Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzing single-read patterns. Over the past years, various metrics have been established to explore this layer of information. However, limited compatibility with alignment tools, reference genomes or the measurements they provide present a bottleneck for most groups to routinely perform read-level analysis. To address this, we developed RLM, a fast and scalable tool for the computation of several frequently used read-level methylation statistics. RLM supports standard alignment tools, works independently of the reference genome and handles most sequencing experiment designs. RLM can process large input files with a billion reads in just a few hours on common workstations

Alkan, Can

Giesselmann, Pay

Hetzel, Sara

Kretzmer, Helene

Meissner, Alexander

Reinert, Knut

English

PubMed

Institutional Repository of the Freie Universität Berlin

Sequence analysisRLM: fast and simplified extraction of read-levelmethylation metrics from bisulfite sequencing dataSara Hetzel1, Pay Giesselmann1, Knut Reinert2, Alexander Meissner1,3,4,5,† andHelene Kretzmer 11Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Mathematics andInformatics, Freie Universität, Berlin, Germany, 3Department of Biology, Chemistry and Pharmacy, Freie Universität, Berlin, Germany,4Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA and 5Broad Institute of MIT andHarvard, Cambridge, MA 02142, USA*To whom correspondence should be addressed.Associate Editor: Can AlkanReceived on May 26, 2021; revised on August 11, 2021; editorial decision on September 7, 2021AbstractSummary: Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzingsingle-read patterns. Over the past years, various metrics have been established to explore this layer of information.However, limited compatibility with alignment tools, reference genomes or the measurements they provide present abottleneck for most groups to routinely perform read-level analysis. To address this, we developed RLM, a fast andscalable tool for the computation of several frequently used read-level methylation statistics. RLM supports standardalignment tools, works independently of the reference genome and handles most sequencing experiment designs.RLM can process large input files with a billion reads in just a few hours on common workstations.Availability and implementation: https://github.com/sarahet/RLM.Contact: meissner@molgen.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionBisulfite sequencing experiments are the gold standard to measureDNA methylation at single-CpG resolution (Frommer et al., 1992).Besides the average CpG methylation level across a cell popula-tion, each read contains information about the methylation of asingle molecule present within a cell (Scherer et al., 2020). This in-formation can be used when analyzing the heterogeneity of a cellpopulation or comparing samples of different conditions such ashealthy and tumor. Different metrics have been established inorder to quantify heterogeneity within and across cells based onsingle-read methylation patterns. Measurements of populationheterogeneity include methylation entropy and epipolymorphism,which are based on so-called epialleles, assessing the patterns ofmethylated and unmethylated CpGs that can occur in a 4-mer ofconsecutive CpGs spanned by the same reads (16 epialleles are pos-sible for a single 4-mer) (Landan et al., 2012; Xie et al., 2011).Additionally, the heterogeneity within a single read can be classi-fied as concordant or discordant, which can then be aggregatedacross reads as the percent of discordant reads (PDR), thus againmeasuring the heterogeneity across different cells (Landau et al.,2014). Similarly, the number of transitions from methylated tounmethylated CpGs on a read can be used and aggregated per CpGto determine the level of heterogeneity (read transition score orRTS) (Charlton et al., 2018).So far, studies that have analyzed read-level DNA methylation het-erogeneity often utilized custom scripts for this purpose (Landanet al., 2012; Landau et al., 2014; Xie et al., 2011). Only few tools areavailable that implement one or more of such metrics; however, theyare limited in their usability by requiring a specific alignment tool, ref-erence genome or additional input such as the exact position of CpGsof interest (He et al., 2013; Scherer et al., 2020; Scott et al., 2020).Here, we present RLM, a fast and scalable tool that implementsestablished and frequently used inter- and intramolecular metrics ofDNA methylation at the read level from bisulfite sequencing experi-ments. RLM is applicable for any reference genome, a wide range oflibrary protocols and works with input alignment files from multiplecommonly used alignment tools. Additionally, it automaticallyaccounts for potential errors and biases caused by sequencingartifacts, mapping quality and overlapping read pairs.2 Implementation and featuresRLM is a standalone Cþþ-based tool implemented using SeqAn(Reinert et al., 2017). As input, RLM accepts BAM or SAM filesVC The Author(s) 2021. Published by Oxford University Press. 