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Comparative Assembly Hub Manual

hal2assemblyHub.py is a python wrapper that produces necessary data and files for creating the comparative assembly hubs through the UCSC genome browser. The program, which is part of the HAL tools package, takes in the multiple sequence alignment in HAL format and (optionally) any available set of annotations, either in BED or WIG format and creates an output directory that contains all necessary data to build browsers for all input genomes and infered ancestral genomes as well as various annotation tracks. The location of the "hub.txt" file, addressable as a public URL, is pasted into the UCSC browser hub page to view the set of browsers.

Quick Start

From a multiple sequence alignment, users can quickly create browsers for all input genomes using the following command:

hal2assemblyHub.py <halFile> <outDir> --lod

or to run in parallel:

hal2assemblyHub.py <halFile> <outDir> --lod --batchSystem <batchType>


hal2assemblyHub.py <halFile> <outDir> --lod --maxThreads <#ofThreads>

  • Option --lod is specified to compute the levels of detail, which is recommended for large datasets.
  • Option --batchSystem: the type of batch system to run the job(s). See [jobTree Manual](https:// github.com/benedictpaten/jobTree) for more details.
  • Option --maxThreads: number of threads (processes) to use when running in single machine mode. Increasing this will allow more jobs to run concurrently when running on a single machine. Default=4. See jobTree Manual for more details.


hal2assemblyHub.py alignment.hal outdir --lod
hal2assemblyHub.py alignment.hal outdir --lod --batchSystem gridEngine
hal2assemblyHub.py alignment.hal outdir --lod --maxThreads 24

Procedural Levels of Detail

To improve browsing speed, especially for browsing at all levels of resolution (from individual bases to whole chromosomes) of large datasets, we compute multiple representations of the original alignment at different levels of detail. HAL Tools provide programs to resample a HAL graph to compute coarser-grained levels of detail to speed up subsequent analysis at different scales. Please see Levels of Detail for more information.

hal2assemblyHub takes care of generating levels of detail for the alignment if option --lod is specified. Example:

hal2assemblyHub.py alignment.hal outdir –lod

By default, no level of detail is generated. Users can independently generate them using the HAL Tools’ halLodInterpolate.py. Generating levels of detail can be time-consuming. Users can provide hal2assemblyHub with pre-computed levels of detail (and avoid re-computing them) by the options --lodTxtFile and --lodDir:

hal2assemblyHub.py alignment.hal outdir --lod --lodTxtFile lod.txt --lodDir loddir/

lod.txt and loddir are output files of halLodInterpolate.py. See halLodInterpolate.py -h for explanations of more options.

  • lod.txt is output text file with links to interpolated hal files, with each file is associated a value stating its minimum suggested query range (in bases).
  • loddir is the path of the directory where interpolated hal files are stored.

###Level of Detail Frequently Asked Questions (LODFAQ)

  1. LOD generation is too slow. What can I do to speed it up? Try using the --maxThreads option to generate each LOD in parallel. If system memory is not an issue, use the --lodInMemory option to disable HAL's disk cache.

  2. Why does my comparative hub looks to "gappy" when I zoom out? The LODs are precomputed using a sampling approach. The more finely the alignment is sampled, the more accurate the LOD will be. But this comes at the cost of an increased number of blocks accessed for each browser query. To increase the number of samples taken per LOD, increase the value of --lodMaxBlock. This should reduce the number of missing blocks at the cost of load time.

  3. My alignment still seems to be missing lots of stuff when I zoom out Maybe your sequences are broken into many small scaffolds or contigs that are getting filtered out by the LOD generation algorithm. In this case, you may be able to resolve this by decreasing --lodMinSeqFrac to adjust the filter.

  4. Why does so much pop in and out when I zoom in and out? Perhaps too few levels of detail are being generated. You can decrease the scale factor between consecutive LODs using the --lodScale option. Increasing --lodMaxBlock may also help (see above).

