dataesr/scientific-paragraphs-categorization huggingface dataset:
The tracking algorithm For the track finding, the coordinate system is defined as indicated in Figure  4 , with the z-axis given by the beam axis, the x-axis pointing left when looking downstream in positive z direction and the y-axis pointing up. The track finding algorithm used for the pattern recognition is a simple implementation of a nearest neighbour algorithm. The algorithm was specifically developed for the test beam data taken with the CALICE AHCAL. It exploits the primary flight direction of incoming beam particles along the z axis by assuming that all particles found by the algorithm have a sizeable momentum component along that axis. This is reflected by the assumption that any MIP-like particle will only create at most one hit in a given layer, and that cells on the same track in adjacent layers are neighbours, sharing at least one corner when projected on the same layer.
Data set and detector simulations The tracks identified in hadronic showers are used to validate the realism of detector simulations based on GEANT4 by comparing results from test-beam data and simulated data. The analysis is based on a data set recorded at the CERN SPS at the H6 beam line in 2007 with energies ranging from 10 GeV to 80 GeV. All runs included in the present analysis used negative polarity secondary or tertiary beams, consisting of a mix of negative pions, muons and electrons. Antiproton contamination as well as kaon content are negligible.
In comparison to data, the four physics lists studied fall into two groups. While the QGSP_BERT and the FTFP_BERT lists reproduce the distribution of the track multiplicity very well, simulations with the LHEP and the QGS_BIC physics lists show significant discrepancies, with an overall shift towards a lower number of identified tracks. This comparison is further expanded by a study of the energy dependence of the mean track multiplicity per event, shown in Figure  8. Again, the statistical errors are omitted except for the residual of data and simulations with QGS_BIC. As for the differential distribution discussed above, the QGSP_BERT and the FTFP_BERT lists reproduce the energy dependence of the mean multiplicity quite well, with QGSP_BERT showing the best agreement with deviations below 5% at all energies.
In GEANT4 simulations the reconstructed track multiplicity is correlated with the total number of charged higher-energetic particles (excluding electrons and positrons), providing the basis for tests of GEANT4 hadronic shower models with observables based on the reconstructed tracks. Four physics lists of GEANT4 9. 4p02 have been investigated. The observations for all identified track segments and those for secondary tracks only are consistent with each other. QGSP_BERT generally provides the best agreement with data with a good reproduction of the track multiplicity, while LHEP shows the largest discrepancies, producing too few secondary tracks overall and strongly underestimating the number of tracks at large angles.
Acknowledgements We gratefully acknowledge the DESY and CERN managements for their support and hospitality, and their accelerator staff for the reliable and efficient beam operation. We would like to thank the  HEP group of the University of Tsukuba  for the loan of drift chambers for the DESY test beam. The authors would like to thank the  RIMST (Zelenograd) group  for their help and sensors manufacturing. This work was supported by the  Bundesministerium für Bildung und Forschung, Germany ; by the the  DFG cluster of excellence 'Origin and Structure of the Universe' of Germany ; by the  Helmholtz-Nachwuchsgruppen  grant  VH-NG-206 ; by the  BMBF , grant no. 05HS6VH1 ; by the  Alexander von Humboldt Foundation  (Research Award IV, RUS1066839 GSA); by joint  Helmholtz Foundation  and  RFBR  grant  HRJRG-002 , SC Rosatom; by the  Russian Ministry of Education and Science  via grants  8174 ,  8411 ,  1366.
Discovery of a gene island correlated with CA4 exhibiting two different configurations The availability of the whole genome sequence of 15 marine Synechococcus isolates and preliminary data from 25 additional Synechococcus unpublished genomes (that will be described in detail elsewhere; G. Farrant and coworkers, unpublished) allowed us to search for genes potentially involved in CA4. Such genes were retrieved in 16 of these 40 sequenced strains and one additional unsequenced strain (M11. 1), originating from a variety of oceanic environments and depths (cf. Table  S1 ). This set encompasses 15 strains belonging to six distinct clades within subcluster 5. 1 (or ten subclades, as defined using the high resolution marker gene petB  [24] ; and two strains from a single clade of subcluster 5. 3 (sensu  [25].
