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# Mapping
Authors:
Joachim Wolff
Saskia Hiltemann
Engy Nasr
Cristóbal Gallardo
Erasmus+ Programme
last_modification
Updated: Feb 25, 2022
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Useful when presenting. --- ## Requirements Before diving into this slide deck, we recommend you to have a look at: - [Introduction to Galaxy Analyses](/training-material/topics/introduction) --- ### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions - What is mapping (alignment)? - What is the BAM format? - How can we view aligned sequences? --- ### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> Objectives - Understand the basic concept of mapping - Learn about factors influencing alignment - See a genome browser used to better understand your aligned data --- # Example NGS pipeline ![High level view of a typical NGS workflow](../../images/mapping/variant_calling_workflow.png) A high level view of a typical NGS bioinformatics workflow ??? - 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? .pull-left[ ![Mapping vs assembly](../../images/mapping/mapping_assembly.png) ] .pull-right[ - Short reads must be combined into longer fragments - **Mapping:** use a reference genome as a guide - **De-novo assembly:** without reference genome ] ??? - 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 --- class: top # 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 ``` ??? - 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.. --- class: top # 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 ``` ??? But if we introduce gaps and allow for some mismatches in bases, this matches up pretty well.. -- - Read could align to multiple places .center[.image-50[![Illustration of multi-mapped read](../../images/mapping/multimap.png)]] - 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 ??? 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.. --- class: top # 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) .center[ .image-25[![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.](../../images/mapping/scoring_example.png)] **Example** (with affine gap penalty) ] ??? - 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 --- class: top # 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 .center[ .image-50[ ![Transitions vs transversions](../../images/mapping/ti_tv.png) ] .image-25[ ![Example scoring matrix](../../images/mapping/ti_tv_scoring.png) ] ] - Knowledge about sequencing platform and biases - Optimize for read length, error rate, homopolymer accuracy, etc.. .footnote[More information about mapping algorithms: [10.1089/cmb.2012.0022](https://doi.org/10.1089/cmb.2012.0022)] ??? 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](../../images/mapping/transition_transversion.gif) --- # Looks easy but.. --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` ??? Suppose we want to map this read (bottom) to this reference sequence (top) --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` <table style="width:100%; table-layout: fixed; font-size:0.8em"> <th>Alignment</th><th></th> <tr><td><pre> AAA-CAGTGAGAA |||-|--|::||| AAATC--TCTGAA </pre></td> <td>Maybe like this?</td> </tr> </table> ??? This is one possibility, is it the only one? --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` <table style="width:100%; table-layout:fixed; font-size:0.8em;"> <th>Alignment</th><th></th> <tr><td><pre> AAA-CAGTGAGAA |||-|--|::||| AAATC--TCTGAA </pre></td> <td>Maybe like this?</td> </tr> <tr><td><pre> AAACAGTGAGAA |||-::|::||| AAA-TCTCTGAA </pre></td> <td> Or this? </td> </tr> </table> ??? This is also a possible alignment. Not easy to say which is better. --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` <table style="width:100%; table-layout:fixed; font-size:0.8em"> <th>Alignment</th><th></th> <tr><td><pre> AAA-CAGTGAGAA |||-|--|::||| AAATC--TCTGAA </pre></td> <td>Maybe like this?</td> </tr> <tr><td><pre> AAACAGTGAGAA |||-::|::||| AAA-TCTCTGAA </pre></td> <td> Or this? </td> </tr> <tr><td><pre> AAACAGTGAGAA |||:-:|::||| AAAT-CTCTGAA </pre></td> <td>Or..? </td> </tr> </table> ??? And a third option --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` <table style="width:100%; table-layout:fixed; font-size:0.8em"> <th>Alignment</th><th></th> <tr><td><pre> AAA-CAGTGAGAA |||-|--|::||| AAATC--TCTGAA </pre></td> <td>Maybe like this?