  5. Why is my comparative hub so slow? Rendering large datasets remotely is a tricky proposition. Apart from finding a faster server to host your data, your only option is probably to decrease the granularity of each LOD. This is best accomplished by lowering --lodMaxBlock. Also, if rendering many contigs/scaffolds is an issue, try increasing --lodMinSeqFraq. Increasing --lodScale will also reduce the number of network queries by decreasing the number of files generated. Basically following the advice from the three previous questions about increasing accuracy in reverse will help increase speed.

  6. When I zoom out, I see too many tiny blocks for millions of different contigs or scaffolds. Can I reduce this? Yes, increase the --lodMinSeqFrac parameter. This will increase the minimum length of a contig in relation to the step size for it to be included in the LOD.

  7. **I am unable to zoom out as far as I want to. The --lodMinCovFrac option was added to mitigate some of the issues with gappy LODs discussed above. It works in conjunction with --lodMinSeqFrac to disallow zooming out past a point which too much of any genome is filtered by -lodMinSeqFrac. For example, --lodMinSeqFrac 0.01 --logMinCovFac 0.8 would specifiy that only LODs such that 80% of all contigs/scafoolds in each genome will be longer than 1% of the stepsize of the LOD.

  8. I am a power user. Are the more options available for fine-tuning LOD generation? Yes, you can regenerate your LODs separately using halLodInterpolate.py which provides a number of additional options to those available during hub generation. You can also easily edit the output lod.txt file by hand to, for example, remove a level of detail or adjust its query range.

Browsers With Annotation Tracks

Annotations computed from the alignment

hal2assemblyHub.py takes care of computing various annotation tracks from the alignment, including “Alignability”, “Conservation”, “GC Content” and “Clade Exclusive Regions”.


hal2assemblyHub.py alignment.hal outdir --lod --cladeExclusiveRegions --alignability --gcContent --conservation conservationRegions.bed --conservationGenomeName hg19

  • --cladeExclusiveRegions: for each node in the phylogenetic tree of the genomes in the alignment, regions that are genome-specific (leaf-node) or clade-specific (internal node), i.e. present only in genomes within the clade and absent in other genomes, are printed out in bigbed-formatted files. These files will be located at outdir/liftoverbed/CladeExclusive. The resulting comparative assembly hub contains one track for each genome. See --maxOutgroupGenomes and --minIngroupGenomes options (see hal2assemblyHub.py --help) for adjusting the definition of “clade exclusive”.

  • --alignability: for each node GENOME in the tree (each genome and each ancestral genome), computes the wiggle track (the Alignability aka Alignment Depth track) using halAlignmentDepth. This track stores the number of other unique genomes (including ancestral genomes) each base aligns to. The computed bigwig-formatted file is located at outdir/GENOME/GENOME.alignability.bw.

  • --conservation: computes the conservation track for each genome, showing measurements of evolutionary conservation using the phyloP program from the PHAST package. See Track Documentation Section for more details. conservationRegions.bed is the bed-formatted file providing neutral regions of a reference genome for creating a neutral model. By default, it expects the neutral regions to be coding genes and uses 4fold degenerate site within those genes (which it extracts automatically). The neutral regions could also be ancestral repeats (or anything else). An example of this file:

chr1 363 2826 gene1
chr1 2827 3760 gene2

The fields above are Chromosome, Start−coordinate, End−coordinate and Gene−name. The chromosome and the coordinates must be consistent with the input HAL file (e.g. same chromosome name, an example of a common inconsistency is when the HAL file has “1” and the bed file has “chr1”).

  • --conservationGenomeName: name of the reference genome used to provide the neutral region information in −−conservation. This must be consistent with the genome name in HAL file as well. The computed conservation tracks are stored at outdir/conservation.

Computing conservation scores could be expensive. Use option --conservationDir to use pre-computed conservation scores:

hal2assemblyHub.py alignment.hal outdir –lod –conservationDir myConservationDir/

  • --conservationDir: Directory contains conservation bigwigs. Format:

Genome1, Genome2, etc. are genome names and must be consistent with names in the input HAL file.

