Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls Article Swipe
Samantha L. Wilson
,
Shu Yi Shen
,
Lauren Harmon
,
Justin Burgener
,
Timothy J. Triche
,
Scott V. Bratman
,
Daniel D. De Carvalho
,
Michael M. Hoffman
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.02.12.430289
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.02.12.430289
Background Cell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). This method allows for DNA methylation profiling of circulating tumour DNA in cancer patients’ blood samples. Such epigenetic profiling of circulating tumour DNA provides information about in which tissues tumour DNA originates, a key requirement of any test for early cancer detection. In addition, DNA methylation signatures provide prognostic information and can detect relapse. For robust quantitative comparisons between samples, immunoprecipitation enrichment methods like cfMeDIP-seq require normalization against common reference controls. Methods To provide a simple and inexpensive reference for quantitative normalization, we developed a set of synthetic spike-in DNA controls for cfMeDIP-seq. These controls account for technical variation in enrichment efficiency due to biophysical properties of DNA fragments. Specifically, we designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). We ensured that the spike-in synthetic DNA sequences do not align to the human genome. We integrated unique molecular indices (UMIs) into cfMeDIP-seq to control for differential amplification after enrichment. To assess enrichment bias according to distinct biophysical properties, we conducted cfMeDIP-seq solely on spike-in DNA fragments. To optimize the amount of spike-in DNA required, we added varying quantities of spike-in control DNA to sheared HCT116 colon cancer genomic DNA prior to cfMeDIP-seq. To assess batch effects, three separate labs conducted cfMeDIP-seq on peripheral blood plasma samples from acute myeloid leukemia (AML) patients. Results We show that cfMeDIP-seq enriches for highly methylated regions, capturing ≥ 97% of methylated spike-in control fragments with ≤ 3% non-specific binding and preference for both high G+C content fragments and fragments with more CpGs. The use of 0.01 ng of spike-in control DNA in each sample provided sufficient sequencing reads to adjust for variance due to fragment length, G+C content, and CpG fraction. Using the known amount of each spiked-in fragment, we created a generalized linear model that absolutely quantifies molar amount from read counts across the genome, while adjusting for fragment length, G+C content, and CpG fraction. Employing our spike-in controls greatly mitigates batch effects, reducing batch-associated variance to ≤ 1% of the total variance within the data. Discussion Incorporation of spike-in controls enables absolute quantification of methylated cfDNA generated from methylated DNA immunoprecipitation-sequencing (MeDIP-seq) experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases. We created an R package, spiky , to convert read counts to picomoles of DNA fragments, while adjusting for fragment properties that affect enrichment. The spiky package is available on Bioconductor ( https://bioconductor.org/packages/spiky ) and GitHub ( https://github.com/trichelab/spiky ). Contact [email protected]
Related Topics
Concepts
Methylated DNA immunoprecipitation
DNA methylation
DNA
Epigenetics
Biology
Methylation
CpG site
Computational biology
Differentially methylated regions
genomic DNA
Molecular biology
DNA sequencing
Human genome
Illumina Methylation Assay
Epigenomics
Genome
Gene
Genetics
Gene expression
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.02.12.430289
- https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdf
- OA Status
- green
- Cited By
- 4
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3131800742
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3131800742Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2021.02.12.430289Digital Object Identifier
- Title
-
Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controlsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-16Full publication date if available
- Authors
-
Samantha L. Wilson, Shu Yi Shen, Lauren Harmon, Justin Burgener, Timothy J. Triche, Scott V. Bratman, Daniel D. De Carvalho, Michael M. HoffmanList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.02.12.430289Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdfDirect OA link when available
- Concepts
-
Methylated DNA immunoprecipitation, DNA methylation, DNA, Epigenetics, Biology, Methylation, CpG site, Computational biology, Differentially methylated regions, genomic DNA, Molecular biology, DNA sequencing, Human genome, Illumina Methylation Assay, Epigenomics, Genome, Gene, Genetics, Gene expressionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3131800742 |
|---|---|
| doi | https://doi.org/10.1101/2021.02.12.430289 |
| ids.doi | https://doi.org/10.1101/2021.02.12.430289 |
| ids.mag | 3131800742 |
| ids.openalex | https://openalex.org/W3131800742 |