</td> </tr> <tr><td><pre> AAACAGTGAGAA |||-::|::||| AAA-TCTCTGAA </pre></td> <td> Or this? </td> </tr> <tr><td><pre> AAACAGTGAGAA |||:-:|::||| AAAT-CTCTGAA </pre></td> <td>Or..? </td> </tr> <tr><td><pre> AAACAGTCA-----GAA |||-----------||| AAA------TCTCTGAA </pre></td> <td> What about this? </td> </tr> </table> ??? 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! --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` <table style="width:100%; table-layout:fixed; font-size:0.8em"> <th>Alignment</th><th>Tool</th> <tr><td><pre> AAA-CAGTGAGAA |||-|--|::||| AAATC--TCTGAA </pre></td> <td>Novoalign</td> </tr> <tr><td><pre> AAACAGTGAGAA |||-::|::||| AAA-TCTCTGAA </pre></td> <td> Ssaha2 </td> </tr> <tr><td><pre> AAACAGTGAGAA |||:-:|::||| AAAT-CTCTGAA </pre></td> <td> BWA </td> </tr> <tr><td><pre> AAACAGTCA-----GAA |||-----------||| AAA------TCTCTGAA </pre></td> <td> Complete Genomics </td> </tr> </table> ??? We didn't just make these up, these real aligners gave these different results --- class: top # Sequence Alignment ``` Reference: AAA CAGTGA GAA Observed: AAA TCTCT GAA ``` <table style="width:100%; table-layout:fixed; font-size:0.8em"> <th>Alignment</th><th>Variant calls</th> <tr><td><pre> AAA-CAGTGAGAA |||-|--|::||| AAATC--TCTGAA </pre></td> <td><pre> ins T del AG sub GA -> CT </pre></td> </tr> <tr><td><pre> AAACAGTGAGAA |||-::|::||| AAA-TCTCTGAA </pre></td> <td><pre> del C sub AG -> TC sub GA -> CT </pre></td> </tr> <tr><td><pre> AAACAGTGAGAA |||:-:|::||| AAAT-CTCTGAA </pre></td> <td><pre> snp C -> T del A snp G -> C sub GA -> CT </pre></td> </tr> <tr><td><pre> AAACAGTGA-----GAA |||-----------||| AAA------TCTCTGAA </pre></td> <td><pre> del CAGTGA ins TCTCT </pre></td> </tr> </table> ??? **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](http://web.archive.org/web/20200411075748/https://teacheng.illinois.edu/SequenceAlignment/): <!-- using webarchive version because game seems broken, once fixed we can update the link back to: http://teacheng.illinois.edu/SequenceAlignment/ --> .image-75[![Recording of alignment game](../../images/mapping/alignment.gif)] .footnote[https://tinyurl.com/sequence-alignment] ??? 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 .center[.image-90[![Paired-end reads](../../images/mapping/pairedend_read.png)]] - **Mapping:** known distance between reads improves accuracy .center[.image-75[![Mapping of paired-end reads](../../images/mapping/pairedend_mapping.png)]] ??? - 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) --- class: top ## Repeats - Multi-mapped reads (e.g. because of repeats) may now be resolved - **Single-end:** ![Cartoon with a reference genome and two repeats marked. Two blue boxes representing a single-ended read map equally well to both repeats.](../../images/mapping/repeats_se.png) ??? In the case of repeats, a single-end read alone would not have be enough for unique mapping.. -- - **Paired-end:** ![Cartoon with a reference genome and two repeats marked. Now the two blue boxes are linked and one of them is red, representing a forward/reverse pair of a paired-end read. The mapping is no longer ambiguous and you can know which repeat the blue box belongs to, as the red box maps upstream.](../../images/mapping/repeats_pe.png) ??? But with the additional information provided by paired-end protocol (distance to mate), this can now be resolved.. --- class: top # InDels (Insertions / Deletions) - Discordant insert size may indicate insertion or deletion between reads - **Deletions:** Longer mapping distance than expected .image-75[![Deletion between two paired reads](../../images/mapping/pairedend_deletion.png)] -- - **Insertions:** Shorter mapping distance than expected .image-75[![Insertions beteween two paired reads](../../images/mapping/pairedend_insertion.png)] ??? - 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 --- class: top ## 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](../../images/mapping/pairedend_fastq.png) ??? 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: One **interleaved** (or **interlaced**) FASTQ file - Most tools require 2 separate files - <i class="fas fa-wrench" aria-hidden="true"></i><span class="visually-hidden">tool</span> De-interlace tools in Galaxy for conversion ??? 