Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline

Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline Simon Holzinger , Leo Schmutterer , Victoria Marie Rothe , Maria Theresia Watzlowik , Uwe Schwartz , View ORCID Profile Gernot Längst doi: https://doi.org/10.1101/2025.08.13.670116 Simon Holzinger 1 Regensburg Center for Biochemistry (RCB), University of Regensburg , 93053 Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leo Schmutterer 1 Regensburg Center for Biochemistry (RCB), University of Regensburg , 93053 Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Victoria Marie Rothe 1 Regensburg Center for Biochemistry (RCB), University of Regensburg , 93053 Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Theresia Watzlowik 1 Regensburg Center for Biochemistry (RCB), University of Regensburg , 93053 Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Uwe Schwartz 2 NGS Analysis Center Biology and Pre-clinical Medicine, University of Regensburg , 93053 Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: gernot.laengst{at}ur.de uwe.schwartz{at}ur.de Gernot Längst 1 Regensburg Center for Biochemistry (RCB), University of Regensburg , 93053 Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gernot Längst For correspondence: gernot.laengst{at}ur.de uwe.schwartz{at}ur.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract High-resolution analysis of cellular chromatin structure is crucial for uncovering developmental and cell-type-specific regulatory networks. We developed the nucDetective pipeline to provide a comprehensive evaluation of chromatin organisation. This involves assessing nucleosome positioning, occupancy, fuzziness, and array regularity. The pipeline was benchmarked by analysing the chromatin structure of the malaria-causing parasite Plasmodium falciparum ( Pf ) during its erythrocytic development cycle. Pf is characterised by a unique chromatin landscape, exhibiting unstable nucleosomes and a genomic AT-content exceeding 80%, which presents challenges for standard MNase-seq analysis of chromatin. The nucDetective pipeline provides specific, high-resolution nucleosome profiles for the different asexual stages of Pf , monitoring the dynamics of individual nucleosomes. In contrast to the current view, suggesting an irregular chromatin structure and lack of nucleosomal DNA, we demonstrate that the transcription start sites exhibit typical eukaryotic features, including +1 nucleosomes, nucleosome-free regions upstream, and phased nucleosome arrays downstream of the TSSs. The global mean nucleosome repeat length varies from 176 bp to 185 bp depending on the developmental stage. Stage specific changes in nucleosome positioning occur locally in intergenic regulatory regions, which are characterized by specific histone modifications and variants. Dynamic nucleosomes correlate with DNA accessibility, gene expression and determine the access to transcription factor binding sites in Pf . The highly regular chromatin structure, with stage-specific structural alterations, emphasises the important role of epigenetic mechanisms in regulating the complex life cycles of Pf. Introduction Eukaryotic genomes are organized in the form of a compact nucleoprotein structure, termed chromatin. The basic packaging unit of chromatin is the nucleosome core, which consists of 147 base pairs (bp) of DNA wrapped around an octameric protein core comprising two copies each of the histones H2A, H2B, H3 and H4 ( Luger et al. 1997 ). The nucleosome cores are arranged in continuous arrays separated by short stretches of linker DNA, resembling a beads-on-a-string-like structure ( Woodcock et al. 1976 ). Chromatin is the template for all DNA-dependent processes, and the positioning of nucleosomes on DNA determines the accessibility of DNA sequences to regulatory factors, such as sequence-dependent transcription factors. The positioning, structure and histone modifications of nucleosomes are dynamically changed during signal transduction and developmental processes ( Harwood et al. 2019 ; West et al. 2014 ; Zhang et al. 2016 ). The alterations in DNA sequence accessibility that result from these changes establish distinct binding platforms for regulatory factors in different cell types or developmental stages. Minor differences in nucleosome organization can alter the binding behaviour of transcription factors and thus regulate gene activity ( Martinez-Campa et al. 2004 ; J. Li et al. 2006 ; Längst et al. 1998 ). Therefore, the analysis and understanding of chromatin organisation and the timing of dynamic changes in nucleosome positioning are crucial to comprehending gene regulatory processes. In a population of cells, nucleosome positioning can be characterised by three main features: fuzziness, occupancy and position. Nucleosome fuzziness reflects the cell-to-cell variability in nucleosome positioning at genomic sites; higher fuzziness indicates greater variability at individual positions; nucleosome occupancy describes the probability of detecting a nucleosome at a particular genomic position and the position refers to the precise genomic location of the nucleosome dyad. Assessing and quantifying nucleosome occupancy is challenging as methods to map nucleosome positions depend on structural and experimental parameters, such as DNA sequence, non-histone proteins bound to DNA, and nucleosome interactions and stability. These factors impact the quantitative and qualitative isolation or detection of the nucleosomal DNA ( Oberbeckmann et al. 2019 ; Schwartz et al. 2019 ; Wernig-Zorc et al. 2024 ; Brogaard et al. 2012 ). A powerful and widely used method to study these features genome-wide is called micrococcal nuclease sequencing (MNase-seq). MNase-seq uses the property of MNase to preferentially hydrolyse the accessible linker DNA. The histone-bound nucleosomal DNA remaining refractory to MNase cleavage ( Yuan et al. 2005 ; Schwartz et al. 2019 ), is subsequently sequenced and presents the nucleosome footprint. Several programs exist to analyse MNase-seq data ( Shtumpf et al. 2022 ), such as the commonly used DANPOS toolkit ( Chen et al. 2013 ) or the Nucleosome Dynamics program suite ( Buitrago et al. 2019 ). However, these tools require specific preprocessing of the sequenced DNA fragments and lack the capability to perform comparative analyses across complex experimental designs involving more than two conditions, such as in time series of developmental stages. Recent advancements in bioinformatics pipeline management systems and software containerization enable the development of robust, reproducible, easily deployable and scalable analysis pipelines ( Di Tommaso et al. 2017 ). This approach has been employed to create comprehensive MNase-seq analysis pipelines, such as the nucMACC pipeline, which assesses nucleosome stability and structure ( Wernig-Zorc et al. 2024 ). The parasite Plasmodium falciparum ( Pf ) causes the disease malaria tropica, responsible for 597,000 deaths in 2023 ( World Health Organization 2024 ). It has an intricate life cycle, encompassing various stages in multiple hosts meticulously orchestrated by a complex transcription network ( Watzlowik et al. 2021 ). The just-in-time regulation of transcription requires the precise coordination of chromatin architecture and gene expression throughout the entirety of the developmental process ( Watzlowik et al. 2025 ). Surprisingly, the Pf genome encodes only for a reduced set of transcription factors, not matching the need for its complex regulatory network, suggesting that additional regulatory mechanisms must contribute to gene expression control ( Bischoff and Vaquero 2010 ). The Pf chromatin landscape deviates significantly from other eukaryotic systems. The parasite encodes for the most divergent histone sequences in eukaryotes, expresses unique histone variants and notably lacks linker histone H1 ( Bischoff and Vaquero 2010 ). In vitro studies have demonstrated that Pf nucleosomes possess reduced stability compared to other eukaryotes ( Silberhorn et al. 2016 ). Moreover, chromatin exhibits predominantly euchromatic features, with heterochromatin confined to subtelomeric regions and a few internal islands (S. A. Fraschka et al. 2018 ). These heterochromatic regions are associated with the regulation of antigenic variation, a mechanism that allows the parasite to evade host immune responses ( Flueck et al. 2009 ). Adding to its unique features, the Pf genome is almost devoid of DNA methylation ( Ponts et al. 2013 ) and exhibits an exceptionally high A/T content—averaging 81% across the genome and reaching up to 95% in intergenic regions ( Gardner et al. 2002 ). This high A/T content, combined with the parasite’s “open” chromatin architecture, requires the development of specialized experimental and bioinformatic approaches to analyse the chromatin structure ( Bártfai et al. 2010 ). Micrococcal nuclease (MNase), which has a sequence preference for A/T-rich regions, poses challenges in analysing the Pf nucleosome organisation. The parasite’s DNA is more susceptible to endonuclease cleavage, and its nucleosomes are comparatively unstable, leading to potential overdigestion and loss of nucleosomal DNA in MNase-seq experiments ( Schwartz et al. 2019 ; Wernig-Zorc et al. 2024 ). This has resulted in MNase-seq studies revealing large variations in nucleosome occupancy across the genome with intergenic regions devoid of nucleosomes and irregular nucleosome positioning at intragenic regions ( Bunnik et al. 2014 ; Ponts et al. 2010 ; Westenberger et al. 2009 ). These studies proposed that Pf chromatin structure is unlike the structure of other eukaryotes. This fact can be explained by the overdigestion of AT-rich