In contrast, there were no strains from subcluster 5. 2, since all those included in the initial dataset lack phycoerythrin and are thus incapable of CA4. The 16 sequenced strains containing genes putatively involved in CA4 were identified using a two-step bioinformatic approach. First, the 40 genomes were screened for the presence of mpeZ (Cyanorak cluster CK_00009110; see www. sb-roscoff. fr/cyanorak/), a gene that encodes a phycobilin lyase-isomerase recently shown to have a key role in CA4 in Synechococcus RS9916  [20]. Orthologs of this gene were retrieved in 8 additional genomes, where the mpeZ gene was consistently located at the 3'-end of a genomic island (hereafter called "CA4-A island"), always downstream of three additional in-frame genes (Figure  1 ). Structural analysis using Phyre2  [26]  predicted that the first two genes encode proteins (Cyanorak clusters CK_00002124 and CK_00002123), which both possess a possible C-terminal α-helix-turn-α-helix (HTH) DNA binding domain (IPR018060; Figures  S1A  and B ).
This domain, known to consist of seven alpha helices, is also found in a large family of proteins whose founding member is AraC, a transcriptional activator involved in the control of the arabinose metabolism in Escherichia coli  [27]. The third gene of the CA4-A island (Cyanorak cluster CK_00009137) is predicted to encode a protein of 55-67 aa and characterized by a Nterminus with a predicted conserved ribbon-helix-helix domain (IPR010985), also found in several bacterial or phage repressors (Figure  S1C ;  [28] ). Thus, together with the two araC-like gene products, the protein encoded by this gene may be involved in regulating the CA4 process. Based on their genomic context and position just upstream of and in the same reading frame as the CA4-specific phycobilin lyase mpeZ  [20] , we propose to call these three genes fciA-C (where "fci" stands for "type four chromatic acclimation island"; Figure  1 ).
Another gene of unknown function was consistently found at the 5'-end of the CA4 island, but was encoded on the reverse strand relative to the fci genes. This gene is an ortholog of unk10 (Cyanorak cluster CK_00002279), previously observed in the phycobilisome rod gene region of pigment type 3c (i. e. high-PUB) strains WH8102 and CC9605, specifically at the 5'-end of the PEII sub-region (see Figure  6  in  [7] ). Unk10, predicted to be 107-118 aa in length, contains a ~43 aa N-terminal Nif11 domain (IPR012903; e-value: 2. 3 10 -8 ), so-called because it was initially found in the product of an uncharacterized gene of the nif cluster of Azotobacter vinelandii  [29]. In four out of the nine mpeZ-containing strains (RS9916, BL107, CC9902 and RCC307), another gene, encoding a predicted 121-123 aa protein of unknown function, was present upstream and headto-head relative to unk10 (Figure  1 ).
This gene, which was designated unk14 (to extend the numbering of unknown genes in the phycobilisome rod region proposed by  [7] ) (Cyanorak cluster CK_00009135), is specific to these four strains, but the encoded protein is paralogous to a protein of unknown function, whose gene is present in all marine Synechococcus strains (Cyanorak cluster CK_00000072). Unk14 belongs to the highly conserved protein family DUF2237 (or PF09996), which is widely distributed among Bacteria. As a second step, we searched for the fciA and fciB genes in other Synechococcus genomes. This led to the discovery of one additional CA4-A strain lacking mpeZ (WH8016) as well as another type of CA4 island in six additional genomes (Figure  1 ). In contrast to the first one, this so-called "CA4-B island" lacks both fciC and mpeZ.
Short CA4 genes of unknown function are named according to  [7] , with unk14 and fciC being novel genes, specific of the CA4-A region. Genes found in the immediate vicinity of CA4 regions (shown in grey) are designated either by their gene names or their cluster number in the Cyanorak v2. 0 database of orthologous sequences (http://www. sb-roscoff. fr/Phyto/cyanorak/; with the prefix CK_ being omitted). One stop codon interrupting WH8016 fciA is indicated by a star. The phylogenetic affiliation of strains, as reported in previous studies (see text), is mentioned between brackets. The pigment type is indicated by circles bi-colored for strains that chromatically adapt and uni-colored for those that do not (see Table  1  for details). Hotspots for DNA recombination, hli (Cluster CK_00000050) and psbA (Cluster CK_00000009), are bordered by a red line.
