Failure to Detect Mutations in U2AF1 due to Changes in the GRCh38 Reference Sequence

Miller, Christopher A.; Walker, Jason; Jensen, Travis L.; Hooper, William F.; Fulton, Robert S.; Painter, Jeffrey S.; Sekeres, Mikkael A.; Ley, Timothy J.; Spencer, David H.; Goll, Johannes; Walter, Matthew J.

doi:10.1016/j.jmoldx.2021.10.013

Cited by 24 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, in our analysis, we found that ONT and HiFi did not cover genes H19 and U2AF1 . Nevertheless, previous studies found that these genes are incorrectly duplicated in GRCh38, which makes it hard, if not possible, to call variants in these genes 19,46 . However, genes CCL3L1 and DUX4L1 are covered, making them only challenging for short reads.…”

Section: Resultsmentioning

confidence: 99%

“…While very challenging to resolve with short reads, over the past few years, multiple groups, including ours, have demonstrated the usefulness of long-reads in identifying these types of events 12,[15][16][17][18] . Thus, gave rise to new research efforts where we uncovered novel sequence elements, found biases in the human reference genomes [19][20][21] , resolved the complete telomere-to-telomere (T2T) human reference genome 22 , and could begin to demonstrate the impact of complex alleles across various human diseases 17,23 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utility of long-read sequencing for All of Us

Mahmoud

Huang

Garimella

et al. 2023

Preprint

View full text Add to dashboard Cite

The All of Us (AoU) initiative aims to sequence the genomes of over one million Americans from diverse ethnic backgrounds to improve personalized medical care. In a recent technical pilot, we compared the performance of traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples representing eight datasets. Our analysis revealed substantial differences in the ability of these technologies to accurately sequence complex medically relevant genes, particularly in terms of gene coverage and pathogenic variant identification. We also considered the advantages and challenges of using low coverage sequencing to increase sample numbers in large cohort analysis. Our results show that HiFi reads produced the most accurate results for both small and large variants. Further, we present a cloud-based pipeline to optimize SNV, indel and SV calling at scale for long-reads analysis. These results will lead to widespread improvements across AoU.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utility of long-read sequencing for All of Us

Mahmoud

Huang

Garimella

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Annotation of CHM13 used Liftoff [3] to map genes from the primary chromosomes, excluding the alternative scaffolds, onto the complete CHM13 genome. GRCh38 contains a number of regions, mostly on chromosome 21, that are known to be erroneous duplications [33, 34]. These regions contain 15 genes on chr21 that are spurious copies, as well as other spurious genes, and we therefore masked out these genes before mapping the remaining genes onto CHM13.…”

Section: Methodsmentioning

confidence: 99%

CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

Varabyou

Sommer

Erdogdu

et al. 2022

Preprint

View full text Add to dashboard Cite

The original CHESS database of human genes was assembled from nearly 10,000 RNA sequencing experiments in 53 human body sites produced by the Genotype-Tissue Expression (GTEx) project, and then augmented with genes from other databases to yield a comprehensive collection of protein-coding and noncoding transcripts. The construction of the new CHESS 3 database employed improved transcript assembly algorithms, a new machine learning classifier, and protein structure predictions to identify genes and transcripts likely to be functional and to eliminate those that appeared more likely to represent noise. The new catalog contains 41,356 genes on the GRCh38 reference human genome, of which 19,839 are protein-coding, and a total of 158,377 transcripts. These include 14,863 novel protein-coding transcripts. The total number of transcripts is substantially smaller than earlier versions due to improved transcriptome assembly methods and to a stricter protocol for filtering out noisy transcripts. Notably, CHESS 3 contains all of the transcripts in the MANE database, and at least one transcript corresponding to the vast majority of protein-coding genes in the RefSeq and GENCODE databases. CHESS 3 has also been mapped onto the complete CHM13 human genome, which gives a more-complete gene count of 43,773 genes and 19,968 protein-coding genes. The CHESS database is available at http://ccb.jhu.edu/chess.

show abstract

“…Most recently, our work identified remaining issues with the most commonly used reference genomes (GRCh37+38), where certain regions of the genome have been duplicated along chromosome 22 [ 13 ]. These include at least three medically relevant genes for inherited diseases, as well as one relevant for somatic variant calling [ 14 ]. Continuing this work together with the T2T group revealed other artifacts along GRCh38, including additional false duplications and missing copies (or collapses) of segmental duplications [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

FixItFelix: improving genomic analysis by fixing reference errors

et al. 2023

View full text Add to dashboard Cite

The current version of the human reference genome, GRCh38, contains a number of errors including 1.2 Mbp of falsely duplicated and 8.04 Mbp of collapsed regions. These errors impact the variant calling of 33 protein-coding genes, including 12 with medical relevance. Here, we present FixItFelix, an efficient remapping approach, together with a modified version of the GRCh38 reference genome that improves the subsequent analysis across these genes within minutes for an existing alignment file while maintaining the same coordinates. We showcase these improvements over multi-ethnic control samples, demonstrating improvements for population variant calling as well as eQTL studies.

show abstract

Failure to Detect Mutations in U2AF1 due to Changes in the GRCh38 Reference Sequence

Cited by 24 publications

References 21 publications

Utility of long-read sequencing for All of Us

Utility of long-read sequencing for All of Us

CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

FixItFelix: improving genomic analysis by fixing reference errors

Contact Info

Product

Resources

About