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 --- class: top ## Paired-end FASTQ files - Order of reads matters! - **`N`<sup>th</sup>** read in forward file <i class="fa fa-arrows-h" aria-hidden="true"></i> **`N`<sup>th</sup>** read in reverse file - Much faster than determining pairing by read names alone - ***Always trim and filter together!*** ??? 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!** -- .pull-left[ .red[ ``` @PAIR-1 forward GGGTGATGGCCGCTGCCGATGGCGTCAAAT + ))%255CCF>>>>>>CCCCCCC65`IIII% ``` ] .orange[ ``` @PAIR-2 forward GATTTGGGGTTCAAAGCAGTATCGATCAA + !''3((((^^d+))%%%++)(%%%%).1) ``` ] .blue[ ``` @PAIR-3 forward TCGCACTCAACGCCCTGCATATGACAAGAC + A64;##=#B9=AAAAAAAAAA9#:AB95%^ ``` ] **`mysample_R1.fastq`** ] .pull-right[ <i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .red[ ``` @PAIR-1 reverse AAGTTACCCTTAACAACTTAAGGGTTTTCA + fffddf`feedB`IABa)^%YBBBRTT\^d ``` ] <i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .orange[ ``` @PAIR-2 reverse AGCAGAAGTCGATGATAATACGCGTCGTTT + IIIIIII^^IIId`?III%IIIGII>IIII ``` ]<i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .blue[ ``` @PAIR-3 reverse AATCCATTTGTTCAACTCACAGTTTACCGT + 9C;=;=<9@4868>9:67AA<9>65<=>59 ``` ] **`mysample_R2.fastq`** ] ??? - 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 --- class: top ## Paired-end FASTQ files - Order of reads matters! - **`N`<sup>th</sup>** read in forward file <i class="fa fa-arrows-h" aria-hidden="true"></i> **`N`<sup>th</sup>** read in reverse file - Much faster than determining pairing by read names alone - ***Always trim and filter together!*** .pull-left[ <i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .red[ ``` @PAIR-1 forward GGGTGATGGCCGCTGCCGATGGCGTCAAAT + ))%255CCF>>>>>>CCCCCCC65`IIII% ``` ] .left[<i class="fa fa-cut" style="width:15%;position:absolute;font-size:5em;"></i>] <i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .orange[ ``` @PAIR-2 forward GATTTGGGGTTCAAAGCAGTATCGATCAA + !''3((((^^d+))%%%++)(%%%%).1) ``` ] <i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .blue[ ``` @PAIR-3 forward TCGCACTCAACGCCCTGCATATGACAAGAC + A64;##=#B9=AAAAAAAAAA9#:AB95%^ ``` ] **`mysample_R1.fastq`** ] .pull-right[ .red[ ``` @PAIR-1 reverse AAGTTACCCTTAACAACTTAAGGGTTTTCA + fffddf`feedB`IABa)^%YBBBRTT\^d ``` ] .orange[ ``` @PAIR-2 reverse AGCAGAAGTCGATGATAATACGCGTCGTTT + IIIIIII^^IIId`?III%IIIGII>IIII ``` ] .blue[ ``` @PAIR-3 reverse AATCCATTTGTTCAACTCACAGTTTACCGT + 9C;=;=<9@4868>9:67AA<9>65<=>59 ``` ] **`mysample_R2.fastq`** ] ??? - 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). --- class: top ## Paired-end FASTQ files - Order of reads matters! - **`N`<sup>th</sup>** read in forward file <i class="fa fa-arrows-h" aria-hidden="true"></i> **`N`<sup>th</sup>** read in reverse file - Much faster than determining pairing by read names alone - ***Always trim and filter together!*** .pull-left[ <i class="fa fa-arrows-h" style="position:absolute;font-size:3em;left:8em;"></i> .red[ ``` @PAIR-1 forward GGGTGATGGCCGCTGCCGATGGCGTCAAAT + ))%255CCF>>>>>>CCCCCCC65`IIII% ``` ] <i class="fa fa-frown-o" style="position:absolute;font-size:3em;left:8em;"></i> .blue[ ``` @PAIR-3 forward TCGCACTCAACGCCCTGCATATGACAAGAC + A64;##=#B9=AAAAAAAAAA9#:AB95%^ ``` ] <i class="fa fa-frown-o" style="position:absolute;font-size:3em;left:8em;"></i> .green[ ``` @PAIR-4 forward AAACTTCGTAGGTCCATTTGACAGCGTGCA + 6664%!!III^(=%3333^^d^d:#32333 ``` ] **`mysample_R1.fastq`** ] .pull-right[ .red[ ``` @PAIR-1 reverse AAGTTACCCTTAACAACTTAAGGGTTTTCA + fffddf`feedB`IABa)^%YBBBRTT\^d ``` ] .orange[ ``` @PAIR-2 reverse AGCAGAAGTCGATGATAATACGCGTCGTTT + IIIIIII^^IIId`?III%IIIGII>IIII ``` ] .blue[ ``` @PAIR-3 reverse AATCCATTTGTTCAACTCACAGTTTACCGT + 9C;=;=<9@4868>9:67AA<9>65<=>59 ``` ] **`mysample_R2.fastq`** ] ??? 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) .center[ .image-40[![Mapping RNA](../../images/mapping/spliced_mapper.png)] ] .footnote[**Figure:** mapping of RNA-seq reads is different than DNA-seq] ??? 