DNA with MNase. The currently unique, high-quality MNase-seq dataset systematically spanning the Pf intraerythrocytic development cycle (IDC) was generated by Kensche and colleagues using a combination of low MNase concentration and additional Exo III digestion to prevent the overdigestion of AT-rich nucleosomal DNA ( Kensche et al. 2016 ). But still, their analysis, identified only a limited number of well-positioned nucleosomes and failed to detect the regular nucleosome arrays typical of other eukaryotic genomes. These findings, along with others, have contributed to the prevailing view that Pf chromatin is atypical, characterized by a loosely organized, highly accessible structure with “fuzzy” nucleosomes and extensive regions of unpackaged DNA ( Le Roch et al. 2012 ; Bunnik et al. 2014 ; Ponts et al. 2010 ). Here we thoroughly re-analyze the stage-specific MNase-seq data from Kensche et al, using our new nucDetective analysis pipeline to improve the understanding of Pf chromatin organisation and dynamics along the IDC. nucDetective includes a comprehensive, easy-to-use and state-of-the-art MNase-seq workflow capable of generating high-resolution nucleosome maps starting from raw reads (nucDetective Profiler). Additionally, it offers a multi-condition analysis workflow (nucDetective Inspector), which combines the results from different cellular states to detect the progressive changes in the chromatin landscape. The pipeline presents a universal tool capable of detecting nucleosome dynamics, such as occupancy, fuzziness and position shifts, comparing two or more functional stages. Re-analysis of Pf MNase-seq data revealed yet undiscovered chromatin features of the malaria parasite. In contrast to the general assumptions, we show that Pf exhibits a chromatin organisation reminiscent of classical eukaryotic chromatin with a well positioned +1 nucleosome, an upstream nucleosome-free region, and phased nucleosome array downstream of the TSS. Improved resolution of nucleosome maps shows changes in chromatin architecture during the IDC, which correlate with DNA accessibility and gene expression, defining actual gene networks being activated or repressed. Results nucDetective uncovers features of nucleosome organisation and dynamics in Pf We developed nucDetective, an easy-to-use and automated pipeline for analysing MNase-seq datasets enabling the analysis of complex experimental designs, such as time-series experiments. The pipeline was employed to gain deeper insights into chromatin dynamics during the IDC of Pf , re-analysing an MNase-seq time-series dataset ( Kensche et al. 2016 ). nucDetective is divided into two consecutive workflows: first, the Profiler, and second, the Inspector. The nucDetective Profiler workflow begins with raw fastq files, generates high-resolution nucleosome profiles, and identifies nucleosome positions. Alignment and postprocessing steps, such as fragment size selection, are optimised for MNase-seq data, and data quality is controlled at every stage (see Methods for details). Even under challenging conditions, such as the AT-rich Pf genome, the results of the Profiler analysis considerably improved the quality of recent nucleosome annotations. A comparison with the previously published MNase-seq analysis clearly shows a gain in structural information providing highly resolved nucleosome positions when using nucDetective Profiler (Figure S1A, S1B and Figure 1A ). Download figure Open in new tab Figure 1. Detection of dynamic nucleosome features in the IDC of Pf using the nucDetective pipeline (A) Genome browser snapshot highlighting the different categories of dynamic nucleosomes. The nucDetective pipeline was used to process MNase-seq data from a time series of the IDC of Pf ( Kensche et al. 2016 ). It provides centered nucleosome coverage tracks (T5-T40 colored coverage tracks) and identifies reference nucleosome positions (grey bars). It assesses nucleosome positioning regularity at each timepoint (grey heatmap) and provides an overview of the average regularity (black heatmap) and the variance in regularity (blue heatmap) across all timepoints. Additionally, it detects nucleosomes showing occupancy changes (yellow bar), position shifts (green bar) and fuzziness changes (red bars) as well as regions, where nucleosome positioning regularity has changed (blue bar). Corresponding areas in the nucleosome coverage tracks are marked in this figure with respectively colored lines and rectangles. (B) Nucleosomes upstream and downstream of the TSS in Pf are positioned in regular arrays. Average, normalised nucleosome occupancy profiles centered on the +1 nucleosomes are shown. (C) Average genome wide NRL in the IDC of Pf changes between 185 bp and 176 bp. The mean NRL with a 95% confidence interval is depicted for each timepoint. The NRL was estimated using the frequencies of same-strand alignment distances through a phasogram. The NRL is determined by the slope of the linear fit to the modes present in the phasogram. (D-G) Dynamic nucleosome calling of (D) occupancy, (E) fuzziness, (F) position and (G) regularity changes. Nucleosomes are sorted by the variance over time of the respective metric. The point where the variance rapidly increases is determined (slope=3) and nucleosomes above this point are considered to reflect changes in occupancy, fuzziness, position or regularity over time. Bottom panels show PCA of the selected nucleosomes. Samples plotted at PC1 and PC2 resemble a cyclic structure as indicated by the arrows reminiscent of the IDC. Percentages in axis labels indicate the proportion of variance explained by each component. These new Pf nucleosome maps reveal a nucleosome organisation at the transcription start site (TSS) reminiscent of the general eukaryotic chromatin structure, featuring a well-positioned +1 nucleosome, an upstream nucleosome-free region (NFR), and a phased nucleosome array downstream of the TSS. Aside from the improved nucleosome resolution, we suggest that the absence of nucleosome arrays downstream of the TSS in previous studies is due to uncertainty in Pf TSS annotation and two additional effects. On the one hand, transcription initiation events have been mapped to relatively wide regions in Pf , often containing multiple initiation site clusters for a single gene; on the other hand, TSS usage changes during parasite development, leading to divergent TSS annotations ( Adjalley et al. 2016 ; Chappell et al. 2020 ; Shaw et al. 2021 ). Aligning nucleosome maps to these variable positions produces inconsistent nucleosome distances, blurring aggregate patterns and obscuring the underlying arrays (Figure S1B). To address this issue, we centered the nucleosome occupancy profile at the positioned +1 nucleosome, using the best positioned nucleosome closest to the assigned TSS within a -100/+300 bp window (Table S1). With this +1 nucleosome annotation, regularly spaced nucleosome arrays downstream of the TSS were detected, revealing a precise nucleosome organisation in Pf (Figure S1B). Due to the high resolution maps of nucleosomes we can now observe variations in nucleosome spacing depending on the developmental stage ( Figure 1B ). For quantifying the average nucleosome repeat length (NRL) at each timepoint, we employed a phasogram-based approach. The largest NRL is noted in the ring stage at T5 (185 bp) and continuously decreases towards trophozoite stage at T30 (176 bp). The improved nucleosome annotation now facilitates an in-depth analysis of the dynamic changes in the nucleosome landscape during Pf IDC ( Figure 1A ). Genomic regions with regularly spaced nucleosomes, which undergo dramatic structural changes over time, can be clearly identified in the high-resolution data. However, as the data set includes multiple time points, identifying dynamic nucleosomes is not trivial, and MNase-seq optimized tools for analysing such data sets are lacking. To tackle this issue, we developed the nucDetective Inspector workflow. The Inspector workflow utilises the output of the Profiler workflow to quantify dynamic changes in chromatin structure by analysing multiple nucleosome features: nucleosome occupancy, fuzziness, dyad positions, and the local nucleosome array regularity ( Figure 1A , S1C and S1D). To identify dynamic nucleosomes, we employed a similar strategy as that used to define super enhancers or unstable nucleosomes ( Whyte et al. 2013 ; Wernig-Zorc et al. 2024 ). Nucleosomes are ranked by the variance of the specific feature (occupancy, fuzziness, position, or regularity) across all samples. The variance is normalised to range between 0 and 1 and is plotted against the rank divided by the total number of events (Figure S1C). To geometrically identify dynamic positions where the signal variability increases rapidly, we determined the points on the curve where the slope first exceeds a certain threshold. Here, for the Pf analysis, we used a cutoff of 3. For a description of array regularity, the spectral power density of the nucleosome signal at the size of 180 bp was plotted using a rolling window approach (Figure S1D). The pipeline identified a total of 49.999 reference nucleosome positions from which 1192 nucleosomes exhibiting high variability in occupancy (occupancy changes, Figure 1D ), 483 nucleosomes showing high variability in fuzziness (fuzziness changes, Figure 1E ), 1740 nucleosomes with pronounced variability in position (position shifts, Figure 1F ), and 1579 nucleosomes displaying high variability in local array regularity (regularity changes, Figure 1G ). To assess the relevance and information content of the extracted features, a Principal Component Analysis (PCA) was employed. Each set of dynamic nucleosomes