Where to download human reference genome

Multiple alignments of 3 vertebrate genomes with Dog Conservation scores for alignments of 3 vertebrate genomes with Dog. Multiple alignments of 7 vertebrate genomes with Fugu Conservation scores for alignments of 7 vertebrate genomes with Fugu. Multiple alignments of 4 vertebrate genomes with Fugu Conservation scores for alignments of 4 vertebrate genomes with Fugu. Multiple alignments of 11 vertebrate genomes with Gorilla Conservation scores for alignments of 11 vertebrate genomes with Gorilla.

Multiple alignments of 6 genomes with Lamprey Conservation scores for alignments of 6 genomes with Lamprey. Multiple alignments of 5 genomes with Lamprey Conservation scores for alignments of 5 genomes with Lamprey. Multiple alignments of 4 genomes with Lancelet Conservation scores for alignments of 4 genomes with Lancelet. Multiple alignments of 5 vertebrate genomes with Malayan flying lemur Conservation scores for alignments of 5 vertebrate genomes with Malyan flying lemur.

Multiple alignments of 8 vertebrate genomes with Marmoset Conservation scores for alignments of 8 vertebrate genomes with Marmoset. Multiple alignments of 4 vertebrate genomes with Medaka Conservation scores for alignments of 4 vertebrate genomes with Medaka.

Multiple alignments of 6 vertebrate genomes with the Medium ground finch Conservation scores for alignments of 6 vertebrate genomes with the Medium ground finch Basewise conservation scores phyloP of 6 vertebrate genomes with the Medium ground finch.

Multiple alignments of 59 vertebrate genomes with Mouse Conservation scores for alignments of 59 vertebrate genomes with Mouse Basewise conservation scores phyloP of 59 vertebrate genomes with Mouse FASTA alignments of 59 vertebrate genomes with Mouse for CDS regions. GRCm38 Patch 6 - Sequence files. Multiple alignments of 29 vertebrate genomes with Mouse Conservation scores for alignments of 29 vertebrate genomes with Mouse Basewise conservation scores phyloP of 29 vertebrate genomes with Mouse FASTA alignments of 29 vertebrate genomes with Mouse for CDS regions.

Multiple alignments of 16 vertebrate genomes with Mouse Conservation scores for alignments of 16 vertebrate genomes with Mouse. Multiple alignments of 9 vertebrate genomes with Mouse Conservation scores for alignments of 9 vertebrate genomes with Mouse. Multiple alignments of 4 vertebrate genomes with Mouse Conservation scores for alignments of 4 vertebrate genomes with Mouse.

Multiple alignments of 8 vertebrate genomes with Opossum Conservation scores for alignments of 8 vertebrate genomes with Opossum.

Multiple alignments of 6 vertebrate genomes with Opossum Conservation scores for alignments of 6 vertebrate genomes with Opossum. Multiple alignments of 7 vertebrate genomes with Orangutan Conservation scores for alignments of 7 vertebrate genomes with Orangutan.

Multiple alignments of 5 vertebrate genomes with Platypus Conservation scores for alignments of 5 vertebrate genomes with Platypus. Multiple alignments of 19 vertebrate genomes with Rat Conservation scores for alignments of 19 vertebrate genomes with Rat Basewise conservation scores phyloP of 19 vertebrate genomes with Rat FASTA alignments of 19 vertebrate genomes with Rat. Multiple alignments of 12 vertebrate genomes with Rat Conservation scores for alignments of 12 vertebrate genomes with Rat Basewise conservation scores phyloP of 12 vertebrate genomes with Rat.

Multiple alignments of 8 vertebrate genomes with Rat Conservation scores for alignments of 8 vertebrate genomes with Rat. Multiple alignments of 8 vertebrate genomes with Stickleback Conservation scores for alignments of 8 vertebrate genomes with Stickleback.

Multiple alignments of 19 mammalian 16 primate genomes with Tariser Conservation scores for alignments of 19 mammalian 16 primate genomes with Tarsier Basewise conservation scores phyloP of 19 mammalian 16 primate genomes with Tarsier FASTA alignments of 19 mammalian 16 primate genomes with Tarsier for CDS regions. Multiple alignments of 10 vertebrate genomes with X. Multiple alignments of 8 vertebrate genomes with X.

Genes are aligned at the transcript level, including introns, so that processed pseudogenes will not be mistakenly identified as genes.

