Download Hg38 Fasta

The most well-known databases to use for downloading the human reference genomes are UCSC Genome Browser, Ensembl and NCBI. The naming convention hg38 is used by UCSC Genome Browser, while Ensembl and NCBI use GRCh38 to refer to the latest human reference genome.

The following location has assembly sequences used in alignment tracks, such as in the 100-species conservation track. For example, you can find the underlying mayZeb1.2bit sequence file for the Zebra Mbuna fish assembly, not yet released but used in the hg38 Vertebrate Multiz Alignment & Conservation (100 Species) track, here: These links also display under a column titled "UCSC version" on the conservation track description page.

Download Hg38 Fasta

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The /gbdb fileserver offers access to all files referenced by the Genome Browser tables, with serversin North America andEurope for faster downloads.Many files in the browser, such as bigBed files, are hosted in binary format. For example, in the hg38 database, thecrispr.bb and crisprDetails.tab files for the CRISPR trackcan be found using the following URLs: North American server: European server: -euro.soe.ucsc.edu/gbdb/hg38/crispr/ Individual regions or whole genome annotations from binary files can be obtained using toolssuch as bigBedToBed, which can be downloaded as a precompiled binary for your system (see the Source and utilities downloads section). The bigBedToBed tool can also be used to obtain aspecific subset of features within a given range, e.g.:

The JSON API can also be used to query and download gbdb data in JSON format. Below are two examplesof how to query and download data using the JSON API, respectively. =hg38;track=ncbiRefSeqOther;chrom=chr21;start=25000000;end=30000000

High level, two versions of GRCh38/hg38 are currently recommended for use with DRAGEN 3.9, the hg38-alt-masked (non-graph) and hg38-alt-masked-graph. They are both available to download [3]. Both of these references contain the alt-masked functionality that was introduced in DRAGEN v3.9 and is described in more detail below. This new alt-masked functionality provides slight accuracy improvements over the previously recommended liftover-based ALT-aware references, the hg38-alt-aware (non-graph) and hg38-alt-aware-graph, shown below in Table 1.

The hg38-alt-masked-graph genome hash table is available to download [3]. The hg38-alt-masked-graph hash table is compatible with pre-3.9 versions of DRAGEN. DRAGEN does not support the users building their own custom graph genomes. This is because altering the population haplotypes can cause an accuracy regression if the new haplotypes compete with other regions of the genome. Great care needs to be taken in building a graph genome and as such a fully automated approach is not currently supported in DRAGEN.

As mentioned earlier, DRAGEN also supports a graph-based reference to improve the mapping accuracy of Illumina reads in the difficult-to-map regions of the genome. The graph functionality does not use native GRCh38/hg38 ALT contigs but uses carefully chosen population haplotype segments which usefully distinguish among homologous regions, and provide alternate paths known to the population to the linear reference.

The choice of population haplotype segments is key to improving the mapping in the difficult parts of the genome. Increasing the number and population diversity can improve the accuracy further but this can have a negative impact if the haplotypes end up competing with each other and result in ambiguous read mappings. Mask-based hg38 ALT-awareness also plays better with graph references, keeping out of the way to allow graph-path liftover to guide mapping without interference.

The full potential of both genome masking and graphs has not yet been reached in terms of accuracy. The Genome in a Bottle (GIAB) Consortium, Genome Reference Consortium (GRC) and Telomere-to-Telomere (T2T) Consortium have also contributed to improvements of the GRCh38/hg38 reference genome [1], with two types of improvements: 1) masked bases in the primary assembly to remove false duplication; 2) include new Decoy contigs. The DRAGEN team is currently in the process of evaluating these changes, and incorporating them into the most recent reference versions. Table 3 shows a roadmap on how we plan to release the reference updates in future DRAGEN releases. The newly masked bases will be incorporated first, and updated references will be name *alt-masked-V2*. The new Decoys are not fully finalized just yet and will be incorporated in a further release.

The GIAB consortium has recently released a new reference which masks false duplications in GRCh38/hg38 [1]. GIAB worked together with the GRC to develop a list of regions in GRCh38 that could be masked without changing coordinates or harming variant calling, because they were erroneously duplicated sequences or contaminations. These duplicated regions were identified by the T2T [7]. The newly masked bases include portions of chr21 which resulted in mapping improvements in some key medical genes CBS, CRYAA and KCNE1.

