E. coli plasmid and gene profiling using Next Generation Sequencing
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1 E. coli plasmid and gene profiling using Next Generation Sequencing Jeroen F. J. Laros Leiden Genome Technology Center Department of Human Genetics Center for Human and Clinical Genetics
2 Introduction General overview Figure: Escherichia coli. Introduction NGS Data Analysis 1/22 Thursday, May 22, 2014
3 Introduction Genome published in Genome size basepairs. 4,288 genes in the assembly. 86 trna genes. 2,584 operons in the assembly. 7 rrna operons. Introduction NGS Data Analysis 2/22 Thursday, May 22, 2014
4 Introduction Genome published in Genome size basepairs. 4,288 genes in the assembly. 86 trna genes. 2,584 operons in the assembly. 7 rrna operons. However, per individual strain: Between 4,000 and 5,500 genes. 16,000 genes in total (pangenome). Introduction NGS Data Analysis 2/22 Thursday, May 22, 2014
5 Introduction Genome published in Genome size basepairs. 4,288 genes in the assembly. 86 trna genes. 2,584 operons in the assembly. 7 rrna operons. However, per individual strain: Between 4,000 and 5,500 genes. 16,000 genes in total (pangenome). Very diverse, only 20% of the genome is shared between all strains. Introduction NGS Data Analysis 2/22 Thursday, May 22, 2014
6 Plasmids Introduction Bacterial DNA Plasmids Figure: Schematic overview of a cell containing plasmids. Introduction NGS Data Analysis 3/22 Thursday, May 22, 2014
7 Plasmids Introduction Bacterial DNA Plasmids Figure: Schematic overview of a cell containing plasmids. Plasmids are small DNA molecules. Separate and independent from the chromosome. Can be transferred to other species. Size between and basepairs. Copy number between 1 and 1,000. Variable between strains and individuals. Introduction NGS Data Analysis 3/22 Thursday, May 22, 2014
8 Introduction Profiling Plasmids: May carry antibiotic resistance genes. Not all of them are known. May be highly similar to other plasmids. Introduction NGS Data Analysis 4/22 Thursday, May 22, 2014
9 Introduction Profiling Plasmids: May carry antibiotic resistance genes. Not all of them are known. May be highly similar to other plasmids. Genes: Multi Locus Sequence Typing (MLST). Uses household genes. Fragments of 450 to 500 basepairs. Introduction NGS Data Analysis 4/22 Thursday, May 22, 2014
10 Introduction Profiling Plasmids: May carry antibiotic resistance genes. Not all of them are known. May be highly similar to other plasmids. Genes: Multi Locus Sequence Typing (MLST). Uses household genes. Fragments of 450 to 500 basepairs. Antibiotic resistance. The gene may be known, the plasmid may not be. Introduction NGS Data Analysis 4/22 Thursday, May 22, 2014
11 Introduction Profiling Plasmids: May carry antibiotic resistance genes. Not all of them are known. May be highly similar to other plasmids. Genes: Multi Locus Sequence Typing (MLST). Uses household genes. Fragments of 450 to 500 basepairs. Antibiotic resistance. The gene may be known, the plasmid may not be. Efflux pumps.... Introduction NGS Data Analysis 4/22 Thursday, May 22, 2014
12 Introduction Antibiotic resistance testing Figure: Classical antibiotic resistance test. Introduction NGS Data Analysis 5/22 Thursday, May 22, 2014
13 Introduction Goals Clinical: Strain identification (MLST). Antibiotic resistance testing. Identifying efflux pumps. Find other important genes. Introduction NGS Data Analysis 6/22 Thursday, May 22, 2014
14 Introduction Goals Clinical: Strain identification (MLST). Antibiotic resistance testing. Identifying efflux pumps. Find other important genes. Technical limitations: The result must be delivered fast. Introduction NGS Data Analysis 6/22 Thursday, May 22, 2014
15 Introduction Goals Clinical: Strain identification (MLST). Antibiotic resistance testing. Identifying efflux pumps. Find other important genes. Technical limitations: The result must be delivered fast. Next Generation Sequencing. Introduction NGS Data Analysis 6/22 Thursday, May 22, 2014
16 Next Generation Sequencing Why Next Generation Sequencing? We analyse everything in one go. The genome, all plasmids are sequenced. Known but also unknown DNA is sequenced. Data can be re-analysed. Is gene X also in there? Introduction NGS Data Analysis 7/22 Thursday, May 22, 2014