We attempted to map all , transcripts from 42, gene loci on the primary chromosomes in GRCh38 to Ash1. In total, we successfully mapped , Of those genes with at least one successfully mapped isoform, 42, Of the genes that initially failed to map, 11 genes mapped to a different chromosome in 7 distinct blocks shown in Table 4 , suggesting a translocation between the two genomes.

Interestingly, 16 of the 22 locations involved in the translocations were in subtelomeric regions, which occurred in 8 pairs where both locations were near telomeres. This is consistent with previous studies reporting that rearrangements involving telomeres and subtelomeres may be a common form of translocation in humans [ 20 , 21 , 22 ]. We examined the translocation between chromosomes 15 and 20, which contains three of the genes in Table 4 , by looking more closely at the alignment between GRCh38 and Ash1.

To confirm the translocation, we aligned an independent set of very long PacBio reads, all from HG, to the Ash1 v1. These alignments show deep, consistent coverage extending many kilobases on both sides of the breakpoint, supporting the correctness of the Ash1 assembly Fig.

Snapshot showing alignments of long PacBio reads to the Ash1 genome, centered on the left end of the location in chromosome 20 position 65,, where a translocation occurred between chromosome 15 GRCh38 and 20 Ash1. The top portion of the figure shows the coordinates on chr Below that is a histogram of read coverage, and the individual reads fill the bottom part of the figure. The indels in the reads, shown as colored bars on each read, are due to the relatively high error rate of the long reads.

All of the genes that failed to map or that mapped partially were members of multi-gene families, and in every case, there was at least one other copy of the missing gene present in Ash1, at an average identity of Thus, there are no cases at all of a gene that is present in GRCh38 and that is entirely absent from Ash1; the genes shown in Table 5 represent cases where Ash1 has fewer members of a multi-gene family.

Three additional genes 2 protein coding, 1 lncRNA mapped to two unplaced contigs, which will provide a guide to placing those contigs in future releases of the Ash1 assembly. Of the 10, variants not in these difficult regions, 10, We then annotated the changes in amino acids caused by variants and incomplete mapping for all protein-coding sequences. Out of , protein-coding transcripts from 20, genes, 92, Another 26, Table 6 shows statistics on all of the changes in protein sequences.

If a protein had more than 1 variant, we counted it under the most consequential variant, i. Of particular interest are those transcripts with variants that significantly disrupt the protein sequence and may result in loss of function. These include transcripts affected by a frameshift , stop loss 58 , stop gain , start loss 58 , or truncation due to incomplete mapping These disrupted isoforms represent gene loci; however, of these genes have at least 1 other isoform that is not affected by a disrupting variant.

This leaves genes in which all isoforms have at least one disruption; the full list is provided in Additional file 1 : Table S1. The assembly and annotation of this first Ashkenazi reference genome, Ash1, are comparable in completeness to the current human reference genome, GRCh We began by creating a high-quality de novo assembly of Ash1, using reads generated by multiple sequencing technologies, and then improved the assembly in multiple ways, using GRCh38 for chromosome-scale scaffolding and then using high-quality variant benchmarks from GIAB, computed on data from the same individual, to correct thousands of small consensus sequence errors.

Unlike GRCh38, which represents a mosaic of multiple individuals, Ash1 is derived almost entirely from a single individual. More precisely, Ash1 v1. As more data and better assemblies become available, we expect this latter portion to shrink. The gene content of Ash1 is nearly identical to GRCh all of the genes are present, with the only differences being 40 protein-coding genes and 54 noncoding genes 0.

Eleven genes were mapped to different chromosomes, suggesting a small number of chromosomal rearrangements that predominately involve exchanges of subtelomeric regions. It is likely that Ash1 contains additional copies of some genes, but we did not attempt to search for these.

Similarly to GRCh38, Ash1 is not yet complete, and we plan to improve the assembly over time, much as GRCh38 has improved since its initial release in Although the estimated quality of Ash1 v1. Additional analysis may also be needed to confirm that the small number of missing and disrupted genes are genuine differences between the genomes rather than incorrectly assembled repeats. Nonetheless, the Ash1 genome provides a ready-to-use reference for any genetic studies involving individuals with an Ashkenazi Jewish background.

In these individuals, alignments to Ash1 should yield fewer variants than alignment against GRCh38, which in turn will allow investigators to spend less time eliminating irrelevant variants. In addition, the computational methods used in this study provide a recipe that should allow the construction of many more human reference genomes, representing the many different populations of humans in the world today.

