PathoSeq-QC : a bioinformatic pipeline that allows robust analysis of next generation sequencing raw data associated to viruses.
Date: 2024-06-03
Institutions: Joint Research Centre
Subjects: bioinformatics, data processing, decision-making, disease prevention, medical computing, medical research, report, research method, viral disease

PathoSeq-QC: a bioinformatic pipeline that 
allows  robust  analysis  of  next  generation 
sequencing raw data associated to viruses 

Leoni, G., Petrillo, M. 

JRC.F.7 
2024 

 
  
  
 
 
This  document  is  a  publication  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science  and  knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking  process. The contents of this 
publication  do  not  necessarily  reflect  the  position  or  opinion  of  the  European  Commission.  Neither  the  European 
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concerning the delimitation of its frontiers or boundaries. 

Contact information 
Name: Gabriele Leoni 
Address: JRC-ISPRA 
Email: Gabriele.LEONI@ec.europa.eu 
Tel.: 39 0332 78 5398 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC135827 

PDF  

ISBN 978-92-68-16698-7   doi:10.2760/316105  

KJ-09-24-369-EN-N 

Luxembourg: Publications Office of the European Union, 2024  

© European Union, 2024  

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How to cite this report: European Commission, Joint Research Centre, Leoni, G. and Petrillo, M., PathoSeq-QC: a bioinformatic pipeline that 
allows robust analysis of next generation sequencing raw data associated to viruses., Publications Office of the European Union, Luxembourg, 
2024, https://data.europa.eu/doi/10.2760/316105, JRC135827. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents  

Abstract ....................................................................................................................................................................................................................................................................... 2 

1 

Introduction..................................................................................................................................................................................................................................................... 4 

2  PathoSeq-QC user guide .................................................................................................................................................................................................................... 5 

2.1  Pipeline and description of modules ......................................................................................................................................................................... 5 

2.1.1 

ReadStrainScanner (ReadSS) ......................................................................................................................................................................... 5 

2.1.2 

CovigatOff module .................................................................................................................................................................................................. 6 

2.1.2.1 

Custom lineage assignation approach .................................................................................................................................... 7 

2.1.3 

InSilicoSeq module................................................................................................................................................................................................... 9 

2.1.4  MakeReport module ................................................................................................................................................................................................ 9 

2.2  Software information and Installation guide ................................................................................................................................................... 9 

2.2.1 

Installation guide ....................................................................................................................................................................................................... 9 

2.2.1.1 

VirStrain database creation ............................................................................................................................................................ 12 

2.3  Run examples ............................................................................................................................................................................................................................... 12 

2.4  Command options .................................................................................................................................................................................................................... 12 

2.5  Output description ................................................................................................................................................................................................................... 13 

2.5.1 

.Report.tab file ........................................................................................................................................................................................................... 13 

2.5.2 

.Report.VarData.tab file .................................................................................................................................................................................... 13 

2.5.3 

.Report.VarDB.tab file ......................................................................................................................................................................................... 14 

2.5.4 

.ReportOverlaps.txt file ...................................................................................................................................................................................... 14 

2.5.5 

.ReportSiteCov.txt file ......................................................................................................................................................................................... 15 

2.5.6 

.Report.VarCov.txt file ......................................................................................................................................................................................... 15 

2.5.7 

.Report.Plots.pdf file ............................................................................................................................................................................................. 16 

2.6  Code availability ........................................................................................................................................................................................................................ 16 

2.7  Terms and conditions ........................................................................................................................................................................................................... 16 

3  Conclusions .................................................................................................................................................................................................................................................. 17 

References ............................................................................................................................................................................................................................................................. 18 

List of abbreviations and definitions ........................................................................................................................................................................................... 20 

List of figures ..................................................................................................................................................................................................................................................... 21 

List of tables ........................................................................................................................................................................................................................................................ 22 

Annexes .................................................................................................................................................................................................................................................................... 23 

Annex 1. PathoSeq-QC command options ..................................................................................................................................................................... 23 

Annex 2. Report.Plots.pdf file from the analyses of the AY.3 consensus sequence (pure lineage). ....................... 24 

Annex 3. Report.Plots.pdf file from the analyses of a 20/80% mix of AY.3/BA.2 reads. .................................................. 25 

1 

 
 
 
Abstract 

High-throughput genomic sequencing emerged as a key technology in combating global health threats such 
as  the  COVID-19  pandemic.  Integrated  with  Bioinformatics  and  epidemiological  data,  it  provides  crucial 
insights  into  pathogenic  threats,  their  evolutionary  dynamics,  and  facilitates  the  monitoring  of  pathogen 
spread. Moreover, it aids in evaluating the efficacy of policy interventions. 

From  a  policymaking  perspective,  standardized  and  harmonized  quality  procedures  are  essential  prior  to 
drawing  any  analysis  and  conclusions,  particularly  during  pandemics  where  timely  and  accurate  results  are 
imperative  for  effective  decision-making.  Without  such  measures,  there  exists  a  significant  risk  of 
misinterpretation of data. For instance, erroneous identification of nucleotide mutations in genomic analyses 
may  lead  to  the  mistaken  assumption  of  the  emergence  of  novel  lineages,  potentially  influencing  policy 
decisions  regarding  countermeasures.  Additionally, the  absence  of  shared  protocols  and workflows  between 
different institutions might lead to different conclusions even when starting from the same input data. 