exhibits a circle-like data structure in PCA, resembling the progression of Pf through every stage of the IDC ( Figure 1D-G ). In summary, the progressive changes in chromatin structure reflect the continuous developmental process in the life cycle of Pf . This finding suggests that changes in chromatin structure are closely associated with the developmental gene expression programme. Dynamic nucleosomes reside in regulatory regions and are linked to active promoters To determine the spatio-temporal distribution of dynamic nucleosomes, we asked whether the various dynamic parameters (position, occupancy, fuzziness) occur at the same or distinct genomic locations. Interestingly, the dynamic parameters show only minor overlaps, indicating that they represent distinct features, potentially associated with specific DNA-dependent processes and chromatin remodeling mechanisms ( Figure 2A ). At the genomic scale, dynamic nucleosomes are relatively evenly distributed throughout the genome, without apparent feature clustering at specific chromosomal locations (Figure S2A). However, we observe a clear enrichment of dynamic nucleosomes at gene promoters ( Figure 2B ). This enrichment is particularly prominent for nucleosomes displaying dynamic changes in fuzziness or occupancy. Dynamic alterations in nucleosome fuzziness or occupancy predominantly occur directly upstream of the TSS at the -1 nucleosome regulating the NFR width ( Figure 2C ). Changes in NFR accessibility or width may be linked to activation or repression of transcription of associated genes ( Klein-Brill et al. 2019 ). In comparison with nucleosomes at the beginning of the gene body, the position of the +1 nucleosome appears to be relatively stable, lacking active position shifts as postulated by the barrier packing model ( Mavrich et al. 2008 ) ( Figure 2C ). In contrast, nucleosomes exhibiting dynamic changes in array regularity are mainly enriched downstream of the TSS, at the start of the gene body ( Figure 2C and S2B), possibly as a consequence of active transcription ( Baldi et al. 2018 ; Singh et al. 2021 ). Download figure Open in new tab Figure 2. Dynamic nucleosomes reside in regulatory regions and are associated with active promoters (A) nucDetective characterizes distinct features of dynamic nucleosomes. Euler diagram of dynamic nucleosomes grouped by occupancy (yellow), fuzziness (red) or position changes (green) over time. (B) Dynamic nucleosomes are enriched at the gene promoters. The genome wide distribution of dynamic nucleosomes was assessed at promoter regions (-500 bp to100 bp of the TSS), 5’UTR, coding regions, introns, 3’UTR and intergenic regions using different sets of nucleosomes:: random genomic positions (genome), all called nucleosome positions, well positioned nucleosomes (defined as the 20% with the lowest fuzziness), nucleosomes showing a position shift over time, dynamic nucleosomes showing a change in array regularity, occupancy or fuzziness. (C) Nucleosome occupancy or fuzziness changes and position shifts primarily occur upstream of the TSS at the -1 nucleosome position. The average scaled (z-score normalization) occurrences of nucleosomes exhibiting occupancy change (yellow), fuzziness change (red), position shift (green) and regularity change (blue) is plotted over the scaled gene body. A magnification of the region around the TSS is provided with an unscaled view. For better orientation the inset plot includes the average nucleosome coverage (grey background). (D) Genomic and epigenetic context of dynamic nucleosomes. Average local change compared to genome wide average in GC content, nucleosome coverage, RNA expression, DNA accessibility measured by ATAC-seq, histone variants H2A.Z and H3.3 and the histone modifications H3K4me3, H3K9ac and H3K9me3 are plotted at random genome positions, random nucleosome dyads and dyads of dynamic nucleosome categories. Shaded areas illustrate the deviation to the genome wide average. Next, the dynamic nucleosome categories were compared to previously published studies on histone variants (S. A.-K. Fraschka et al. 2016 ; Bártfai et al. 2010 ), histone modifications ( Bártfai et al. 2010 ; Jeninga et al. 2023 ), DNA-accessibility ( Toenhake et al. 2018 ) and transcription ( Kensche et al. 2016 ). These and other studies suggested that the histone variant H2A.Z is a marker for regulatory regions in Plasmodium , guiding chromatin modifying and transcription initiating complexes ( Bártfai et al. 2010 ). H2A.Z occupancy remains constant throughout the erythrocytic lifecycle, whereas the H3K4me3 and H3K9ac histone marks associated with H2A.Z are stage-specific and correlate with the regulation of the developmental cycle ( Bártfai et al. 2010 ). The H3.3 variant binding sites in Pf are suggested to depend on the GC content of DNA, marking coding and subtelomeric repetitive regions, irrespective of transcriptional activity (S. A.-K. Fraschka et al. 2016 ). Our analysis shows that dynamic nucleosomes, changing occupancy and fuzziness, preferentially occur in the H2A.Z/H3K4me3/H3K9ac marked regions and are also linked to regions containing the histone variant H3.3 ( Figure 2D ). Heterochromatin in Plasmodium falciparum is characterised by the presence of H3K9me3 and heterochromatin protein 1 (HP1). It is observed in subtelomeric regions and small internal regions where it is involved in silencing virulence factors such as multi-gene surface antigens, while also playing a role in life cycle stage transitions (S. A. Fraschka et al. 2018 ; Flueck et al. 2009 ). Heterochromatin domains, as indicated by H3K9me3 ChIP, are characterised by depleted nucleosome occupancy and fuzziness changes, indicating a stable chromatin organisation ( Figure 2D ). This underscores the tight epigenetic control of these essential regions involved in parasite adaptation and survival. While there is a strong association between fuzziness and occupancy dynamics with the histone variants and the H3K4me3/H3K9ac marks, the data can be further subdivided according to the ATAC-seq pattern. We observed a strong ATAC peak at nucleosome positions undergoing occupancy changes; however, only a few ATAC sites coincided with changes in nucleosome fuzziness, and even fewer with position shifts ( Figure 2D ). In summary, nucleosome dynamics is intimately connected with specific histone modifications and variants at active genomic sites, primarily located at gene promoters, emphasising their essential role in regulating gene expression. Dynamic nucleosomes (anti-)correlate with DNA accessibility but exhibit distinct features Next, we examined the dynamics of nucleosomes in accessible chromatin regions, defined by ATAC-seq positive domains ( Toenhake et al. 2018 ). The Profiler workflow annotated a total of 5300 nucleosomes in these open chromatin domains ( Figure 3A ). While most of these nucleosomes remained stable over time (n=4129) a significant subset (n=1171, p < 0.0001, permutation test) of nucleosome positions exhibited a dynamic behaviour ( Figure 3A ). As indicated above ( Figure 2D ), open chromatin regions are predominantly associated with changes in nucleosome occupancy, accounting for approximately 58 % of all nucleosomes in this class (n=689, p < 0.0001, permutation test) ( Figure 3A ). Analysis of the association between chromatin accessibility and nucleosome occupancy dynamics revealed a negative relationship (median Pearson correlation of ρ = -0.635) ( Figure 3B-C ), indicating that a decrease in nucleosome occupancy accompanies chromatin opening. Notably, we observed that the eviction of a single nucleosome is sufficient to open broader regions ( Figure 3C ). Similarly, but to a lesser extent, we observed a positive correlation with nucleosome fuzziness (median Pearson correlation of ρ = 0.485) ( Figure 3A and 3B ). However, nucleosome shifts are rarely present in open regions (15%, n=261) and do not correlate with chromatin accessibility ( Figure 3A-C ). As ATAC-seq is not suitable for resolving nucleosome shifts, not all chromatin features can be detected by this method. MNase-seq analysis by the nucDetective pipeline achieves a more comprehensive and better resolved view of chromatin dynamics. Download figure Open in new tab Figure 3. Dynamic nucleosomes (anti-)correlate with DNA accessibility yet show distinct features (A) Mainly nucleosome occupancy and fuzziness changes coincide with open chromatin regions. Venn diagrams illustrating the overlap of nucleosomes in open chromatin regions derived from ATAC-seq in at least one timepoint (lightblue) and dynamic nucleosomes (grey). Below the overlap with individual nucleosome features are shown. (B) Loss of nucleosome occupancy and increase in fuzziness are correlated with DNA accessibility changes. Linear correlations were computed between the accessibility score derived from the ATAC-seq fragment coverage at each nucleosome position and the corresponding occupancy, fuzziness and summit position (shift) at every timepoint. The density plot illustrates the Pearson correlation coefficients of dynamic nucleosomes, alongside the density of Pearson correlations for randomly selected, equally sized sets of nucleosomes (grey, 1000 iterations). The median Pearson correlation coefficient ρ and the number of nucleosomes (n) are indicated. (C) Genome browser snap shot showing representative examples of the anti-correlation between nucleosome occupancy and DNA accessibility (left) and a region showing nucleosome dynamics but no changes in DNA accessibility (right). Black boxes and lines are provided for easier visual identification of dynamic nucleosomes. Transcription factor binding motifs are associated with distinct nucleosome occupancy kinetics