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

You will need to install the required programs and tools listed in the following table, or to verify that they are already installed with the required version.

Mandatory programs

Optional programs

Standard GNU packages and utilities

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 $PathTo/FASTA2AGP.jar".

D. 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
                  G   Germs:      DT(C)E + [N|MN] + dna + polishing + annotation + classification
                  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, -s G 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: 2)
  -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
                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 infer 3' alien oligonucleotide sequence(s) for step [T]; inferred oligo(s) are
                completed with those from "POLY" (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
  -e            extensive de novo assembly (slow; default: not set)
  -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            check dependencies and exit
  -h            print this help and exit

  EXAMPLES:
    fq2dna.sh  -1 r1.fastq -2 r2.fastq  -o out -b ek12  -s G -T "Escherichia coli K12" -a NEXTERA
    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 AUTO
    fq2dna.sh  -1 hts.1.fq.gz -2 hts.2.fq.gz  -o out -b bact -s B  -a IUDI  -t 24  -e

Notes

• fq2dna is able to consider up to three paired-ends libraries (PE; options -1 to -6). FASTQ input files are read by fqCleanER; they 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 using CoPro. Based on the coverage confidence interval derived from the coverage profile, insufficiently covered scaffold sequences are discarded, whereas the remaining ones are enhanced.
  Depending on the specified strategy (option -s), selected scaffold sequences can be classified as chromosome/plasmid/undetermined (i.e. CPU) using Platon (Schwengers et al. 2020) and/or annotated using Bakta (Schwengers et al. 2021), provided that these two optional tools are available.

• Is is generally not recommended to modify the default values for trimming options -q , -l and -p. Increasing options -q and -l and/or decreasing option -p can negatively affect the local coverage of some sequenced regions.

• Use option -a AUTO each time you are unaware of the DNA library preparation kit. Setting -a AUTO will run the tool AlienDiscover to infer the technical oligonucleotide occurring in 3’ end (if any).

• By default, fq2dna generally assembles scaffold sequences with great N50 and auN accuracy metrics when run on high-quality input data (e.g. high %Q20, coverage depth ≥ 100×). However, you can use option -e to run SPAdes extensively, but at the cost of longer running times.

• 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).

• 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:

• A large set of descriptive statistics and metrics are available in the some output files. The descriptive statistics of the three FASTA files are written in <prefix>.dna.info.txt (e.g. no. Nseq and Nres of assembled sequences and bases, respectively, auN, N50,…). The residue content of each final scaffold sequence is written in <prefix>.scf.info.txt (e.g. length, GC-content, …). Statistics associated to the genome coverage profile analysis are summarized in <prefix>.cov.info.txt (e.g. low coverage cutoff).

• In order to quickly assess the overall accuracy of the assembled genome, different quality control metrics are summarized in <prefix>.QCsum.txt:

• Default options lead to a de novo assembly inferred from a subset of high-quality HTS reads corresponding to 60× coverage depth (option -C), which is a good tradeoff to observe accurate results with fast running times. It is therefore expected that the resulting genome coverage profile (output file <prefix>.cov.info.txt) is a unimodal histogram with both mean (covO) and mode close to the default 60×. Strong deviation from the specified coverage depth (option -C) can be indicative of a sequencing problem, e.g. sample contamination (bimodal histogram, assessed by e.g. momo ≤ 0.99), low coverage depth (i.e. small covO and/or covF), locally insufficient coverage depth (negative skewness, assessed by e.g. R2 ≤ 0.6). For more details about these quality control metrics, see the documentation of CoPro.

• Of important note is that the genome coverage profile (<prefix>.cov.info.txt) is also used to fit a (Generalized Poisson; GP) theoretical distribution. Such a GP distribution enables to determine a coverage depth cutoff, under which any observed coverage depth is considered as significantly low (p-value ≤ 0.00005; for more details, see the documentation of CoPro). Every assembled base with coverage depth lower than the estimated coverage cutoff (low in <prefix>.cov.info.txt) is written in lowercase into <prefix>.scf.fasta and should be considered with caution. If the selected scaffold sequences (i.e. with average coverage depth greater than the low coverage cutoff) do not seems to represent the whole genome, try to consider the entire set of assembled scaffolds (output file <prefix>.all.fasta) and/or rerun fq2dna with smaller expected coverage (option -C).

• An ambiguous base N is a multi-allelic position that is sufficiently covered by its generating HTS reads. These Namb bases are 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). It is worth noting that a large number Namb of ambiguous bases can sometimes be the result of a contaminated dataset.

• 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 G), see the documentation of Platon.