Annotations provided by users

Currently, hal2assemblyHub.py supports two annotation formats: bed (or big bed) and wiggle (or bigwig), see http://genome.ucsc.edu/FAQ/FAQformat.html. Example annotations are genes, transcription levels, histone modifications, etc.

#####Example 1: hal2assemblyHub.py alignment.hal outdir --lod --bedDirs Genes --tabBed

  • --bedDirs: comma separated list of paths to different annotation directories, one directory per annotation type. In this example, there is only one annotation type, which is Genes. The format of each annotation directory is:

bedfile1.bed and bedfile2.bed are gene annotations of Genome1. By default, these annotations will be lifted-over(/mapped/translated) to all other genomes in the alignment, unless options --noBedLiftover is specified. Please only include genomes that have annotations. For example, if only Genome1 and Genome2 have gene annotations, the Genes/ directory should only have Genome1/ and Genome2/. Note, names of Genome1 and Genome2 must be consistent with the genome names in the alignment. The bed file names may be anything as long as they have they .bed extension. The name of each annotation directory (Genes in this case) will be used as the track name on the browser. For example, browser of Genome1 will have a track named “Genome1 Genes” and a track named “Genome2 Lifted-over Genes”.

  • --tabBed: if the input bed files are tab-separated (recommended), this option must be specified. The default settings assume space-delimited. If the bed files are space-delimited, the field values must not contain any space.
Example 2:

hal2assemblyHub.py alignment.hal outdir --lod --bedDirs allAnnotations/Genes,allAnnotations/CpG-Islands,allAnnotations/Variations --tabBed

In this example, there are three different annotation types: Genes, CpG-Islands, and Variations, all located within the directory allAnnotations/.

Example 3:

hal2assemblyHub.py alignment.hal outdir --lod --bedDirs Genes,CpG-Islands,Variations --tabBed --noBedLiftover

  • --noBedLiftover: if specified, the lift−over step is disable, i.e. only creates track for the input annotations and does not lift/map these annotations to other genomes.
Example 4:

hal2assemblyHub.py alignment.hal outdir --lod --finalBigBedDirs Genes,CpG-Islands,Variations --tabBed

  • --finalBigBedDirs: comma separated list of directories containing final big bed files to be displayed. No liftover will be done for these files. Each directory represents a type of annotation. This option is useful when annotations have been previous lifted-over and can just be fed to the pipeline, to avoid rerunning the lift-over processes. Format of each directory:

Annotations of queryGenome have been lifted-over (mapped) to targetGenomes and will be displayed on each targetGenome’s browser. For example, if bbDir is Genes, targetGenome1.bb contains the gene annotations of queryGenome1 mapped to targetGenome1, in bigBed format. queryGenome and targetGenome are the same for the original (non lifted-over) annotations (e.g. gene annotations of queryGenome1). Note: it is not required that each annotation must be lifted over to all other genomes. The pipeline prepares one track for each bigBed file - users can choose which tracks to include.

Example 5:

hal2assemblyHub.py alignment.hal outdir --lod --bedDirs Genes --finalBigBedDirs CpG-Islands,Variations --tabBed

In this example, the pipeline will not perform lifting-over for the CpG-Islands and Variations annotations (in bigBed format) - the corresponding tracks will be displayed on the resulting comparative hubs “as is”, while the Genes annotations (in bed format) will be lifted-over. This is applicable when users wish to include new annotations into their comparative assembly hubs, or to update some annotations while keeping the rest intact.

