Detecting and annotating genetic variations using the HugeSeq pipeline

Lam, Hugo Y. K.; Pan, Cuiping; Clark, Michael J.; Lacroute, Phil; Chen, Rui; Haraksingh, Rajini; O’Huallachain, Maeve; Gerstein, Mark; Kidd, Jeffrey M.; Bustamante, Carlos D.; Snyder, M

doi:10.1038/nbt.2134

Cited by 108 publications

(90 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For deletions, we see significant overlap as well as variant calls that are specific to each method ( Figure S5A), but overlap is less for duplications ( Figure S5B) and inversions ( Figure S5C). This is consistent with what has been shown previously (Lam et al 2012) as inversions and duplications are more difficult in principle to accurately resolve. Since each SV detection method is designed to use different types of signals and also optimized to identify different classes of SVs, such overlaps are also expected.…”

Section: Cc-by-nc-ndsupporting

confidence: 80%

“…Consistent with previous analysis (Lam et al 2012), deletions show the highest concordance among the various methods of detection compared to duplication and inversion calls ( Figure S5). As expected, we detected a 520 bp deletion in exon 3 of the β-catenin (CTNNB1) gene (Dataset 4, Supplemental Data), which was previously documented to exist in HepG2 (López-Terrada et al 2009b).…”

Section: Svs Identified From Deep Short-insert Wgssupporting

confidence: 77%

See 1 more Smart Citation

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

Zhou

Greer

Spies

et al. 2018

Preprint

View full text Add to dashboard Cite

HepG2 is one of the most widely used human cell lines in biomedical research and one of the main cell lines of ENCODE. Although the functional genomic and epigenomic characteristics of HepG2 are extensively studied, its genome sequence has never been comprehensively analyzed and higher-order structural features of its genome beyond its karyotype were only cursorily known. The high degree of aneuploidy in HepG2 renders traditional genome variant analysis methods challenging and partially ineffective. Correct and complete interpretation of the extensive functional genomics data fromHepG2 requires an understanding of the cell line's genome sequence and genome structure. We performed deep whole-genome sequencing, mate-pair sequencing and linked-read sequencing to identify a wide spectrum of genome characteristics in HepG2: copy numbers of chromosomal segments, SNVs and Indels (both corrected for copynumber), phased haplotype blocks, structural variants (SVs) including complex genomic rearrangements, and novel mobile element insertions. A large number of SVs were phased, sequence assembled and experimentally validated. Several chromosomes show striking loss of heterozygosity. We re-analyzed HepG2 RNA-Seq and wholegenome bisulfite sequencing data for allele-specific expression and phased DNA methylation. We show examples where deeper insights into genomic regulatory complexity could be gained by taking knowledge of genomic structural contexts into account. Furthermore, we used the haplotype information to produce an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize HepG2.

show abstract

Section: Cc-by-nc-ndsupporting

confidence: 80%

Section: Svs Identified From Deep Short-insert Wgssupporting

confidence: 77%

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

Zhou

Greer

Spies

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Single nucleotide variants, short insertions and deletions (<100 bp Indels), and structural variants will be scored using the HugeSeq pipeline (Lam et al, 2012). For each PBMC sample, the transcriptome will be analyzed using RNA-seq, involving the random primed synthesis of cDNA from RNA depleted of rRNA.…”

Section: Ihmp Study: Microbiome and Host Changes During Respiratory Amentioning

confidence: 99%

The Integrative Human Microbiome Project: Dynamic Analysis of Microbiome-Host Omics Profiles during Periods of Human Health and Disease

2014

Cell Host & Microbe

409

207

View full text Add to dashboard Cite

Much has been learned about the diversity and distribution of human-associated microbial communities, but we still know little about the biology of the microbiome, how it interacts with the host, and how the host responds to its resident microbiota. The Integrative Human Microbiome Project (iHMP, http://hmp2.org), the second phase of the NIH Human Microbiome Project, will study these interactions by analyzing microbiome and host activities in longitudinal studies of disease-specific cohorts and by creating integrated data sets of microbiome and host functional properties. These data sets will serve as experimental test beds to evaluate new models, methods, and analyses on the interactions of host and microbiome. Here we describe the three models of microbiome-associated human conditions, on the dynamics of preterm birth, inflammatory bowel disease, and type 2 diabetes, and their underlying hypotheses, as well as the multi-omic data types to be collected, integrated, and distributed through public repositories as a community resource.

show abstract

“…We ultimately used the genotypes from the available “MasterVar Beta” files provided by CGI directly and did not consider additional filtering steps for the analysis of genotypes beyond those that went into the construction of the public domain files. The 69 individual genomes consisted of 22 individuals of Northern European ancestry (abbreviated here as CE for the CEPH or CEU HapMap Population (Consortium, 2005; Frazer et al, 2007), 10 individuals of Yoruban ancestry (YR), five individuals each of Mexican (ME), and African ancestry living in Dallas (AS), four individuals each of Japanese (JP), Han Chinese (CH), Italian (TS), East Indian (GI), Maasai Kenyan (MK), and Luhya Kenyan (LW) ancestry, and three individuals of Puerto Rican ancestry (PU). Thirteen CE individuals were the offspring of a couple of other CE individuals and were excluded from the analysis.…”

Section: Methodsmentioning

confidence: 99%

Clinical Implications of Human Population Differences in Genome-Wide Rates of Functional Genotypes

Torkamani¹,

Pham²,

Libiger³

et al. 2012

Front. Gene.

View full text Add to dashboard Cite

There have been a number of recent successes in the use of whole genome sequencing and sophisticated bioinformatics techniques to identify pathogenic DNA sequence variants responsible for individual idiopathic congenital conditions. However, the success of this identification process is heavily influenced by the ancestry or genetic background of a patient with an idiopathic condition. This is so because potential pathogenic variants in a patient’s genome must be contrasted with variants in a reference set of genomes made up of other individuals’ genomes of the same ancestry as the patient. We explored the effect of ignoring the ancestries of both an individual patient and the individuals used to construct reference genomes. We pursued this exploration in two major steps. We first considered variation in the per-genome number and rates of likely functional derived (i.e., non-ancestral, based on the chimp genome) single nucleotide variants and small indels in 52 individual whole human genomes sampled from 10 different global populations. We took advantage of a suite of computational and bioinformatics techniques to predict the functional effect of over 24 million genomic variants, both coding and non-coding, across these genomes. We found that the typical human genome harbors ∼5.5–6.1 million total derived variants, of which ∼12,000 are likely to have a functional effect (∼5000 coding and ∼7000 non-coding). We also found that the rates of functional genotypes per the total number of genotypes in individual whole genomes differ dramatically between human populations. We then created tables showing how the use of comparator or reference genome panels comprised of genomes from individuals that do not have the same ancestral background as a patient can negatively impact pathogenic variant identification. Our results have important implications for clinical sequencing initiatives.

show abstract

Detecting and annotating genetic variations using the HugeSeq pipeline

Cited by 108 publications

References 17 publications

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

The Integrative Human Microbiome Project: Dynamic Analysis of Microbiome-Host Omics Profiles during Periods of Human Health and Disease

Clinical Implications of Human Population Differences in Genome-Wide Rates of Functional Genotypes

Contact Info

Product

Resources

About