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Bacterial WGS training : Exercise 6

Title Chromosome, plasmid, resistance and virulence annotation
Training dataset:
Questions:
  • How many genes there are in my sample?
  • Are there virulence and/or antibiotic resistance genes?
  • Where are the genes located?
  • Which plasmids are present in the sample?
  • How do I visualize the results?
Objectives:
  • Annotate virulence and ABR genes
  • Determine gene variants
  • Determine plasmidome
  • Locate annotated genes
  • Results interpretation
Time estimation: 1 h
Key points:
  • Comparing annotation using mapping vs assembly
  • Plasmid, virulence and resistance determination

Fastqc_1

Introduction

In this exercise we are going to determine the genomic content of a multidrug-resistant (MDR) K. neumoniae isolate. First we will usse srst2 to asses the resistome and later, we will use plasmidID to infer biological and positional information to sequences and see where the genes, detected with mapping strategy, are located.

Training dataset description

The sample we are going to analyse is an in silico dataset obtained with wgsim using a sample of Klebsiella pneumoniae subsp. pneumoniae HS11286 available at ncbi.

Exercise

Mapping based annotation

To execute srst2, which maps the reads against a antibiotic resistance genes database (ARGannot), lets execute this command:

cd
cd wgs/bacterial_wgs_training_dataset/ANALYSIS
nextflow run ../../bacterial_wgs_training/main.nf \
--reads '../REFERENCES/plasmidid_test/KPN*_R{1,2}.fastq.gz' \
--fasta ../REFERENCES/listeria_NC_021827.1_NoPhagues.fna \
-profile conda \
--gtf ../REFERENCES/listeria_NC_021827.1_NoPhagues.gff \
--srst2_resistance ../REFERENCES/ARGannot.r1.fasta \
--srst2_virulence ../REFERENCES/EcOH.fasta \
--step mapAnnotation \
--outdir 07-mapAnnotation \
-resume

Results should look like that

Sample DB gene allele coverage depth diffs uncertainty divergence length maxMAF clusterid seqid annotation
KPN_TEST_R ARGannot.r1 RmtB_AGly RmtB_1580 100.0 12.09 1snp 0.132 756 0.125 309 1580 no;no;RmtB;AGly;AB263754;2843-3598;756
KPN_TEST_R ARGannot.r1 TEM-1D_Bla TEM-117_968 100.0 33.386 2snp 0.262 764 0.382 205 968 no;no;TEM-117;Bla;AY130282;1-764;764
KPN_TEST_R ARGannot.r1 KPC-1_Bla KPC-14_809 100.0 5.412 1indel 0.0 876 0.333 184 809 no;no;KPC-14;Bla;JX524191;396-1271;876
KPN_TEST_R ARGannot.r1 AmpH_Bla AmpH_634 100.0 11.373 14snp 1.206 1161 0.143 86 634 no;no;AmpH;Bla;CP003785;4208384-4209544;1161
KPN_TEST_R ARGannot.r1 CTX-M-9_Bla CTX-M-14_102 100.0 26.676 1snp 0.114 876 0.412 190 102 no;yes;CTX-M-14;Bla;AF252622;1741-2616;876
KPN_TEST_R ARGannot.r1 StrA_AGly StrA_1501 100.0 12.502 2snp 0.249 804 0.167 263 1501 no;no;StrA;AGly;AJ627643;3725-4528;804
KPN_TEST_R ARGannot.r1 StrB_AGly StrB_1614 100.0 9.545 1snp 0.119 837 0.167 227 1614 no;no;StrB;AGly;KR091911;169145-169981;837
KPN_TEST_R ARGannot.r1 AadA_AGly AadA2_1605 100.0 9.306 2snp 0.256 780 0.167 229 1605 yes;no;AadA2;AGly;X68227;166-945;780
KPN_TEST_R ARGannot.r1 SHV-OKP-LEN_Bla SHV-11_1287 100.0 9.401 0.0 861 0.143 164 1287 yes;no;SHV-11;Bla;HM751098;1-861;861
KPN_TEST_R ARGannot.r1 TetRG_Tet TetRG_605 96.209 6.48 10snp24holes edge0.0 1.642 633 0.5 373 605 no;no;TetRG;Tet;S52438;113-745;633
KPN_TEST_R ARGannot.r1 DfrA_Tmt DfrA12_1089 99.799 8.389 1indel 0.0 498 0.143 418 1089 yes;no;DfrA12;Tmt;Z21672;310-807;498
KPN_TEST_R ARGannot.r1 TetG_Tet TetG_632 100.0 9.963 0.0 1176 0.25 80 632 no;no;TetG;Tet;NC_010410;3672607-3671432;1176
KPN_TEST_R ARGannot.r1 SulII_Sul SulII_1219 100.0 11.094 1snp 0.123 816 0.2 256 1219 no;no;SulII;Sul;KR091911;167466-168281;816

This table is a full report of all the ARG found with all mapping stats.

Assembly based annotation

Now, using the contigs assembled using those same reads, we can determine the exact location of those ARG. ARG can be located on the chromosome but motly on plasmids. In that case, we are going to focus on plasmid derived ARG using the annotation feature of plasmidID. To run the analysis lets use this command:

cd
cd wgs/bacterial_wgs_training_dataset/REFERENCES/
cp -r /mnt/ngs_course_shared/bacterial_wgs_training_dataset/REFERENCES/plasmidid_test .

Now we can run the nextflow

cd ../ANALYSIS/
nextflow run ../../bacterial_wgs_training/main.nf \
--reads '../REFERENCES/plasmidid_test/KPN*_R{1,2}.fastq.gz' \
--fasta ../REFERENCES/listeria_NC_021827.1_NoPhagues.fna \
-profile conda \
--gtf ../REFERENCES/listeria_NC_021827.1_NoPhagues.gff \
--plasmidid_database ../REFERENCES/plasmidid_test/plasmids_TEST_database.fasta \
--plasmidid_config ../REFERENCES/plasmidid_test/plasmidid_config.txt \
--step plasmidID \
--outdir 08-plasmidID \
-resume

Results should look like these

NC_016838.1 NC_016839.1 NC_016840.1
NC_016841.1 NC_016846.1 NC_016847.1

Those are the 6 plasmids that this isolate had, have a look at those pictures and find out if the genes are the same allele.

Are all the genes located with srst2 bound to plasmids?