Nucleosomal stability and positioning regulate the availability of specific DNA elements for regulatory proteins ( Zhu et al. 2018 ). Modulating nucleosome positioning at specific sites affects transcription factor binding and ultimately controls gene expression programs. Therefore, we examined the kinetics of changes in nucleosome occupancy in greater detail. Clustering analysis revealed groups of nucleosomes that open and close in a concerted manner at different time points of the erythrocytic life cycle ( Figure 4A ). Cluster 1 comprises genomic sites with low nucleosome occupancy during the first 20 hours of the erythrocytic life cycle, thereby allowing regulatory proteins access to the underlying DNA. Starting at 25 hours, nucleosome deposition occurs at these sites, thus restricting factor access to these DNA sequences. To uncover recurring sequence elements in regions characterized by similar nucleosome kinetics, we performed de novo motif analysis (Figure S3), and identified motifs were compared to known ApiAP2 transcription factor motifs ( Campbell et al. 2010 ) (Figure S3, last 3 columns). ApiAP2 transcription factors are, with 27 known members, the largest family of transcription factors in Pf . These are involved in the regulation of IDC progression and differentiation ( Balaji et al. 2005 ; Campbell et al. 2010 ). Several overrepresented motifs displayed high similarities to the binding motifs of the ApiAP2 transcription factor family, such as AP2-FG, AP2-O4, AP2-G5 and AP2-I ( Figure 4B ). Remarkably, the time point of nucleosome eviction in the nucleosome group (cluster 5) associated with the AP2-I transcription factor motif correlates with the peak of AP2-I expression and its putative role in erythrocyte invasion ( Santos et al. 2017 ). The analysis also revealed novel sequence motifs ( Figure 4 ), suggesting the existence of other sequence specific DNA binding factors in Pf , binding to regulatory elements and playing a role in the intricate regulation of the life cycle of Pf . Download figure Open in new tab Figure 4. Transcription factor binding motifs are associated with distinct nucleosome occupancy kinetics. (A) Nucleosome occupancy dynamics can be grouped by distinct kinetics in the lifecycle of Pf. Self-organizing map clustering of nucleosome occupancy changes resulted in 6 distinct clusters. Heatmap (left) showing z-score scaled occupancy scores across all time points ordered by cluster assignment as indicated by the color bar on the left side. Boxplots (right) depicting the z-score scaled occupancy changes over time of the individual clusters as indicated on the right side. (B) Distinct DNA motifs are associated with nucleosome occupancy kinetics. The top de novo – derived motif for each cluster is shown. The transcription factor names of the best-matching known DNA binding motifs derived from ( Campbell et al. 2010 ) are shown. Motifs where we did not find a corresponding known DNA binding motif (similarity score < 0.6) are marked as novel. Additional enriched motifs along with the significance of motif enrichment and the fraction of motifs at the respective nucleosome positions are shown in Figure S3 Nucleosome dynamics in promoter regions are associated with gene expression changes The accumulation of dynamic nucleosomes in promoter regions ( Figure 2B ) suggests a role for nucleosome positioning in the regulation of gene expression. The high-resolution maps of nucleosome positions obtained with the nucDetective pipeline allow for the first time, a detailed analysis of the structural changes at Pf promoter regions. Selecting genes that are activated upon entry into the trophozoite stage at T25 shows a concomitant opening of the promoter region and the formation of a nucleosome-depleted region upstream of the +1 nucleosome ( Figure 5A and 5B ). Active transcription also results in a loss of regularly positioned nucleosomes in the gene body ( Figure 5A-B , S4A). However, by T40, the chromatin structure of these genes is completely reverted, closing the promoter and re-establishing the regular nucleosome array in the gene body. This can be observed even though the transcript abundance remains high, as shown by the RNA-seq data ( Figure 5A ). It must be noted that the RNA-seq data provides steady-state transcript levels, not allowing the conclusion that chromatin closure is occurring during active transcription. Consistently, nascent RNAs detected by GRO-Seq reveal transcriptional repression of this gene set in the late schizont stage, correlating with promoter closure ( Lu et al. 2017 ) (Figure S4B). Furthermore, classifying genes according to their nascent transcript profiles into four groups reveals characteristic chromatin structures at the gene promoters, temporally correlating with ongoing transcription (Figure S4C). Interestingly, as most genes are repressed in the early ring and late schizont stage, we observe a high similarity in the promoter chromatin structure for T5 and T40, except for those genes which are expressed late (Figure S4C, cluster A8). Download figure Open in new tab Figure 5. Nucleosome dynamics in promoter regions are associated with gene expression changes (A) The opening of the promoter region correlates with the initiation of transcription. Genes with a low transcript abundance at the beginning and a high abundance at the end of the IDC were selected (n = 898, left). Average nucleosome occupancy profiles centered at the +1 nucleosomes show an opening of promoter region at T30 and T40 resulting in a nucleosome-depleted region upstream of the TSS (right panel). Nucleosome occupancy profiles were first scaled by the underlying profile of MNase digested gDNA and then the scaled coverage profile at each time point was divided by its region median coverage value. (B) Genome browser snapshot illustrating the correlation between nucleosome dynamics (right) and gene expression (left). Gene expression becomes detectable simultaneously with nucleosome eviction upstream of the TSS (black box, yellow bars). Stars mark the changes in nucleosome occupancy. (C) Nucleosome eviction, loss of position regularity and an increase in fuzziness in promoter regions (-500 to +100bp from TSS) correlate with transcriptional activity. Linear correlations were computed for each dynamic nucleosome in the promoter region and the corresponding gene expression. The density plot illustrates Pearson correlation coefficients for dynamic nucleosomes, alongside the density of Pearson correlations for randomly selected, equally sized sets of nucleosomes in promoter areas (grey, 1000 iterations). The median Pearson correlation coefficient ρ and the number of nucleosomes (n) are indicated. The data shows globally that transcriptional effects are associated with dynamic nucleosomes in promoter regions (-500 to + 100 bp from TSS), where nucleosome eviction (loss of occupancy, median Pearson correlation ρ = -0.446) and loss of positioning (increased fuzziness and loss of regularity, median Pearson correlations of ρ = 0.547 and ρ = -0.53 respectively) correlate with transcriptional activity ( Figure 5C ). A similar trend is observed for all nucleosomes in coding regions, where occupancy, fuzziness and regularity (anti-)correlate with gene expression (Figure S4A). Discussion We developed an optimised MNase-seq analysis pipeline called nucDetective, which is designed to annotate nucleosome positions at high resolution (Profiler workflow) and to identify dynamic changes in nucleosome positioning (Inspector workflow). The Profiler workflow commences with raw sequencing files, automates the process to produce high-resolution nucleosome profiles, and incorporates necessary quality checks (QC), making it a universal tool for any MNase-seq dataset. This is achieved using MNase-seq optimized alignment settings, and proper selection of the fragment sizes corresponding to mono-nucleosomal DNA to increase resolution. Implementing these features into a seamless workflow resulted in a clearer definition of nucleosome positions and detection of well-positioned nucleosomes. In this study, we demonstrate that the Profiler workflow effectively analyses challenging MNase-seq data in Pf . The consecutive Inspector workflow allows for a comprehensive comparison of nucleosome features for experimental designs that exceed a simple two-condition comparison, setting it apart from other MNase-seq analysis tools. The method is versatile and can be used in various experimental settings, such as cell differentiation, knock-down approaches, and time-series treatment experiments, or even for comparing multiple functional states, like the IDC of Pf presented here. Simultaneously assessing changes in nucleosome occupancy, position, fuzziness, and array regularity enables an in-depth analysis of chromatin structure dynamics. This provides a higher resolution and more insights into nucleosome features than is possible using ATAC-seq. The nucDetective pipeline is user-friendly, allowing experimental scientists to utilise a state-of-the-art pipeline with relevant QC metrics and implement required software packages without complex installation. Its implementation as an open-source nextflow pipeline allows for modular extension and adaptation to emerging demands in the future ( Di Tommaso et al. 2017 ). Here, we re-analysed the MNase-seq dataset of Kensche and colleagues to investigate the poorly understood chromatin architecture of Pf . The parasite exhibits a complex life cycle in two hosts, revealing dramatic changes in its gene expression programme. A scarcity of transcription factors suggests a large contribution of epigenetic mechanisms to transcriptional regulation ( Templeton et al. 2004 ). Indeed, specialised