Literature DB >> 24598259

Impact of sequencing depth in ChIP-seq experiments.

Youngsook L Jung1, Lovelace J Luquette2, Joshua W K Ho1, Francesco Ferrari2, Michael Tolstorukov3, Aki Minoda4, Robbyn Issner5, Charles B Epstein5, Gary H Karpen4, Mitzi I Kuroda6, Peter J Park7.   

Abstract

In a chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experiment, an important consideration in experimental design is the minimum number of sequenced reads required to obtain statistically significant results. We present an extensive evaluation of the impact of sequencing depth on identification of enriched regions for key histone modifications (H3K4me3, H3K36me3, H3K27me3 and H3K9me2/me3) using deep-sequenced datasets in human and fly. We propose to define sufficient sequencing depth as the number of reads at which detected enrichment regions increase <1% for an additional million reads. Although the required depth depends on the nature of the mark and the state of the cell in each experiment, we observe that sufficient depth is often reached at <20 million reads for fly. For human, there are no clear saturation points for the examined datasets, but our analysis suggests 40-50 million reads as a practical minimum for most marks. We also devise a mathematical model to estimate the sufficient depth and total genomic coverage of a mark. Lastly, we find that the five algorithms tested do not agree well for broad enrichment profiles, especially at lower depths. Our findings suggest that sufficient sequencing depth and an appropriate peak-calling algorithm are essential for ensuring robustness of conclusions derived from ChIP-seq data.
© The Author(s) 2014. Published by Oxford University Press.

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Year:  2014        PMID: 24598259      PMCID: PMC4027199          DOI: 10.1093/nar/gku178

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  19 in total

1.  High-resolution profiling of histone methylations in the human genome.

Authors:  Artem Barski; Suresh Cuddapah; Kairong Cui; Tae-Young Roh; Dustin E Schones; Zhibin Wang; Gang Wei; Iouri Chepelev; Keji Zhao
Journal:  Cell       Date:  2007-05-18       Impact factor: 41.582

2.  Genome-wide remodeling of the epigenetic landscape during myogenic differentiation.

Authors:  Patrik Asp; Roy Blum; Vasupradha Vethantham; Fabio Parisi; Mariann Micsinai; Jemmie Cheng; Christopher Bowman; Yuval Kluger; Brian David Dynlacht
Journal:  Proc Natl Acad Sci U S A       Date:  2011-05-05       Impact factor: 11.205

3.  Genome-wide mapping of in vivo protein-DNA interactions.

Authors:  David S Johnson; Ali Mortazavi; Richard M Myers; Barbara Wold
Journal:  Science       Date:  2007-05-31       Impact factor: 47.728

4.  Systematic evaluation of factors influencing ChIP-seq fidelity.

Authors:  Yiwen Chen; Nicolas Negre; Qunhua Li; Joanna O Mieczkowska; Matthew Slattery; Tao Liu; Yong Zhang; Tae-Kyung Kim; Housheng Hansen He; Jennifer Zieba; Yijun Ruan; Peter J Bickel; Richard M Myers; Barbara J Wold; Kevin P White; Jason D Lieb; X Shirley Liu
Journal:  Nat Methods       Date:  2012-04-22       Impact factor: 28.547

5.  ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.

Authors:  Stephen G Landt; Georgi K Marinov; Anshul Kundaje; Pouya Kheradpour; Florencia Pauli; Serafim Batzoglou; Bradley E Bernstein; Peter Bickel; James B Brown; Philip Cayting; Yiwen Chen; Gilberto DeSalvo; Charles Epstein; Katherine I Fisher-Aylor; Ghia Euskirchen; Mark Gerstein; Jason Gertz; Alexander J Hartemink; Michael M Hoffman; Vishwanath R Iyer; Youngsook L Jung; Subhradip Karmakar; Manolis Kellis; Peter V Kharchenko; Qunhua Li; Tao Liu; X Shirley Liu; Lijia Ma; Aleksandar Milosavljevic; Richard M Myers; Peter J Park; Michael J Pazin; Marc D Perry; Debasish Raha; Timothy E Reddy; Joel Rozowsky; Noam Shoresh; Arend Sidow; Matthew Slattery; John A Stamatoyannopoulos; Michael Y Tolstorukov; Kevin P White; Simon Xi; Peggy J Farnham; Jason D Lieb; Barbara J Wold; Michael Snyder
Journal:  Genome Res       Date:  2012-09       Impact factor: 9.043

6.  Comprehensive analysis of the chromatin landscape in Drosophila melanogaster.

Authors:  Peter V Kharchenko; Artyom A Alekseyenko; Yuri B Schwartz; Aki Minoda; Nicole C Riddle; Jason Ernst; Peter J Sabo; Erica Larschan; Andrey A Gorchakov; Tingting Gu; Daniela Linder-Basso; Annette Plachetka; Gregory Shanower; Michael Y Tolstorukov; Lovelace J Luquette; Ruibin Xi; Youngsook L Jung; Richard W Park; Eric P Bishop; Theresa K Canfield; Richard Sandstrom; Robert E Thurman; David M MacAlpine; John A Stamatoyannopoulos; Manolis Kellis; Sarah C R Elgin; Mitzi I Kuroda; Vincenzo Pirrotta; Gary H Karpen; Peter J Park
Journal:  Nature       Date:  2010-12-22       Impact factor: 49.962