Phylogenetic analyses of genes putatively involved in CA4 In order to better understand the evolution of the CA4 process and notably the origin of the differentiation into two distinct CA4 islands, we performed a number of phylogenetic analyses using genes that are common to the two island types CA4-A and CA4-B (fciA, fciB, unk10 and mpeW or mpeZ). Figure  2  shows a phylogenetic tree based on FciA and FciB sequences and rooted with two more distant, uncharacterized members of the AraC family retrieved from marine Synechococcus spp. (Syn8016DRAFT_2956 and WH7805_05326; Cyanorak cluster CK_00002282). As for other cyanobacterial members of the AraC family, their homology with FciA and FciB mostly resides at the C-terminal HTH domain level (data not shown). Orthologs of both Fci proteins separated into two main branches, the first one including all strains that have a CA4-A island (except BIOS-E4-1, which lacks fciAB genes) and the second one encompassing five out of the six strains having a CA4-B island (Figures  1  and 2 ).
CA4 induces a change in the expression of phycobilin lyase genes The inability of Synechococcus spp. WH8016 and BIOS-E4-1 to acclimate to changes in light quality may be due to the absence of key CA4 genes in these genomes (i. e. , mpeZ and fciAB, respectively; Figure  1 ). Less predictably, strains WH8109, WH8103, MVIR-18-1 and RCC307, which possess a complete set of CA4 genes (Figure  1 ), are also affected in their ability to perform CA4 (see above). Comparative analyses of protein sequences did not reveal any obvious mutation/deletion or insertion in the most conserved regions of FciA/B (data not shown) or MpeW/Z (Figure  S4 ) that could explain this loss of CA4 phenotype. To check whether the absence of response could result from an alteration of the regulation of the transcript abundance of the characterized and putative CA4-specific lyase-encoding genes, mpeZ or mpeW transcript levels were measured in exponentially growing strains acclimated to either LGL or LBL in a representative selection of strains.
DNA was quantified using Qubit (Invitrogen, Carlsbad, USA) before sequencing using the Illumina technology starting from both pair-end (2 x 100bp) and mate-pair (2 x 100 bp, insert size: 4-10 kbp) libraries. Assembly into contigs was performed using the CLC AssemblyCell software (CLCBio, Prismet, Denmark). CA4 gene regions extracted from these genomes were deposited in the GenBank nucleotide sequence database under the following accession numbers: KF177881-KF177905 (Table  S1 ), after manual annotation using the Artemis software  [56]  and the Cyanorak v2. 0 information system (http://www. sbroscoff. fr/cyanorak). The presence of genes involved (or potentially involved) in chromatic acclimation was checked in marine Synechococcus genomes, by BLASTP searches  [57]  using BioEdit v7. 0. 9. 0  [58]  against all 43 available marine Synechococcus genomes.
Growth rates (µ in d -1 ) were computed as the slope of a Ln(Nt) vs. time plot, where Nt is the cell concentration at time t. The half acclimation time (T 50 in d) was calculated using the regression wizard of SigmaPlot (v. 12, Systat Software, San Jose, CA) by fitting a sigmoidal non-linear regression to data with the following equation: f(x) = min + (max-min) / (1 + 10 logT50-x x a), where a is the slope of the curve. Minimum (min) and maximum (max) values were defined as average values of the Exc 495:545 ratio of the GL and BL controls, respectively. Kinetics of acclimation over successive shifts was also monitored in Synechococcus strain BL107. During the whole experiment, the two biological replicates were maintained in exponential growth phase by diluting cultures every two days to a final concentration of ~8.
qPCR analyses were performed using the 480 II (Roche, Boulogne-Billancourt, France) and the absolute SYBR Green ROX Mix (Abgene, Epsom, UK) using for each data point three biological and three technical replicates, corresponding to independent cultures and qPCR measurements, respectively. Reactions were incubated for 15 min at 95°C, followed by 45 cycles of 10 s at 95°C, 10 s at 60°C and 15 s at 72°C, terminated by a ramping from 65 to 97°C in order to perform a fusion curve. Quantification of the relative fold change in mRNA levels was.