chromatin remodelling enzymes organise chromatin architecture temporally and structurally, shaping the interaction landscape for transcription factors ( Watzlowik et al. 2021 ; 2025 ). To better understand these epigenetic processes, we provide a comprehensive analysis of the nucleosome landscape and its dynamics in Pf in unprecedented detail. Our results demonstrate that Pf exhibits a typical eukaryotic chromatin structure, including well-defined nucleosome positioning at the TSS and regularly spaced nucleosome arrays ( Schones et al. 2008 ; Yuan et al. 2005 ), which has not been clear in the field up to now. Furthermore, we show temporal dynamics of nucleosomes and link these to chromatin accessibility and gene expression patterns. Our analysis also identifies putative cis-regulatory elements that may operate in a coordinated manner during transcriptional regulation. Together, these findings advance our understanding of chromatin-based gene regulation in Pf and challenge prevailing views about its chromatin architecture. Studies on Pf have demonstrated that it has a general transcription machinery akin to that of other eukaryotes, which implies the existence of a classical promoter organisation ( Bischoff and Vaquero 2010 ). However, using the annotated TSS or the translational start site (ATG) as reference points to visualise nucleosome positions did not reveal a eukaryotic-like chromatin architecture ( Kensche et al. 2016 ). However, the TSS in Pf is not well-defined, occurring within a relatively broad window rather than at a single, fixed location ( Adjalley et al. 2016 ). Furthermore, the TSS varies according to the developmental stage of the parasite, often using multiple TSS windows and multiple promoters for the same gene ( Adjalley et al. 2016 ), adding additional complexity to TSS annotation. The ATG codon, on the other hand, is also suboptimal as a reference point due to its variable distance from the TSS and its independence from transcription initiation and the associated chromatin structure. By focusing on the nucleosome closest to the annotated TSS (the +1 nucleosome) as a reference point in this study, we were able to obtain a clear nucleosome positioning pattern at promoters, demonstrating the important role of the +1 nucleosome in determining the transcriptionally competent promoter structure. Accordingly, we show that the chromatin structure of the Pf promoter indeed resembles a typical eukaryotic promoter. The improved resolution of nucleosome positions also enables us to calculate the average nucleosome repeat length (NRL) throughout the parasite’s life cycle. Early studies on Pf chromatin structure suggested very short NRLs of 150 to 155 bp at specific genes, which was confirmed using the fragment length of isolated di-nucleosomes in MNase-seq experiments ( Silberhorn et al. 2016 ; Lanzer et al. 1994 ; Horrocks et al. 2002 ). However, employing the nucDetective pipeline on the Kensche dataset allows to assess the average genome wide NRL, which range from 176 to 185 bp and align with the typical sizes of eukaryotic NRL‘s ( Compton et al. 1976 ; Prunell and Kornberg 1982 ). Our result are also consistent with previous findings that cells with high transcriptional activity exhibit a reduction in nucleosome-to-nucleosome distance ( Baldi et al. 2018 ). We observed the shortest NRL in the trophozoite stage of Pf which coincides with elevated transcriptional activity and significant opening of chromatin ( Bunnik et al. 2014 ; Ay et al. 2014 ; Ponts et al. 2010 ; Lu et al. 2017 ; Kensche et al. 2016 ). Conversely, the longest NRL is observed during the ring stage, shortly after erythrocyte invasion, when transcriptional activity is very minimal ( Kensche et al. 2016 ; Lu et al. 2017 ). This suggests a potential link between nucleosome spacing and transcriptional activity in Pf . We demonstrate that nucleosome dynamics occur predominantly in the promoter region of genes in Pf , and we can trace the alterations to specific nucleosomes and genes. We find that the specific regulation of individual nucleosomes directly correlates with changes in accessibility and downstream transcription, thereby enhancing the resolution of biologically relevant regions of chromatin as compared to other methods, such as ATAC-seq. Furthermore, the analysis of concerted nucleosome dynamics has facilitated the identification of novel sequence motifs that become accessible, revealing potential regulatory elements and suggesting the existence of yet undiscovered DNA sequence specific factors. Our discovery supports previous studies indicating that there are undiscovered Plasmodium-specific transcription factors ( Bischoff and Vaquero 2010 ; Militello et al. 2004 ). Here we demonstrate that our high-resolution chromatin structure analysis provides new insights into the complex regulatory network. Moreover, apart from the information revealed by ATAC-seq data, which monitors chromatin accessibility, we show that nucleosome position shifts do not result in chromatin opening and are not detected by ATAC-seq. The function of these nucleosome position switches still needs to be elucidated. The nucDetective pipeline highlights the unique ability of MNase-seq to extract relevant features that cannot be inferred from other methods used to probe chromatin structure. Investigating the processes associated with chromatin dynamics that do not lead to increased accessibility may present an interesting area of research in the future. Materials and Methods The nucDetective pipeline All the steps from fastq files to detection of dynamic nucleosome features across multiple conditions were integrated and automated within the nucDetective pipeline, which is available on GitHub ( https://github.com/uschwartz/nucDetective.git ). The version of the nucDetective pipeline used in this study was v1.1. The nucDetective pipeline is executed using nextflow workflow management system and runs the software within stable Docker containers, ensuring a reproducible, scalable and portable analysis workflow ( Di Tommaso et al. 2017 ). The nucDetecitve pipeline is split into: 1) the Profiler workflow, comprising mapping, pre-/post-processing, QCs, NRL analysis and nucleosome profiling, and 2) the consecutive Inspector workflow, comprising nucleosome profile normalization, exploratory data analysis, nucleosome position annotation and detection of dynamic nucleosome features. The Profiler workflow The input of the workflow are raw paired-end sequencing data in the form of fastq files. First sequencing and read quality is checked using FastQC ( Andrews 2010 ) and adaptor sequences or low quality bases are trimmed using Trim Galore with the parameter: --stringency 2 and -q 10 ( Krueger 2023 ). Reads are mapped against the indexed reference genome (here Pfalciparium3D7 release 57 from PlasmoDB) using bowtie2 with following settings: --very-sensitive, --no-discordant, --no-mix, --dovetail ( Langmead and Salzberg 2012 ). Aligned fragments are filtered by MAPQ scores of at least 20 and reads mapped in proper pair using samtools (H. Li et al. 2009 ). Quality of the aligned sequence data, such as the fragment length distribution, is controlled using qualimap ( García-Alcalde et al. 2012 ). The fragments are further filtered to mono-nucleosome sized fragments (here we used 75 – 175 bp) and optionally fragments mapping to blacklisted elements are removed (here the mitochondrial and apicoplast chromosome) using the alignmentSieve function of deepTools package ( Ramírez et al. 2016 ). Nucleosome fragment coverage profiles are normalized by the supplied mappable genome size (here 23292622 bp) and generated using the dpos function of the DANPOS2 package with following options: -m 1, --extend 70, -u 0, -z 20, -e 1, --distance 75 –width 10 ( Chen et al. 2013 ). Nucleosome repeat lengths (NRLs) are calculated based on a phasogram approach described in (Valouev et al. 2011) and implemented in the swissknife (v0.40) package. Frequencies of distances between 5’ ends of reads in the same orientation are calculated and linear regression between peak maxima allows estimation of the average phase between nucleosomes. The Inspector workflow The nucleosome fragment coverage profiles in the form of wig files, which are the result of the Profiler workflow, are used in the consecutive Inspector workflow as input. First the nucleosome profiles are quantile normalized to a selected reference profile (here T20) using the wiq function of the DANPOS2 package ( Chen et al. 2013 ). Optionally, if a TSS annotation is provided (here we used our +1 nucleosome centered TSS annotation, Table1), a TSS plot of the normalized profiles can be generated using the computeMatrix and plotProfile functions of the deepTools package ( Ramírez et al. 2016 ). Next, nucleosome positions of each sample are called using the dpos function of DANPOS2 package with following settings: -z 20, -e 1, -- width 10, --height 25. The nucleosome annotation result of DANPOS2 is converted to bed file format and the best 20% positioned nucleosomes in each sample are filtered based on the provided fuzziness score. Next, the selected nucleosome positions of each sample are stepwise merged into a common reference nucleosome map. Nucleosome positions that overlap by at least 100 bp are stitched together. Nucleosome occupancy of the reference nucleosome map is assessed in each sample using the deeptools multiBigwigSummary function on the normalized nucleosome profiles. The resulting nucleosome occupancy table is than used for exploratory data analysis, such as PCA and correlation clustering. Nucleosome fuzziness, occupancy, shift and regularity dynamics Nucleosome fuzziness scores as reported from