Based  on  the  previous,  we  introduce  PathoSeq-QC,  a  bioinformatics  "decision-tree"  workflow  designed  to 
ensure that conclusions drawn from genomic data are based on robust and high-quality inputs. This versatile 
tool not only evaluates sample and sequencing data quality but also robustly identifies variants and assigns 
viral  lineages.  While  initially  tailored  for  SARS-CoV-2,  PathoSeq-QC  can  be  adapted  to  various  viral  threats. 
PathoSeq-QC is anticipated to enhance the resilience of pathogen surveillance protocols by applying stringent 
quality  criteria,  thereby  mitigating  the  risk  of  erroneous  conclusions  that  could  impact  both  the  work  of 
researches and the decision-making processes. 

2 

 
 
 
 
Authors 

Gabriele Leoni (JRC.F.7), Mauro Petrillo (Seidor Srl)1. 

Acknowledgments 

The  authors  express  their  gratitude  to  Sandra  Coecke  (JRC.F.7),  Maddalena  Querci  (JRC.F.7),  and  Tobias 
Wiesenthal  (JRC.F.7)  for  their  unwavering  support  throughout  this  project.  Special  appreciation  is  also 
extended to Victoria Ruiz Serra (JRC.F.7) for her valuable ideas and insightful comments. 

1 This work was carried out for the JRC.F.7 Unit at JRC Ispra. 

3 

 
 
 
 
                                                        
 
1 

Introduction  

Recommendations regarding the generation and utilization of genomic data for surveillance signify the pivotal 
role genomics plays in combatting global health threats  such as the COVID-19 pandemic. Genomic analysis 
not  only  yields  valuable  insights  into  emerging  pathogenic  threats  like  SARS-CoV-2  but  also  aids  in 
understanding their evolutionary trajectory and assessing the effectiveness of implemented countermeasures 
at the policymaking level. This integration of genomics with  Bioinformatics, along with epidemiological data, 
forms a robust framework for monitoring pathogen spread and informing public health decisions. 

For  instance,  genomic  sequencing  of  SARS-CoV-2  enables  the  identification  of  lineages  and  variants  of 
concern (VOCs) through the detection of specific sequence mutations relative to a reference sequence. These 
mutations and VOCs are instrumental in epidemiological modelling, shedding light on the dynamics of disease 
transmission within populations. 

However,  the  bioinformatics  processing  of  sequence  data  entails  a  series  of  procedures  that  are  neither 
uniform nor infallible. For example, variant calling, a crucial bioinformatics step for determining nucleic acid 
mutations  from  raw  sequence  data,  which  is  essential  for  a  proper  virus  lineage  assignation,  is  highly 
dependent on the choice of bioinformatics software and parameters, leading to potentially divergent results if 
altered.  Therefore,  particularly  in  decision-making  contexts,  it  is  imperative  to  rigorously  evaluate  the 
robustness of bioinformatics workflows, as they directly impact the reliability of sequence data interpretation, 
including  mutation  identification  and  lineage  characterization.  Adding  to  this  challenge  is  the  lack  of 
standardized guidelines and protocols for conducting thorough and replicable analyses of SARS-CoV-2 data. 
This  has  led  to  a  proliferation  of  tools  and  pipelines  utilizing  diverse  methodologies  and  software,  further 
complicating the situation. 

In  response  to  these  challenges,  we  have  developed  PathoSeq-QC,  a  modular  bioinformatics  "decision-tree" 
workflow initially tailored for SARS-CoV-2 but adaptable to any pathogenic threat. This pipeline is designed to 
assist users in assessing the integrity of their conclusions by ensuring the use of high-quality input data. By 
analysing  next-generation  sequencing  (NGS)  raw  data,  PathoSeq-QC  offers  capabilities  such  as  evaluating 
data  quality,  determining  lineage  composition,  and  identifying  recombinant  viruses  or  novel  lineages, 
providing also information relative to the quality of the original sample and of the sequencing data. 

This report serves as a comprehensive  user  guide for utilizing the  PathoSeq-QC  pipeline and interpreting its 
outputs  effectively.  A  separate  technical  report  (JRC137175)  delves  into  the  pipeline's  underlying  logic, 
strengths, limitations, and includes practical applications on both simulated and real-world datasets. 

4 

 
 
 
 
2  PathoSeq-QC user guide 

In this chapter we describe how to install the tool, how to run it and how to interpret its outputs. 

2.1  Pipeline and description of modules 

PathoSeq-QC is a command line tool meant to run on UNIX environments. It is composed of 4 main modules: 

- 

- 

- 

ReadStrainScanner. 

CovigatOff. 

InSilicoSeq. 

-  MakeReport. 

2.1.1  ReadStrainScanner (ReadSS) 

The  ReadSS  module  can  perform  raw  reads  alignment  versus  a  set  of  SARS-CoV-2  consensus  sequences 
using the bbmap2 tool. These consensus sequences were generated based on the variants observed in at least 
95% of the sequences retrieved from the Global Initiative on Sharing All Influenza Data3 (GISAID) database. 
The module calculates the frequencies of UnAmbiguously mapped (i.e., reads that specifically map only onto 1 
consensus)  and  Ambiguously  mapped  reads  (i.e.,  reads  that  can  be  mapped  equally  to  different  consensus 
sequences)  for each  consensus  sequence.  This  information  is  used  to  highlight  reads  that  might  potentially 
belong to a recombinant sequence or might support the assigned lineage instead (usually performed with the 
Pangolin4 tool). This module relies on the following bbmap command: 

bbmap.sh  -Xmx$MEM  threads=$THREADS  ref=$CONSENSUS  perfectmode=T  maxsites=1  out=$MAPPED.bam 
outu=$UNMAPPED.BAM scafstats=$SCAFSTATS bamscript=bs.sh 

Mapped and unmapped reads are stored and saved into different files to allow further investigations by the 
user. The possibility to exclude the ReadSS step is also available, as it represents the most consuming step in 
terms of both hardware and time, and similar results can be achieved with VirStrain analyses (implemented 
into the CovigatOff module and discussed below). 

