Your screenshot of the finder showing the genomes directory seems to be of your main igv directory, not the IGVTools directory. I just tried downloading igvtools from and the info required for mm10 was there. Did you move the igvtools.jar to the main igv directory and run it from there? If so, it is trying to pick up the genome from the igv/genomes directory, not from the IGVTools/genomes directory. The igv/genomes directory will not necessarily have all the genomes, as its contents depend on which genomes you have used with the IGV desktop application.
I am trying to use R packages which require BSgenome to specify the genome. My datasets are from mouse aligned aginst mm10 from ensembl rather than UCSC. So, am I supposed to forge the BSgenome myself?
Mm10 Genome Download
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Do NOT believe what is reported on the UCSC page at the above link that mm10 is based on the GRCm38 assembly from the Genome Reference Consortium. Even though this has been the case for years (since the beginning of the mm10 genome), the UCSC folks updated mm10 in June 2021, so now it's based on the GRCm38.p6 assembly. However they never bothered to update what's displayed at -bin/hgGateway?db=mm10
GRCm38.p6 is a patched version of GRCm38 that only _adds_ new sequences to it. I encourage you to spend some time reading about how the Genome Reference Consortium manages assembly releases/versions/names/patches. This goes beyond Bioconductor and is general knowledge useful to any computational biologist.
So if you've aligned your data against GRCm38, then you should be able to use GRCm38.p6 (a.k.a. mm10) for your downstream analysis. Note that the opposite wouldn't work in general because of the risk that a small subset of your data got aligned to sequences in GRCm38.p6 that are not in GRCm38.
Finally note that even though the sequences in GRCm38.p6 and mm10 are the same, their names differ (the UCSC folks love to rename sequences). But you can easily switch between the UCSC names and the original names with seqlevelsStyle():
Further to what Herv told you, the difference between Ensembl and UCSC/NCBI is primarily where the genes/transcripts/exons are in the genome, and how many of each a given gene might have. Here is a random gene we can use as an example.
That's supposed to be the same exact transcript, but the genomic positions are different. In fact, none of the transcripts from Ensembl overlap any of the transcripts from UCSC/NCBI! Here is a plot of UCSC (above) and Ensembl (below).
The ENCODE project uses Reference Genomes from NCBI or UCSC to provide a consistent framework for mapping high-throughput sequencing data. In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability. Drosophia melanogaster experiments are mapped to either dm3 or dm6 and Caenorhabdilis elegans experiments are mapped to ce10 or ce11. The official reference files for each Uniform processing pipeline can be found in the table below, organized by organism and pipeline. In addition to the genome sequences (we generally use the "no alt" version for each genome), a variety of other crucial files can be found there as well (GENCODE transcript references, chromosome size files, the phage lambda genome, etc.).
The table below includes files used by each pipeline for uniform processing by the ENCODE DCC, with associated details on genome assembly and annotation, if applicable. For your convenience, the GRC genome assembly and GENCODE annotation files are directly linked below. For further information, please contact encode-help@lists.stanford.edu
Some of the experiments at the ENCODE portal have not been processed by the DCC uniform processing pipelines and may have used different reference files. The References search page includes all the reference datasets used by the different projects whose data could be found on the portal.
However I can't find the full genomic fasta and gtf files for mm10/GRCm38, instead just separate fasta files for each of the chromosomes and no gtf annotation file?( _genbank/Eukaryotes/vertebrates_mammals/Mus_musculus/GRCm38/). Instead they only seem to exist for the patch 6 release ( _000001635.8_GRCm38.p6).
I'm getting really confused where to get them from, do people tend to just align to the patch release? If not where can I find the full genomic fasta and gtf files for the major release? I've looked on ncbi and ensembl and keep looping back to the newest patch release version...
for the GTF file, You will probably want the Comprehensive gene annotation for CHR region. just copy the link and use wget -c to download, then gunzip. The link Im talking about is _mouse/release_M22/gencode.vM22.annotation.gtf.gz
While GRCm38 from NCBI is technically the same build (in terms of sequence content), the sequence identifiers will differ between the original at NCBI and what UCSC produces. Then ERCC RNA data is an extra layer of annotation added to base genomes available at certain sources (GEO and Ensembl host these, I believe, and perhaps others). The source mm10 from UCSC used at Galaxy Main does not include this content.