0 database of orthologous sequences (http://www. sb-roscoff. fr/Phyto/cyanorak/; with the prefix CK_ being omitted). One stop codon interrupting WH8016 fciA is indicated by a star. The phylogenetic affiliation of strains, as reported in previous studies (see text), is mentioned between brackets. The pigment type is indicated by circles bi-colored for strains that chromatically adapt and uni-colored for those that do not (see Table1for details). Hotspots for DNA recombination, hli (Cluster CK_00000050) and psbA (Cluster CK_00000009), are bordered by a red line.
Acknowledgements We are grateful to  Anton Post  who provided unpublished genomic data from Synechococcus sp. WH8020. We thank the Genoscope, particularly  Karine Labadie ,  Jean-Marc Aury  and  Patrick Wincker  for whole genome sequencing of 25 additional Synechococcus strains and assembly of preliminary data, in the framework of the  METASYN  project. We warmly thank  David J. Scanlan  and  Florence Legall  for isolating some of the Synechococcus strains used in this study as well as the  Roscoff Culture Collection  for maintaining them.
This work was supported by the  French "Agence Nationale de la Recherche "  Microbial Genomics Programme  ( PELICAN ,  ANR-09-GENM-030 ), the  European Union 's  Seventh Framework Programmes FP7 MicroB3  (grant agreement  287589 , http://www. microb3. eu)and  MaCumba  (grant agreement  311975 , http://www. macumbaproject. eu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: Cell-specific gene expression is controlled by epigenetic modifications and transcription factor binding. While genome-wide maps for these protein-DNA interactions have become widely available, quantitative comparison of the resulting ChIP-Seq data sets remains challenging. Current approaches to detect differentially bound or modified regions are mainly borrowed from RNA-Seq data analysis, thus focusing on total counts of fragments mapped to a region, ignoring any information encoded in the shape of the peaks. Results: Here, we present MMDiff, a robust, broadly applicable method for detecting differences between sequence count data sets. Based on quantifying shape changes in signal profiles, it overcomes challenges imposed by the highly structured nature of the data and the paucity of replicates.
We first use a simulated data set to compare the performance of MMDiff with results obtained by four alternative methods. We demonstrate that MMDiff excels when peak profiles change between samples. We next use MMDiff to re-analyse a recent data set of the histone modification H3K4me3 elucidating the establishment of this prominent epigenomic marker. Our empirical analysis shows that the method yields reproducible results across experiments, and is able to detect functional important changes in histone modifications. To further explore the broader applicability of MMDiff, we apply it to two ENCODE data sets: one investigating the histone modification H3K27ac and one measuring the genome-wide binding of the transcription factor CTCF. In both cases, MMDiff proves to be complementary to count-based methods.
In addition, we can show that MMDiff is capable of directly detecting changes of homotypic binding events at neighbouring binding sites. MMDiff is readily available as a Bioconductor package. Conclusions: Our results demonstrate that higher order features of ChIP-Seq peaks carry relevant and often complementary information to total counts, and hence are important in assessing differential histone modifications and transcription factor binding. We have developed a new computational method, MMDiff, that is capable of exploring these features and therefore closes an existing gap in the analysis of ChIP-Seq data sets.
Background Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is rapidly becoming the main experimental technique in functional genomic and epigenomic studies. ChIP-Seq's ability to profile genome-wide patterns of transcription factor binding and histone modifications has led to its extensive use by the ENCODE consortium  [1]  in an endeavour to identify and characterise all functional elements encoded in the human genome. Despite the widespread use of ChIP-Seq, data analysis is still a challenging task  [2]  and a typical computational pipeline includes a number of steps, each posing its own difficulties. An initial crucial step is the identification of regions with significant signal enrichment relative to a control sample in a process called peak calling. Over the last years, several tools for this task have been suggested and they have recently been compared in  [3].
