0015 22536   6000   4813   3643   8080   0   26.62% 21.35% 16.16% 35.85% 0.00% 62.48% 37.52% 0.1791 U
NODE_0016 4992    1394   1052   1469   1071   6   27.92% 21.07% 29.42% 21.45% 0.12% 49.44% 50.56% 0.0000 U
NODE_0017 2806    724    661    386    1035   0   25.80% 23.55% 13.75% 36.88% 0.00% 62.69% 37.31% 0.5037 U
NODE_0018 1664    524    292    266    582    0   31.49% 17.54% 15.98% 34.97% 0.00% 66.47% 33.53% 0.0080 U
NODE_0019 1251    317    215    219    498    2   25.33% 17.18% 17.50% 39.80% 0.15% 65.26% 34.74% 0.0523 C
NODE_0020 1126    398    199    265    263    1   35.34% 17.67% 23.53% 23.35% 0.08% 58.76% 41.24% 0.1237 U
NODE_0021 846     181    200    234    227    4   21.39% 23.64% 27.65% 26.83% 0.47% 48.46% 51.54% 0.0150 U
NODE_0022 628     239    86     114    186    3   38.05% 13.69% 18.15% 29.61% 0.47% 68.00% 32.00% 0.0648 U
NODE_0023 509     135    101    120    153    0   26.52% 19.84% 23.57% 30.05% 0.00% 56.59% 43.41% 0.0251 U
NODE_0027 407     80     99     70     158    0   19.65% 24.32% 17.19% 38.82% 0.00% 58.48% 41.52% 0.1986 C

The last column CPU also shows the classification of each scaffold sequence into the category ‘Chromosome’, ‘Plasmid’ or ‘Undetermined’ (i.e. C, P, U, respectively). Such a classification shows that the 12th scaffold sequence (i.e. NODE_0012) is likely a plasmid (subsequent BLAST searches indeed show that it is almost identical to pLM6179).

The tab-delimited file Lm.2HF33.scf.amb.txt (displayed below) lists the different assembled positions that are likely non mono-allelic.

#seq       lgt      pos     base  %A    %C    %G    %T    %gap
NODE_0001  1091380  481973  N     48.9  0     51.1  0     0
NODE_0001  1091380  481988  N     51.1  0     0     48.9  0
NODE_0004  206465   203562  N     26.1  0     0     73.9  0
NODE_0004  206465   203837  N     0     40.0  0     60.0  0
NODE_0004  206465   203838  N     0     40.8  0     59.2  0
NODE_0004  206465   203840  N     62.5  0     37.5  0     0
NODE_0004  206465   204068  N     38.3  0     61.7  0     0
NODE_0008  103527   13568   N     73.5  0     26.5  0     0
NODE_0008  103527   13622   N     28.8  0     71.2  0     0
NODE_0008  103527   13673   N     0     69.8  0     30.2  0
NODE_0016  4992     374     N     77.8  0     22.2  0     0
NODE_0016  4992     376     N     20.3  0     79.7  0     0
NODE_0019  1251     392     N     60.6  0     0     39.4  0
NODE_0021  846      314     N     0     0     0     59.7  40.3
NODE_0021  846      495     N     75.5  0     0     0     24.5
NODE_0021  846      497     N     0     0     76.0  24.0  0
NODE_0021  846      498     N     23.1  0     75.0  1.9   0
NODE_0022  628      468     N     0     71.7  0     28.3  0
NODE_0022  628      475     N     0     71.1  0     28.9  0

As the overall sequencing seems to not suffer from contamination (see above), (some of) these different ambiguous positions can be indicative of repeat regions of the genome.

References

Bankevich A, Nurk S, Antipov D, Gurevich A, Dvorkin M, Kulikov AS, Lesin V, Nikolenko S, Pham S, Prjibelski A, Pyshkin A, Sirotkin A, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA (2012) SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology, 19(5):455-477. doi:10.1089/cmb.2012.0021.

Duru IC, Andreevskaya M, Laine P, Rode TN, Ylinen A, Løvdal T, Bar N, Crauwels P, Riedel CU, Bucur FI, Nicolau AI, Auvinen P (2020) Genomic characterization of the most barotolerant Listeria monocytogenes RO15 strain compared to reference strains used to evaluate food high pressure processing. BMC Genomics, 21:455. doi:10.1186/s12864-020-06819-0.

Lindner MS, Kollock M, Zickmann F, Renard BY (2013) Analyzing genome coverage profiles with applications to quality control in metagenomics. Bioinformatics, 29(10):1260-1267. doi:10.1093/bioinformatics/btt147.

Roguski L, Deorowicz S (2014) DSRC 2: Industry-oriented compression of FASTQ files. Bioinformatics, 30(15):2213-2215. doi:10.1093/bioinformatics/btu208.