Example 6:

hal2assemblyHub.py alignment.hal outdir --lod --bedDirs Genes,CpG-Islands --bedDirs2 Variations --tabBed

  • --bedDirs2: Similar to --bedDirs, except the tracks for the annotations specified here will be kept separately and out of the composite track. In this case, the Genes and CpG-Islands tracks will be included in the composite track (hubCentral) while the V ariations tracks will be on its own.
Example 7:

hal2assemblyHub.py alignment.hal outdir --lod --finalBigBedDirs Genes,CpG-Islands --finalBigBedDirs2 Variations --tabBed

  • --finalBigBedDirs2: Similar to --finalBigBedDirs, except these tracks will be kept separately and out of the composite track.
Example 8:

hal2assemblyHub.py alignment.hal outdir --lod --bedDirs Genes,CpG-Islands --tabBed --wigDirs Transcription,Methylation

  • --wigDirs: similar to --bedDirs, but for wiggle format files.

Item Searching of Annotation Tracks

By default, hal2assemblyHub.py index the name column of the input bed files so that when browsing the hubs, users can quickly search for specific items using their names. Additional fields can be added to the bed files and the pipeline will index them for searching. When there are additional fields in the bed files, an “.as” (AutoSQL) format file is required for each input bed directory. See http://genome.ucsc.edu/goldenPath/help/bigBed.html#Ex3 for the format of the “.as” file.

This is applicable when users want to be able to search genes by various IDs, such as accession numbers and common names. If the name column in the bed file is the accession number, add an additional field common-name to the bed file, and use the .as file to specify this additional field. In Example1, the input Genes directory will be as followed:


Example of an .as file:

table geneEscherichiaColi042Uid161985
“EscherichiaColi042Uid161985 genes with additional fields commonName, synonym and product”
string chrom; “Reference sequence chromosome or scaffold”
uint chromStart; “Start position of feature on chromosome”
uint chromEnd; “End position of feature on chromosome”
string name; “Name of gene”
uint score; “Score”
char[1] strand; “+ or - for strand”
uint thickStart; “Coding region start”
uint thickEnd; “Coding region end”
uint reserved; “RGB value”
int blockCount; “Number of blocks”
int[blockCount] blockSizes; “A comma-separated list of block sizes”
int[blockCount] chromStarts; “A comma-separated list of block starts” string commonName; “Gene common name”
string synonym; “Gene synonym”
string product; “Gene product”

In this case, there are three extra fields: commonName, synonym and product, and those three fields, together with the name field, will be indexed for searching, i.e. when browsing the resulting hub browsers, users can search a gene by its name, common name, synonym or product.

Update Comparative Assembly Hubs

The simplest way to update comparative assembly hubs is to rerun hal2assemblyHub.py and utilize the following options:

See the above sections and hal2assemblyHub.py --help for more details.

Manipulate Hub Display

To manipulate the hub displays, see the following options:



Comparative Assembly Hubs are built utilizing the Assembly Hub function of the UCSC Genome Browser. Many of the output files produced by the Comparative Assembly Hub Pipeline are explained in details here: http://genomewiki.ucsc.edu/index.php/Assembly_Hubs. To avoid potential problems, we recommend users to provide an empty outdir when running hal2assemblyHub.py.

The output directory may contain:

  1. hub.txt: The primary URL reference for the constructed comparative assembly hubs. Please paste the URL of the location of this file to the UCSC genome browser to load the hubs. This is similar to how a track hub is created, please see http://genome.ucsc.edu/goldenPath/help/hgTrackHubHelp.html for more instructions. This file contains a short description of the hub properties, including the hub name, short label, long label and contact email.

  2. genomes.txt: list of genome assemblies included in the hub.

  3. groups.txt: definitions of track groups. Track groups are the sections of related tracks grouped together under the primary genome browser graphics display image.

