Mapping

Last updated on 2025-06-24 | Edit this page

In this episode we will try to pinpoint single nucleotide polymorphism (SNPs), also called single nucleotide variants (SNVs), between our samples and the reference. The SNPs are determined by a process called read mapping in which reads are aligned to the reference sequence.

Overview

Questions

How to generate a phylogenetic tree from SNP data?

Objectives

Map reads against a reference genome
Extract single nucleotide polymorphisms
Establish a phylogenetic tree from the SNP data

The identified SNPs will be used to compare the isolates to each other and to establish a phylogenetic tree.

SNP calling

snippy is a pipeline that contains different tools to determine SNPs in sequencing reads against a reference genome. It takes forward and reverse reads of paired-end sequences and aligns them against a reference.

Challenge

First we’ll start the interactive session, load the snippy module and create a snps folder in the molepi/results to hold the results from snippy.

Show me the solution

BASH

interactive -A uppmax2025-3-4 -M snowy -t 04:00:00
module load bioinfo-tools snippy
cd /proj/g2020004/nobackup/3MK013/<username>/molepi/results
mkdir snps

Snippy is fast but a single run still takes about 15 minutes. We would therefore tell snippy to run all the samples after each other. However this time we cannot use a wildcard to do so. We would instead run all the samples in a loop. The “real” loop is commented out, and we use one that runs only the sample we’ve analyzed so far.

BASH

cd ../data/trimmed_fastq/
#for sample in ERR026473 ERR026474 ERR026478 ERR026481 ERR026482 ERR029206 ERR029207
for sample in ERR029206
do
snippy --ram 8 --outdir ../../results/snps/"${sample}" --ref ../GCF_000195955.2_ASM19595v2_genomic.fna --R1 "${sample}"_1.fastq.gz_trim.fastq --R2 "${sample}"_2.fastq.gz_trim.fastq
done

Here, we provide snippy with an output folder (--outdir), the location of the reference genome (--ref), and the trimmed read files for each end of the pair (--R1 and --R2). We also indicate that snippy should use 8 Gb of memory (--ram 8).

BASH

head -n10 ../../results/snps/ERR029206/snps.tab

OUTPUT

CHROM	POS	TYPE	REF	ALT	EVIDENCE	FTYPE	STRAND	NT_POS	AA_POS	EFFECT	LOCUS_TAG	GENE	PRODUCT
NC_000962.3	1849	snp	C	A	A:217 C:0
NC_000962.3	1977	snp	A	G	G:118 A:0
NC_000962.3	4013	snp	T	C	C:176 T:0
NC_000962.3	7362	snp	G	C	C:156 G:0
NC_000962.3	7585	snp	G	C	C:143 G:0
NC_000962.3	9304	snp	G	A	A:127 G:0
NC_000962.3	11820	snp	C	G	G:164 C:0
NC_000962.3	11879	snp	A	G	G:125 A:0
NC_000962.3	14785	snp	T	C	C:163 T:1

This list gives us information on every SNP that was found by snippy when compared to the reference genome. The first SNP is found at the position 1849 of the reference genome, and is a C in the H37Rv (reference strain) and an A in isolate ERR029206. There is a high confidence associated with this SNP: an A has been found 217 times in the sequencing reads and never a C at this position.

Now copy a reduced version of the results obtained from the other samples, save it in the results folder. We will also rename the existing snps folder to save it. We extract them from the archive in the data folder, using tar. tar is a program to take multiple files, including their folder structure and put them in a single tar “ball”. Often the tarballs are compressed with gzip.

BASH

cd /proj/g2020004/nobackup/3MK013/<username>/molepi/results
mv snps snps_partial
mkdir snps
cd snps
tar xvzf ../../../../data/snps.tar.gz
ls *

OUTPUT

ERR026473:
ref.fa.          snps.bam.bai  snps.filt.vcf  snps.log	     snps.tab  snps.vcf.gz
ref.fa.fai	     snps.bed      snps.gff       snps.raw.vcf   snps.txt  snps.vcf.gz.csi
snps.aligned.fa  snps.csv      snps.html      snps.subs.vcf  snps.vcf

ERR026474:
ref.fa		       snps.bam.bai  snps.filt.vcf  snps.log	     snps.tab  snps.vcf.gz
ref.fa.fai	     snps.bed      snps.gff       snps.raw.vcf   snps.txt  snps.vcf.gz.csi
snps.aligned.fa  snps.csv      snps.html      snps.subs.vcf  snps.vcf

Note the unusual way to pass arguments to tar with just xvzf instead of -x -v -z -f or -xvzf. x tells tar to extract the archive (c would be used to create the archive); v is for verbose, providing more information on the output; z tells tar to decompress the file; f tells what file to work on.