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 .footnote[ [DOI: 10.1186/1471-2105-14-184](https://doi.org/10.1186/1471-2105-14-184) ] ??? Know the data you are working with and pick the right mapper and parameters for the job! Not an easy task.. --- class: top ## Mapping tools ![Timeline of mapping tools](../../images/mapping/ngs_read_mappers_timeline.jpeg) .footnote[60+ different mappers, many comparison papers. Figure from [10.1093/bioinformatics/bts605](https://doi.org/10.1093/bioinformatics/bts605) ] ??? Many different tools available Different strengths and weaknesses, comparison table in link --- class: top # Mapping tools **Mapping tool** | **Uses** | **Characteristics** --- | --- | --- HISAT2 | DNA/RNA | Short reads. Based on [GCSA](https://doi.org/10.1109/TCBB.2013.2297101). [Reference](https://www.nature.com/articles/s41587-019-0201-4). RNASTAR | RNA | Short reads. Extremely fast. High sensitive and accuracy. Based on Maximal Mappable Prefixes (MMPs). [Reference](https://pubmed.ncbi.nlm.nih.gov/23104886/). BWA-MEM2 | DNA | Short reads. Twice as faster as BWA-MEM. Memory efficient. Based on [Burrows-Wheeler](https://academic.oup.com/bioinformatics/article/25/14/1754/225615). [Reference](https://arxiv.org/abs/1907.12931). Minimap2 | DNA/RNA | Long reads (PacBio and ONT). Extremely fast. Based on [DALIGN](https://link.springer.com/chapter/10.1007/978-3-662-44753-6_5) and [MHAP](https://www.nature.com/articles/nbt.3238). [Reference](https://doi.org/10.1093/bioinformatics/bty191). Bismark | DNA/RNA | Short reads. Bisulfite treated sequencing. Based on [GCSA](https://doi.org/10.1109/TCBB.2013.2297101). [Reference](https://pubmed.ncbi.nlm.nih.gov/21493656/). BBMap | DNA/RNA | Short and long reads (PacBio and ONT). Memory demanding. [Reference](https://bib.irb.hr/datoteka/773708.Josip_Maric_diplomski.pdf). Whisper 2 | DNA | Short reads. Indel sensitive. Variant-calling oriented. [Reference](https://academic.oup.com/bioinformatics/article/35/12/2043/5165374). S-conLSH | DNA | Long reads (ONT). High sensitivity and accuracy. [Reference](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03918-3). --- # File Formats --- # SAM/BAM file format ![Example of SAM file format](../../images/bam_file.png "SAM/BAM file") **SAM:** **S**equence **A**lignment **M**ap **BAM:** Binary (compressed) SAM; not human-readable --- # SAM/BAM file format ![More detailed view of SAM format](../../images/bam_file_explained.png "SAM/BAM file detail") - Original read information (from FASTQ) plus mapping information - Position on reference, alignment, quality score, uniqueness, .. ??? Alignment given in CIGAR string. - in screenshot "37M" means 37 matches - in screenshot "18M2D19M" means 18 matches, then 2 deletions, then 19 matches --- class: top # Genome Browsers - Visualise aligned reads (BAM files) ![IGV Genome Browser](../../images/mapping/igv_animation.gif) .footnote[This is [IGV (Integrative Genome Browser)](https://software.broadinstitute.org/software/igv/) DOI: [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754)] ??? - 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 --- class: top # Genome Browsers in Galaxy - [JBrowse](https://jbrowse.org/) <i class="fas fa-wrench" aria-hidden="true"></i><span class="visually-hidden">tool</span> Genome Browser as Galaxy tool .image-90[![Screenshot of JBrowse in the Galaxy Interface showing transcripts, various box plots, heatmaps, sequencing depth and variation plots.](../../images/mapping/jbrowse.png)] .footnote[[JBrowse.org](https://jbrowse.org) DOI: [10.1186/s13059-016-0924-1](https://doi.org/10.1186/s13059-016-0924-1)] ??? 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. --- class: top # Genome Browsers in Galaxy - **External Genome Browsers** in Galaxy - BAM datasets in Galaxy have display links - [UCSC Genome Browser](https://genome-euro.ucsc.edu/cgi-bin/hgTracks), [Ensemble](https://www.ensembl.org), [IGV](https://software.broadinstitute.org/software/igv/), [IGB](https://bioviz.org/), [BAM.iobo](https://bam.iobio.io/home) ![Display application links in Galaxy](../../images/mapping/igv_link.png) - 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](https://java.com/en/download/faq/java_webstart.xml) be installed on your machine ??? In the mapping hands-on tutorial you will use JBrowse and IGV --- ### <i class="fas fa-key" aria-hidden="true"></i><span class="visually-hidden">keypoints</span> Key points - 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 --- ## Thank You! This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!
Authors:
Joachim Wolff
Saskia Hiltemann
Engy Nasr
Cristóbal Gallardo
Erasmus+ Programme
This material is licensed under the Creative Commons Attribution 4.0 International License
.