| fwci | 0.21353519 |
| type | preprint |
| title | Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11287 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1306 |
| topics[0].subfield.display_name | Cancer Research |
| topics[0].display_name | Cancer Genomics and Diagnostics |
| topics[1].id | https://openalex.org/T10269 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9998000264167786 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Epigenetics and DNA Methylation |
| topics[2].id | https://openalex.org/T11482 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9927999973297119 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | RNA modifications and cancer |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C116114238 |
| concepts[0].level | 5 |
| concepts[0].score | 0.9100703001022339 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q6823960 |
| concepts[0].display_name | Methylated DNA immunoprecipitation |
| concepts[1].id | https://openalex.org/C190727270 |
| concepts[1].level | 4 |
| concepts[1].score | 0.8357748985290527 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q874745 |
| concepts[1].display_name | DNA methylation |
| concepts[2].id | https://openalex.org/C552990157 |
| concepts[2].level | 2 |
| concepts[2].score | 0.674344539642334 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7430 |
| concepts[2].display_name | DNA |
| concepts[3].id | https://openalex.org/C41091548 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6188832521438599 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q26939 |
| concepts[3].display_name | Epigenetics |
| concepts[4].id | https://openalex.org/C86803240 |
| concepts[4].level | 0 |
| concepts[4].score | 0.559260904788971 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[4].display_name | Biology |
| concepts[5].id | https://openalex.org/C33288867 |
| concepts[5].level | 3 |
| concepts[5].score | 0.4993166923522949 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q518328 |
| concepts[5].display_name | Methylation |
| concepts[6].id | https://openalex.org/C140173407 |
| concepts[6].level | 5 |
| concepts[6].score | 0.49710896611213684 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1138358 |
| concepts[6].display_name | CpG site |
| concepts[7].id | https://openalex.org/C70721500 |
| concepts[7].level | 1 |
| concepts[7].score | 0.49510470032691956 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[7].display_name | Computational biology |
| concepts[8].id | https://openalex.org/C154253233 |
| concepts[8].level | 5 |
| concepts[8].score | 0.49148356914520264 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q14862240 |
| concepts[8].display_name | Differentially methylated regions |
| concepts[9].id | https://openalex.org/C17757408 |
| concepts[9].level | 3 |
| concepts[9].score | 0.46654248237609863 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q4135516 |
| concepts[9].display_name | genomic DNA |
| concepts[10].id | https://openalex.org/C153911025 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4643113613128662 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7202 |
| concepts[10].display_name | Molecular biology |
| concepts[11].id | https://openalex.org/C51679486 |
| concepts[11].level | 3 |
| concepts[11].score | 0.4598335921764374 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q380546 |
| concepts[11].display_name | DNA sequencing |
| concepts[12].id | https://openalex.org/C197077220 |
| concepts[12].level | 4 |
| concepts[12].score | 0.43809089064598083 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q720988 |
| concepts[12].display_name | Human genome |
| concepts[13].id | https://openalex.org/C136953436 |
| concepts[13].level | 5 |
| concepts[13].score | 0.43008869886398315 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q6000286 |
| concepts[13].display_name | Illumina Methylation Assay |
| concepts[14].id | https://openalex.org/C121912465 |
| concepts[14].level | 5 |
| concepts[14].score | 0.4278712868690491 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3589153 |
| concepts[14].display_name | Epigenomics |
| concepts[15].id | https://openalex.org/C141231307 |
| concepts[15].level | 3 |
| concepts[15].score | 0.3138936161994934 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7020 |
| concepts[15].display_name | Genome |
| concepts[16].id | https://openalex.org/C104317684 |
| concepts[16].level | 2 |
| concepts[16].score | 0.30556029081344604 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[16].display_name | Gene |
| concepts[17].id | https://openalex.org/C54355233 |
| concepts[17].level | 1 |
| concepts[17].score | 0.2642250657081604 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[17].display_name | Genetics |
| concepts[18].id | https://openalex.org/C150194340 |
| concepts[18].level | 3 |
| concepts[18].score | 0.2008027732372284 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q26972 |
| concepts[18].display_name | Gene expression |
| keywords[0].id | https://openalex.org/keywords/methylated-dna-immunoprecipitation |