Many contigs aligned end-to-end to a single chromosome, and these were easy to place. The script then considered the contigs that aligned to GRCh38 in multiple disjoint chunks. The scaffolding script then aligned the ONT reads to the Ash1 v0. This procedure identified breakpoints and then split the Ash0. Note that if a mis-assembly occurred in a low coverage region, the contig was split at the weak point.

If the mis-assembly occurred in a high-coverage region, then it was likely due to a repetitive sequence, and the contig was split at the alignment breakpoint location.

After splitting, the script re-aligned the split contigs to the GRCh38 reference and used the best alignments to assign each contig or partial contig to a chromosome location. The remaining contigs were left unplaced. Some gaps in the initial Ash1 assembly occurred in areas where GRCh38 is ungapped, sometimes corresponding to regions that were manually curated to capture especially difficult repetitive regions.

To capture these regions, we took two additional gap-filling steps. First, for every gap in Ash1 v0. In these cases, we filled the gap in Ash1 with the GRCh38 sequence. This step closed gaps, yielding Ash1 v1.

Note that in the Ash1 genome, these GRCh38 sequences are recorded in lowercase, to distinguish them from the Ashkenazi-origin sequence, which is in uppercase.

Next, for the gaps where we could not find contiguous GRCh38 sequence that aligned to both sides of the gap in Ash1 v0. This second step added sequences from GRCh38 into the gaps, making the gaps smaller but leaving a pair of bp gaps for each inserted contig. This assembly, Ash1 v1. We next searched Ash1 v1. We then aligned each region to Ash1 using nucmer [ 24 ] and filtered the results to determine which SVs were present and which were missing from Ash1 v1.

We also made small variant calls from Ash1 v1. To ignore errors due to Ash1 representing a single haplotype and identify potential errors in Ash1, we excluded FPs where the v4.

Using the remaining FPs, we corrected 32, substitution errors, insertion errors, and 14, deletion errors in the Ash1 assembly. This did not correct any regions in Ash1 that aligned outside the v4. These corrections yielded Ash1 v1. To create Ash1 v1. These sequences, which are part of the pseudo-autosomal regions, are nearly identical between X and Y in GRCh38 and in Ash1. To create v1. We then re-polished the v1. For these sites, we replaced the Ash1 reference allele with the Ashkenazi major allele.

These single-base changes resulted in Ash1 v1. After chromosome assignment was done, contigs remained unplaced. We aligned them to Ash1 v1. We filtered the alignments using samtools to include only reads aligning with a quality of 40 and above. Variant calls for Ash V1. Because the assembly represents a single haplotype, FPs were calculated differently from the standard hap. We aligned these 2-kb sequences using nucmer [ 24 ] with a requirement that seed matches be at least 50 bases -l 50 and that anchors be unique in the reference and query --mum , to help eliminate spurious mappings in repetitive regions, though this reduced the number of SNVs considered.

Coordinates were then converted to Ash1 by using the delta2paf utility from paftools [ 29 ], followed by paftools liftover on the paf file to obtain the Ash1 genome coordinates of each original SNV site. Gffread was used to extract the coding sequences from GRCh38 and Ash1. The alignments were used to determine the GRCh38 location, sequence, and functional consequence of each variant. We compared the variants to the benchmark set using vcfeval from RealTimeGenomics tools [ 31 ]. A draft sequence of the Neandertal genome.

International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Article Google Scholar. Next generation disparities in human genomics: concerns and remedies.

Trends Genet. Genomics is failing on diversity. Is it time to change the reference genome? Genome Biol. De novo human genome assemblies reveal spectrum of alternative haplotypes in diverse populations. Nat Commun. Characterization and identification of hidden rare variants in the human genome.

BMC Genomics. The use of non-variant sites to improve the clinical assessment of whole-genome sequence data. PLoS One. Catching hidden variation: systematic correction of reference minor allele annotation in clinical variant calling. Download all variants GVF. Variant Effect Predictor. DNA methylation, transcription factor binding sites, histone modifications, and regulatory features such as enhancers and repressors, and microarray annotations.

More about the Ensembl regulatory build and microarray annotation. Experimental data sources. Download all regulatory features GFF. Permanent link - View in archive site. Privacy policy. Search terms. Search Human. Search all species.