The folders don’t contain everything that is output by snippy, to save space. But it is enough to run snippy-core, which summarizes several snippy runs.

Challenge: How many SNPs were identified in each sample??

Find out how many SNPs were identified in the M. tuberculosis isolates when compared to H37Rv. Hint: The .txt file in the snippy output contains summary information:

Show me the solution

From the molepi/results/snps folder:

BASH

cat ERR029207/snps.txt

Challenge: How many SNPs were identified in each sample?? (continued)

OUTPUT

DateTime        2023-05-17T13:51:57
ReadFiles       [...]/data/trimmed_fastq/ERR029207_1.fastq.gz_trim.fastq [...]/data/trimmed_fastq/ERR029207_2.fastq.gz_trim.fastq
Reference       [...]/data/GCF_000195955.2_ASM19595v2_genomic.fna
ReferenceSize   4411532
Software        snippy 4.6.0
Variant-COMPLEX 32
Variant-DEL     57
Variant-INS     52
Variant-MNP     2
Variant-SNP     1290
VariantTotal    1433

This M. tuberculosis isolate contains 1290 SNPs compared to H37Rv.

Repeat this for the other isolates. How about using a for loop?

Show me the solution

BASH

for accession in ERR*; do
  echo <accession>
  cat <accession/snps.txt | grep <pattern>
done

Core SNPs

In order to compare the identified SNPs with each other we need to know if a certain position exists in all isolates. A core site can have the same nucleotide in every sample (monomorphic) or some samples can be different (polymorphic).

In this second part, after identifying them, snippy will concatenate the core SNPs, i.e. ignoring sites that are monomorphic in all isolates and in the reference. Concatenation of the SNP sites considerably reduces the size of the alignment.

The --ref argument provides the reference genome. Each folder containing the result of the previous step of snippy is then added to the command line.

BASH

cd /proj/g2020004/nobackup/3MK013/<username>/molepi/results/snps
snippy-core --ref=../../data/GCF_000195955.2_ASM19595v2_genomic.fna ERR026473 ERR026474 ERR026478 ERR026481 ERR026482 ERR029206 ERR029207

Instead of writing all samples explicitly, we could use wildcards:

BASH

cd /proj/g2020004/nobackup/3MK013/<username>/molepi/results/snps
snippy-core --ref=../../data/GCF_000195955.2_ASM19595v2_genomic.fna ERR*

The last few lines look like this:

OUTPUT

...
Loaded 1 sequences totalling 4411532 bp.
Will mask 0 regions totalling 0 bp ~ 0.00%
0	ERR026473	snp=1378	del=174	ins=165	het=747	unaligned=125683
1	ERR026474	snp=1369	del=180	ins=143	het=811	unaligned=123020
2	ERR026478	snp=1381	del=221	ins=143	het=638	unaligned=112145
3	ERR026481	snp=1349	del=215	ins=138	het=754	unaligned=126217
4	ERR026482	snp=1348	del=215	ins=145	het=911	unaligned=127554
5	ERR029206	snp=1352	del=152	ins=98	het=1839	unaligned=122677
6	ERR029207	snp=1355	del=152	ins=97	het=1900	unaligned=118570
Opening: core.tab
Opening: core.vcf
Processing contig: NC_000962.3
Generating core.full.aln
Creating TSV file: core.txt
Running: snp-sites -c -o core.aln core.full.aln
This analysis is totally hard-core!
Done.

Our output was written to ‘core.aln’. But let’s have a look at the results.

Discussion: What’s in the output of snippy??

Have a look at the content of these three files>

core.aln

core.full.aln

core.tab

core.txt

What is the difference between these files? Why is core.aln smaller than core.full.aln? What is in core.aln?

Phylogenetic tree

We will here infer a phylogenetic tree from the file ‘core.aln’ with IQ-TREE.

In this case, we want IQ-TREE to automatically select the best (nucleotide) substitution model. IQ-TREE does that by testing many (among a very large collection) substitution models. We also have SNP data, which by definition do not contain constant (invariable) sites. We thus input the alignment with the -s option and the model with -m MFP+ASC. MFP will tell IQ-TREE to test a range of models, and ASC will correct for the fact that there is no constant sites.

