Mapping

Updated: PURL: gxy.io/GTN:S00081

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Requirements

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Introduction to Galaxy Analyses

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What is mapping (alignment)?
What is the BAM format?
How can we view aligned sequences?

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Understand the basic concept of mapping
Learn about factors influencing alignment
See a genome browser used to better understand your aligned data

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Example NGS pipeline

High level view of a typical NGS workflow

A high level view of a typical NGS bioinformatics workflow

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Mapping step occurs if a reference genome is available for the organism of interest
- else: de-novo assembly
Variant calling step is just an example, after mapping can do many steps
- Structural Variants / Fusion genes
- Differential Gene expression
- Alternative Splicing
- ..

What is mapping?

Mapping vs assembly

Short reads must be combined into longer fragments
Mapping: use a reference genome as a guide
De-novo assembly: without reference genome

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Mapping is also referred to as alignment
Short reads produced by sequencer must be combined into larger contigs
- e.g. reconstruct the chromosomes
- mapping uses a reference genome as a guide
- can subsequently find where our sample differs from reference (variants)

This tutorial only deals with mapping/alignment
There are other tutorials available for de-novo assembly

Sequence alignment

Determine position of short read on the reference genome

Reference: . . . A A C G C C T T . . .
Read:            A G G G G C C T T

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Consider situation where we must map this (short) read to this (long) reference
- e.g. human genome ~ 3.2 billion base pairs
We scan the reference genome until we find an area that's similar to our read
This area looks pretty similar, but not quite identical..

Sequence alignment

Determine position of short read on the reference genome

Reference: . . . A A - C G C C T T . . .              | = match
.                | : - : | | | | |                    : = mismatch
Read:            A G G G G C C T T                    - = gap

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But if we introduce gaps and allow for some mismatches in bases, this matches up pretty well..

Sequence alignment

Determine position of short read on the reference genome

Reference: . . . A A - C G C C T T . . .              | = match
.                | : - : | | | | |                    : = mismatch
Read:            A G G G G C C T T                    - = gap

Read could align to multiple places

How to handle multi-mapped reads? Depends on tool:
- Map to best region (but what is "best"? And what about ties?)
- Map to all regions
- Map to one region randomly
- Discard read

How do we determine best region?
- Assign alignment score to every mapping

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But if we introduce gaps and allow for some mismatches in bases, this matches up pretty well..

Some reads may map to multiple locations

repeat regions, short reads, highly variable regions, sequencing errors, ..

We want a way to determine best alignment if none are perfect matches..

Alignment Scoring (basics)

Reward for a match (e.g. +10), penalty for a mismatch (e.g. -5)
Penalty for gaps
- Linear: every gap same penalty (e.g. -5)
- Affine: gap open vs gap extend (e.g. -5 and -1)

Different tools use different scoring values (and give different results)

Screenshot of a sequence scoring game where two sequences are being aligned across the top (GGCTGG and GAGG) and the per-base and cumulative scores from left to right.

Example (with affine gap penalty)

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Each locus get scored independently (first row of scores in example)
Scores from all loci are added up (cumulative score row)
Final score for entire alignment in this example is 19
These reward and penalty values are just examples and will vary

Alignment Scoring (advanced)

Base quality
- Mismatch of low-confidence base: lower penalty
- Mismatch of high-confidence base: higher penalty

Transitions vs transversions
- Transitions about 2x as frequent as transversions

Transitions vs transversions Example scoring matrix

Knowledge about sequencing platform and biases
- Optimize for read length, error rate, homopolymer accuracy, etc..

More information about mapping algorithms: 10.1089/cmb.2012.0022

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Many more complexities may be considered, different tools make different choices

Transitions are more likely to occur in real sequences, so may give lower penalty than transversions

Transitions are interchanges of two-ring purines (A G) or of one-ring pyrimidines (C T): they therefore involve bases of similar shape.

Transversions are interchanges of purine for pyrimidine bases, which therefore involve exchange of one-ring and two-ring structures.

Transitions and transversions

Looks easy but..

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Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

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Suppose we want to map this read (bottom) to this reference sequence (top)

Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

Alignment
AAA-CAGTGAGAA \|\|\|-\|--\|::\|\|\| AAATC--TCTGAA	Maybe like this?