Schwengers O, Barth P, Falgenhauer L, Hain T, Chakraborty T, Goesmann A (2020) Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores. Microbial Genomics, 6(10):mgen000398. doi:10.1099/mgen.0.000398.

Seemann T (2014) Prokka: rapid prokaryotic genome annotation. Bioinformatics, 30(14):2068-2069. doi:10.1093/bioinformatics/btu153.

Citations

Abou Fayad A, Rafei R, Njamkepo E, Ezzeddine J, Hussein H, Sinno S, Gerges J-R, Barada S, Sleiman A, Assi M, Baakliny M, Hamedeh L, Mahfouz R, Dabboussi F, Feghali R, Mohsen Z, Rady A, Ghosn N, Abiad F, Abubakar A, Barakat A, Wauquier N, Quilici M-L, Hamze M, Weill F-X, Matar GM (2024) An unusual two-strain cholera outbreak in Lebanon, 2022-2023: a genomic epidemiology study. Nature Communications, 15:6963. doi:10.1038/s41467-024-51428-0

Crippa C, Pasquali F, Rodrigues C, De Cesare A, Lucchi A, Gambi L, Manfreda G, Brisse S, Palma F (2023) Genomic features of Klebsiella isolates from artisanal ready-to-eat food production facilities. Scientific Reports, 13:10957. doi:10.1038/s41598-023-37821-7

Hawkey J, Frézal L, Tran Dien A, Zhukova A, Brown D, Chattaway MA, Simon S, Izumiya H, Fields PI, De Lappe N, Kaftyreva L, Xu X, Isobe J, Clermont D, Njamkepo E, Akeda Y, Issenhuth-Jeanjean S, Makarova M, Wang Y, Hunt M, Jenkins BM, Ravel M, Guibert V, Serre E, Matveeva Z, Fabre L, Cormican M, Yue M, Zhu B, Morita M, Iqbal Z, Nodari CS, Pardos de la Gandara M, Weill F-X (2024) Genomic perspective on the bacillus causing paratyphoid B fever. Nature Communications, 15:10143. doi:10.1038/s41467-024-54418-4

Kämpfer P, Glaeser SP, Busse H-J, McInroy JA, Clermont D, Criscuolo A (2022) Pseudoneobacillus rhizosphaerae gen. nov., sp. nov., isolated from maize root rhizosphere. International Journal of Systematic and Evolutionary Biology, 72:5. doi:10.1099/ijsem.0.005367

Kämpfer P, Lipski A, McInroy JA, Clermont D, Criscuolo A, Glaeser SP (2022) Bacillus rhizoplanae sp. nov. from maize roots. International Journal of Systematic and Evolutionary Biology, 72:7. doi:10.1099/ijsem.0.005450

Kämpfer P, Lipski A, Lamothe L, Clermont D, Criscuolo A, McInroy JA, Glaeser SP (2023) Paenibacillus plantiphilus sp. nov. from the plant environment of Zea mays. Antonie van Leeuwenhoek, 116:883-892. doi:10.1007/s10482-023-01852-x

Kämpfer P, Glaeser SP, McInroy JA, Busse H-J, Clermont D, Criscuolo A (2024) Description of Cohnella rhizoplanae sp. nov., isolated from the root surface of soybean (Glycine max). Antonie van Leeuwenhoek, 118:41. doi:10.1007/s10482-024-02051-y

Palma F, Mangone I, Janowicz A, Moura A, Chiaverini A, Torresi M, Garofolo G, Criscuolo A, Brisse S, Di Pasquale A, Cammà C, Radomski N (2022) In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes. BMC Genomics, 23:235. doi:10.1186/s12864-022-08437-4

Rahi P, Mühle E, Scandola C, Touak G, Clermont D (2024) Genome sequence-based identification of Enterobacter strains and description of Enterobacter pasteurii sp. nov. Microbiology Spectrum, 12:e03150-23. doi:10.1128/spectrum.03150-23

Shelomi M, Han C-J, Chen W-M​, Chen H-K​, Liaw S-J​, Mühle E​, Clermont5 D (2023) Chryseobacterium oryctis sp. nov., isolated from the gut of the beetle Oryctes rhinoceros, and Chryseobacterium kimseyorum sp. nov., isolated from a stick insect rearing cage. International Journal of Systematic and Evolutionary Biology, 73:4. doi:10.1099/ijsem.0.005813

Vautrin N, Alexandre K, Pestel-Caron M, Bernard E, Fabre R, Leoz M, Dahyot S, Caron F (2023) Contribution of Antibiotic Susceptibility Testing and CH Typing Compared to Next-Generation Sequencing for the Diagnosis of Recurrent Urinary Tract Infections Due to Genetically Identical Escherichia coli Isolates: a Prospective Cohort Study of Cystitis in Women. Microbiology Spectrum, 11(4):e02785-22. doi:10.1128/spectrum.02785-22