Challenge

Infer a phylogenetic tree with IQ-TREE, using the aligned polymorphic sites identified by snippy-core above. IQ-TREE should find the best possible model, adding the correction for no constant sites. Don’t forget to load the right module.

Give me a hint

BASH

iqtree -h

Show me the solution

BASH

module load <module>
iqtree2 <alignment> <model testing>

OUTPUT

...
Total wall-clock time used: 0.312 sec (0h:0m:0s)

Analysis results written to:
  IQ-TREE report:                core.aln.iqtree
  Maximum-likelihood tree:       core.aln.treefile
  Likelihood distances:          core.aln.mldist
  Screen log file:               core.aln.log

Date and Time: Thu May 18 09:00:37 2023

With this small data set, IQ-TREE finishes very quickly. Let’s put the resulting files into a separate folder and let’s rename our resulting tree.

BASH

cd /proj/g2020004/nobackup/3MK013/<username>/molepi/results
mkdir tree
mv snps/core.aln.* tree

Let’s inspect our tree, and give it another extension to make it clear it is a newick file.

BASH

cd tree
mv core.aln.treefile core.aln.newick 
cat core.aln.newick

OUTPUT

(ERR026473:0.0004854701,ERR026474:0.0004356437,((((ERR026478:0.0000010000,ERR029207:0.0000010000):0.0000010000,ERR029206:0.0000131789):0.0006712219,Reference:0.0075887710):0.0000152316,(ERR026481:0.0000066238,ERR026482:0.0000131899):0.0005602421):0.0002373720);

This does not look much like a tree yet. The tree is written in a bracket annotation, the Newick format. In order to make sense of it we can better view this in a tree viewer.

Challenge: Can you identify what substitution model IQ-TREE used?

What model was selected? How does IQ-TREE choose between different models? What does e.g. “JC+ASC+G4” means?

Give me a hint

The log file of IQ-TREE contains a lot of information.

The IQ-TREE website contains a description of the DNA substitution models.

Show me the solution

BASH

cat core.aln.iqtree

OUTPUT

...
Best-fit model according to BIC: TVMe+ASC
...

IQ-TREE chose the TVMe+ASC model. We’ve discussed the ASC above, and you can read more about the TVMe in the IQ-TREE manual. In short, it is a mdoel where base frequencies are deemed equal, and all rates estimated independently, except the rates of transversions (A <-> G and C <-> T) are equal.

The model selection process is also described on the IQ-TREE website.

Challenge: Visualize the tree on your computer

Copy the tree in Newick format (core.aln.newick) to your computer using scp and use FigTree or (phylo.io)[https://beta.phylo.io/] to visualize it. Do you see a way to assess the quality of the tree?

Show me the solution

BASH

scp <username>@rackham.uppmax.uu.se:<file with path> .

Challenge: Rerun the tree, calculating bootstraps this time

In the Phylogenetics module, we calculated 1000 UltraFast bootstraps using the -B 1000 option. Here, calculate 100 non-parametric bootstraps. Non-parametric bootstraps are bootstraps that are calculated without making assumptions about the data. In that case, it means actually resampling the alignment and calculating trees for each of the bootstrapped alignments. It is a much slower process than the UltraFast method (which is parametric), but in that case, since the alignment is very small, the computational load is acceptable.

With IQ-TREE, non-parametric bootstraps are inferred using the -b <n bootstraps> option. Make sure all files are prefixed with core.aln.100npb (option --prefix).

The process will take a minute or so. Then in the results folder, next to the tree folder, create a tree100npb folder and move the results file there.

Show me the solution

BASH

cd /proj/g2020004/nobackup/3MK013/<username>/molepi/results/snps
iqtree2 <alignment> <model as before> <100 non-parametric bootstraps> <prefix>
cd ..
mkdir tree100npb
mv snps/core.aln.100npb* tree100npb/

Challenge: Can you identify what substitution model IQ-TREE used? (continued)

When that finishes, copy the resulting tree to your local computer and visualize it. Display support values: - With FigTree: on the left menu, tick “Branch labels”, and choose the last row (what you entered when prompted to do so, label by default) - With phylo.io: do not use the “beta” service, as it displays the support values incorrectly. Use the stable version, but compare both!

As shown by phylo.io, beta version. Note the position of the “99”.

Key Points

Single nucleotide polymorphisms can be identified by mapping reads to a reference genome
Parameters for the analysis have to be selected based on expected outcomes for this organism
Concatenation of SNPs helps to reduce analysis volume
Phylogenetic trees can be written with a bracket syntax in Newick format