| keywords[0].score | 0.9100703001022339 |
| keywords[0].display_name | Methylated DNA immunoprecipitation |
| keywords[1].id | https://openalex.org/keywords/dna-methylation |
| keywords[1].score | 0.8357748985290527 |
| keywords[1].display_name | DNA methylation |
| keywords[2].id | https://openalex.org/keywords/dna |
| keywords[2].score | 0.674344539642334 |
| keywords[2].display_name | DNA |
| keywords[3].id | https://openalex.org/keywords/epigenetics |
| keywords[3].score | 0.6188832521438599 |
| keywords[3].display_name | Epigenetics |
| keywords[4].id | https://openalex.org/keywords/biology |
| keywords[4].score | 0.559260904788971 |
| keywords[4].display_name | Biology |
| keywords[5].id | https://openalex.org/keywords/methylation |
| keywords[5].score | 0.4993166923522949 |
| keywords[5].display_name | Methylation |
| keywords[6].id | https://openalex.org/keywords/cpg-site |
| keywords[6].score | 0.49710896611213684 |
| keywords[6].display_name | CpG site |
| keywords[7].id | https://openalex.org/keywords/computational-biology |
| keywords[7].score | 0.49510470032691956 |
| keywords[7].display_name | Computational biology |
| keywords[8].id | https://openalex.org/keywords/differentially-methylated-regions |
| keywords[8].score | 0.49148356914520264 |
| keywords[8].display_name | Differentially methylated regions |
| keywords[9].id | https://openalex.org/keywords/genomic-dna |
| keywords[9].score | 0.46654248237609863 |
| keywords[9].display_name | genomic DNA |
| keywords[10].id | https://openalex.org/keywords/molecular-biology |
| keywords[10].score | 0.4643113613128662 |
| keywords[10].display_name | Molecular biology |
| keywords[11].id | https://openalex.org/keywords/dna-sequencing |
| keywords[11].score | 0.4598335921764374 |
| keywords[11].display_name | DNA sequencing |
| keywords[12].id | https://openalex.org/keywords/human-genome |
| keywords[12].score | 0.43809089064598083 |
| keywords[12].display_name | Human genome |
| keywords[13].id | https://openalex.org/keywords/illumina-methylation-assay |
| keywords[13].score | 0.43008869886398315 |
| keywords[13].display_name | Illumina Methylation Assay |
| keywords[14].id | https://openalex.org/keywords/epigenomics |
| keywords[14].score | 0.4278712868690491 |
| keywords[14].display_name | Epigenomics |
| keywords[15].id | https://openalex.org/keywords/genome |
| keywords[15].score | 0.3138936161994934 |
| keywords[15].display_name | Genome |
| keywords[16].id | https://openalex.org/keywords/gene |
| keywords[16].score | 0.30556029081344604 |
| keywords[16].display_name | Gene |
| keywords[17].id | https://openalex.org/keywords/genetics |
| keywords[17].score | 0.2642250657081604 |
| keywords[17].display_name | Genetics |
| keywords[18].id | https://openalex.org/keywords/gene-expression |
| keywords[18].score | 0.2008027732372284 |
| keywords[18].display_name | Gene expression |
| language | en |
| locations[0].id | doi:10.1101/2021.02.12.430289 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2021.02.12.430289 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5103218787 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4346-9696 |
| authorships[0].author.display_name | Samantha L. Wilson |
| authorships[0].countries | CA |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[0].affiliations[0].raw_affiliation_string | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[0].institutions[0].id | https://openalex.org/I2802654988 |
| authorships[0].institutions[0].ror | https://ror.org/03zayce58 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[0].institutions[0].country_code | CA |
| authorships[0].institutions[0].display_name | Princess Margaret Cancer Centre |
| authorships[0].institutions[1].id | https://openalex.org/I1325899441 |
| authorships[0].institutions[1].ror | https://ror.org/042xt5161 |
| authorships[0].institutions[1].type | healthcare |
| authorships[0].institutions[1].lineage | https://openalex.org/I1325899441 |
| authorships[0].institutions[1].country_code | CA |
| authorships[0].institutions[1].display_name | University Health Network |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Samantha L. Wilson |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[1].author.id | https://openalex.org/A5044630168 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7036-2291 |
| authorships[1].author.display_name | Shu Yi Shen |
| authorships[1].countries | CA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[1].affiliations[0].raw_affiliation_string | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[1].institutions[0].id | https://openalex.org/I2802654988 |
| authorships[1].institutions[0].ror | https://ror.org/03zayce58 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[1].institutions[0].country_code | CA |
| authorships[1].institutions[0].display_name | Princess Margaret Cancer Centre |
| authorships[1].institutions[1].id | https://openalex.org/I1325899441 |
| authorships[1].institutions[1].ror | https://ror.org/042xt5161 |