17 Next Generation Sequencing Why Next Generation Sequencing? We analyse everything in one go. The genome, all plasmids are sequenced. Known but also unknown DNA is sequenced. Data can be re-analysed. Is gene X also in there? We did a pilot on the HiSeq Successful. A bit slow (it takes two weeks for a HiSeq to finish). Way too much data per sample. Over 200 times more data per sample than needed. Found a contamination (Streptococcus). Introduction NGS Data Analysis 7/22 Thursday, May 22, 2014
18 Next Generation Sequencing Sequencers: Ion Torrent Characteristics: 3 hours per run. 1 day sampleprep, 1 day emulsion PCR reads. Read length ±300bp. 2 E. coli per run. Figure: Ion torrent. Introduction NGS Data Analysis 8/22 Thursday, May 22, 2014
19 Next Generation Sequencing Sequencers: Ion Torrent Characteristics: 3 hours per run. 1 day sampleprep, 1 day emulsion PCR reads. Read length ±300bp. 2 E. coli per run. Figure: Ion torrent. Fast and inexpensive. Introduction NGS Data Analysis 8/22 Thursday, May 22, 2014
20 Data analysis General overview We screen for 130 known plasmids and 400 genes. Introduction NGS Data Analysis 9/22 Thursday, May 22, 2014
21 Data analysis General overview We screen for 130 known plasmids and 400 genes. Output: MLST. List of plasmids. Otherwise, similar plasmids. List of genes of interest. Introduction NGS Data Analysis 9/22 Thursday, May 22, 2014
22 Data analysis General overview We screen for 130 known plasmids and 400 genes. Output: MLST. List of plasmids. Otherwise, similar plasmids. List of genes of interest. For the MLST, we need a consensus sequence. As opposed to a list of variants, which we normally use. Introduction NGS Data Analysis 9/22 Thursday, May 22, 2014
23 Data analysis General overview We screen for 130 known plasmids and 400 genes. Output: MLST. List of plasmids. Otherwise, similar plasmids. List of genes of interest. For the MLST, we need a consensus sequence. As opposed to a list of variants, which we normally use. Forthelistof plasmidsandgenes, wewantalistwecanopen in Excel. Introduction NGS Data Analysis 9/22 Thursday, May 22, 2014
24 Alignment Data analysis Figure: Variant calling. Introduction NGS Data Analysis 10/22 Thursday, May 22, 2014
25 Data analysis MLST Pipeline: Map all reads to the genome. Make a consensus sequence. Select genes. Introduction NGS Data Analysis 11/22 Thursday, May 22, 2014
26 Data analysis MLST Pipeline: Map all reads to the genome. Make a consensus sequence. Select genes. Tools: tmap for alignment. samtools/bcftools for builing a consensus sequence. In house program to select a region. Introduction NGS Data Analysis 11/22 Thursday, May 22, 2014
27 Data analysis Plasmid detection Pipeline: Select all reads that do not map to the genome. Map these reads to each plasmid individually. Calculate the horizontal coverage. Introduction NGS Data Analysis 12/22 Thursday, May 22, 2014
28 Data analysis Plasmid detection Pipeline: Select all reads that do not map to the genome. Map these reads to each plasmid individually. Calculate the horizontal coverage. Tools: samtools to extract unmapped reads. tmap for alignment. In house program to make a wiggletrack. In house program to find covered regions. Introduction NGS Data Analysis 12/22 Thursday, May 22, 2014
29 Coverage Data analysis Figure: Coverage/ depth histogram. Introduction NGS Data Analysis 13/22 Thursday, May 22, 2014
30 Coverage Data analysis Figure: Coverage/ depth histogram. Introduction NGS Data Analysis 13/22 Thursday, May 22, 2014
31 Coverage Data analysis Figure: Coverage/ depth histogram. Figure: Coverage summary. Introduction NGS Data Analysis 13/22 Thursday, May 22, 2014
32 Data analysis Antibiotic resistance genes detection Pipeline: Select genes from the genome or plasmids. Calculate the non-n content of the consensus sequence. Introduction NGS Data Analysis 14/22 Thursday, May 22, 2014
33 Data analysis Antibiotic resistance genes detection Pipeline: Select genes from the genome or plasmids. Calculate the non-n content of the consensus sequence. Tools: In house program to select a region. In house program to calculate the non-n percentage. Introduction NGS Data Analysis 14/22 Thursday, May 22, 2014
34 Challenges Technical issues Between 66% and 80% of the reads map to the genome. Introduction NGS Data Analysis 15/22 Thursday, May 22, 2014