A second approach which can improve the accuracy of the mapping and variant calling is through the use of decoy sequences. A joint effort between the GIAB and Baylor College of Medicine [8] is currently assessing augmenting the GRCh38/hg38 reference with new decoys to correct for regions which have been falsely collapsed in the reference e.g., the reference contains a single copy of a region whereas it should contain a second homologous region. The decoys, which are similar but not identical, will remove false positive variants by providing alternative mapping locations instead of forcing the reads to map to the wrong copy of the sequence.

Please note that starting from DRAGEN 3.9, if no liftover or masked bed is specified on the HT building command line, the default behavior of DRAGEN is to automatically apply the alt-masked bed, to generate hg38-alt-masked (or hg19-alt-masked) reference by default. The alt-masking does not apply to GRCH37 or hs37d5, since those references do not include ALT contigs natively.

DRAGEN provides the functionality to make custom masked genomes by modifying the hg38_alt_mask.bed file packaged with DRAGEN or even by the user creating one. DRAGEN will create a hash table by treating any position contained in the bed file as an N in the FASTQ for mapping purposes. This essentially creates the same hash table as modifying the reference directly. If there are contigs in the bed file which are not in the FASTA then DRAGEN 3.9 will abort. In future versions, DRAGEN will not abort and only mask the regions which are present.

I downloaded the hg38.fa.gz, but I couldn't upload it to galaxy since it is larger than 2 GB and It asked to do so via ftp?So far, I found this link:Install some ftp server.I did install and configured proftpd. But I am not sure how to connect it to Galaxy's database. Is there any information on this?

You would only need to upload the fasta to Galaxy if you intend to customize the index (not use one of the originals) or use it as a Custom reference genome (not recommended for a genome of this size). For the Data Manager path, this would involve uploading the fasta to a Galaxy history and using that fasta as an input to a Data Manager that indexes the base genome. These are the "Fetch genome" DMs, then the others (samtools, picard, 2bit, and other indexes per-tool).

If you plan to just use the base genome, just use the Data Managers directly. It will fetch the genome without you needing to do anything special (no uploading files, etc). This is about same process: use a fetch genome data manager sourcing from UCSC - instead of an uploaded fasta - then run the other DMs (in order).

UCSC tools are not needed to create indexes. These do many things, but in this context would only be used to convert formats (twoBitToFasta, or the reverse, which is not needed in your case since the fasta is already available and there is a DM to convert and index a fasta already loaded with a DM to a 2bit index).

The final option is to create all indexes manually. This is really not recommended unless you are experienced with it and are willing to troubleshoot. Data Managers should be used if at all possible - things will go much smoother. But I'll link the help pages for manually creating indexes and other related tasks below - just be aware that these docs are a bit older and as I said, might require you to do some troubleshooting. We don't provide step-by-step manual index install documents at a detailed level anymore - the DMs have replaced that need, as even customized genomes can be used with them (if you load the target fasta by FTP into a history or into a Data Library first from the file system then into a working history, link also included):

Use refgenie populate to replace registry paths (e.g. refgenie://hg38/fasta) in text files with asset file paths (e.g. /home/johndoe/genomes/hg38/fasta/default/hg38.fa). For use in an ephemeral compute environment, the remote version, refgenie populatr, will replace your registry path with a URI, like s3://path/to/asset.xyz or This powerful feature allows you to write configuration files and scripts with maximum portability for anything you might need to configure with reference genome paths.

Now, just add sample attributes in your sample take with refgenie registry paths, like refgenie://hg38/fasta. You can add these either as sample attributes directly in the sample table, or using a derived attribute. Looper will automatically use refgenie to pre-populate the registry paths into correct local paths before submitting the jobs.

Minimap2 should be able to map from fastq or fasta files. Galaxy Minimap2 cannot see fastq files. When galaxy minimap2 is run on fasta files it fails where it previously ran effectively. This same data was processed through galaxy a few weeks ago, something is different.

I am running a local version of galaxy (17.09) trying to analyze RNA seq data with Bowtie2, DEseq2. I am the admin and have gotten this instance mostly configured. I have managed to get all the sequences loaded in to the Data libraries off my hard drive as I haven't had time to set up a FTP server. I have been trying like crazy to get the ref genomes and GFF/GTF files loaded to start my analyses. I have been all over the wiki and here on biostars looking at how to load a reference genome for hg38. I watched the video "managing galaxy's built in data and data managers." as well as looked at several very similar questions. I used the used the tool shed to obtain DMs (create DBkey and ref genome) and followed the directions in the Video, tried to build Bowtie2 indexes, and got error. I realized then I might need to do some additional indexes first as per several threads. So I got the tools for Samtools indexes, Picard indexes, and twoBit indexes. When I attempted to run the samtools index from the data manger. I still get the following error telling me it can't find a file? e24fc04721