2 https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/bbmap-guide/ 
3 https://gisaid.org/ 
4 Áine O’Toole, Emily Scher, Anthony Underwood, Ben Jackson, Verity Hill, John T McCrone, Rachel Colquhoun, Chris Ruis, Khalil Abu-Dahab, 
en Taylor, Corin Yeats, Louis Du Plessis, Daniel Maloney, Nathan Medd, Stephen W Attwood, David M Aanensen, Edward C Holmes, 
Oliver G Pybus, Andrew Rambaut (2021) Virus Evolution DOI:10.1093/ve/veab064 

5 

 
 
 
 
 
 
 
 
                                                        
 
2.1.2  CovigatOff module 

The  CovigatOff  module  is  an  off-line  implementation  of  the  CoVigator5  pipeline.  Briefly,  this  module  can 
perform:  

1. Raw reads quality controls (using Fastp6); 

2. Read preprocessing (using GATK7); 

3. Mapping (using Bwa); 

4. Variant Calling (using GATK and LoFreq8); 

5. Variant Annotation (using Vafator9 and SnpEff10); 

6. Lineage assignation (using Pangolin/VirStrain11/Freyja12/Custom approach). 

7. Canonical sgRNA discovery (using Periscope13); 

A significant enhancement from the original CoVigator pipeline is the capability to execute it offline, following 
the  setup  of  all  necessary  conda  environments  (see  chapter  2.2).  This  enables  users  to  leverage  High-
Performance  Computing  (HPC)  infrastructures,  typically  isolated  from  the  internet  for  security  purposes. 
Another notable improvement is the utilization of multithreaded versions of fastp and LoFreq tools, enhancing 
the speed of certain processes. 

5  Bukur,  T.; Riesgo-Ferreiro,  P.;  Sorn, P.;  Gudimella, R.; Hausmann,  J.; Rösler,  T.;  Löwer,  M.;  Schrörs,  B.;  Sahin, U.  CoVigator—A  Knowledge 

Base for Navigating SARS-CoV-2 Genomic Variants. Viruses 2023, 15, 1391. https://doi.org/10.3390/v15061391 

6 Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu, fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics, Volume 34, Issue 17, 

September 2018, Pages i884–i890, https://doi.org/10.1093/bioinformatics/bty560 

7 Poplin R, Ruano-Rubio V, DePristo MA, Fennell TJ, Carneiro MO, Van der Auwera GA, Kling DE, Gauthier LD, Levy-Moonshine A, Roazen D, 
Shakir  K,  Thibault  J,  Chandran  S,  Whelan  C, Lek  M,  Gabriel  S,  Daly  MJ,  Neale  B,  MacArthur  DG,  Banks  E.  (2017).  Scaling accurate 
genetic variant discovery to tens of thousands of samples bioRxiv, 201178. DOI: 10.1101/201178 

8  Wilm  et  al.  LoFreq:  A  sequence-quality  aware,  ultra-sensitive  variant  caller  for  uncovering  cell-population  heterogeneity  from  high-

throughput sequencing datasets. Nucleic Acids Res. 2012; 40(22):11189-201. 

9 https://github.com/TRON-Bioinformatics/vafator 
10 "A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila 
melanogaster strain w1118; iso-2; iso-3.", Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. Fly 
(Austin). 2012 Apr-Jun;6(2):80-92. PMID: 22728672. 

11  Liao,  H.,  Cai,  D.  &  Sun,  Y.  VirStrain:  a  strain 

identification  tool  for  RNA  viruses.  Genome  Biol  23,  38 

(2022). 

https://doi.org/10.1186/s13059-022-02609-x 

12 Karthikeyan S, Levy JI, De Hoff P, Humphrey G, Birmingham A, Jepsen K, Farmer S, Tubb HM, Valles T, Tribelhorn CE, Tsai R, Aigner S, 
Sathe  S,  Moshiri  N,  Henson  B,  Mark  AM,  Hakim  A,  Baer  NA,  Barber  T,  Belda-Ferre  P,  Chacón  M,  Cheung  W,  Cresini  ES,  Eisner  ER, 
Lastrella AL, Lawrence ES, Marotz CA, Ngo TT, Ostrander T, Plascencia A, Salido RA, Seaver P, Smoot EW, McDonald D, Neuhard RM, 
Scioscia  AL,  Satterlund  AM,  Simmons  EH,  Abelman  DB,  Brenner  D,  Bruner  JC,  Buckley  A,  Ellison  M,  Gattas  J,  Gonias  SL,  Hale  M, 
Hawkins F, Ikeda L, Jhaveri H, Johnson T, Kellen V, Kremer B, Matthews G, McLawhon RW, Ouillet P, Park D, Pradenas A, Reed S, Riggs 
L, Sanders A, Sollenberger B, Song  A, White B, Winbush T, Aceves CM, Anderson C, Gangavarapu K, Hufbauer E, Kurzban E, Lee J, 
Matteson NL, Parker E, Perkins SA, Ramesh KS, Robles-Sikisaka R, Schwab MA, Spencer E, Wohl S, Nicholson L, Mchardy IH, Dimmock 
DP, Hobbs CA, Bakhtar O, Harding A, Mendoza A, Bolze A, Becker D, Cirulli ET, Isaksson M, Barrett KMS, Washington NL, Malone JD, 
Schafer AM, Gurfield N, Stous S, Fielding-Miller R, Garfein RS, Gaines T, Anderson C, Martin NK, Schooley R, Austin B, MacCannell DR, 
Kingsmore SF, Lee W, Shah S, McDonald E, Yu AT, Zeller M, Fisch KM, Longhurst C, Maysent P, Pride D, Khosla PK, Laurent LC, Yeo 
GW,  Andersen  KG,  Knight  R.  Wastewater  sequencing  uncovers  early,  cryptic  SARS-CoV-2  variant  transmission.  medRxiv  [Preprint]. 
2022 Apr 4:2021.12.21.21268143.  doi: 10.1101/2021.12.21.21268143. Update in: Nature. 2022 Jul 7;: PMID: 35411350; PMCID: 
PMC8996633. 