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This is one possibility, is it the only one?

Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

Alignment
AAA-CAGTGAGAA \|\|\|-\|--\|::\|\|\| AAATC--TCTGAA	Maybe like this?
AAACAGTGAGAA \|\|\|-::\|::\|\|\| AAA-TCTCTGAA	Or this?

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This is also a possible alignment. Not easy to say which is better.

Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

Alignment
AAA-CAGTGAGAA \|\|\|-\|--\|::\|\|\| AAATC--TCTGAA	Maybe like this?
AAACAGTGAGAA \|\|\|-::\|::\|\|\| AAA-TCTCTGAA	Or this?
AAACAGTGAGAA \|\|\|:-:\|::\|\|\| AAAT-CTCTGAA	Or..?

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And a third option

Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

Alignment
AAA-CAGTGAGAA \|\|\|-\|--\|::\|\|\| AAATC--TCTGAA	Maybe like this?
AAACAGTGAGAA \|\|\|-::\|::\|\|\| AAA-TCTCTGAA	Or this?
AAACAGTGAGAA \|\|\|:-:\|::\|\|\| AAAT-CTCTGAA	Or..?
AAACAGTCA-----GAA \|\|\|-----------\|\|\| AAA------TCTCTGAA	What about this?

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There is no one right way to do alignment

Hard to say which of these is "better" or "worse"
Just different choices, but all valid

Mapping is a non-trivial problem!

Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

Alignment	Tool
AAA-CAGTGAGAA \|\|\|-\|--\|::\|\|\| AAATC--TCTGAA	Novoalign
AAACAGTGAGAA \|\|\|-::\|::\|\|\| AAA-TCTCTGAA	Ssaha2
AAACAGTGAGAA \|\|\|:-:\|::\|\|\| AAAT-CTCTGAA	BWA
AAACAGTCA-----GAA \|\|\|-----------\|\|\| AAA------TCTCTGAA	Complete Genomics

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We didn't just make these up, these real aligners gave these different results

Sequence Alignment

Reference: AAA CAGTGA GAA
Observed:  AAA TCTCT  GAA

Alignment	Variant calls
AAA-CAGTGAGAA \|\|\|-\|--\|::\|\|\| AAATC--TCTGAA	ins T del AG sub GA -> CT
AAACAGTGAGAA \|\|\|-::\|::\|\|\| AAA-TCTCTGAA	del C sub AG -> TC sub GA -> CT
AAACAGTGAGAA \|\|\|:-:\|::\|\|\| AAAT-CTCTGAA	snp C -> T del A snp G -> C sub GA -> CT
AAACAGTGA-----GAA \|\|\|-----------\|\|\| AAA------TCTCTGAA	del CAGTGA ins TCTCT

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Important: Mapping can affect downstream analysis!

These different mappings led to different variants, and hard to tell they are equivalent.

Try it yourself!

Lego time! Who wants to volunteer?
Or try this online sequence alignment game:

Recording of alignment game

https://tinyurl.com/sequence-alignment

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Can have learners play around with this alignment game now

Or use Lego bricks, each nucleotide a different colour

Paired-end sequencing

Sequencing: Cut longer fragments of DNA, sequence only the ends
Mapping: known distance between reads improves accuracy

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The fragments are too long to sequence entirely, but we can sequence the ends.
Then we have the added information of how far apart these two reads must map
This improves our mapping
For example for multi-mapped reads, or repeats (next slide)

Repeats

Multi-mapped reads (e.g. because of repeats) may now be resolved
Single-end:

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In the case of repeats, a single-end read alone would not have be enough for unique mapping..

Repeats

Multi-mapped reads (e.g. because of repeats) may now be resolved
Single-end:
Paired-end:

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In the case of repeats, a single-end read alone would not have be enough for unique mapping..

But with the additional information provided by paired-end protocol (distance to mate), this can now be resolved..

InDels (Insertions / Deletions)

Discordant insert size may indicate insertion or deletion between reads
Deletions: Longer mapping distance than expected

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InDels (Insertions / Deletions)

Discordant insert size may indicate insertion or deletion between reads
Deletions: Longer mapping distance than expected
Insertions: Shorter mapping distance than expected

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Unexpected mapping distance between two reads in a pair may indicate a variant.
Exact location of variant unknown unless more reads covering the area
- Only know it it somewhere between the two reads

FAQ: "What about mate-pair sequencing?"