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

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 AUTO -A phiX.fasta -s G   \\
            -o 2HF33 -b Lm.2HF33 -T "Listeria monocytogenes 2HF33" -t 12 -w /tmp

Of note, option -a AUTO was set to let fq2dna infer the technical oligonucleotide used during library preparation, whereas the putative contaminating sequence file phiX.fasta was set using option -A. The strategy ‘Germs’ was set (option -s G) in order to also perform both scaffold annotation and classification.

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 v25.12
# 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]  541e697e87d7d80a70d29d3827f98d6d  ERR4032786_1.fastq.gz
+ FQ12: [127.9 Mb]  651a7443ba56bb52cdf1b43ba9e9d844  ERR4032786_2.fastq.gz
# output directory
+ OUTDIR=2HF33
# tmp directory
+ TMP_DIR=/tmp/fq2dna.Lm.2HF33.lh617yARn
# STRATEGY G: Germs
[00:01] Deduplicating, Trimming, Clipping, Decontaminating and Correcting reads ... [ok]
[02:46] Merging and/or Normalizing reads ... [ok]
[04:01] Approximating genome size ... [ok]
> 3112666 bps (N=3112091 M=3113241)
[04:05] Assembling genome with/without PE read merging (dnaM/dnaN) .... [ok]
> [dnaN]  covr=62   arl=285   k=21,33,55,77,99,121
> [dnaM]  covr=60   arl=281   k=21,33,55,77,99,121
[06:26] Comparing genome assemblies ... [ok]
> [dnaN]  Nseq=31   Nres=3099160   NG50=361897   auGN=462498
> [dnaM]  Nseq=34   Nres=3099305   NG50=452690   auGN=346318
> selecting dnaN
> [dna]   Nseq=31   Nres=3099160    N50=361897    auN=464514
[06:27] Aligning PE reads against scaffolds .... [ok]
[06:40] Polishing scaffolds ... 
> [dna]   Nseq=31   Nres=3099288   NG50=361909   auGN=462513
> [dna]   Nseq=30   Nres=3099356   NG50=361921   auGN=462523
> [dna]   Nseq=30   Nres=3099356    N50=361921    auN=464506
[07:24] Processing scaffolds ... [ok]
> expect. cov.        60
> avg. cov.           60.45
> cov. mode           60
> momo index          0.99913539
> accuracy            0.878995
> min. cov. cutoff    43
> ambiguous pos.      5
> [dna]   Nseq=23   Nres=3096373    N50=361921    auN=464956
[07:43] 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
+ quality control summary metrics:          2HF33/Lm.2HF33.QCsum.txt
[16:15] exit 

This log output shows that the complete analysis was performed on 12 threads in ~16 minutes (HTS read processing in ~4 minutes, de novo assemblies in ~2 minutes, scaffold sequence post-processing in ~2 minutes, annotation and classification in ~8 minutes). One can also observe that the selected de novo assembly was dnaN (i.e. lower number of contigs, greater auGN metric), therefore showing that the PE HTS read merging step (M) does not always enable to obtain better genome assemblies. The final genome assembly has a total size of 3.09 Mbps, represented in 23 contigs, with N50=361,921 bps (for comparison sake, the assembly accession of the original assembly is GCF_902838845).

Output files

The genome coverage profile summary file (Lm.2HF33.cov.info.txt; see excerpt below) shows an average coverage depth of ~60×, corresponding to a symmetrical coverage depth distribution with mode 60×, identical to the expected value (default option -C 60). The momo index is 0.9991 (above the mono-modality cutoff, e.g. 0.99), therefore suggesting that no contamination occurred in the HTS reads selected for assembly. The fitted Generalized Poisson (GP) distribution has a mean of 60.09, and its derived metric R2 (= 0.8181) suggests an expected uniform coverage across the assembled positions.

== observed coverage distribution
   no.pos  3101232
   avg     60.45
   mode    60
   momo    0.99913539
** GP(lambda,theta) distribution
   lambda  109.26055591
   theta   -0.81815798
   mean    60.09
   R2      0.87975610
-- confidence interval (p-value=0.00005)
   low     43
   sup     76