13 Parker MD, Lindsey BB, Leary S, Gaudieri S, Chopra A, Wyles M, Angyal A, Green LR, Parsons P, Tucker RM, Brown R, Groves D, Johnson 
K, Carrilero L, Heffer J, Partridge DG, Evans C, Raza M, Keeley AJ, Smith N, Filipe ADS, Shepherd JG, Davis C, Bennett S, Sreenu VB, 
Kohl A, Aranday-Cortes E, Tong L, Nichols J, Thomson EC; COVID-19 Genomics UK (COG-UK) Consortium; Wang D, Mallal S, de Silva 
TI.  Subgenomic  RNA  identification  in  SARS-CoV-2  genomic  sequencing  data.  Genome  Res.  2021  Apr;31(4):645-658.  doi: 
10.1101/gr.268110.120. Epub 2021 Mar 15. PMID: 33722935; PMCID: PMC8015849. 

6 

 
 
 
 
 
                                                        
 
 
Besides  the  standard  analyses  present  in  the  original  CoVigator  pipeline,  we  implemented  additional  tasks 
that  were  deemed  necessary  to  assure  robustness  of  the  lineage  characterisation  and  to  evaluate  the 
presence of subgenomics RNA:  

- 

- 

sgRNA  analyses.  The  importance  of  subgenomics  RNAs  in  SARS-CoV-2  has  been  extensively 
reported14.  Nonetheless,  to  our  knowledge,  no  available  analyses  pipelines  integrate  tools  able  to 
investigate  sgRNA  presence  and  abundance.  We  therefore  implemented  PathoSeq-QC  with  a 
customized  version  of  Periscope15,  a  software  that  starting  from  raw  sequencing  data  is  able  to 
evaluate the presence of sgRNA from raw Illumina sequencing data. 

Lineage  assignation.  The  software  most  commonly  used  for  this  step  is  Pangolin.  PathoSeq-QC  is 
implemented  with  a  standard  Pangolin  analyses.  However,  to  increase  the  robustness  of  one  the 
most crucial analyses steps, we added the possibility to perform parallel additional analyses based 
on two publicly available software, VirStrain and Freyja, and a third one that is an in-house tool: 

o  VirStrain-based  analyses.  By  using  VirStrain,  the  analysis  tries  to  guess  the  most 
probable  strain  based  on  the  analyses  of  raw  reads  data  using  a  database  of  variants 
generated from the set of reference consensus sequences. 

o  Freyja-based analyses. This analysis uses the Freyja software to perform a variant calling 
analysis based on the iVAR variant caller, and to deconvolute such information to determine 
the  lineage  composition  of  the  sample.  This  is  extremely  suitable  in  case  mixed  samples 
(e.g., wastewater samples) are processed. 

o  Custom lineage assignation approach (explained in details below). 

2.1.2.1  Custom lineage assignation approach 

This  lineage  assignation  method  is  based  on  a  database  of  known  lineages  and  variants  (about  1,500 
lineages provided within the tool repository and fully customizable). Briefly, for each lineage of the database, 
two frequencies are evaluated: 

1. The percentage of lineage variants observed in the sample with respect to the whole set of variants called 
in the sample;  

2.  The  percentage  of  lineage  variants  observed  in  the  sample  with  respect  to  the  whole  set  of  all  lineage 
variants. 

The  percentages  are  next  ranked  and  indexed  individually.  Next,  a  total  “overlapping  score”  is  generated  by 
summing the individual ranking indexes of each single lineage. Lastly, a final ranking is made, based on the 
“overlapping”  score.  Depending  to  the  value  of  the  top  lineage  score,  the  sample  might  be  assigned  to  the 
relative lineage or might represent a potential recombinant lineage (Figure 1). 

14  Long  S.  SARS-CoV-2  Subgenomic  RNAs:  Characterization,  Utility,  and  Perspectives.  Viruses.  2021  Sep  24;13(10):1923.  doi: 

10.3390/v13101923. Erratum in: Viruses. 2022 Jun 28;14(7): PMID: 34696353; PMCID: PMC8539008. 

15 Parker MD, Lindsey BB, Leary S, Gaudieri S, Chopra A, Wyles M, Angyal A, Green LR, Parsons P, Tucker RM, Brown R, Groves D, Johnson 
K, Carrilero L, Heffer J, Partridge DG, Evans C, Raza M, Keeley AJ, Smith N, Filipe ADS, Shepherd JG, Davis C, Bennett S, Sreenu VB, 
Kohl A, Aranday-Cortes E, Tong L, Nichols J, Thomson EC; COVID-19 Genomics UK (COG-UK) Consortium; Wang D, Mallal S, de Silva 
TI.  Subgenomic  RNA  identification  in  SARS-CoV-2  genomic  sequencing  data.  Genome  Res.  2021  Apr;31(4):645-658.  doi: 
10.1101/gr.268110.120. Epub 2021 Mar 15. PMID: 33722935; PMCID: PMC8015849. 