| authorships[1].institutions[1].type | healthcare |
| authorships[1].institutions[1].lineage | https://openalex.org/I1325899441 |
| authorships[1].institutions[1].country_code | CA |
| authorships[1].institutions[1].display_name | University Health Network |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Shu Yi Shen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[2].author.id | https://openalex.org/A5079736295 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2248-0602 |
| authorships[2].author.display_name | Lauren Harmon |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1317189400 |
| authorships[2].affiliations[0].raw_affiliation_string | Van Andel Institute, Grand Rapids, MI, United States of America |
| authorships[2].institutions[0].id | https://openalex.org/I1317189400 |
| authorships[2].institutions[0].ror | https://ror.org/00wm07d60 |
| authorships[2].institutions[0].type | nonprofit |
| authorships[2].institutions[0].lineage | https://openalex.org/I1317189400 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Van Andel Institute |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lauren Harmon |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Van Andel Institute, Grand Rapids, MI, United States of America |
| authorships[3].author.id | https://openalex.org/A5079712911 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9522-8865 |
| authorships[3].author.display_name | Justin Burgener |
| authorships[3].countries | CA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I185261750 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[3].affiliations[1].raw_affiliation_string | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[3].institutions[0].id | https://openalex.org/I2802654988 |
| authorships[3].institutions[0].ror | https://ror.org/03zayce58 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[3].institutions[0].country_code | CA |
| authorships[3].institutions[0].display_name | Princess Margaret Cancer Centre |
| authorships[3].institutions[1].id | https://openalex.org/I1325899441 |
| authorships[3].institutions[1].ror | https://ror.org/042xt5161 |
| authorships[3].institutions[1].type | healthcare |
| authorships[3].institutions[1].lineage | https://openalex.org/I1325899441 |
| authorships[3].institutions[1].country_code | CA |
| authorships[3].institutions[1].display_name | University Health Network |
| authorships[3].institutions[2].id | https://openalex.org/I185261750 |
| authorships[3].institutions[2].ror | https://ror.org/03dbr7087 |
| authorships[3].institutions[2].type | education |
| authorships[3].institutions[2].lineage | https://openalex.org/I185261750 |
| authorships[3].institutions[2].country_code | CA |
| authorships[3].institutions[2].display_name | University of Toronto |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Justin M. Burgener |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[4].author.id | https://openalex.org/A5041929142 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5665-946X |
| authorships[4].author.display_name | Timothy J. Triche |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1317189400 |
| authorships[4].affiliations[0].raw_affiliation_string | Van Andel Institute, Grand Rapids, MI, United States of America |
| authorships[4].institutions[0].id | https://openalex.org/I1317189400 |
| authorships[4].institutions[0].ror | https://ror.org/00wm07d60 |
| authorships[4].institutions[0].type | nonprofit |
| authorships[4].institutions[0].lineage | https://openalex.org/I1317189400 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Van Andel Institute |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Tim Triche |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Van Andel Institute, Grand Rapids, MI, United States of America |
| authorships[5].author.id | https://openalex.org/A5063842271 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8610-4908 |
| authorships[5].author.display_name | Scott V. Bratman |
| authorships[5].countries | CA |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I185261750 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[5].affiliations[1].raw_affiliation_string | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[5].institutions[0].id | https://openalex.org/I2802654988 |
| authorships[5].institutions[0].ror | https://ror.org/03zayce58 |
| authorships[5].institutions[0].type | healthcare |
| authorships[5].institutions[0].lineage | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[5].institutions[0].country_code | CA |
| authorships[5].institutions[0].display_name | Princess Margaret Cancer Centre |
| authorships[5].institutions[1].id | https://openalex.org/I1325899441 |
| authorships[5].institutions[1].ror | https://ror.org/042xt5161 |
| authorships[5].institutions[1].type | healthcare |
| authorships[5].institutions[1].lineage | https://openalex.org/I1325899441 |
| authorships[5].institutions[1].country_code | CA |
| authorships[5].institutions[1].display_name | University Health Network |
| authorships[5].institutions[2].id | https://openalex.org/I185261750 |