Same concept as paired-end
Much longer distance between ends
Very different library prep
Useful for detection of larger Structural Variations (SVs) / Fusion Genes
- longer than expected distance between mates: deletion in sample
- shorter than expected distance beetween mates: insertion in sample
- unexpected orientation of one mate: inversion in sample

Paired-end FASTQ files

Sequencer produces two FASTQ files:
- Forward reads (usually _1 or _R1 in file name)
- Reverse reads (usually _2 or _R2 in file name)

Paired-end reads as two separate FASTQ files

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When you have paired-end data, you will usually get 2 files.

File names identical except for e.g. _1/_2 or _R1/_R2
First file contains all the forward reads ("left" sides of pairs)
Other file contains all the reverse reads

Pairing also visible in read names

/1 /2 at end or 1: and 2: in read ID

Paired-end FASTQ files

Sequencer produces two FASTQ files:
- Forward reads (usually _1 or _R1 in file name)
- Reverse reads (usually _2 or _R2 in file name)

Paired-end reads as two separate FASTQ files

Sometimes: One interleaved (or interlaced) FASTQ file
- Most tools require 2 separate files
- tool De-interlace tools in Galaxy for conversion

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When you have paired-end data, you will usually get 2 files.

File names identical except for e.g. _1/_2 or _R1/_R2
First file contains all the forward reads ("left" sides of pairs)
Other file contains all the reverse reads

Pairing also visible in read names

/1 /2 at end or 1: and 2: in read ID

Sometimes data can be in a single interleaved file (aka interlaced)

alternating forward and reverse read
de-interlace tools in Galaxy to convert this to two separate files
- because many tools require two separate files

Paired-end FASTQ files

Order of reads matters!
- N^th read in forward file N^th read in reverse file
- Much faster than determining pairing by read names alone

Always trim and filter together!

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Most tools blindly assume that first read in forward file is paired with first read in reverse file etc

Otherwise too slow

for every read, worst case have to scan all reads in other file
for files with millions of reads, that is millions ^ millions

When trimming and filtering, if a read is removed from one file, its mate must be removed from other one too!

Always trim together in paired-end mode!

Paired-end FASTQ files

Order of reads matters!
- N^th read in forward file N^th read in reverse file
- Much faster than determining pairing by read names alone

Always trim and filter together!

@PAIR-1 forward
GGGTGATGGCCGCTGCCGATGGCGTCAAAT
+
))%255CCF>>>>>>CCCCCCC65`IIII%

@PAIR-2 forward
GATTTGGGGTTCAAAGCAGTATCGATCAA
+
!''3((((^^d+))%%%++)(%%%%).1)

@PAIR-3 forward
TCGCACTCAACGCCCTGCATATGACAAGAC
+
A64;##=#B9=AAAAAAAAAA9#:AB95%^

mysample_R1.fastq

@PAIR-1 reverse
AAGTTACCCTTAACAACTTAAGGGTTTTCA
+
fffddffeedBIABa)^%YBBBRTT\^d

@PAIR-2 reverse
AGCAGAAGTCGATGATAATACGCGTCGTTT
+
IIIIIII^^IIId`?III%IIIGII>IIII

@PAIR-3 reverse
AATCCATTTGTTCAACTCACAGTTTACCGT
+
9C;=;=<9@4868>9:67AA<9>65<=>59

mysample_R2.fastq

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Most tools blindly assume that first read in forward file is paired with first read in reverse file etc

Otherwise too slow

for every read, worst case have to scan all reads in other file
for files with millions of reads, that is millions ^ millions

When trimming and filtering, if a read is removed from one file, its mate must be removed from other one too!

Always trim together in paired-end mode!

Nth read in forward file belongs in a pair with Nth read in reverse file
So red reads in this slide form a pair, orange ones, etc

Paired-end FASTQ files

Order of reads matters!
- N^th read in forward file N^th read in reverse file
- Much faster than determining pairing by read names alone

Always trim and filter together!