7 

 
 
 
 
 
 
 
                                                        
 
 
Figure 1. Custom lineage assignation approach. The custom lineage assignation approach involves several steps. First, 
the 'A ∩ B x 100 / A' column calculates the percentage of lineage variants observed in the sample relative to the entire set 
of variants called in the sample. These percentages are then ranked in decreasing order and assigned an increasing index, 
which is reported in the 'score_AB' column. Similarly, the 'A ∩ B x 100 / B' column determines the percentage of lineage 
variants observed in the sample relative to the total set of all lineage variants and assigns them a second increasing 
index, reported in the 'score_BA' column. These two indexes are then summed and presented in the 'Score' column. 
Subsequently, the results are arranged based on the 'Score' column, with the most probable lineage being the one with 
the lowest assigned score. 

8 

 
 
 
2.1.3 

InSilicoSeq module 

This module is meant for development and test purposes as it allows the user to in-silico generate raw reads 
sequencing  data  just  using  a  consensus  sequence  as  input  file.  For  further  information  related  to  the 
InSilicoSeq tool, please refer to https://insilicoseq.readthedocs.io/en/latest/).  

2.1.4  MakeReport module 

The  MakeReport  module  takes  care  of  integrating  the  data  coming  from  all  the  analyses,  of  filtering  them 
based on coverage thresholds and of creating several file reports. Currently, it generates 8 outputs: 2 pdf files 
and 6 table files, which are explained in detail in chapter 2.5. 

2.2  Software information and Installation guide 

PathoSeq-QC is a Linux command/line tool that can also be used in cluster environments. It has been tested 
on Ubuntu-20 and Centos7 OS releases. The tool is written in Python and R program languages. 

2.2.1 

Installation guide 

The pipeline does not require to be compiled nor to be installed. However, it relies on the presence of several 
tools installed within specific conda16 environments. Conda environments, with the relative tools and versions, 
are listed in Table 1. Please refer to the conda site page for further information on how to install conda. 

Currently there are two solutions to create the conda environments. The first and easiest one is to perform an 
automatic  installation,  which  can  be  achieve  with  the  following  commands  after  having  downloaded  the 
PathoSeq-QC repository: 

# Change directory 

cd /PathoSeq-QC/Repository/Directory 

# Launch the installation procedure 

bash ./INSTALL.sh 

Additionally, user can manually create and install all environments and dependencies.  

For  each  needed  environment,  the  required  software  and  libraries  can  be  installed  with  either  conda  or 
mamba17 and with the standard install.packages() R function. Here below there is an example: 

# Virtual environment creation 

conda create -n gatk  

# Environment activation 

16 https://docs.conda.io/en/latest/ 
17 https://github.com/mamba-org/mamba 

9 

 
 
 
 
 
 
 
 
 
 
 
                                                        
 
conda activate gatk 

# Installation of gatk4 software inside environment 

conda/mamba install -c bioconda gatk4 

# Deactivation of environment 

conda deactivate 

To install R using conda, please see the  guide available  at: https://www.biostars.org/p/498049/.  After having 
installed InSilicoSeq within the “iss” conda environment, it is highly recommended to change the code since, as 
it is, it stores all the generated reads into RAM, potentially resulting in memory issues. A turnaround is to let 
InSilicoSeq  save  all  the  reads  directly  to  disk  instead,  bypassing  the  RAM.  The  solution  can  be  found  here: 
https://github.com/HadrienG/InSilicoSeq/pull/210. 

10 

 
 
 
Table 1. Conda environment and software dependencies. 

Conda environment 
name 

Software/Library 

Version 

Required 

base 

base 

bbmap 

fastp 

gatk 

lofreq 

bwa 

Python 

Python/coloredlogs 

Bbmap 

fastp 

>3.7 

15.0.1 

38.96 

0.22.0 

Gatk; bcftools 

4.2.6.1; 1.15.1 

Lofreq; bcftools 

2.1.5; 1.15.1 

Bwa; samtools 

0.7.17-r1188; 1.15.1 

samtools 

Samtools 

1.15.1 

vafator 

snpeff 

pangolin 

virstrain 

freyja 

R 

R 

iss 

iss 

Vafator 

Snpeff 

Pangolin 

VirStrain 

freyja 

R 

1.3.0 

5.1d 

4.0.6 

1.12 

1.3.8 

4.1.3 

R/data.table; R/ggplot2; 
R/patchwork; R/argparse; 
R/UpSetR; R/ColorBrewer; 
R/stringdist 

1.14.6; 3.4.0; 1.1.2; 
2.2.1; 1.4.0; 1.1_3; 
0.9.8 

IsSilicoSeq 

pigz 

1.5.3 

2.6 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Only if –VirStrain is used 

Only if --freyja is used 

Yes 

Yes 

Yes 

Yes 

11 

 
 
 
 
2.2.1.1  VirStrain database creation 

In order to successfully run a VirStrain analysis, the user must first create a VirStrain database:  

-  Decompress the consensus database file using the command: 

gunzip /PATH/TO/YOUR/REPOSITORY/virstrain/consensus.95percent.09.01.22.multialignment.gz 

- 

Activate the VirStrain environment, and launch the virstrain_build command: 

conda activate virstrain; 

cd /PATH/TO/YOUR/REPOSITORY/reference/virstrain 

virstrain_build –I 
/PATH/TO/YOUR/REPOSITORY/reference/virstrain/consensus.95percent.09.01.22.multialignment -d 
/PATH/TO/YOUR/REPOSITORY/reference/virstrain/ConsensusVirstrainDB -s 0.8 

conda deactivate 

Please note that the following warning is expected to occur during this phase and should be ignored: 

Use  of  uninitialized  value  $char  in  pattern  match  (m//)  at  /your/condaEnv/path/virstrain/lib/python3.7/site-
packages/VirStrain/aln2cluster-overlap-kmer-withd.pl line 81 

2.3  Run examples 

To test the installation, users can run the following command: 

python PathoSeq-QC.pl --wd /yourDir/Test/ --fastq1 //PATH/TO/YOUR/REPOSITORY/examples/Test_R1.fastq.gz -
-fastq2 //PATH/TO/YOUR/REPOSITORY/examples/Test_R2.fastq.gz  --condaBin /your/Conda/Bin/Dir  --condaEnv 
/your/Conda/Env/Dir -t 1 

2.4  Command options 

The full list of parameters available for PathoSeq-QC, coupled with their descriptions, is available in Annex 1. 