| authorships[5].institutions[2].ror | https://ror.org/03dbr7087 |
| authorships[5].institutions[2].type | education |
| authorships[5].institutions[2].lineage | https://openalex.org/I185261750 |
| authorships[5].institutions[2].country_code | CA |
| authorships[5].institutions[2].display_name | University of Toronto |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Scott V. Bratman |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[6].author.id | https://openalex.org/A5069130022 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-8572-5259 |
| authorships[6].author.display_name | Daniel D. De Carvalho |
| authorships[6].countries | CA |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I185261750 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[6].affiliations[1].raw_affiliation_string | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[6].institutions[0].id | https://openalex.org/I2802654988 |
| authorships[6].institutions[0].ror | https://ror.org/03zayce58 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[6].institutions[0].country_code | CA |
| authorships[6].institutions[0].display_name | Princess Margaret Cancer Centre |
| authorships[6].institutions[1].id | https://openalex.org/I1325899441 |
| authorships[6].institutions[1].ror | https://ror.org/042xt5161 |
| authorships[6].institutions[1].type | healthcare |
| authorships[6].institutions[1].lineage | https://openalex.org/I1325899441 |
| authorships[6].institutions[1].country_code | CA |
| authorships[6].institutions[1].display_name | University Health Network |
| authorships[6].institutions[2].id | https://openalex.org/I185261750 |
| authorships[6].institutions[2].ror | https://ror.org/03dbr7087 |
| authorships[6].institutions[2].type | education |
| authorships[6].institutions[2].lineage | https://openalex.org/I185261750 |
| authorships[6].institutions[2].country_code | CA |
| authorships[6].institutions[2].display_name | University of Toronto |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Daniel D. De Carvalho |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[7].author.id | https://openalex.org/A5043633648 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-4517-1562 |
| authorships[7].author.display_name | Michael M. Hoffman |
| authorships[7].countries | CA |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I185261750 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Toronto, Toronto, ON, Canada |
| authorships[7].affiliations[1].institution_ids | https://openalex.org/I4210127509 |
| authorships[7].affiliations[1].raw_affiliation_string | Vector Institute for Artificial Intelligence, Toronto, ON, Canada |
| authorships[7].affiliations[2].institution_ids | https://openalex.org/I185261750 |
| authorships[7].affiliations[2].raw_affiliation_string | Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada |
| authorships[7].affiliations[3].institution_ids | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[7].affiliations[3].raw_affiliation_string | Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada |
| authorships[7].institutions[0].id | https://openalex.org/I2802654988 |
| authorships[7].institutions[0].ror | https://ror.org/03zayce58 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I1325899441, https://openalex.org/I2802654988 |
| authorships[7].institutions[0].country_code | CA |
| authorships[7].institutions[0].display_name | Princess Margaret Cancer Centre |
| authorships[7].institutions[1].id | https://openalex.org/I1325899441 |
| authorships[7].institutions[1].ror | https://ror.org/042xt5161 |
| authorships[7].institutions[1].type | healthcare |
| authorships[7].institutions[1].lineage | https://openalex.org/I1325899441 |
| authorships[7].institutions[1].country_code | CA |
| authorships[7].institutions[1].display_name | University Health Network |
| authorships[7].institutions[2].id | https://openalex.org/I185261750 |
| authorships[7].institutions[2].ror | https://ror.org/03dbr7087 |
| authorships[7].institutions[2].type | education |
| authorships[7].institutions[2].lineage | https://openalex.org/I185261750 |
| authorships[7].institutions[2].country_code | CA |
| authorships[7].institutions[2].display_name | University of Toronto |
| authorships[7].institutions[3].id | https://openalex.org/I4210127509 |
| authorships[7].institutions[3].ror | https://ror.org/03kqdja62 |
| authorships[7].institutions[3].type | facility |
| authorships[7].institutions[3].lineage | https://openalex.org/I4210127509 |
| authorships[7].institutions[3].country_code | CA |
| authorships[7].institutions[3].display_name | Vector Institute |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Michael M. Hoffman |
| authorships[7].is_corresponding | True |
| authorships[7].raw_affiliation_strings | Department of Computer Science, University of Toronto, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Vector Institute for Artificial Intelligence, Toronto, ON, Canada |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11287 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1306 |