  4. Genome assembly directories: one directory is created for each genome assembly, one directory for each ancestral genome, and one for the pangenome, if appropriate. Example:

  • GenomeAssembly1/
    • GenomeAssembly1.2bit
    • chrom.sizes
    • trackDb.txt
    • description.html
    • GenomeAssembly1.alignability.bw: Bigwig file for the alignability track of GenomeAssembly1, generated if option --alignability is specified when running hal2assemblyHub.py. Alignability is the number of genome assemblies that have bases aligned with each base of the current assembly (mappability).
    • GenomeAssembly1.gc.bw: Bigwig file for the GC Content track of GenomeAssembly1, generated if options --gcContent is specified when running hal2assemblyHub.py.
    • repeatMasker/: subdirectory containing repeatMasker files of the GenomeAssembly1, present if option --rmskDir is specified when running hal2asssemblyHub.py.

*** For more details on items (1) to (4), see: http://genome.ucsc.edu/goldenPath/help/hgTrackHubHelp.htmlSetup ***

  1. conservation/: Files necessary for the Conservation Track of each GenomeAssembly Browser, generated if option --conservation is used.

  2. hubTree.png: Phylogenetic tree image of the genome assemblies that is displayed in the configuration page of each genome assembly’s hub browser.

  3. liftoverbed/: All bed annotation files, including both input bed files and lifted-over bed files. Example:

  • Annotation1/
    • GenomeAssembly1/
      • GenomeAssembly1.bb : annotation1 of GenomeAssembly1
      • GenomeAssembly2.bb : annotation1 of GenomeAssembly2 mapped onto GenomeAssembly1
      • ...
    • GenomeAssembly2/
      • ...
    • ...
  • Annotation#/
  1. documentation/: documentation files automatically generated by hal2assemblyHub.py. These files are used for documentation of the various tracks on the hub browsers (see Section Track Documentation).

  2. lod.txt: (Level of details) the lod text file generated by halLodInterpolate.py, or by the pipeline (which calls halLodInterpolate.py) if option --lod is specified. The text file contains links to interpolated hal files, with each file is associated a value stating its minimum suggested query range (in bases).

  3. lod/: the output directory of halLodInterpolate.py, containing the interpolated lod files

  4. alignment.hal: the multiple sequence alignment of the input genome assemblies, in HAL format.

Track Documentation

The following track documentation are automatically generated by hal2assemblyHub.py with each corresponding track, and is displayed by the browser on the track information page.

1. Alignability

The documentation for the Alignability track of all genomes is located at outdir/documentation/alignability.html. To edit the track documentation, please edit the alignability.html file.


This track shows the number of genomes aligned to each position of the ref- erence. The values range from 0 to the total number of input genomes and imputed ancestral genomes.


Alignability was generated using the halAlignability script of the HAL tools package.


Hickey et al. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics. 2013 May;29(10):1341-1342

2. GC Percent

The documentation for the gcPercent track of all genomes is located at outdir/documentation/gcPercent.html. To edit the track documentation, please edit the gcPercent.html file.


The GC percent track shows the percentage of G (guanine) and C (cytosine) bases in 5-base windows. High GC content is typically associated with gene-rich areas.

This track may be configured in a variety of ways to highlight different as- pects of the displayed information. Click the ”Graph configuration help” link for an explanation of the configuration options.


This track was generated following the [UCSC GC Percent Track Construction instructions](http://genomewiki.ucsc.edu/index.php/Browser_Track_ Construction#GC_Percent), using the sequence information extracted from the multiple sequence alignments.


The GC Percent graph presentation is by Hiram Clawson. The data was automatically generated using the HAL tools package.

3. Conservation

The documentation for the Conservation track of all genomes is located at outdir/documentation/conservation.html. To edit the track documentation, please edit the conservation.html file.


This track shows measurements of evolutionary conservation using the phyloP program from the PHAST package, for all genomes in the comparative assembly hub. The multiple alignments were generated using progressiveCactus.

PhyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. PhyloP is appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon posi- tions, or first positions of miRNA target sites). PhyloP can measure acceleration (faster evolution than expected under neutral drift) as well as conservation (slower than expected evolution). In the phyloP plots, sites predicted to be conserved are assigned positive scores (and shown in blue), while sites predicted to be fast-evolving are assigned negative scores (and shown in red). The absolute values of the scores represent -log p-values under a null hypothesis of neutral evolution. PhyloP treat alignment gaps and unaligned nucleotides as missing data.