Users can see the full list of commands also with the following commands: 

python PathoSeq-QC.py --help 

python PathoSeq-QC.py -h 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.5  Output description 

As  already  mentioned,  the  pipeline  generates  several  tables  and  plots.  They  all  are  named  with  a  prefix 
$NAME,  which  refers  either  to  the  prefix  of  the  analysed  sample  or  to  the  text  provided  to  --name  input 
argument. Here below, we provide short descriptions and examples of all outputs. 

2.5.1 

.Report.tab file 

This  file  represents  the  main  output  of  the  pipeline  in  which  all  the  major  results  of  the  analyses  are 
identify  Co-
summarized.  Users  can 
infections/Contaminations, 
SampleInLineageOutTotLineage-SampleInLineageOutTotSample-
TopOverlapScore  combined  with  the  assigned  lineage,  to  try  discriminating  novel  strains  and/or  potential 
recombinants from known lineages. Additionally, a useful wrapper script (PathoSeq-QC.wrapper.py), especially 
when  analysing  multiple  samples,  can  be  used  to  parse  this  file  to  generate  a  summary  table  as  the  one 
reported in Table 22. 

take  advantage  of 
the 
and 

the  GATK  and/or  Freyja 

fields 

to 

Table 2. PathoSeq-QC wrapper output example. SAMPLE: prefix of the analysed sample or to the text provided to --name 
input argument; CHECK_SITES: Reports the results of the coverage checks made on the regions of the 
“/PATH/TO/YOUR/REPOSITORY/reference/refSites.bed” file; CHECK_VARS: Reports the results of the coverage checks made 
on the variants available in the “/PATH/TO/YOUR/REPOSITORY/reference/refVar file; CHECK_PURE: Warns the user if 
problems with sample homogeneity are found; CHECK_LINEAGE: Reports the lineages identified by the different 
approaches; OUTCOME: Final sample evaluation. 

SAMPLE 

CHECK_SITES  CHECK_VARS  CHECK_PURE 

CHECK_LINEAGE 

(Pangolin\VirStrain\Freyja\Custom) 

OUTCOME 

ERR10024698 

Pass 

ERR10088143 

Pass 

ERR10103260 

Pass 

ERR10108718 

Pass 

ERR10111314 

Pass 

Pass 

Pass 

Pass 

Pass 

Pass 

NotPass 

BA.5.2.1\nan\BA.5.2.1\BF.4 

PotentialMixture 

NotPass 

C.36.3\nan\C.36.3.1\C.36.3 

PotentialMixture 

Pass 

BA.1.1\nan\BA.1.1\BA.1.1.2 

KnownLineage 

NotPass 

B.1\nan\B.1\B.1.527 

PotentialMixture 

NotPass 

BA.5.2\nan\BF.26\BF.4 

PotentialMixture 

2.5.2 

.Report.VarData.tab file 

This file includes a table listing the variants called by GATK and LoFreq, coupled with information used by the 
pipeline, such as the Allele Frequency (AF) from GATK analyses and depth of coverage at the precise loci (DP). 
The “Filter” column can be used to observe which variants are passing or not the coverage threshold (Table 3). 

13 

 
 
 
 
 
 
 
 
 
Table 3. Report.VarData.tab file example. CHROM: Chromosome name; POS: Genomic position; REF: Reference nucleotide; 
ALT: Alternative nucleotide; AF: Alternative allele frequency; DP: Depth of coverage; sample: prefix of the analysed sample 
or to the text provided to --name input argument; caller: Tool that identified the variant; Var: Variant; Filter: Whether the 
variant passed internal DP filters or not; OverlappingCall: Whether the variant has been identified by both GATK and 
LoFreq or not. 

CHROM 

POS 

REF  ALT  AF 

DP 

sample 

caller 

Var 

Filter  OverlappingCall 

MN908947.3 

241 

MN908947.3 

670 

MN908947.3 

1931 

MN908947.3 

2790 

C 

T 

C 

C 

T 

G 

A 

T 

1 

1 

28 

SRR23060839 

GATK 

241:C-T 

NA 

36 

SRR23060839 

GATK 

670:T-G 

NA 

1 

176 

SRR23060839 

GATK 

1931:C-A 

PASS 

1 

3531 

SRR23060839 

GATK 

2790:C-T 

PASS 

Yes 

Yes 

Yes 

Yes 

2.5.3 

.Report.VarDB.tab file 

This file contains all the variants present in the Consensus file (mentioned in 2.2.1.1), together with:  

1. Counts of lineages sharing the same variant; 

2. If a variant was observed in the sample;  

3. Novel variants observed within the sample but not present within the database of consensus sequences. 

An example is shown in Table 4. 