7.  Picking ChIP-seq peak detectors for analyzing chromatin modification experiments.

Authors:  Mariann Micsinai; Fabio Parisi; Francesco Strino; Patrik Asp; Brian D Dynlacht; Yuval Kluger
Journal:  Nucleic Acids Res       Date:  2012-02-03       Impact factor: 16.971

8.  An integrated encyclopedia of DNA elements in the human genome.

Authors: 
Journal:  Nature       Date:  2012-09-06       Impact factor: 49.962

9.  Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data.

Authors:  Raja Jothi; Suresh Cuddapah; Artem Barski; Kairong Cui; Keji Zhao
Journal:  Nucleic Acids Res       Date:  2008-08-06       Impact factor: 16.971

10.  Model-based analysis of ChIP-Seq (MACS).

Authors:  Yong Zhang; Tao Liu; Clifford A Meyer; Jérôme Eeckhoute; David S Johnson; Bradley E Bernstein; Chad Nusbaum; Richard M Myers; Myles Brown; Wei Li; X Shirley Liu
Journal:  Genome Biol       Date:  2008-09-17       Impact factor: 13.583

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  29 in total

1.  ChIP-Seq Assays from Mammalian Cartilage and Chondrocytes.

Authors:  Akira Yamakawa; Hironori Hojo; Shinsuke Ohba
Journal:  Methods Mol Biol       Date:  2021

2.  Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions.

Authors:  Jana Molitor; Jan-Philipp Mallm; Karsten Rippe; Fabian Erdel
Journal:  Biophys J       Date:  2017-01-26       Impact factor: 4.033

Review 3.  Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research.

Authors:  Vijender Chaitankar; Gökhan Karakülah; Rinki Ratnapriya; Felipe O Giuste; Matthew J Brooks; Anand Swaroop
Journal:  Prog Retin Eye Res       Date:  2016-06-11       Impact factor: 21.198

4.  Genome-Wide Epigenetic Studies in Human Disease: A Primer on -Omic Technologies.

Authors:  Huihuang Yan; Shulan Tian; Susan L Slager; Zhifu Sun; Tamas Ordog
Journal:  Am J Epidemiol       Date:  2015-12-30       Impact factor: 4.897

5.  Improved detection of epigenomic marks with mixed-effects hidden Markov models.

Authors:  Pedro L Baldoni; Naim U Rashid; Joseph G Ibrahim
Journal:  Biometrics       Date:  2019-10-17       Impact factor: 2.571

6.  Distinct epigenetic signatures delineate transcriptional programs during virus-specific CD8(+) T cell differentiation.

Authors:  Brendan E Russ; Moshe Olshanksy; Heather S Smallwood; Jasmine Li; Alice E Denton; Julia E Prier; Angus T Stock; Hayley A Croom; Jolie G Cullen; Michelle L T Nguyen; Stephanie Rowe; Matthew R Olson; David B Finkelstein; Anne Kelso; Paul G Thomas; Terry P Speed; Sudha Rao; Stephen J Turner
Journal:  Immunity       Date:  2014-11-06       Impact factor: 31.745

7.  Efficient low-cost chromatin profiling with CUT&Tag.

Authors:  Hatice S Kaya-Okur; Derek H Janssens; Jorja G Henikoff; Kami Ahmad; Steven Henikoff
Journal:  Nat Protoc       Date:  2020-09-10       Impact factor: 13.491

8.  Practical Guidelines for High-Resolution Epigenomic Profiling of Nucleosomal Histones in Postmortem Human Brain Tissue.

Authors:  Marija Kundakovic; Yan Jiang; David H Kavanagh; Aslihan Dincer; Leanne Brown; Venu Pothula; Elizabeth Zharovsky; Royce Park; Rivka Jacobov; Isabelle Magro; Bibi Kassim; Jennifer Wiseman; Kristen Dang; Solveig K Sieberts; Panos Roussos; Menachem Fromer; Brent Harris; Barbara K Lipska; Mette A Peters; Pamela Sklar; Schahram Akbarian
Journal:  Biol Psychiatry       Date:  2016-03-09       Impact factor: 13.382

Review 9.  Identifying and mitigating bias in next-generation sequencing methods for chromatin biology.

Authors:  Clifford A Meyer; X Shirley Liu
Journal:  Nat Rev Genet       Date:  2014-09-16       Impact factor: 53.242

Review 10.  Considerations in the analysis of plant chromatin accessibility data.

Authors:  Kerry L Bubb; Roger B Deal
Journal:  Curr Opin Plant Biol       Date:  2020-02-26       Impact factor: 7.834

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