If you wish to use a different genome version for mouse than what is available at Galaxy Main, a local/cloud Galaxy can be used with a genome added with a Data Manager (from any source) or you can try using the Custom Genome feature at Galaxy Main - just be aware that using such a large genome as a custom genome may create jobs that run out of memory.
To be clear, in practical terms, the start coordinate format (0-based or 1-based) is dependent on the datatype of the dataset/file. This is independent of the underlying version of the reference genome.
According to my understanding, when a new genome is annotated, it should contain new information plus the old information though there might be a case, when the some entries are removed from the genome if they are updated and recognised later to be something else, than what was annotated.
The coordinates between genome builds change, by design, since sequence has been added, revised, and sometimes removed from the chromosomes. In general, all analyses will need to be on the same genome build, so some analyses might need to be redone. In some cases, the easiest thing to do is to redo the entire analysis. In some cases, a tool like the UCSC liftover tool will be good enough.
The steps are: 1) build the new genome (differs by organism) and 2) annotate the genome with features of interest. For step 2, UCSC, Ensembl, and NCBI each do their own annotation. Each feature set (transcripts, regulatory elements, miRNA, etc.) requires its own solution, so if you want to know details, the best bet is to pick your track or data set of interest and investigate. UCSC makes this easy since each track has a detailed description.
The first thing we do is change to our desired our working directory, set the number of threads we would like to use, and load our gene and genome annotations. Depending on the configuration of your local environment, you may need to modify the number of threads used below in addArchRThreads(). By default ArchR uses half of the total number of threads available but you can adjust this manually as you see fit. If you are using Windows, the usable threads will automatically be set to 1 because the parallel processing in ArchR is built for Unix-based operating systems.
Next, we set the default number of threads for ArchR functions. This is something you will have to do during each new R session. We recommend setting threads to 1/2 to 3/4 of the total available cores. The memory usage in ArchR will often scale with the number of threads used so allowing ArchR to use more threads will also lead to higher memory usage.
Then, we set the genome to be used for gene and genome annotations. As above, this is something you will have to do during each new R session. Of course, this genome version must match the genome version that was used for alignment. For the data used in this tutorial, we will use the hg19 reference genome but ArchR natively supports additional genome annotations and custome genome annotations as outlined in the next section.
Providing this information to ArchR is streamlined through the addArchRGenome() function. This function tells ArchR that, for all analyses in the current session, it should use the genomeAnnotation and geneAnnotation associated with the defined ArchRGenome. Each of the natively supported genomes are composed of a BSgenome object that defines the genomic coordinates and sequence of each chromosome, a GRanges object containing a set of blacklisted regions, a TxDb object that defines the positions and structures of all genes, and an OrgDb object that provides a central gene identifier and contains mappings between this identifier and other kinds of identifiers.
The precompiled version of the hg19 genome in ArchR uses BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, and a blacklist that was merged using ArchR::mergeGR() from the hg19 v2 blacklist regions and from mitochondrial regions that show high mappability to the hg19 nuclear genome from Caleb Lareau and Jason Buenrostro. To set a global genome default to the precompiled hg19 genome:
The precompiled version of the hg38 genome in ArchR uses BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, and a blacklist that was merged using ArchR::mergeGR() from the hg38 v2 blacklist regions and from mitochondrial regions that show high mappability to the hg38 nuclear genome from Caleb Lareau and Jason Buenrostro. To set a global genome default to the precompiled hg38 genome:
The precompiled version of the mm9 genome in ArchR uses BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, and a blacklist that was merged using ArchR::mergeGR() from the mm9 v1 blacklist regions from Anshul Kundaje and from mitochondrial regions that show high mappability to the mm9 nuclear genome from Caleb Lareau and Jason Buenrostro. To set a global genome default to the precompiled mm9 genome: 152ee80cbc
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