@PAIR-1 forward
GGGTGATGGCCGCTGCCGATGGCGTCAAAT
+
))%255CCF>>>>>>CCCCCCC65`IIII%

@PAIR-2 forward
GATTTGGGGTTCAAAGCAGTATCGATCAA
+
!''3((((^^d+))%%%++)(%%%%).1)

@PAIR-3 forward
TCGCACTCAACGCCCTGCATATGACAAGAC
+
A64;##=#B9=AAAAAAAAAA9#:AB95%^

mysample_R1.fastq

@PAIR-1 reverse
AAGTTACCCTTAACAACTTAAGGGTTTTCA
+
fffddffeedBIABa)^%YBBBRTT\^d

@PAIR-2 reverse
AGCAGAAGTCGATGATAATACGCGTCGTTT
+
IIIIIII^^IIId`?III%IIIGII>IIII

@PAIR-3 reverse
AATCCATTTGTTCAACTCACAGTTTACCGT
+
9C;=;=<9@4868>9:67AA<9>65<=>59

mysample_R2.fastq

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Important to always provide both files to trimming and filtering tools together
If a read in one file gets removed (e.g. because it is below quality threshold), but it's mate is not, the pairing between the two files is no longer correct.
If one half of pair is trimmed, the other
- also removed, or
- put into separate "singletons" FASTQ file that some mappers can use
FAQ:" why not look at read names to determine pairing?"
- analysis would be much slower if for every read must scan (max) entire other file for mate, since often millions or reads (for whole-genome sequencing).

Paired-end FASTQ files

Order of reads matters!
- N^th read in forward file N^th read in reverse file
- Much faster than determining pairing by read names alone

Always trim and filter together!

@PAIR-1 forward
GGGTGATGGCCGCTGCCGATGGCGTCAAAT
+
))%255CCF>>>>>>CCCCCCC65`IIII%

@PAIR-3 forward
TCGCACTCAACGCCCTGCATATGACAAGAC
+
A64;##=#B9=AAAAAAAAAA9#:AB95%^

@PAIR-4 forward
AAACTTCGTAGGTCCATTTGACAGCGTGCA
+
6664%!!III^(=%3333^^d^d:#32333

mysample_R1.fastq

@PAIR-1 reverse
AAGTTACCCTTAACAACTTAAGGGTTTTCA
+
fffddffeedBIABa)^%YBBBRTT\^d

@PAIR-2 reverse
AGCAGAAGTCGATGATAATACGCGTCGTTT
+
IIIIIII^^IIId`?III%IIIGII>IIII

@PAIR-3 reverse
AATCCATTTGTTCAACTCACAGTTTACCGT
+
9C;=;=<9@4868>9:67AA<9>65<=>59

mysample_R2.fastq

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By cutting the yellow read only from the forward reads file, but leaving the other side of pair in the other file, an incorrect pairing is now assumed by downstream tools

Choosing an Aligner

Each tool makes different choices during alignment
- Choice of aligner may affect downstream results
- Default options may not be best for your data!

Best tool for your data depends on many factors
- Type of experiment (e.g. DNA, RNA, Bisulphite)
- Sequencing platform
- Compute resources vs sensitivity
- Read characteristics (paired/single end, read length)

Mapping RNA

Figure: mapping of RNA-seq reads is different than DNA-seq

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Choice of mapper depends on your experiment

Some mappers are good for DNA but not RNA
Some mappers do well in highly rearranged genomes (e.g. cancer), others less so
Some mappers do well on some platforms but worse on others
- e.g. Oxford Nanopore with its long reads but high error rates

Or other factors

STAR needs a LOT of RAM
Do you need results fast? or accurate? (e.g. medical setting)

FAQ: "Why not map RNA reads to the transcriptome?"

you can, and it is done, but then cannot find novel genes or alternative splicing

FAQ: "Why not BLAST or BLAT?"

optimized for longer sequences
not base quality aware
too slow

Know your data!

“... there is no tool that outperforms all of the others in all the tests. Therefore, the end user should clearly specify [their] needs in order to choose the tool that provides the best results.” - Hatem et al BMC Bioinformatics 2013, 14:184

DOI: 10.1186/1471-2105-14-184

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Know the data you are working with and pick the right mapper and parameters for the job!

Not an easy task..