35 Challenges Technical issues Between 66% and 80% of the reads map to the genome. The other needs to be mapped to the 130 plasmids and 278 additional genes. Alignment is not much faster for small reference sequences. Introduction NGS Data Analysis 15/22 Thursday, May 22, 2014
36 Challenges Technical issues Between 66% and 80% of the reads map to the genome. The other needs to be mapped to the 130 plasmids and 278 additional genes. Alignment is not much faster for small reference sequences. In total, the analysis would take around = 136 times longer than the initial alignment. Introduction NGS Data Analysis 15/22 Thursday, May 22, 2014
37 Clusters Challenges Figure: Dell M610 blade server Introduction NGS Data Analysis 16/22 Thursday, May 22, 2014
38 Challenges Automatic scheduling on a cluster 1 %.bam: %.sam 2 $(SAMTOOLS) view bt $( call MAKEREF, $@) o $@ $< 3 4 %.flagstat : %.bam 5 $(SAMTOOLS) flagstat $< > $@ Listing 1: Makefile snippet. Introduction NGS Data Analysis 17/22 Thursday, May 22, 2014
39 Challenges Automatic scheduling on a cluster 1 %.bam: %.sam 2 $(SAMTOOLS) view bt $( call MAKEREF, $@) o $@ $< 3 4 %.flagstat : %.bam 5 $(SAMTOOLS) flagstat $< > $@ Listing 1: Makefile snippet. To fully exploit a cluster, we use the Make language. Only describe dependencies. Implicit workflow. Error control. Introduction NGS Data Analysis 17/22 Thursday, May 22, 2014
40 Challenges Automatic scheduling on a cluster 1 %.bam: %.sam 2 $(SAMTOOLS) view bt $( call MAKEREF, $@) o $@ $< 3 4 %.flagstat : %.bam 5 $(SAMTOOLS) flagstat $< > $@ Listing 1: Makefile snippet. To fully exploit a cluster, we use the Make language. Only describe dependencies. Implicit workflow. Error control. The pipeline we made is only 122 lines long. Introduction NGS Data Analysis 17/22 Thursday, May 22, 2014
41 MLST Results 1 CAATGATGATCGACAGTATGGCTGTGCTCGATATCTTCATTCTTGCGGCT 2 AAAGCGGCGGCGAACCACCACAAAGAATACCGGAACGAAGAAGATTGCCA 3 GTACCGTTGCGGTCACCATCCCGCCCATTACACCGGTACCTACTGCGTTC 4 TGCGCGCCGGAACCAGCACCAGTACTGATAACCAGCGGCATAACGCCGAG 5 GATAAACGCCAGCGAGGTCATCAGGATCGGACGTAAACGCATCCGCACCG 6 CATCAAGCGTCGCTTCAATCAGACCTTTACCTTCTTTATCCATCAAGTCT 7 TTGGCGAATTCGACGATAAGGATCGCGTTCTTCGCCGACAACCCAATGGT 8 TGTGAGCAGGCCTACCTGGAAGTAAACGTCATTGGTCAGGCCACGGAAGG Listing 2: Part of the consensus sequence of acrb. Introduction NGS Data Analysis 18/22 Thursday, May 22, 2014
42 MLST Results 1 CAATGATGATCGACAGTATGGCTGTGCTCGATATCTTCATTCTTGCGGCT 2 AAAGCGGCGGCGAACCACCACAAAGAATACCGGAACGAAGAAGATTGCCA 3 GTACCGTTGCGGTCACCATCCCGCCCATTACACCGGTACCTACTGCGTTC 4 TGCGCGCCGGAACCAGCACCAGTACTGATAACCAGCGGCATAACGCCGAG 5 GATAAACGCCAGCGAGGTCATCAGGATCGGACGTAAACGCATCCGCACCG 6 CATCAAGCGTCGCTTCAATCAGACCTTTACCTTCTTTATCCATCAAGTCT 7 TTGGCGAATTCGACGATAAGGATCGCGTTCTTCGCCGACAACCCAATGGT 8 TGTGAGCAGGCCTACCTGGAAGTAAACGTCATTGGTCAGGCCACGGAAGG Listing 2: Part of the consensus sequence of acrb. These sequences can be analysed directly by existing MLST classification software. Introduction NGS Data Analysis 18/22 Thursday, May 22, 2014
43 Results Plasmid detection Plasmid Size Reads #3/#2 Cov #5/#2 NC NC NC NC NC NC NC NC NC Table: Part of the plasmids Excel file. Introduction NGS Data Analysis 19/22 Thursday, May 22, 2014
44 Results Gene detection Reference Gene Length Cov #4/#3 AB CMY AB IMP AB ACT AB IMP AB IMP AB IMP AC accd AC acra AC acrb Table: Part of the genes Excel file. Introduction NGS Data Analysis 20/22 Thursday, May 22, 2014
45 Results Reusability Plasmids and genes can be added easily. Introduction NGS Data Analysis 21/22 Thursday, May 22, 2014
46 Results Reusability Plasmids and genes can be added easily. Plasmids. Download a reference sequence. Index the reference sequence. Put the files in the right folder. Introduction NGS Data Analysis 21/22 Thursday, May 22, 2014
47 Results Reusability Plasmids and genes can be added easily. Plasmids. Download a reference sequence. Index the reference sequence. Put the files in the right folder. Genes: Download a reference sequence. Find the gene in this reference sequence. Write down the coordinates of the gene. This part is automated. Introduction NGS Data Analysis 21/22 Thursday, May 22, 2014
48 Questions? Acknowledgements: Sunita Paltansing Henk Buermans Sandra Bernards Johan den Dunnen Introduction NGS Data Analysis 22/22 Thursday, May 22, 2014
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