DANPOS2 and nucleosome occupancy scores assessed with multiBigwigSummary (see above) are taken for subsequent analysis. Nucleosome occupancy scores are further rlog transformed to minimize differences between samples and stabilize the variance ( Love et al. 2014 ). To detect nucleosome shifts the nucleosome profiles are loaded for each nucleosome position on the nucleosome reference map and a locally weighted scatterplot smoothing (LOESS) is applied using an alpha parameter of 0.6. The summit of the smoothed curve is assessed as nucleosome dyad position in each sample and used for further analysis. To quantify nucleosome regularity, spectral power density (PSD) analysis was applied to nucleosome occupancy profiles derived from normalized bigWig coverage files. Genomic coverage was extracted and processed in a rolling window manner (width: 1025 bp; step size: 100 bp), and power spectra were estimated using the smoothed periodogram function with a spectral smoothing span of 3 and a padding factor of 1. Spectral components were computed for each window, and the nucleosome repeat length (NRL) was derived as the inverse of the frequency. For each window, the spectral power at the frequency nearest to the expected average NRL (180 bp) was extracted, yielding a genome-wide track of regularity scores. These scores were log-transformed and used to compute variance and mean tracks across all samples. Regularity scores were assigned to individual nucleosomes by averaging PSD values over centered bins spanning 800 bp around each reference nucleosome dyad. To identify the most dynamic nucleosomes in regularity, fuzziness, occupancy or position (shift) the respective scores are normalized to a deviation from the highest to lowest value of 1 and plotted against their ranks, which are normalized by the total number of ranks. A LOESS smoothing was applied and the first derivate of the LOESS fit was calculated to deduce the slope of the curve. The most dynamic nucleosomes are determined as the highest scores after the slope of the curve exceeds 3. +1 nucleosome annotation To get +1 nucleosome annotation, mono-nucleosome sized fragments of all timepoints were merged and used as input for Nucleosome Dynamics program suite with PlasmoDB57 as reference annotation ( Buitrago et al. 2019 ). The 3’-end of the resulting gff from txstart was used as +1 nucleosome dyad annotation for all timepoints. Nucleosome coverage maps are aligned to regions +/- 1000 bp around the +1 dyad annotation and the average coverage is plotted. Downstream Analysis PCA was performed using nucleosome features (occupancy, fuzziness, position, regularity) of each timepoint on nucleosomes with high variance of the respective features (e.g. dynamic nucleosomes; results of nucDetective Inspector). Overlaps of nucleosome labels were visualised using the eulerR package ( Larsson 2024 ). The genome wide distribution of dynamic nucleosomes was assessed using the ChIPseeker package with PlasmoDB57 annotation ( Yu et al. 2015 ). Promoter region was defined as -500 to 100 bp around the TSS. The frequency profile of dynamic nucleosomes at the TSS and in gene bodies uses PlasmoDB56 annotation and deeptools for meta- and composite plots ( Ramírez et al. 2016 ). The profiles are scaled and smoothed by a rolling average with window length of 25 bp and 5 bp for gene bodies and TSS area plots respectively. Association with histone marks For comparability samples from ring stages or 20 hours past infection were used. Datasets for H3K9me3 (GSE202214) ( Jeninga et al. 2023 ), H3K9ac, H3K9me3 and H2A.Z (GSE23787) ( Bártfai et al. 2010 ) and H3.3 (GSE80466) (S. A.-K. Fraschka et al. 2016 ) were analysed as follows: Reads were trimmed with trimmomatic v0.39 using the following options: ILLUMINACLIP::2:30:10 MAXINFO:30:0.2 MINLEN:35. Alignment was done using bowtie2 v2.5.1 ( Langmead and Salzberg 2012 ) with “--very-sensitive “, “--no-mixed “ and “--no-unal “ options. Further processing was done with deeptools v3.5.1 ( Ramírez et al. 2016 ). First bamCoverage with options “-bs 10 --normalizeUsing RPKM “ was used to get coverage bigwigs and subsequently bamCoverage -b1 -b2 -bs 10 – operation log2 was used to get log2(ChIP/Input) bigwigs. ATAC-seq data was obtained from GEO (GSE104075) ( Toenhake et al. 2018 ) as bedgraph files, which were converted to bigwigs using the ucsc bedgraphtobigwig tool. RNA-seq data (GSE66185) ( Kensche et al. 2016 ) were downloaded and adjusted to PlasmoDB57 annotation with 10 bp stepsize by a custom R script. bedtools v2.30.0 “makewindows” and “nuc” functions were used to get the GC content of the Pf genome in 150 bp windows ( Quinlan and Hall 2010 ). The average, centred log2FC or coverage of histone marks, RNA-, MNase- and ATAC-seq around nucleosome positions is plotted. Overlaps and Correlation of Dynamic Nucleosomes with ATAC Peaks ATAC peaks are obtained from GEO (GSE104075) ( Toenhake et al. 2018 ) Nucleosomes that overlap by at least 100 bp were assigned to the corresponding ATAC peak. Overlaps are visualised using the eulerR package ( Larsson 2024 ). Correlation of nucleosome features with accessibility Chromatin accessibility at the eight developmental timepoints was quantified by averaging ATAC-seq signal intensity across each reference nucleosome position. Fuzziness scores, occupancy, and positional shift data for the reference nucleosomes were obtained from the nucDetective Inspector workflow. For each nucleosome, Pearson correlation coefficients between ATAC measured accessibility and individual nucleosome features were calculated across all timepoints. To assess the statistical significance of these correlations a permutation-based approach was applied. Null distributions were generated by randomly sampling nucleosomes from the genome 999 times per feature. Correlation values from the set of highly dynamic nucleosomes were compared to these simulated backgrounds in the promoter regions. Clustering of nucleosome occupancy dynamics Self-organising maps (SOM) were used to group changes in nucleosome occupancy according to the time at which they occurred ( Wehrens and Buydens 2007 ). A hexagonal grid was initialised to the size of 13 x 13. The learning rate was set to α = (0.05,0.01) and the number of iterations was set to 500. Hierarchical clustering was applied to the build SOM to obtain six cluster using the agglomeration method “ward.D” in the R function hclust. Motif analysis of nucleosome occupancy cluster The function findMotifsGenome of the HOMER suit was applied to the nucleosome positions of each cluster to find enriched sequence motifs ( Heinz et al. 2010 ). As background the 20% best positioned nucleosome reference map was provided. Identified de novo motifs were compared to the set of known motifs for transcription factors of the ApiAP2 protein family from Pf ( Campbell et al. 2010 ). For each cluster the three best ranked (according to their p-value) de novo motifs were reported. Furthermore, for each of these top ranked motifs matches with known motifs are shown using a similarity score cutoff of 0.6. If the similarity score was below this threshold, only the top match was included. Correlation of nucleosome features with gene expression Nucleosomes from the reference nucleosome map (result of nucDetective Inspector) were assigned to genes according to their respective promoters (500 bp upstream to 100 bp downstream from TSS) or coding regions. Gene expression data (GSE66185) in the form of rescaled RPKM values were adopted from Kensche et al. ( Kensche et al. 2016 ). The gene expression data and nucleosome features (fuzziness, occupancy and shifts) were aggregated and the Pearson correlation was calculated for each nucleosome and feature across all developmental time points. The statistical significance of these correlations was assessed utilizing a permutation-based approach, randomly sampling nucleosomes from the respective genomic region (promoter or coding regions) 999 times per feature to create a null distribution. The correlation values from dynamic nucleosomes were compared to these simulated distributions in the promoter region and to all nucleosomes in the coding regions. Public datasets used in this study Pf MNase-seq data and RNA-seq data analysed in this paper are available at GEO with accession GSE66185. Datasets for H3K9me3 (GSE202214), H3K9ac, H3K9me3 and H2A.Z (GSE23787), H3.3 (GSE80466) and ATAC-seq (GSE104075) were obtained from GEO. Author contributions Conceptualization: US, SH, GL, MTW Methodology: US, SH, LS, VMR Formal analysis: US, SH, LS, VMR Investigation: US, SH, LS, VMR Visualization: US, SH, LS Software: US, SH, LS, VMR Supervision: US, GL Funding Acquisition: GL Resources: GL Writing—original draft: US, SH, GL Writing—review & editing: US, SH, GL Competing interests Authors declare that they have no competing interests. Data and materials availability All data needed to evaluate the conclusions in the paper are present in the paper and/or the supplementary Materials. The nucDetective pipeline v1.1 is available on GitHub with the repository uschwartz/nucDetective . Additional scripts to replicate the analysis presented in this paper are found at SimHolz/Holzinger_et_al_2025 . The results of the nucDetective Pf analysis including nucleosome profiles, nucleosome reference positions and dynamic nucleosomes, as well as the used Pf annotation are deposited on https://zenodo.org/ with the DOI: 10.5281/zenodo.16779899. Acknowledgments We thank Richard Bartfai and Manuel Llinas for fruitful discussions and comments on this manuscript. The work was funded by the Detusche Forschungsgemeinschaft (DFG) with the project number 534335380. Funder Information Declared Deutsche Forschungsgemeinschaft , 534335380 References ↵ Adjalley , Sophie H. , Christophe D. Chabbert , Bernd Klaus , Vicent Pelechano , and Lars M. Steinmetz . 