Mapping tools

Timeline of mapping tools

60+ different mappers, many comparison papers. Figure from 10.1093/bioinformatics/bts605

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Many different tools available

Different strengths and weaknesses, comparison table in link

Mapping tools

Mapping tool	Uses	Characteristics
HISAT2	DNA/RNA	Short reads. Based on GCSA. Reference.
RNASTAR	RNA	Short reads. Extremely fast. High sensitive and accuracy. Based on Maximal Mappable Prefixes (MMPs). Reference.
BWA-MEM2	DNA	Short reads. Twice as faster as BWA-MEM. Memory efficient. Based on Burrows-Wheeler. Reference.
Minimap2	DNA/RNA	Long reads (PacBio and ONT). Extremely fast. Based on DALIGN and MHAP. Reference.
Bismark	DNA/RNA	Short reads. Bisulfite treated sequencing. Based on GCSA. Reference.
BBMap	DNA/RNA	Short and long reads (PacBio and ONT). Memory demanding. Reference.
Whisper 2	DNA	Short reads. Indel sensitive. Variant-calling oriented. Reference.
S-conLSH	DNA	Long reads (ONT). High sensitivity and accuracy. Reference.

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File Formats

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SAM/BAM file format

Example of SAM file format

SAM: Sequence Alignment Map

BAM: Binary (compressed) SAM; not human-readable

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SAM/BAM file format

More detailed view of SAM format

Original read information (from FASTQ) plus mapping information
- Position on reference, alignment, quality score, uniqueness, ..

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Alignment given in CIGAR string.

in screenshot "37M" means 37 matches
in screenshot "18M2D19M" means 18 matches, then 2 deletions, then 19 matches

Genome Browsers

Visualise aligned reads (BAM files)

IGV Genome Browser

This is IGV (Integrative Genome Browser) DOI: 10.1038/nbt.1754

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Can zoom in and out, drag left and right, explore your sample
Zoom in for more information, mismatches, read information
Many different genome browsers exist

Genome Browsers in Galaxy

JBrowse tool Genome Browser as Galaxy tool

Screenshot of JBrowse in the Galaxy Interface showing transcripts, various box plots, heatmaps, sequencing depth and variation plots.

JBrowse.org DOI: 10.1186/s13059-016-0924-1

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Jbrowse tool builds up a small website for you, and pre-processes the reference genome into a more efficient format. If you wanted to share this with your colleagues, you could download this dataset and directly place it on your webserver.

Genome Browsers in Galaxy

External Genome Browsers in Galaxy
- BAM datasets in Galaxy have display links
- UCSC Genome Browser, Ensemble, IGV, IGB, BAM.iobo

Display application links in Galaxy

Two different links for IGV
- local:
  - Start IGV on your machine first
  - Then click link to automatically load data from Galaxy
- [Reference genome name] ("Human hg19" in screenshot)
  - Downloads and starts IGV for you
  - Requires Java web start be installed on your machine

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In the mapping hands-on tutorial you will use JBrowse and IGV

Mapping is not trivial
There are many mapping tools, best choice depends on your data
Choice of mapper can affect downstream results
Know your data!
Genome browsers can be used to view aligned reads

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Thank You!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Author(s)	Joachim Wolff Saskia Hiltemann Engy Nasr Cristóbal Gallardo
Reviewers

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Tutorial Content is licensed under Creative Commons Attribution 4.0 International License.

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Mapping

Requirements

question Questions

objectives Objectives

Example NGS pipeline

What is mapping?

Sequence alignment

Sequence alignment

Sequence alignment

Alignment Scoring (basics)

Alignment Scoring (advanced)

Looks easy but..

Sequence Alignment

Sequence Alignment

Sequence Alignment

Sequence Alignment

Sequence Alignment

Sequence Alignment

Sequence Alignment

Try it yourself!

Paired-end sequencing

Repeats

Repeats

InDels (Insertions / Deletions)

InDels (Insertions / Deletions)

Paired-end FASTQ files

Paired-end FASTQ files

Paired-end FASTQ files

Paired-end FASTQ files

Paired-end FASTQ files

Paired-end FASTQ files

Choosing an Aligner

Know your data!

Mapping tools

Mapping tools

File Formats

SAM/BAM file format

SAM/BAM file format

Genome Browsers

Genome Browsers in Galaxy

Genome Browsers in Galaxy

keypoints Key points

Thank You!

Requirements

Help