| primary_topic.subfield.display_name | Cancer Research |
| primary_topic.display_name | Cancer Genomics and Diagnostics |
| related_works | https://openalex.org/W4233664389, https://openalex.org/W2127540087, https://openalex.org/W2155605783, https://openalex.org/W2775229688, https://openalex.org/W112435004, https://openalex.org/W2150651400, https://openalex.org/W2019369654, https://openalex.org/W1480390089, https://openalex.org/W2099688182, https://openalex.org/W4320158726 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2021 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2021.02.12.430289 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2021.02.12.430289 |
| primary_location.id | doi:10.1101/2021.02.12.430289 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2021/11/19/2021.02.12.430289.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2021.02.12.430289 |
| publication_date | 2021-02-16 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2900771557, https://openalex.org/W2970087737, https://openalex.org/W2009458183, https://openalex.org/W2201642794, https://openalex.org/W2024800350, https://openalex.org/W2477281194, https://openalex.org/W2952841869, https://openalex.org/W2000225203, https://openalex.org/W2916127307, https://openalex.org/W2950199861, https://openalex.org/W2900295924, https://openalex.org/W2150887741, https://openalex.org/W2953263404, https://openalex.org/W2077237334, https://openalex.org/W2523592638, https://openalex.org/W2951912016, https://openalex.org/W2119923823, https://openalex.org/W2170551349, https://openalex.org/W2952109315, https://openalex.org/W2108234281, https://openalex.org/W2102619694, https://openalex.org/W2582660596, https://openalex.org/W2108871359, https://openalex.org/W2259938310, https://openalex.org/W2953622121, https://openalex.org/W2887474968, https://openalex.org/W2056040742, https://openalex.org/W2946766521, https://openalex.org/W2559923889, https://openalex.org/W2060774914, https://openalex.org/W2121044470, https://openalex.org/W2898598946, https://openalex.org/W6608097372, https://openalex.org/W2752378479, https://openalex.org/W4247413519, https://openalex.org/W2797121391, https://openalex.org/W2754712980, https://openalex.org/W1963990067, https://openalex.org/W1973094248, https://openalex.org/W2020541351, https://openalex.org/W1567333450, https://openalex.org/W3035917317, https://openalex.org/W2121016876, https://openalex.org/W2580767461, https://openalex.org/W2950493549, https://openalex.org/W2132468289, https://openalex.org/W2014677321 |
| referenced_works_count | 47 |
| abstract_inverted_index.( | 463, 468 |
| abstract_inverted_index.) | 465 |
| abstract_inverted_index., | 438 |
| abstract_inverted_index.R | 435 |
| abstract_inverted_index.a | 14, 60, 102, 112, 346 |
| abstract_inverted_index.). | 470 |
| abstract_inverted_index.1% | 384 |
| abstract_inverted_index.3% | 291 |
| abstract_inverted_index.54 | 140 |
| abstract_inverted_index.In | 70 |
| abstract_inverted_index.It | 410 |
| abstract_inverted_index.To | 100, 208, 225, 251 |
| abstract_inverted_index.We | 178, 193, 272, 432 |
| abstract_inverted_index.an | 434 |
| abstract_inverted_index.do | 186 |
| abstract_inverted_index.in | 39, 54, 127, 316, 418 |
| abstract_inverted_index.is | 459 |
| abstract_inverted_index.ng | 311 |
| abstract_inverted_index.of | 35, 47, 63, 114, 134, 145, 166, 229, 237, 284, 309, 312, 340, 385, 394, 400, 425, 445 |
| abstract_inverted_index.on | 221, 260, 461 |
| abstract_inverted_index.to | 17, 131, 189, 201, 213, 241, 249, 323, 328, 382, 421, 439, 443 |
| abstract_inverted_index.we | 110, 138, 217, 233, 344 |
| abstract_inverted_index.(80 | 153 |
| abstract_inverted_index.160 | 155 |
| abstract_inverted_index.320 | 157 |
| abstract_inverted_index.97% | 283 |
| abstract_inverted_index.CpG | 167, 334, 369 |
| abstract_inverted_index.DNA | 4, 11, 21, 26, 32, 38, 50, 58, 72, 117, 135, 141, 184, 223, 231, 240, 247, 315, 406, 426, 446 |
| abstract_inverted_index.For | 82 |
| abstract_inverted_index.G+C | 159, 299, 331, 366 |
| abstract_inverted_index.The | 307, 456 |
| abstract_inverted_index.and | 23, 78, 104, 149, 164, 294, 302, 333, 368, 414, 428, 466 |
| abstract_inverted_index.any | 64 |
| abstract_inverted_index.bp, | 154, 156, 173, 175 |
| abstract_inverted_index.can | 79 |
| abstract_inverted_index.due | 130, 327, 420 |
| abstract_inverted_index.for | 31, 66, 107, 119, 124, 203, 277, 296, 325, 363, 416, 450 |
| abstract_inverted_index.key | 61 |
| abstract_inverted_index.not | 187 |
| abstract_inverted_index.our | 372 |
| abstract_inverted_index.set | 113 |
| abstract_inverted_index.the | 170, 181, 190, 227, 337, 359, 386, 390 |
| abstract_inverted_index.use | 308 |
| abstract_inverted_index.≤ | 290, 383 |
| abstract_inverted_index.≥ | 282 |
| abstract_inverted_index.0.01 | 310 |
| abstract_inverted_index.1/20 | 176 |
| abstract_inverted_index.1/40 | 174 |
| abstract_inverted_index.50%, | 162 |
| abstract_inverted_index.Such | 44 |
| abstract_inverted_index.This | 28 |
| abstract_inverted_index.bias | 211 |