Display Convention and Configuration

In full and pack display modes, conservation scores are displayed as a wiggletrack (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options.


The conservation tracks of this comparative assembly hub were created using the [phyloP package](https://github.com/glennhickey/hal/tree/development/ phyloP), which is part of HAL tools. The HAL’s phyloP is a python wrapper for running the PHAST package phyloP program and building conservation tracks for all genomes in the HAL multiple alignment. The process starts with creating a neutral model using a reference genome and neutral regions provided. By default, it expects the neutral regions to be coding genes and uses 4fold degenerate site within those genes (which it extracts automatically). The neutral regions could also be ancestral repeats (or anything else). After the model is created, the program proceeds to compute the conservation scores for positions along the root genome. These scores are subsequently lifted over to the children genomes using the multiple alignment. Lastly, HAL phyloP computes the conservation scores for regions in the children genomes that do not align to the root genome. (Of note, the program uses the phylogenetic tree extracted from the HAL file if the tree is not specified.)


We thank Melissa Jane Hubisz and Adam Siepel for the PHAST package and their help with HAL phyloP. The HAL phyloP package: Glenn Hickey, Joel Armstrong, Ngan Nguyen, Benedict Paten.


Siepel, A., Pollard, K. and Haussler, D.. New methods for detecting lineage-specific selection. ResearchinComputationalMolecularBiology. 2006:190-205.

Hickey et al.. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics. 2013 May;29(10):1341-1342.

4. Alignments and Lifted-Over Annotations

The documentation for the Alignment snake tracks, lifted-over annotation tracks and all other tracks in the hubCentral is located at outdir/documentation/hubCentral.html. To edit the documentation, please edit the hubCentral.html file.



An alignment track, or snake track, shows the relationship between the cho- sen browser genome, termed the reference (genome), and another genome, termed the query (genome). The snake display is capable of showing all possible types of structural rearrangement.

Display Convention and Configuration

In full display mode, a snake track can be decomposed into two primitive drawing elements, segments, which are the colored rectangles, and adjacencies, which are the lines connecting the segments. Segments represent subsequences of the target genome aligned to the given portion of the reference genome. Adjacencies represent the covalent bonds between the aligned subsequences of the target genome. Segments can be configured to be colored by chromosome, strand or left a single color under the SelecttrackType, Alignments, then Blockcoloringmethod.

Red tick-marks within segments represent substitutions with respect to the reference, shown in windows of the reference of (by default) up to 50 kilo- bases. This default can be adjusted under SelecttrackType, Alignments, then Maximumwindowsizeinwhichtoshowmismatches. Zoomed in to the base-level these substitutions are labeled with the non-reference base.

An insertion in the reference relative to the target creates a gap between abutting segment sides that is connected by an adjacency. An insertion in the target relative to the reference is represented by an orange tick mark that splits a segment at the location the extra bases would be inserted. Simultaneous independent insertions in both target and reference look like an insertion in the reference relative to the target, except that the corresponding adjacency connecting the two segments is colored orange. More complex structural rearrangements create adjacencies that connect the sides of non-abutting segments in a natural fashion.

Duplications within the target genome create extra segments that overlap along the reference genome axis. Duplications within the reference imply self-alignments, intervals of the reference genome that align to other intervals of the eference genome. To show these self-alignments within the reference genome we draw colored coded sets of lines along the reference genome axis that indicate these self homologies, and align any target segments that align to these regions arbitrarily to just one copy of the reference self alignment.

The pack display option can be used to display a larger number of Snake tracks in limited vertical browser. This mode eliminates the adjacencies from the display and forces the segments onto as few rows as possible, given the constraint of still showing duplications in the target sequence.

The dense display further eliminates these duplications so that each Snake track is compactly represented along just one row.

