A Method for Localizing Non-Reference Sequences to the Human Genome

Chrisman, Brianna; Paskov, Kelley; He, Chloe; Jung, Jae-Yoon; Washington, Peter; Wall, Dennis P.

doi:10.1142/9789811250477_0029

Cited by 6 publications

(8 citation statements)

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“…In order to better understand patterns of contamination in human whole genome sequencing, we analyzed sequences from the iHART dataset[37]. Originally curated to study genetic determinants of autism, the iHART dataset contains whole genome sequences from blood samples from children with autism, their siblings, and their parents, but also stands as an invaluable genomics resource due to its unique family structure [38, 39, 40]. iHART was sequenced at the New York Genome Sequencing Center, a common site for large sequencing studies, using commonly followed storage, prep, and sequencing protocols [37], making it a good model dataset to understand common sequencing issues.…”

Section: Introductionmentioning

confidence: 99%

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

Chrisman

Jung

et al. 2022

Preprint

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BackgroundThe unmapped readspace of whole genome sequencing data tends to be large but is often ignored. We posit that it contains valuable signals of both human infection and contamination. Using unmapped and poorly aligned reads from whole genome sequences (WGS) of over 1,000 families and 5,000 individuals, we present insights into common viral, bacterial, and computational contamination that plague whole genome sequencing studies.ResultsWe present several notable results: (1) In addition to known contaminants such as Epstein-Barr virus and phiX, sequences from whole blood and lymphocyte cell lines contain many other contaminants, likely originating from storage, prep, and sequencing pipelines. (2) Sequencing plate and biological sample source of a sample strongly influence contamination profile. And, (3) Y-chromosome fragments not on the human reference genome commonly mismap to bacterial reference genomes.ConclusionBoth experiment-derived and computational contamination is prominent in next-generation sequencing data. Such contamination can compromise results from WGS as well as metagenomics studies, and standard protocols for identifying and removing contamination should be developed to ensure the fidelity of sequencing-based studies.genomics WGS contamination

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Section: Introductionmentioning

confidence: 99%

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

Chrisman

Jung

et al. 2022

Preprint

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“…We previous developed and validated a proof-of-concept algorithm to localize 100-bp k -mers extracted from 150 bp reads [7]. We review the mathematics of this maximum likelihood model, and discuss the modifications that we added in order to allow localization of tandem repeats and for sequence originating from the sex chromosomes.…”

Section: Methodsmentioning

confidence: 99%

“…Originally, we aimed to develop an algorithm to localize unmapped reads to coarse regions of the genome, with the hope that these regions could function as “bins” and that from these bins, we could ultimately assemble longer contigs that represented alternative haplotypes or sections of the genome missing from GRCh38. We presented a proof-of-concept version of such a localization algorithm for non-repetitive sequences in autosomes, with discussion about a final de novo step would look like [7].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Sequence Location Approximation using Nuclear Families (ASLAN) Validates Regions of the Telomere-to-Telomere Assembly and Identifies New Hotspots for Genetic Diversity

Chrisman

Paskov

et al. 2022

Preprint

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Although it is heavily relied on to study genetic contributors to health and disease, the current human reference genome (GRCh38) is incomplete in two major ways: firstly, it is missing large sections of heterochromatic sequence, and secondly, as a singular, linear reference genome it does not represent the full spectrum of genetic diversity that exists in the human species. In order to better understand and characterize gaps in GRCh38 and genetic diversity, we developed a method - ASLAN, an Algorithm for Sequence Location Approximation using Nuclear families - that identifies the region of origin of short reads that do not align to the GRCh38. Using unmapped reads and variant calls from whole genome sequencing (WGS) data from nuclear families, ASLAN relies on a maximum likelihood model to identify the most likely region of the genome that a subsequence belongs to, given the phasing information of family and the distribution of the subsequence in the unmapped reads. Validating ASLAN on a synthetically generated dataset, and on true reads originating from the alternative haplotypes in the decoy genome, we show that ASLAN can localize more than 90% of 100-basepair sequences with above 92% accuracy and around 1 megabase of resolution. We then run ASLAN on 100-mers from unmapped reads from WGS from over 700 families, and compare ASLAN localizations to alignment of the 100-mers to the T2T-CHM13 assembly, recently released by the Telomere-to-telomere (T2T) consortia. We find that many unmapped reads in GRCh38 originate from telomeres and centromeres that are gaps in the GRCh38 reference. We also confirm that ASLAN localizations are in high concordance with T2T-CHM13 alignments, except in the centromeres of the acrocentric chromosomes. Comparing ASLAN localizations and T2T-CHM13 alignments, we identify sequences missing from T2T-CHM13 or sequences with high divergence from their aligned region in T2T-CHM13, thus highlighting new hotspots for genetic diversity.

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“…This means that population-specific rare variants, haplotypes and structural variants cannot be captured well for certain populations. This can have implications for the development of individualized therapies based on those markers (Chrisman et al, 2022). The recent Human Pangenome Reference Consortium (HPRC) is working to establish a human genome reference that reflects the existing worldwide human diversity (https://humanpangenome.org/).…”

Section: Resources For Including Diverse and Admixed Populationsmentioning

confidence: 99%

Including diverse and admixed populations in genetic epidemiology research

Caliebe

Tekola‐Ayele

Darst

et al. 2022

Genetic Epidemiology

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The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of

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A Method for Localizing Non-Reference Sequences to the Human Genome

Cited by 6 publications

References 0 publications

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

An Algorithm for Sequence Location Approximation using Nuclear Families (ASLAN) Validates Regions of the Telomere-to-Telomere Assembly and Identifies New Hotspots for Genetic Diversity

Including diverse and admixed populations in genetic epidemiology research

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