Table 4. Report.VarDB.tab file example. Var: Variant; lineage: lineage; CountsOfLineagesWithVar: Number of lineages with 
the selected variant; VarIsLineageSpecific: Whether the variant is present in a single lineage or not; ObservedInSample: 
Whether the variant has been identified in the sample; Pos: Genomic position of the variant. 

var 

lineage 

CountsOfLineagesWithVar 

VarIsLineageSpecific 

ObservedInSample 

Pos 

10015:C-T 

B.1.1.524 

10015:C-T 

B.1.606 

10029:C-T 

A.2.5 

10029:C-T 

A.2.5.1 

2 

2 

424 

424 

2.5.4 

.ReportOverlaps.txt file 

No 

No 

No 

No 

No 

No 

Yes 

Yes 

10015 

10015 

10029 

10029 

This file contains the results from the custom lineage assignation step ordered by “Overlapping Score”. Briefly, 
the  top  result  is  the  lineage  with  the  highest  frequency  of  variants  observed  within  the  lineage  (column 
SampleInLineageOutTotLineage) and the highest frequency of variants observed within the  analysed sample 
(column SampleInLineageOutTotSample). An example is shown in Table 55. 

14 

 
 
 
 
 
 
 
 
 
Table 5. ReportOverlaps.txt file example. Lineages: Lineages tested; SampleInLineageOutTotSamples: percentage of 
lineage variants observed in the sample relative to the entire set of variants called in the sample; Score_AB: Index of the 
SampleInLineageOutTotSample column; SampleInLineageOutTotLineage: percentage of lineage variants observed in the 
sample relative to the total set of all lineage variants; score_BA: Index of the SampleInLineageOutTotLineage column; 
score: sum of the score_AB and score_BA indexes. 

lineages  SampleInLineageOutTotSample 

score_AB  SampleInLineageOutTotLineage 

score_BA  score 

BE.1 

79.746835443038 

BE.1.1 

75.9493670886076 

BA.5.3.1 

74.6835443037975 

BF.6 

77.2151898734177 

3 

6 

7 

5 

96.9230769230769 

95.2380952380952 

95.1612903225806 

93.8461538461539 

2 

3 

4 

7 

5 

9 

11 

12 

2.5.5 

.ReportSiteCov.txt file 

This file reports the coverage (DP) and the Z score based on the coverage distribution evaluated from a set of 
standards  genomic  regions  (provided  within  the  “/PATH/TO/YOUR/REPOSITORY/reference/refSites.bed  file  and 
fully customizable). An example can be found in Table 6.  

Table 6. ReportSiteCov.txt file example. CHROM: Chromosome ID; POS: Genomic position; DP: Depth of coverage; zScore: Z 
score values evaluated for every variants based on the standards genomic regions provided. 

CHROM 

MN908947.3 

MN908947.3 

MN908947.3 

MN908947.3 

POS 

4595 

4596 

4597 

4598 

2.5.6 

.Report.VarCov.txt file 

DP 

632 

653 

649 

659 

zScore 

-2.49 

-2.46 

-2.47 

-2.45 

This files reports the Z score evaluated for the set of selected variants, based on the coverage distribution of 
the  regions  present  in  the  “/PATH/TO/YOUR/REPOSITORY/reference/refSites.bed  file  (Table  7).  The  set  of 
variants can be found in the “/PATH/TO/YOUR/REPOSITORY/reference/refVar file, which is fully customizable by 
the user. 

15 

 
 
 
 
 
 
 
 
Table 7. Report.VarCov.txt file example. VAR: Variant; DP: Depth of coverage; zScore: Z score value evaluated for every 
variant using the coverage distribution evaluated from the standards Genomics regions. 

VAR 

3037:C-T 

14408:C-T 

23403:A-G 

DP 

2238 

2238 

2238 

zScore 

-0.67 

-0.67 

-0.67 

2.5.7 

.Report.Plots.pdf file 

This pdf file contains several plots which are automatically generated by the MakeReport module. These plots 
represent:  

1. The distribution of Allele Frequencies (AF) scores (from GATK and LoFreq) for all detected variants;  

2. AF scores of called variants which are within the "Pangolin assigned or top UnAmbiguously mapped reads 
lineage"; 

3. The ReadStrainScanner results (top 5 UnAmbiguously mapped and Ambiguously mapped lineages); 

4. The Raw counts of variants detected per lineage; 

5. The Frequencies of variants detected per lineage.  

Examples  from  the  analyses  of  a  pure  sample  and  a  mixture  are  provided  in  Annex  2  and  Annex  3, 
respectively. 

2.6  Code availability 

The code is currently stored  at the code Europa  public repository:  https://code.europa.eu/dighealth/pathoseq-
qc. 

Additionally, 
(https://apps.jrc.cec.eu.int/eureca/catalogue/asset/SW0000864). 

the  PathoSeq-QC  project 

registered 

is 

in  EURECA  under 

the  SW0000864 

id 

2.7  Terms and conditions 

The  tool  is  released  under  the  “GNU  GENERAL  PUBLIC  LICENSE”,  Version  3,  29  June  2007.  For  further 
information, please read the LICENSE file available at the official tool repository. 

16 

 
 
 
 
 
 
 
 
 
 
 
 
3  Conclusions  

PathoSeq-QC emerges as a crucial state-of-the-art tool in the realm of pathogen surveillance, particularly in 
the  context  of  combating  global  health  threats  like  the  COVID-19  pandemic.  By  providing  a  bioinformatics 
"decision-tree" workflow easily adaptable to various viral threats, PathoSeq-QC facilitates the assessment of 
conclusions  derived  from  genomic  data,  ensuring  they  are  grounded  in  solid  and  high-quality  inputs.  The 
availability  of  the  code  and  user  guide  presented  here  signifies a  significant  step  forward  in  enhancing  the 
accessibility and usability of this tool. 

Moreover, PathoSeq-QC aims to improve the  robustness of pathogen  surveillance protocols by aiding in the 
establishment  of  quality  standard  criteria.  This  proactive  approach  serves  to  mitigate  the  risk  of  erroneous 
conclusions  that  could  potentially  influence  decision-making  processes  in  public  health  domains.  By 
empowering  researchers  and  policymakers  with  a  reliable  means  to  assess  genomic  data,  PathoSeq-QC 
contributes to the collective effort in fortifying our defences against emerging pathogenic threats. 