2016 . “ Landscape and Dynamics of Transcription Initiation in the Malaria Parasite Plasmodium Falciparum .” Cell Reports 14 ( 10 ): 2463 – 75 . doi: 10.1016/j.celrep.2016.02.025 . OpenUrl CrossRef PubMed ↵ Andrews , Simon . 2010 . FASTQC. A Quality Control Tool for High Throughput Sequence Data . Released April 26. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/4 . ↵ Ay , Ferhat , Evelien M. Bunnik , Nelle Varoquaux , et al. 2014 . “ Three-Dimensional Modeling of the P. Falciparum Genome during the Erythrocytic Cycle Reveals a Strong Connection between Genome Architecture and Gene Expression .” Genome Research 24 ( 6 ): 974 – 88 . doi: 10.1101/gr.169417.113 . OpenUrl Abstract / FREE Full Text ↵ Balaji , S. , M. Madan Babu , Lakshminarayan M. Iyer , and L. Aravind . 2005 . “ Discovery of the Principal Specific Transcription Factors of Apicomplexa and Their Implication for the Evolution of the AP2-Integrase DNA Binding Domains .” Nucleic Acids Research 33 ( 13 ): 3994 – 4006 . doi: 10.1093/nar/gki709 . OpenUrl CrossRef PubMed Web of Science ↵ Baldi , Sandro , Stefan Krebs , Helmut Blum , and Peter B. Becker . 2018 . “ Genome-Wide Measurement of Local Nucleosome Array Regularity and Spacing by Nanopore Sequencing .” Nature Structural & Molecular Biology 25 ( 9 ): 894 – 901 . doi: 10.1038/s41594-018-0110-0 . OpenUrl CrossRef PubMed ↵ Bártfai , Richárd , Wieteke A. M. Hoeijmakers , Adriana M. Salcedo-Amaya , et al. 2010 . “ H2A.Z Demarcates Intergenic Regions of the Plasmodium Falciparum Epigenome That Are Dynamically Marked by H3K9ac and H3K4me3 .” PLoS Pathogens 6 ( 12 ): e1001223 . doi: 10.1371/journal.ppat.1001223 . OpenUrl CrossRef PubMed ↵ Bischoff , Emmanuel , and Catherine Vaquero . 2010 . “ In Silico and Biological Survey of Transcription-Associated Proteins Implicated in the Transcriptional Machinery during the Erythrocytic Development of Plasmodium Falciparum .” BMC Genomics 11 ( January ): 34 . doi: 10.1186/1471-2164-11-34 . OpenUrl CrossRef PubMed ↵ Brogaard , Kristin R. , Liqun Xi , Ji-Ping Wang , and Jonathan Widom . 2012 . “ A Chemical Approach to Mapping Nucleosomes at Base Pair Resolution in Yeast .” Methods in Enzymology 513 : 315 – 34 . doi: 10.1016/B978-0-12-391938-0.00014-8 . OpenUrl CrossRef PubMed Web of Science ↵ Buitrago , Diana , Laia Codó , Ricard Illa , et al. 2019 . “ Nucleosome Dynamics: A New Tool for the Dynamic Analysis of Nucleosome Positioning .” Nucleic Acids Research 47 ( 18 ): 9511 – 23 . doi: 10.1093/nar/gkz759 . OpenUrl CrossRef PubMed ↵ Bunnik , Evelien M. , Anton Polishko , Jacques Prudhomme , et al. 2014 . “ DNA-Encoded Nucleosome Occupancy Is Associated with Transcription Levels in the Human Malaria Parasite Plasmodium Falciparum .” BMC Genomics 15 ( 1 ): 347 . doi: 10.1186/1471-2164-15-347 . OpenUrl CrossRef PubMed ↵ Campbell , Tracey L. , Erandi K. De Silva , Kellen L. Olszewski , Olivier Elemento , and Manuel Llinás . 2010 . “ Identification and Genome-Wide Prediction of DNA Binding Specificities for the ApiAP2 Family of Regulators from the Malaria Parasite .” PLoS Pathogens 6 ( 10 ): e1001165 . doi: 10.1371/journal.ppat.1001165 . OpenUrl CrossRef PubMed ↵ Chappell , Lia , Philipp Ross , Lindsey Orchard , et al. 2020 . “ Refining the Transcriptome of the Human Malaria Parasite Plasmodium Falciparum Using Amplification-Free RNA-Seq .” BMC Genomics 21 ( 1 ): 395 . doi: 10.1186/s12864-020-06787-5 . OpenUrl CrossRef PubMed ↵ Chen , Kaifu , Yuanxin Xi , Xuewen Pan , et al. 2013 . “ DANPOS: Dynamic Analysis of Nucleosome Position and Occupancy by Sequencing .” Genome Research 23 ( 2 ): 341 – 51 . doi: 10.1101/gr.142067.112 . OpenUrl Abstract / FREE Full Text ↵ Compton , J. L. , M. Bellard , and P. Chambon . 1976 . “ Biochemical Evidence of Variability in the DNA Repeat Length in the Chromatin of Higher Eukaryotes .” Proceedings of the National Academy of Sciences of the United States of America 73 ( 12 ): 4382 – 86 . doi: 10.1073/pnas.73.12.4382 . OpenUrl Abstract / FREE Full Text ↵ Di Tommaso , Paolo , Maria Chatzou , Evan W. Floden , Pablo Prieto Barja , Emilio Palumbo , and Cedric Notredame . 2017 . “ Nextflow Enables Reproducible Computational Workflows .” Nature Biotechnology 35 ( 4 ): 316 – 19 . doi: 10.1038/nbt.3820 . OpenUrl CrossRef PubMed ↵ Flueck , Christian , Richard Bartfai , Jennifer Volz , et al. 2009 . “ Plasmodium Falciparum Heterochromatin Protein 1 Marks Genomic Loci Linked to Phenotypic Variation of Exported Virulence Factors .” PLoS Pathogens 5 ( 9 ): e1000569 . doi: 10.1371/journal.ppat.1000569 . OpenUrl CrossRef PubMed ↵ Fraschka , Sabine A. , Michael Filarsky , Regina Hoo , et al. 2018 . “ Comparative Heterochromatin Profiling Reveals Conserved and Unique Epigenome Signatures Linked to Adaptation and Development of Malaria Parasites .” Cell Host & Microbe 23 ( 3 ): 407 – 420 .e8. doi: 10.1016/j.chom.2018.01.008 . OpenUrl CrossRef PubMed ↵ Fraschka , Sabine Anne-Kristin , Rob Wilhelmus Maria Henderson , and Richárd Bártfai . 2016 . “ H3.3 Demarcates GC-Rich Coding and Subtelomeric Regions and Serves as Potential Memory Mark for Virulence Gene Expression in Plasmodium Falciparum .” Scientific Reports 6 (August): 31965 . doi: 10.1038/srep31965 . OpenUrl CrossRef PubMed ↵ García-Alcalde , Fernando , Konstantin Okonechnikov , José Carbonell , et al. 2012 . “Qualimap: Evaluating next-Generation Sequencing Alignment Data.” Bioinformatics (Oxford , England ) 28 ( 20 ): 2678 – 79 . doi: 10.1093/bioinformatics/bts503 . OpenUrl CrossRef PubMed Web of Science ↵ Gardner , Malcolm J. , Neil Hall , Eula Fung , et al. 2002 . “ Genome Sequence of the Human Malaria Parasite Plasmodium Falciparum .” Nature 419 ( 6906 ): 498 – 511 . doi: 10.1038/nature01097 . OpenUrl CrossRef PubMed Web of Science ↵ Harwood , Janet C. , Nicholas A. Kent , Nicholas D. Allen , and Adrian J. Harwood . 2019 . “ Nucleosome Dynamics of Human iPSC during Neural Differentiation .” EMBO Reports 20 ( 6 ): e46960 . doi: 10.15252/embr.201846960 . OpenUrl Abstract / FREE Full Text ↵ Heinz , Sven , Christopher Benner , Nathanael Spann , et al. 2010 . “ Simple Combinations of Lineage-Determining Transcription Factors Prime Cis-Regulatory Elements Required for Macrophage and B Cell Identities .” Molecular Cell 38 ( 4 ): 576 – 89 . doi: 10.1016/j.molcel.2010.05.004 . OpenUrl CrossRef PubMed Web of Science ↵ Horrocks , Paul , Robert Pinches , Neline Kriek , and Chris Newbold . 2002 . “ Stage-Specific Promoter Activity from Stably Maintained Episomes in Plasmodium Falciparum .” International Journal for Parasitology 32 ( 10 ): 1203 – 6 . doi: 10.1016/s0020-7519(02)00123-6 . OpenUrl CrossRef PubMed ↵ Jeninga , Myriam D. , Jingyi Tang , Shamista A. Selvarajah , Alexander G. Maier , Michael F. Duffy , and Michaela Petter . 2023 . “ Plasmodium Falciparum Gametocytes Display Global Chromatin Remodelling during Sexual Differentiation .” BMC Biology 21 ( 1 ): 65 . doi: 10.1186/s12915-023-01568-4 . OpenUrl CrossRef PubMed ↵ Kensche , Philip Reiner , Wieteke Anna Maria Hoeijmakers , Christa Geeke Toenhake , et al. 2016 . “ The Nucleosome Landscape of Plasmodium Falciparum Reveals Chromatin Architecture and Dynamics of Regulatory Sequences .” Nucleic Acids Research 44 ( 5 ): 2110 – 24 . doi: 10.1093/nar/gkv1214 . OpenUrl CrossRef PubMed ↵ Klein-Brill , Avital , Daphna Joseph-Strauss , Alon Appleboim , and Nir Friedman . 2019 . “ Dynamics of Chromatin and Transcription during Transient Depletion of the RSC Chromatin Remodeling Complex .” Cell Reports 26 ( 1 ): 279 – 292 .e5. doi: 10.1016/j.celrep.2018.12.020 . OpenUrl CrossRef PubMed ↵ Krueger , Felix . 2023 . Trim Galore . Released February 2. https://github.com/FelixKrueger/TrimGalore?tab=readme-ov-file . ↵ Langmead , Ben , and Steven L. Salzberg . 2012 . “ Fast Gapped-Read Alignment with Bowtie 2 .” Nature Methods 9 ( 4 ): 357 – 59 . doi: 10.1038/nmeth.1923 . OpenUrl CrossRef PubMed Web of Science ↵ Längst , G. , P. B. Becker , and I. Grummt . 1998 . “ TTF-I Determines the Chromatin Architecture of the Active rDNA Promoter .” The EMBO Journal 17 ( 11 ): 3135 – 45 . doi: 10.1093/emboj/17.11.3135 . OpenUrl Abstract / FREE Full Text ↵ Lanzer , M. , S. P. Wertheimer , D. de Bruin , and J. V. Ravetch . 1994 . “ Chromatin Structure Determines the Sites of Chromosome Breakages in Plasmodium Falciparum .” Nucleic Acids Research 22 ( 15 ): 3099 – 103 . doi: 10.1093/nar/22.15.3099 . OpenUrl CrossRef PubMed ↵ Larsson , Johan . 2024 . eulerr: Area-Proportional Euler and Venn Diagrams with Ellipses . Manual. https://CRAN.R-project.org/package=eulerr . ↵ Le Roch , K. G. , D.-W. D. Chung , and N. Ponts . 2012 . “ Genomics and Integrated Systems Biology in Plasmodium Falciparum: A Path to Malaria Control and Eradication .” Parasite Immunology 34 ( 2–3 ): 50 – 60 . doi: 10.1111/j.1365-3024.2011.01340.x . OpenUrl CrossRef PubMed Web of Science ↵ Li , Heng , Bob Handsaker , Alec Wysoker , et al. 2009 . “The Sequence Alignment/Map Format and SAMtools.” Bioinformatics (Oxford , England ) 25 ( 16 ): 2078 – 79 . doi: 10.1093/bioinformatics/btp352 . OpenUrl CrossRef PubMed Web of Science ↵ Li , Junwei , Gernot Längst , and Ingrid Grummt . 2006 . “ NoRC-Dependent Nucleosome Positioning Silences rRNA Genes .” The EMBO Journal 25 ( 24 ): 5735 – 41 . doi: 10.1038/sj.emboj.7601454 . OpenUrl Abstract / FREE Full Text ↵ Love , Michael I. , Wolfgang Huber , and Simon Anders . 2014 . “ Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2 .” Genome Biology 15 ( 12 ): 550 . doi: 10.1186/s13059-014-0550-8 . OpenUrl CrossRef PubMed ↵ Lu , Xueqing Maggie , Gayani Batugedara , Michael Lee , Jacques Prudhomme , Evelien M. Bunnik , and Karine G. Le Roch . 