To compound both of these problems, ChIP-Seq produces spatially distributed patterns of binding or histone modifications localised to specific regions of the genome (peaks); this feature, in particular, renders standard differential testing methods unsuited for the comparison of ChIP-Seq data sets. Currently, two strategies are predominantly followed for the differential analysis of ChIP-Seq data sets: The most naive approach is to identify overlaps in the sets of genomic peak intervals detected in the different samples, e. g. ,  [4] [5] [6]. This simplifies the problem to a basic occupancy analysis which is insensitive to changes in the affinity of TF binding or in the prevalence of histone modifications. In addition, the results are strongly dependent on the thresholds which are set heuristically in the peak calling step and differences in the noise background may further confound the outcome of this analysis.
An alternative strategy is to compute the total number of reads mapping to each peak in each data set and to test for significant fold-changes across multiple tissues or conditions, e. g. ,  [7]. These count-based approaches have mostly advocated the adaptation of methods for RNA-Seq data analysis to the more structured ChIP-Seq data. For example, the frequently used methods DBChIP  [8]  and DiffBind  [7]  are based on the RNA-Seq methods DESeq  [9]  and EdgeR  [10]. They employ a negative binomial distribution to model both biological and technical noise in the total counts of expressed genes. To circumvent the problems of low experimental replication, they apply an elegant approach in which information is shared across genes, effectively pooling together genes with similar total counts. An immediate problem arising for countbased methods is finding the right normalisation.
In this context a sample consists of all the reads mapping to a given peak region in one data set. As there is a large variability of observed peak profiles at different genomic locationssome may weakly resemble a Gaussian distribution, however most are strongly skewed and/or multi-modal (see Figure  1 ) -we cannot make any assumption about the type of distribution. We therefore adopt recent advances in machine learning research  [21, 22] , which enable us to include features of any order in the prediction of differential binding without making assumptions of the underlying distributions. MMDiff is specifically designed to detect differences between different ChIP-Seq data sets, however, its main idea can also be used to address the more general problem of detecting differences in other sequencing based experiments, for example in DNase-Seq or CAGE-Seq data sets.
Recently, a similar approach has been employed for the detection of differential RNA isoforms from RNA-Seq data  [23]. We illustrate and compare our method on a simulation study, and on three independent ChIP-seq data sets of both transcription factor binding and epigenomic modifications. Our results show that MMDiff can capture biologically meaningful changes and is highly complementary to count-based approaches. We propose that MMDiff provides an important new tool for bioinformaticians and biologists interested in epigenomic data analysis, conveniently available as a Bioconductor tool. The rest of the paper is organised as follows: we start with a discription of the statistical foundations of our method and a discussion on how the MMD statistic of  [21]  is modified to account for biological variability.
We complete the Methods section with a thorough simulation study which compares the results of our method to four different competitors in a controlled environment. This enables us to discuss the strengths and weaknesses of the various methods, and in particular highlights the complementarity of the MMDiff approach w. r. t. count-based methods. We then present results on three different data sets: we start with an in-depth analysis on the H3K4me3 data set of  [24]. As this study constitutes our main biological motivation, we present multiple complementary analyses that demonstrate the functional significance of our results. To establish the broad applicability of our method, we also present results on two ENCODE data sets: a comparison of the histone mark H3K27ac across different human cell lines (K562 and GM12878), and a comparison of binding patterns of the transcription factor CTCF across different mouse tissues (cortex, cerebellum and liver).
Accounting for biological variability The bootstrap procedure for computing MMD statistics proposed in  [21]  has strong theoretical guarantees for discriminating between different distributions, given sufficient number of samples (i. e. reads mapped to a peak). A simulation study shows that the procedure appears to be well calibrated when comparing technical replicates of ChIP-Seq data (see Additional file 1). However, biological variability implies that the histogram distributions of the same peak in different biological replicates will be more different than expected. This turns out to be true, and the testing procedure of  [21]  rejects the null hypothesis in almost all comparisons between biological replicates (see Additional file 1). In order to avoid this problem, we adopt a data-driven method to estimate biological variability from biological replicates.