To ensure that the snake alignments track loads quickly at any resolution, from windows showing individual bases up to entire scaffolds or chromosomes, the LOD (Levels-Of-Detail) algorithm (part of the HAL tools package) is used, which creates scaleable levels of detail for the alignments. The additional use of the hdf5 caching scheme further aides scaling.

Various mouse overs are implemented and clicking on segments navigates to the corresponding region in the target genome, making it simple to instantly switch the alignment view between reference points.


A snake is a way of viewing a set of pairwise gap-less alignments that may overlap on both the reference and query genomes. Alignments are always repre- sented as being on the positive strand of the reference species, but can be on either strand on the query sequence.

A snake plot puts all the query segments within a reference chromosome range on a set of one or more levels. All the segments on a level are on the same strand, do not overlap in reference coordinate space, and are in the same order and orientation in both sequences. This is the same requirement as the alignments in a chain on the UCSC browser. Before the algorithm is started, all the segments are sorted by their starting coordinate on the query, and the current level is set to one. Then in a recursive fashion, the algorithm places the first segment on the current list on the current level, and then adds all the rest of the segments on the list that will fit onto the current level with the requirements that all the segments on a level are on the same strand, and that the proposed segment be non-overlapping and have a reference start address that is greater than the query end address of the previously added segment on that level. All segments that will not fit on the current level are then added to subsequent levels following the same rules. Once all the segments have been assigned a level, lines are drawn between the segments to show the adjacencies in the list when sorted by query start address.


The snake alignment display was implemented by Brian Raney. HAL supports and track generations: Glenn Hickey, Ngan Nguyen, Joel Armstrong, Benedict Paten.

###Lifted-over Annotations


Lifted-over annotation tracks show the annotations of any genome translated onto the reference genome, via a process of lift-over. All the alignments and lifted over annotations shown are mutually consistent with one another, because the annotation lift over and alignment display is symmetrically driven by one reference free alignment process, rather than a mixture of different pairwise and reference based multiple alignments.


The lifted-over tracks were generated using the halLiftover and/or the halWiggleLiftover scripts of the HAL tools package.


Glenn Hickey, Ngan Nguyen, Joel Armstrong, Benedict Paten.


Hickey et al.. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics. 2013 May;29(10):1341-1342. Comparative Assembly Hubs: Web Accessible Browsers for Comparative Genomics

5. RepeatMasker

The documentation for the repeatMasker track of all genomes is located at outdir/documentation/repeatMasker.html. To edit the documentation, please edit the repeatMasker.html file.


This track was created by using Arian Smit’s RepeatMasker program, which screens DNA sequences for inter- spersed repeats and low complexity DNA sequences. The program outputs a detailed annotation of the repeats that are present in the query sequence (repre- sented by this track), as well as a modified version of the query sequence in which all the annotated repeats have been masked. RepeatMasker uses the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below.

Display Conventions and Configuration

In full display mode, this track displays up to ten different classes of repeats:

  • Short interspersed nuclear elements (SINE), which include ALUs Long interspersed nuclear elements (LINE)
  • Long terminal repeat elements (LTR), which include retroposons DNA repeat elements (DNA)
  • Simple repeats (micro-satellites)
  • Low complexity repeats
  • Satellite repeats
  • RNA repeats (including RNA, tRNA, rRNA, snRNA, scRNA, srpRNA) Other repeats, which includes class RC (Rolling Circle)
  • Unknown

The level of color shading in the graphical display reflects the amount of base mismatch, base deletion, and base insertion associated with a repeat element. The higher the combined number of these, the lighter the shading.


Data are generated using the RepeatMasker. Repeats are soft-masked. Alignments may extend through repeats, but are not permitted to initiate in them.


Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track.


Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010.

Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. TrendsGenet. 2000 Sep;16(9):418-420. PMID: 10973072

For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. CurrOpinGenetDev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. CurrOpinGenetDev. 1996 Dec;6(6):743-8. PMID: 8994846