The continued refinement and widespread adoption of tools like PathoSeq-QC are paramount in ensuring the 
agility and effectiveness of our response strategies in the face of evolving health challenges. As we navigate 
the complexities of pathogen surveillance and response, PathoSeq-QC stands as a beacon of progress, guiding 
us towards more informed and impactful decision-making in safeguarding global health. 

17 

 
 
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Chris Ruis, Khalil Abu-Dahab, Ben Taylor, Corin Yeats, Louis Du Plessis, Daniel Maloney, Nathan Medd, Stephen 
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DOI:10.1093/ve/veab064 

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Long  S.  SARS-CoV-2  Subgenomic  RNAs:  Characterization,  Utility,  and  Perspectives.  Viruses.  2021  Sep 
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ADS, Shepherd JG, Davis C, Bennett S, Sreenu VB, Kohl A, Aranday-Cortes E, Tong L, Nichols J, Thomson EC; 
COVID-19 Genomics UK (COG-UK) Consortium; Wang D, Mallal S, de Silva TI. Subgenomic RNA identification in 
SARS-CoV-2  genomic  sequencing  data.  Genome  Res.  2021  Apr;31(4):645-658.  doi:  10.1101/gr.268110.120. 
Epub 2021 Mar 15. PMID: 33722935; PMCID: PMC8015849. 

Poplin  R,  Ruano-Rubio  V,  DePristo  MA,  Fennell  TJ,  Carneiro  MO,  Van  der  Auwera  GA,  Kling  DE,  Gauthier  LD, 
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Shifu  Chen,  Yanqing  Zhou,  Yaru  Chen,  Jia  Gu,  fastp:  an  ultra-fast  all-in-one  FASTQ  preprocessor, 
Bioinformatics, 
i884–i890, 
https://doi.org/10.1093/bioinformatics/bty560 

September 

Volume 

Pages 

2018, 

Issue 

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34, 

Wilm  et  al.  LoFreq:  A  sequence-quality  aware,  ultra-sensitive  variant  caller  for  uncovering  cell-population 
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19 

 
List of abbreviations and definitions  

AF 

ALT 

Alternative Frequency 

Alternative 

CHROM   

Chromosome 

DP 

GISAID 

HPC 

NGS 

OS 

POS 

REF 

VAR 

Depth 

Global Initiative on Sharing All Influenza Data 

High-Performance Computing 

Next Generation Sequencing 

Operative System 

Position 

Reference 

Variants 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures  

Figure 1. Custom lineage assignation approach. ..........................................................................................................................................8 

21 

 
 
 
List of tables  

Table 1. Conda environment and software dependencies. ................................................................................................................. 11 

Table 2. PathoSeq-QC wrapper output example. ...................................................................................................................................... 13 

Table 3. Report.VarData.tab file example. ..................................................................................................................................................... 14 

Table 4. Report.VarDB.tab file example. ......................................................................................................................................................... 14 

Table 5. ReportOverlaps.txt file example. ...................................................................................................................................................... 15 

Table 6. ReportSiteCov.txt file example. ......................................................................................................................................................... 15 

Table 7. Report.VarCov.txt file example. ......................................................................................................................................................... 16 

22 

 
 
 
Annexes  

Annex 1. PathoSeq-QC command options 

Option 

--wd 

--fastq1 

--fastq2 

--fasta 

--condaBin 

--condaEnv 

-t | --threads 

-m | --memory 

MakeReport options: 

--skip_upSet 

--skip_plots 

--skip_covigatOff 

--skip_readSS 

--minDP 

--percDP 

Specific of Fasta mode: 

--model 

--nreads 

Additional options: 

--virstrain 

--periscope 

--freyja 

--consensus 

--reference 

-n | --name 

--condor 

-man|--help|-h 

-V 

-d | --debug 

-v | --verbose 

Description 

Working directory 

First Set of reads 

Second Set of reads 

Fasta mode (in alternative to --fastq1 and --fastq2) 

Conda Bin absolute path 

Conda Env absolute path 

Number of threads to use 

Memory  limit  [20g].  It  applies  to  bbmap/gatk  and  others 
java steps 

To skip UpSet diagram generation 

To skip plots plots 

To skip CovigatOff plots 

To  skip  readStrainScanner  analyses  and  plots  (this  will 
drastically reduce also overall computational time) 

Minimum Absolute DP value for variant filtering. By default, 
5  or  100  is  selected  according  to  the  magnitude  of  the 
sample  median  DP.  Not  used  if  percDP  results  in  a  higher 
DP threshold value. 

Minimum  DP  percentage  threshold  for  variant  filtering.  By 
default  selected  to  10%  of  the  sample  median  DP.  Not 
used if minDP results in a higher DP threshold value. 

ISS generate model option 

ISS generate nreads 

Trigger a VirStrain analyses 

Trigger a periscope analyses 

Trigger a freyja analyses 

Reference genome file (defaults: set of 1500 consensus) 

wuhCor1 reference genome 

Output prefix 

For condor compatibility (do not store results in folders that 
cannot  be  copied  by  condor).  Already  set  in  PathoSeq-
QC.submit 

Print the doc (not yet fully implemented) 

Version 

Print DEBUG and INFO logs 

Print only Warnings and Errors 

23 

 
 
 
 
 
 
Annex 2. Report.Plots.pdf file from the analyses of the AY.3 consensus sequence (pure lineage).  

24 

 
 
 
Annex 3. Report.Plots.pdf file from the analyses of a 20/80% mix of AY.3/BA.2 reads.  

25 

 
 
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