| abstract_inverted_index.both | 297 |
| abstract_inverted_index.bp), | 158 |
| abstract_inverted_index.bp). | 177 |
| abstract_inverted_index.each | 317, 341 |
| abstract_inverted_index.from | 265, 355, 404 |
| abstract_inverted_index.high | 298 |
| abstract_inverted_index.into | 199 |
| abstract_inverted_index.labs | 257 |
| abstract_inverted_index.like | 91 |
| abstract_inverted_index.more | 305 |
| abstract_inverted_index.read | 356, 441 |
| abstract_inverted_index.show | 273 |
| abstract_inverted_index.test | 65 |
| abstract_inverted_index.that | 180, 274, 350, 453 |
| abstract_inverted_index.with | 10, 19, 24, 143, 289, 304 |
| abstract_inverted_index.work | 18 |
| abstract_inverted_index.(1/80 | 172 |
| abstract_inverted_index.(35%, | 161 |
| abstract_inverted_index.(AML) | 269 |
| abstract_inverted_index.65%), | 163 |
| abstract_inverted_index.CpGs. | 306 |
| abstract_inverted_index.These | 121 |
| abstract_inverted_index.Using | 336 |
| abstract_inverted_index.about | 53 |
| abstract_inverted_index.acute | 266 |
| abstract_inverted_index.added | 234 |
| abstract_inverted_index.after | 206 |
| abstract_inverted_index.align | 188 |
| abstract_inverted_index.batch | 253, 377, 412 |
| abstract_inverted_index.blood | 42, 262 |
| abstract_inverted_index.cfDNA | 402 |
| abstract_inverted_index.colon | 244 |
| abstract_inverted_index.data. | 391 |
| abstract_inverted_index.early | 67 |
| abstract_inverted_index.human | 191 |
| abstract_inverted_index.known | 338, 422 |
| abstract_inverted_index.model | 349 |
| abstract_inverted_index.molar | 353 |
| abstract_inverted_index.other | 429 |
| abstract_inverted_index.prior | 248 |
| abstract_inverted_index.reads | 322 |
| abstract_inverted_index.spiky | 437, 457 |
| abstract_inverted_index.three | 255 |
| abstract_inverted_index.total | 387 |
| abstract_inverted_index.using | 13 |
| abstract_inverted_index.which | 55 |
| abstract_inverted_index.while | 361, 448 |
| abstract_inverted_index.(UMIs) | 198 |
| abstract_inverted_index.GitHub | 467 |
| abstract_inverted_index.HCT116 | 243 |
| abstract_inverted_index.across | 358 |
| abstract_inverted_index.adjust | 324 |
| abstract_inverted_index.affect | 454 |
| abstract_inverted_index.allows | 30 |
| abstract_inverted_index.amount | 228, 339, 354 |
| abstract_inverted_index.assess | 209, 252 |
| abstract_inverted_index.biases | 417 |
| abstract_inverted_index.cancer | 40, 68, 245 |
| abstract_inverted_index.common | 96 |
| abstract_inverted_index.counts | 357, 442 |
| abstract_inverted_index.detect | 80 |
| abstract_inverted_index.highly | 278 |
| abstract_inverted_index.length | 152 |
| abstract_inverted_index.linear | 348 |
| abstract_inverted_index.method | 29 |
| abstract_inverted_index.plasma | 263 |
| abstract_inverted_index.robust | 83 |
| abstract_inverted_index.sample | 318 |
| abstract_inverted_index.simple | 103 |
| abstract_inverted_index.solely | 220 |
| abstract_inverted_index.status | 147 |
| abstract_inverted_index.tumour | 37, 49, 57 |
| abstract_inverted_index.unique | 195 |
| abstract_inverted_index.within | 169, 389 |
| abstract_inverted_index.Contact | 471 |
| abstract_inverted_index.Methods | 99 |
| abstract_inverted_index.Results | 271 |
| abstract_inverted_index.account | 123 |
| abstract_inverted_index.adapted | 16 |
| abstract_inverted_index.against | 95 |
| abstract_inverted_index.between | 86 |
| abstract_inverted_index.biases. | 431 |
| abstract_inverted_index.binding | 293 |
| abstract_inverted_index.content | 160, 300 |
| abstract_inverted_index.control | 202, 239, 287, 314 |
| abstract_inverted_index.convert | 440 |
| abstract_inverted_index.created | 345, 433 |
| abstract_inverted_index.effects | 413 |
| abstract_inverted_index.enables | 397 |
| abstract_inverted_index.ensured | 179 |
| abstract_inverted_index.genome, | 360 |
| abstract_inverted_index.genome. | 192 |
| abstract_inverted_index.genomic | 8, 246 |
| abstract_inverted_index.greatly | 375 |
| abstract_inverted_index.indices | 197 |
| abstract_inverted_index.length, | 330, 365 |
| abstract_inverted_index.methods | 90 |
| abstract_inverted_index.myeloid | 267 |
| abstract_inverted_index.package | 458 |
| abstract_inverted_index.provide | 75, 101 |
| abstract_inverted_index.regions | 9 |
| abstract_inverted_index.require | 93 |
| abstract_inverted_index.samples | 22, 264 |
| abstract_inverted_index.sheared | 242 |
| abstract_inverted_index.tissues | 56 |
| abstract_inverted_index.varying | 235 |
| abstract_inverted_index.(cfDNA). | 27 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.absolute | 398 |
| abstract_inverted_index.content, | 332, 367 |
| abstract_inverted_index.controls | 118, 122, 374, 396 |
| abstract_inverted_index.corrects | 415 |
| abstract_inverted_index.designed | 139 |
| abstract_inverted_index.distinct | 214 |
| abstract_inverted_index.effects, | 254, 378 |
| abstract_inverted_index.enriches | 276 |
| abstract_inverted_index.fraction | 165 |
| abstract_inverted_index.fragment | 151, 171, 329, 364, 451 |
| abstract_inverted_index.leukemia | 268 |