3934This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contactjournals.permissions@oup.comBioinformatics, 37(21), 2021, 3934–3935doi: 10.1093/bioinformatics/btab663Advance Access Publication Date: 2 October 2021Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article/37/21/3934/6380544 by Charité - Med. Bibliothek user on 11 January 2022from the common bisulfite alignment tools BSMAP, BISMARK,segemehl and GEM (Krueger and Andrews, 2011; Otto et al., 2012;Santiago et al., 2012; Xi and Li, 2009). RLM can run with single- orpaired-end sequencing data from different protocols such as wholegenome bisulfite sequencing and target enrichment approaches butalso reduced representation bisulfite sequencing (RRBS). To accountfor the artificially introduced nucleotides in RRBS experiments,CpGs at the end of the first read (and beginning of the second read)can be omitted from the analysis. Generally, reads that do not repre-sent a primary alignment, are polymerase chain reaction duplicates,fail the quality control or do not pass a user-defined mapping qualityfilter are discarded.For single-end input, RLM streams across the records of the in-put file and extracts information about the methylation status of aCpG based on the corresponding reference genome and the BAMfile tag that represents the origin and mapping orientation of eachread (e.g. ‘ZS’ tag for BSMAP). Reads with sequencing errors atthe position of a CpG, indels or reads that span less than three CpGsare discarded.For paired-end sequencing experiments, reads are filtered analo-gous to single-end experiments. Both mates are processed independ-ently except for overlapping mates, which are merged and processedas a single, contiguous read maximizing the information that can beextracted when using downstream measurements dependent on fourconsecutive CpGs such as entropy. Additionally, we avoid twoquantifications of the same genomic fragment, which would bias theanalysis of cell population heterogeneity. To enable this, reads arekept in memory until the mate has been read and are removedfrom memory afterwards. If one mate needs to be excluded from theanalysis, the other read will be processed independently to improvecoverage.Depending on the research question of interest, RLM offersmultiple outputs that can be requested separately or all at once bythe user.Single-read information: For every read with at least three CpGs,the methylation status for each CpG, the average methylation, thetransition score and the discordance are reported. The transitionscore is defined as the number of transitions between methylatedand unmethylated CpGs divided by the number of possible transi-tions. This file is mandatory output as the information collectedhere is used for the other output files.RTS and PDR: For every CpG spanned by a user-defined min-imum number of reads, the RTS and PDR across all reads spanningthe CpG are reported. Additionally, the corresponding methylationrate based on the reads considered for the read-level measurementsis provided.Entropy and epipolymorphism: For every 4-mer of consecutiveCpGs spanned by a user-defined minimum number of reads, the en-tropy and epipolymorphism are calculated and reported togetherwith the average methylation rate of the complete 4-mer based onthe reads considered for read-level metrics. Additionally, the fre-quencies for all epialleles leading to the respective metrics areprovided.To complement the reported scores, RLM ships a standalone Rscript that provides users with a report including summary statisticsand figures. Additionally, the documentation contains guidelines forits interpretation and use cases. The runtime of RLM scales linearlywith the input size for both paired- and single-end modes. Largedatasets with up to one billion reads can be processed in few hourswith modest memory requirements (detailed performance analysisand comparison with other existing tools in the SupplementaryData).AcknowledgementWe thank R. Rahn and E. Seiler for helpful suggestions regarding theimplementation.FundingThis work has been supported by the Max Planck Society.Conflict of Interest: none declared.ReferencesCharlton,J. et al. (2018) Global delay in nascent strand DNA methylation.Nat. Struct. Mol. Biol., 25, 327–332.Frommer,M. et al. (1992) A genomic sequencing protocol that yields a positivedisplay of 5-methylcytosine residues in individual DNA strands. Proc. Natl.Acad. Sci. USA, 89, 1827–1831.He,J. et al. (2013) DMEAS: DNA methylation entropy analysis software.Bioinformatics, 29, 2044–2045.Krueger,F. and Andrews,S.R. (2011) Bismark: a flexible aligner andmethylation caller for Bisulfite-Seq applications. Bioinformatics, 27,1571–1572.Landan,G. et al. (2012) Epigenetic polymorphism and the stochastic forma-tion of differentially methylated regions in normal and cancerous tissues.Nat. Genet., 44, 1207–1214.Landau,D.A. et al. (2014) Locally disordered methylation forms the basis ofintratumor methylome variation in chronic lymphocytic leukemia. CancerCell, 26, 813–825.Otto,C. et al. (2012) Fast and sensitive mapping of bisulfite-treated sequencingdata. Bioinformatics, 28, 1698–1704.Reinert,K. et al. (2017) The SeqAn Cþþ template library for efficient sequenceanalysis: a resource for programmers. J. Biotechnol., 261, 157–168.Santiago,M.-S. et al. (2012) The GEM mapper: fast, accurate and versatilealignment by filtration. Nat. Methods, 9, 1185–1188.Scherer,M. et al. (2020) Quantitative comparison of within-sample heterogen-eity scores for DNA methylation data. Nucleic Acids Res., 48, e46.Scott,C.A. et al. (2020) Identification of cell type-specific methylation signalsin bulk whole genome bisulfite sequencing data. Genome Biol., 21, 156.Xi,Y. and Li,W. (2009) BSMAP: whole genome bisulfite sequence MAPpingprogram. BMC Bioinformatics, 10, 232.Xie,H. et al. (2011) Genome-wide quantitative assessment of variation inDNA methylation patterns. Nucleic Acids Res., 39, 4099–4108.RLM 3935Downloaded from https://academic.oup.com/bioinformatics/article/37/21/3934/6380544 by Charité - Med. Bibliothek user on 11 January 2022

RLM: fast and simplified extraction of read-level methylation metrics from bisulfite sequencing data

Hetzel, S.

Gießelmann, P.

Reinert, K.

Meissner, A.

Kretzmer, H.

MPG.PuRe

Sequence analysisRLM: fast and simplified extraction of read-levelmethylation metrics from bisulfite sequencing dataSara Hetzel1, Pay Giesselmann1, Knut Reinert2, Alexander Meissner1,3,4,5,† andHelene Kretzmer 11Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Mathematics andInformatics, Freie Universität, Berlin, Germany, 3Department of Biology, Chemistry and Pharmacy, Freie Universität, Berlin, Germany,4Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA and 5Broad Institute of MIT andHarvard, Cambridge, MA 02142, USA*To whom correspondence should be addressed.Associate Editor: Can AlkanReceived on May 26, 2021; revised on August 11, 2021; editorial decision on September 7, 2021AbstractSummary: Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzingsingle-read patterns. Over the past years, various metrics have been established to explore this layer of information.However, limited compatibility with alignment tools, reference genomes or the measurements they provide present abottleneck for most groups to routinely perform read-level analysis. To address this, we developed RLM, a fast andscalable tool for the computation of several frequently used read-level methylation statistics. RLM supports standardalignment tools, works independently of the reference genome and handles most sequencing experiment designs.RLM can process large input files with a billion reads in just a few hours on common workstations.Availability and implementation: https://github.com/sarahet/RLM.Contact: meissner@molgen.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionBisulfite sequencing experiments are the gold standard to measureDNA methylation at single-CpG resolution (Frommer et al., 1992).Besides the average CpG methylation level across a cell popula-tion, each read contains information about the methylation of asingle molecule present within a cell (Scherer et al., 2020). This in-formation can be used when analyzing the heterogeneity of a cellpopulation or comparing samples of different conditions such ashealthy and tumor. Different metrics have been established inorder to quantify heterogeneity within and across cells based onsingle-read methylation patterns. Measurements of populationheterogeneity include methylation entropy and epipolymorphism,which are based on so-called epialleles, assessing the patterns ofmethylated and unmethylated CpGs that can occur in a 4-mer ofconsecutive CpGs spanned by the same reads (16 epialleles are pos-sible for a single 4-mer) (Landan et al., 2012; Xie et al., 2011).Additionally, the heterogeneity within a single read can be classi-fied as concordant or discordant, which can then be aggregatedacross reads as the percent of discordant reads (PDR), thus againmeasuring the heterogeneity across different cells (Landau et al.,2014). Similarly, the number of transitions from methylated tounmethylated CpGs on a read can be used and aggregated per CpGto determine the level of heterogeneity (read transition score orRTS) (Charlton et al., 2018).So far, studies that have analyzed read-level DNA methylation het-erogeneity often utilized custom scripts for this purpose (Landanet al., 2012; Landau et al., 2014; Xie et al., 2011). Only few tools areavailable that implement one or more of such metrics; however, theyare limited in their usability by requiring a specific alignment tool, ref-erence genome or additional input such as the exact position of CpGsof interest (He et al., 2013; Scherer et al., 2020; Scott et al., 2020).Here, we present RLM, a fast and scalable tool that implementsestablished and frequently used inter- and intramolecular metrics ofDNA methylation at the read level from bisulfite sequencing experi-ments. RLM is applicable for any reference genome, a wide range oflibrary protocols and works with input alignment files from multiplecommonly used alignment tools. Additionally, it automaticallyaccounts for potential errors and biases caused by sequencingartifacts, mapping quality and overlapping read pairs.2 Implementation and featuresRLM is a standalone Cþþ-based tool implemented using SeqAn(Reinert et al., 2017). As input, RLM accepts BAM or SAM filesVC The Author(s) 2021. Published by Oxford University Press. 3934This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contactjournals.permissions@oup.comBioinformatics, 37(21), 2021, 3934–3935doi: 10.1093/bioinformatics/btab663Advance Access Publication Date: 2 October 2021Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article/37/21/3934/6380544 by Max-Planck-Institut für molekulare Genetik user on 30 November 2021from the common bisulfite alignment tools BSMAP, BISMARK,segemehl and GEM (Krueger and Andrews, 2011; Otto et al., 2012;Santiago et al., 2012; Xi and Li, 2009). RLM can run with single- orpaired-end sequencing data from different protocols such as wholegenome bisulfite sequencing and target enrichment approaches butalso reduced representation bisulfite sequencing (RRBS). To accountfor the artificially introduced nucleotides in RRBS experiments,CpGs at the end of the first read (and beginning of the second read)can be omitted from the analysis. Generally, reads that do not repre-sent a primary alignment, are polymerase chain reaction duplicates,fail the quality control or do not pass a user-defined mapping qualityfilter are discarded.For single-end input, RLM streams across the records of the in-put file and extracts information about the methylation status of aCpG based on the corresponding reference genome and the BAMfile tag that represents the origin and mapping orientation of eachread (e.g. ‘ZS’ tag for BSMAP). Reads with sequencing errors atthe position of a CpG, indels or reads that span less than three CpGsare discarded.For paired-end sequencing experiments, reads are filtered analo-gous to single-end experiments. Both mates are processed independ-ently except for overlapping mates, which are merged and processedas a single, contiguous read maximizing the information that can beextracted when using downstream measurements dependent on fourconsecutive CpGs such as entropy. Additionally, we avoid twoquantifications of the same genomic fragment, which would bias theanalysis of cell population heterogeneity. To enable this, reads arekept in memory until the mate has been read and are removedfrom memory afterwards. If one mate needs to be excluded from theanalysis, the other read will be processed independently to improvecoverage.Depending on the research question of interest, RLM offersmultiple outputs that can be requested separately or all at once bythe user.Single-read information: For every read with at least three CpGs,the methylation status for each CpG, the average methylation, thetransition score and the discordance are reported. The transitionscore is defined as the number of transitions between methylatedand unmethylated CpGs divided by the number of possible transi-tions. This file is mandatory output as the information collectedhere is used for the other output files.RTS and PDR: For every CpG spanned by a user-defined min-imum number of reads, the RTS and PDR across all reads spanningthe CpG are reported. Additionally, the corresponding methylationrate based on the reads considered for the read-level measurementsis provided.Entropy and epipolymorphism: For every 4-mer of consecutiveCpGs spanned by a user-defined minimum number of reads, the en-tropy and epipolymorphism are calculated and reported togetherwith the average methylation rate of the complete 4-mer based onthe reads considered for read-level metrics. Additionally, the fre-quencies for all epialleles leading to the respective metrics areprovided.To complement the reported scores, RLM ships a standalone Rscript that provides users with a report including summary statisticsand figures. Additionally, the documentation contains guidelines forits interpretation and use cases. The runtime of RLM scales linearlywith the input size for both paired- and single-end modes. Largedatasets with up to one billion reads can be processed in few hourswith modest memory requirements (detailed performance analysisand comparison with other existing tools in the SupplementaryData).AcknowledgementWe thank R. Rahn and E. Seiler for helpful suggestions regarding theimplementation.FundingThis work has been supported by the Max Planck Society.Conflict of Interest: none declared.ReferencesCharlton,J. et al. (2018) Global delay in nascent strand DNA methylation.Nat. Struct. Mol. Biol., 25, 327–332.Frommer,M. et al. (1992) A genomic sequencing protocol that yields a positivedisplay of 5-methylcytosine residues in individual DNA strands. Proc. Natl.Acad. Sci. USA, 89, 1827–1831.He,J. et al. (2013) DMEAS: DNA methylation entropy analysis software.Bioinformatics, 29, 2044–2045.Krueger,F. and Andrews,S.R. (2011) Bismark: a flexible aligner andmethylation caller for Bisulfite-Seq applications. Bioinformatics, 27,1571–1572.Landan,G. et al. (2012) Epigenetic polymorphism and the stochastic forma-tion of differentially methylated regions in normal and cancerous tissues.Nat. Genet., 44, 1207–1214.Landau,D.A. et al. (2014) Locally disordered methylation forms the basis ofintratumor methylome variation in chronic lymphocytic leukemia. CancerCell, 26, 813–825.Otto,C. et al. (2012) Fast and sensitive mapping of bisulfite-treated sequencingdata. Bioinformatics, 28, 1698–1704.Reinert,K. et al. (2017) The SeqAn Cþþ template library for efficient sequenceanalysis: a resource for programmers. J. Biotechnol., 261, 157–168.Santiago,M.-S. et al. (2012) The GEM mapper: fast, accurate and versatilealignment by filtration. Nat. Methods, 9, 1185–1188.Scherer,M. et al. (2020) Quantitative comparison of within-sample heterogen-eity scores for DNA methylation data. Nucleic Acids Res., 48, e46.Scott,C.A. et al. (2020) Identification of cell type-specific methylation signalsin bulk whole genome bisulfite sequencing data. Genome Biol., 21, 156.Xi,Y. and Li,W. (2009) BSMAP: whole genome bisulfite sequence MAPpingprogram. BMC Bioinformatics, 10, 232.Xie,H. et al. (2011) Genome-wide quantitative assessment of variation inDNA methylation patterns. Nucleic Acids Res., 39, 4099–4108.RLM 3935Downloaded from https://academic.oup.com/bioinformatics/article/37/21/3934/6380544 by Max-Planck-Institut für molekulare Genetik user on 30 November 2021

RLM: Fast and simplified extraction of Read-Level Methylation metrics from bisulfite sequencing data

Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzing single-read patterns. Over the past years, various metrics have been established to explore this layer of information. However, limited compatibility with alignment tools, reference genomes or the measurements they provide present a bottleneck for most groups to routinely perform read-level analysis. To address this, we developed RLM, a fast and scalable tool for the computation of several frequently used read-level methylation statistics. RLM supports standard alignment tools, works independently of the reference genome and handles most sequencing experiment designs. RLM can process large input files with a billion reads in just a few hours on common workstations.



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RLM: fast and simplified extraction of read-level methylation metrics from bisulfite sequencing data

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