These different statistics therefore assess that the assembled sequences are accurate and well-supported by sufficient data. Finally, the GP distribution enables to determine the low coverage cutoff (i.e. 43×) 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  35    3101208  943528 604087 568021 985272 300  30.42% 19.47% 18.31% 31.77% 0.00%  62.21% 37.79%  237 372  1664  99066  1091349 88605.94  559194 532745 125907 97509  2   6   10
Lm.2HF33.scf.fasta  21    3096552  942169 603187 566991 984026 179  30.42% 19.47% 18.31% 31.77% 0.00%  62.21% 37.79%  628 4992 64421 125913 1091380 147454.85 560067 532745 125913 97509  2   6   10
Lm.2HF33.agp.fasta  23    3096385  942169 603187 566991 984026 12   30.42% 19.48% 18.31% 31.77% 0.00%  62.21% 37.79%  628 4992 64421 126576 935183  134625.43 464956 361921 125913 90129  3   7   11

Of note, 14 small (min. length = 300 bps; option -L) and/or insufficiently covered sequences (cutoff = 43×; see Lm.2HF33.cov.info.txt) were not selected for the final scaffold sequence set (i.e. Lm.2HF33.scf.fasta), therefore leading to the final assembled genome of total length Nres = 3,096,552 bps, represented in Nseq = 21 scaffold sequences (scaffold and contig N50 = 532,745 and 361,921 bps, respectively).

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 file Lm.2HF33.scf.fasta contains the k-mer coverage depth returned by SPAdes (covk), the read 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 330567 212931 195684 352105 93 30.28% 19.51% 17.92% 32.26% 0.00% 62.56% 37.44% 0.0116 C
NODE_0002 532745  154855 110898 91560  175432 0  29.06% 20.81% 17.18% 32.92% 0.00% 62.00% 38.00% 0.6892 C
NODE_0003 361921  113236 65663  74544  108478 0  31.28% 18.14% 20.59% 29.97% 0.00% 61.27% 38.73% 0.0032 C
NODE_0004 206455  68177  35148  42251  60874  5  33.02% 17.02% 20.46% 29.48% 0.00% 62.51% 37.49% 0.0091 C
NODE_0005 126576  37673  26289  21299  41315  0  29.76% 20.76% 16.82% 32.64% 0.00% 62.41% 37.59% 0.2115 C
NODE_0006 125913  41257  22119  25733  36804  0  32.76% 17.56% 20.43% 29.22% 0.00% 62.00% 38.00% 0.8299 C
NODE_0007 109341  32663  22787  17974  35916  1  29.87% 20.84% 16.43% 32.84% 0.00% 62.73% 37.27% 0.0147 C
NODE_0008 103533  29753  21604  16750  35350  76 28.73% 20.86% 16.17% 34.14% 0.07% 62.93% 37.07% 0.0003 C
NODE_0009 99090   32747  17157  20514  28672  0  33.04% 17.31% 20.70% 28.93% 0.00% 61.99% 38.01% 0.9410 C
NODE_0010 97509   28253  20285  17248  31723  0  28.97% 20.80% 17.68% 32.53% 0.00% 61.51% 38.49% 0.0245 C
NODE_0011 64421   21749  11088  13365  18219  0  33.76% 17.21% 20.74% 28.28% 0.00% 62.05% 37.95% 0.6433 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% 0.1143 C
NODE_0014 35062   10004  7708   6005   11345  0  28.53% 21.98% 17.12% 32.35% 0.00% 60.89% 39.11% 0.0229 C
NODE_0015 22536   6000   4813   3643   8080   0  26.62% 21.35% 16.16% 35.85% 0.00% 62.48% 37.52% 0.1343 U
NODE_0016 4992    1395   1052   1470   1073   2  27.94% 21.07% 29.44% 21.49% 0.04% 49.46% 50.54% 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% 0.4654 U
NODE_0018 1664    524    292    266    582    0  31.49% 17.54% 15.98% 34.97% 0.00% 66.47% 33.53% 0.0078 U
NODE_0019 1126    398    199    264    263    2  35.34% 17.67% 23.44% 23.35% 0.17% 58.81% 41.19% 0.1331 U
NODE_0020 846     182    200    236    228    0  21.51% 23.64% 27.89% 26.95% 0.00% 48.47% 51.53% 0.0017 U
NODE_0021 628     240    88     114    186    0  38.21% 14.01% 18.15% 29.61% 0.00% 67.84% 32.16% 0.0219 U

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), as assessed by Platon via the selected strategy -s G. 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 Namb = 5 assembled positions that are likely multi-allelic.

#seq      lgt     pos    base %A    %C    %G    %T    %gap
NODE_0001 1091380 481973 N    50.0  0     50.0  0     0
NODE_0004 206455  203837 N    0     39.6  0     60.4  0
NODE_0004 206455  203838 N    0     42.6  0     57.4  0
NODE_0004 206455  203840 N    59.6  0     40.4  0     0
NODE_0004 206455  204068 N    37.0  0     63.0  0     0

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.

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Citations

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