2017 . “ Nascent RNA Sequencing Reveals Mechanisms of Gene Regulation in the Human Malaria Parasite Plasmodium Falciparum .” Nucleic Acids Research 45 ( 13 ): 7825 – 40 . doi: 10.1093/nar/gkx464 . OpenUrl CrossRef PubMed ↵ Luger , Karolin , Armin W. Mäder , Robin K. Richmond , David F. Sargent , and Timothy J. Richmond . 1997 . “ Crystal Structure of the Nucleosome Core Particle at 2.8 Å Resolution .” Nature 389 ( 6648 ): 251 – 60 . doi: 10.1038/38444 . OpenUrl CrossRef PubMed Web of Science ↵ Martinez-Campa , Carlos , Panagiotis Politis , Jean-Luc Moreau , et al. 2004 . “ Precise Nucleosome Positioning and the TATA Box Dictate Requirements for the Histone H4 Tail and the Bromodomain Factor Bdf1 .” Molecular Cell 15 ( 1 ): 69 – 81 . doi: 10.1016/j.molcel.2004.05.022 . OpenUrl CrossRef PubMed Web of Science ↵ Mavrich , Travis N. , Ilya P. Ioshikhes , Bryan J. Venters , et al. 2008 . “ A Barrier Nucleosome Model for Statistical Positioning of Nucleosomes throughout the Yeast Genome .” Genome Research 18 ( 7 ): 1073 – 83 . doi: 10.1101/gr.078261.108 . OpenUrl Abstract / FREE Full Text ↵ Militello , Kevin T. , Matthew Dodge , Lara Bethke , and Dyann F. Wirth . 2004 . “ Identification of Regulatory Elements in the Plasmodium Falciparum Genome .” Molecular and Biochemical Parasitology 134 ( 1 ): 75 – 88 . doi: 10.1016/j.molbiopara.2003.11.004 . OpenUrl CrossRef PubMed Web of Science ↵ Oberbeckmann , Elisa , Michael Wolff , Nils Krietenstein , et al. 2019 . “Absolute Nucleosome Occupancy Map for the Saccharomyces Cerevisiae Genome.” Genome Research , ahead of print , November 6 . doi: 10.1101/gr.253419.119 . OpenUrl Abstract / FREE Full Text ↵ Ponts , Nadia , Lijuan Fu , Elena Y. Harris , et al. 2013 . “ Genome-Wide Mapping of DNA Methylation in the Human Malaria Parasite Plasmodium Falciparum .” Cell Host & Microbe 14 ( 6 ): 696 – 706 . doi: 10.1016/j.chom.2013.11.007 . OpenUrl CrossRef PubMed ↵ Ponts , Nadia , Elena Y. Harris , Jacques Prudhomme , et al. 2010 . “ Nucleosome Landscape and Control of Transcription in the Human Malaria Parasite .” Genome Research 20 ( 2 ): 228 – 38 . doi: 10.1101/gr.101063.109 . OpenUrl Abstract / FREE Full Text ↵ Prunell , A. , and R. D. Kornberg . 1982 . “ Variable Center to Center Distance of Nucleosomes in Chromatin .” Journal of Molecular Biology 154 ( 3 ): 515 – 23 . doi: 10.1016/s0022-2836(82)80010-7 . OpenUrl CrossRef PubMed Web of Science ↵ Quinlan , Aaron R. , and Ira M. Hall . 2010 . “BEDTools: A Flexible Suite of Utilities for Comparing Genomic Features.” Bioinformatics (Oxford , England ) 26 ( 6 ): 841 – 42 . doi: 10.1093/bioinformatics/btq033 . OpenUrl CrossRef PubMed Web of Science ↵ Ramírez , Fidel , Devon P. Ryan , Björn Grüning , et al. 2016 . “ deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis .” Nucleic Acids Research 44 ( W1 ): W160 – 165 . doi: 10.1093/nar/gkw257 . OpenUrl CrossRef PubMed ↵ Santos , Joana Mendonca , Gabrielle Josling , Philipp Ross , et al. 2017 . “ Red Blood Cell Invasion by the Malaria Parasite Is Coordinated by the PfAP2-I Transcription Factor .” Cell Host & Microbe 21 ( 6 ): 731 – 741 .e10. doi: 10.1016/j.chom.2017.05.006 . OpenUrl CrossRef PubMed ↵ Schones , Dustin E. , Kairong Cui , Suresh Cuddapah , et al. 2008 . “ Dynamic Regulation of Nucleosome Positioning in the Human Genome .” Cell 132 ( 5 ): 887 – 98 . doi: 10.1016/j.cell.2008.02.022 . OpenUrl CrossRef PubMed Web of Science ↵ Schwartz , Uwe , Attila Németh , Sarah Diermeier , et al. 2019 . “ Characterizing the Nuclease Accessibility of DNA in Human Cells to Map Higher Order Structures of Chromatin .” Nucleic Acids Research 47 ( 3 ): 1239 – 54 . doi: 10.1093/nar/gky1203 . OpenUrl CrossRef PubMed ↵ Shaw , Philip J. , Jittima Piriyapongsa , Pavita Kaewprommal , et al. 2021 . “ Identifying Transcript 5’ Capped Ends in Plasmodium Falciparum .” PeerJ 9 : e11983 . doi: 10.7717/peerj.11983 . OpenUrl CrossRef PubMed ↵ Shtumpf , Mariya , Kristan V. Piroeva , Shivam P. Agrawal , Divya R. Jacob , and Vladimir B. Teif . 2022 . “ NucPosDB: A Database of Nucleosome Positioning in Vivo and Nucleosomics of Cell-Free DNA .” Chromosoma 131 ( 1–2 ): 19 – 28 . doi: 10.1007/s00412-021-00766-9 . OpenUrl CrossRef PubMed ↵ Silberhorn , Elisabeth , Uwe Schwartz , Patrick Löffler , et al. 2016 . “ Plasmodium Falciparum Nucleosomes Exhibit Reduced Stability and Lost Sequence Dependent Nucleosome Positioning .” PLoS Pathogens 12 ( 12 ): e1006080 . doi: 10.1371/journal.ppat.1006080 . OpenUrl CrossRef PubMed ↵ Singh , Ashish Kumar , Tamás Schauer , Lena Pfaller , Tobias Straub , and Felix Mueller-Planitz . 2021 . “ The Biogenesis and Function of Nucleosome Arrays .” Nature Communications 12 ( 1 ): 7011 . doi: 10.1038/s41467-021-27285-6 . OpenUrl CrossRef PubMed ↵ Templeton , Thomas J. , Lakshminarayan M. Iyer , Vivek Anantharaman , et al. 2004 . “ Comparative Analysis of Apicomplexa and Genomic Diversity in Eukaryotes .” Genome Research 14 ( 9 ): 1686 – 95 . doi: 10.1101/gr.2615304 . OpenUrl Abstract / FREE Full Text ↵ Toenhake , Christa Geeke , Sabine Anne-Kristin Fraschka , Mahalingam Shanmugiah Vijayabaskar , David Robert Westhead , Simon Jan van Heeringen , and Richárd Bártfai . 2018 . “ Chromatin Accessibility-Based Characterization of the Gene Regulatory Network Underlying Plasmodium Falciparum Blood-Stage Development .” Cell Host & Microbe 23 ( 4 ): 557 – 569 .e9. doi: 10.1016/j.chom.2018.03.007 . OpenUrl CrossRef PubMed ↵ Watzlowik , Maria Theresia , Sujaan Das , Markus Meissner , and Gernot Längst . 2021 . “ Peculiarities of Plasmodium Falciparum Gene Regulation and Chromatin Structure .” International Journal of Molecular Sciences 22 ( 10 ): 5168 . doi: 10.3390/ijms22105168 . OpenUrl CrossRef PubMed ↵ Watzlowik , Maria Theresia , Elisabeth Silberhorn , Sujaan Das , et al. 2025 . “ Plasmodium Blood Stage Development Requires the Chromatin Remodeller Snf2L .” Nature 639 ( 8056 ): 1069 – 75 . doi: 10.1038/s41586-025-08595-x . OpenUrl CrossRef PubMed ↵ Wehrens , Ron , and Lutgarde M. C. Buydens . 2007 . “ Self- and Super-Organizing Maps in R : The Kohonen Package .” Journal of Statistical Software 21 ( 5 ). doi: 10.18637/jss.v021.i05 . OpenUrl CrossRef ↵ Wernig-Zorc , Sara , Fabian Kugler , Leo Schmutterer , et al. 2024 . “ nucMACC: An MNase-Seq Pipeline to Identify Structurally Altered Nucleosomes in the Genome .” Science Advances 10 ( 27 ): eadm9740 . doi: 10.1126/sciadv.adm9740 . OpenUrl CrossRef PubMed ↵ West , Jason A. , April Cook , Burak H. Alver , et al. 2014 . “ Nucleosomal Occupancy Changes Locally over Key Regulatory Regions during Cell Differentiation and Reprogramming .” Nature Communications 5 ( August ): 4719 . doi: 10.1038/ncomms5719 . OpenUrl CrossRef PubMed ↵ Westenberger , Scott J. , Long Cui , Neekesh Dharia , Elizabeth Winzeler , and Liwang Cui . 2009 . “ Genome-Wide Nucleosome Mapping of Plasmodium Falciparum Reveals Histone-Rich Coding and Histone-Poor Intergenic Regions and Chromatin Remodeling of Core and Subtelomeric Genes .” BMC Genomics 10 ( December ): 610 . doi: 10.1186/1471-2164-10-610 . OpenUrl CrossRef PubMed ↵ Whyte , Warren A. , David A. Orlando , Denes Hnisz , et al. 2013 . “ Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes .” Cell 153 ( 2 ): 307 – 19 . doi: 10.1016/j.cell.2013.03.035 . OpenUrl CrossRef PubMed Web of Science ↵ Woodcock , C. L. , J. P. Safer , and J. E. Stanchfield . 1976 . “ Structural Repeating Units in Chromatin. I. Evidence for Their General Occurrence .” Experimental Cell Research 97 ( January ): 101 – 10 . doi: 10.1016/0014-4827(76)90659-5 . OpenUrl CrossRef PubMed Web of Science ↵ World Health Organization . 2024 . World Malaria Report 2024 . World Health Organization . https://www.who.int/publications/i/item/9789240104440 . ↵ Yu , Guangchuang , Li-Gen Wang , and Qing-Yu He . 2015 . “ChIPseeker: An R/Bioconductor Package for ChIP Peak Annotation, Comparison and Visualization.” Bioinformatics (Oxford , England ) 31 ( 14 ): 2382 – 83 . doi: 10.1093/bioinformatics/btv145 . OpenUrl CrossRef PubMed ↵ Yuan , Guo-Cheng , Yuen-Jong Liu , Michael F. Dion , et al. 2005 . “Genome-Scale Identification of Nucleosome Positions in S. Cerevisiae.” Science (New York , N.Y .) 309 ( 5734 ): 626 – 30 . doi: 10.1126/science.1112178 . OpenUrl Abstract / FREE Full Text ↵ Zhang , Wenjuan , Yaping Li , Michael Kulik , et al. 2016 . “ Nucleosome Positioning Changes during Human Embryonic Stem Cell Differentiation .” Epigenetics 11 ( 6 ): 426 – 37 . doi: 10.1080/15592294.2016.1176649 . OpenUrl CrossRef PubMed ↵ Zhu , Fangjie , Lucas Farnung , Eevi Kaasinen , et al. 2018 . “ The Interaction Landscape between Transcription Factors and the Nucleosome .” Nature 562 ( 7725 ): 76 – 81 . doi: 10.1038/s41586-018-0549-5 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 14, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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Share Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline Simon Holzinger , Leo Schmutterer , Victoria Marie Rothe , Maria Theresia Watzlowik , Uwe Schwartz , Gernot Längst bioRxiv 2025.08.13.670116; doi: https://doi.org/10.1101/2025.08.13.670116 Share This Article: Copy Citation Tools Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline Simon Holzinger , Leo Schmutterer , Victoria Marie Rothe , Maria Theresia Watzlowik , Uwe Schwartz , Gernot Längst bioRxiv 2025.08.13.670116; doi: https://doi.org/10.1101/2025.08.13.670116 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41911) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13371) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22482) Immunology (17728) Microbiology (40363) Molecular Biology (17163) Neuroscience (88536) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)