| abstract_inverted_index.optimize | 226 |
| abstract_inverted_index.package, | 436 |
| abstract_inverted_index.protocol | 15 |
| abstract_inverted_index.provided | 319 |
| abstract_inverted_index.provides | 51 |
| abstract_inverted_index.reducing | 379 |
| abstract_inverted_index.regions, | 280 |
| abstract_inverted_index.relapse. | 81 |
| abstract_inverted_index.samples, | 87 |
| abstract_inverted_index.samples. | 43 |
| abstract_inverted_index.separate | 256 |
| abstract_inverted_index.spike-in | 116, 182, 222, 230, 238, 286, 313, 373, 395 |
| abstract_inverted_index.variance | 326, 381, 388 |
| abstract_inverted_index.Cell-free | 2 |
| abstract_inverted_index.Employing | 371 |
| abstract_inverted_index.according | 212 |
| abstract_inverted_index.addition, | 71 |
| abstract_inverted_index.adjusting | 362, 449 |
| abstract_inverted_index.available | 460 |
| abstract_inverted_index.capturing | 281 |
| abstract_inverted_index.cell-free | 25 |
| abstract_inverted_index.conducted | 218, 258 |
| abstract_inverted_index.controls. | 98 |
| abstract_inverted_index.developed | 111 |
| abstract_inverted_index.fraction. | 335, 370 |
| abstract_inverted_index.fragment, | 343 |
| abstract_inverted_index.fragments | 142, 288, 301, 303, 427 |
| abstract_inverted_index.generated | 403 |
| abstract_inverted_index.low-input | 20 |
| abstract_inverted_index.mitigates | 376, 411 |
| abstract_inverted_index.molecular | 196 |
| abstract_inverted_index.patients. | 270 |
| abstract_inverted_index.picomoles | 444 |
| abstract_inverted_index.profiling | 34, 46 |
| abstract_inverted_index.reference | 97, 106 |
| abstract_inverted_index.required, | 232 |
| abstract_inverted_index.sequences | 185 |
| abstract_inverted_index.spiked-in | 342 |
| abstract_inverted_index.synthetic | 115, 183 |
| abstract_inverted_index.technical | 125, 430 |
| abstract_inverted_index.variation | 126 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Discussion | 392 |
| abstract_inverted_index.absolutely | 351 |
| abstract_inverted_index.detection. | 69 |
| abstract_inverted_index.efficiency | 129 |
| abstract_inverted_index.enrichment | 89, 128, 210, 419 |
| abstract_inverted_index.epigenetic | 45 |
| abstract_inverted_index.fragments, | 447 |
| abstract_inverted_index.fragments. | 136, 224 |
| abstract_inverted_index.identifies | 7 |
| abstract_inverted_index.integrated | 194 |
| abstract_inverted_index.methylated | 3, 279, 285, 401, 405 |
| abstract_inverted_index.peripheral | 261 |
| abstract_inverted_index.preference | 295 |
| abstract_inverted_index.prognostic | 76 |
| abstract_inverted_index.properties | 133, 424, 452 |
| abstract_inverted_index.quantifies | 352 |
| abstract_inverted_index.quantities | 236 |
| abstract_inverted_index.sequencing | 321 |
| abstract_inverted_index.signatures | 74 |
| abstract_inverted_index.sufficient | 320 |
| abstract_inverted_index.(MeDIP-seq) | 408 |
| abstract_inverted_index.(methylated | 148 |
| abstract_inverted_index.biophysical | 132, 215, 423 |
| abstract_inverted_index.cfMeDIP-seq | 92, 200, 219, 259, 275 |
| abstract_inverted_index.circulating | 36, 48 |
| abstract_inverted_index.comparisons | 85 |
| abstract_inverted_index.enrichment. | 207, 455 |
| abstract_inverted_index.generalized | 347 |
| abstract_inverted_index.inexpensive | 105 |
| abstract_inverted_index.information | 52, 77 |
| abstract_inverted_index.methylation | 33, 73, 146 |
| abstract_inverted_index.originates, | 59 |
| abstract_inverted_index.patients’ | 41 |
| abstract_inverted_index.properties, | 216 |
| abstract_inverted_index.requirement | 62 |
| abstract_inverted_index.Bioconductor | 462 |
| abstract_inverted_index.cfMeDIP-seq. | 120, 250 |
| abstract_inverted_index.combinations | 144 |
| abstract_inverted_index.differential | 204 |
| abstract_inverted_index.experiments. | 409 |
| abstract_inverted_index.methylation, | 12 |
| abstract_inverted_index.non-specific | 292 |
| abstract_inverted_index.quantitative | 84, 108 |
| abstract_inverted_index.(cfMeDIP-seq) | 6 |
| abstract_inverted_index.Incorporation | 393 |
| abstract_inverted_index.Specifically, | 137 |
| abstract_inverted_index.amplification | 205 |
| abstract_inverted_index.dinucleotides | 168 |
| abstract_inverted_index.normalization | 94 |
| abstract_inverted_index.normalization, | 109 |
| abstract_inverted_index.quantification | 399 |
| abstract_inverted_index.unmethylated), | 150 |
| abstract_inverted_index.batch-associated | 380 |
| abstract_inverted_index.immunoprecipitation | 88 |
| [email protected] | 472 |
| abstract_inverted_index.immunoprecipitation-sequencing | 5, 407 |
| abstract_inverted_index.https://github.com/trichelab/spiky | 469 |
| abstract_inverted_index.https://bioconductor.org/packages/spiky | 464 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5043633648 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I1325899441, https://openalex.org/I185261750, https://openalex.org/I2802654988, https://openalex.org/I4210127509 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.43766108 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |