dataesr/scientific-paragraphs-categorization huggingface dataset:
The Analysis of ChIP-seq Data Sequencing Depth Effective analysis of ChIP-seq data requires sufficient coverage by sequence reads (sequencing depth). The required depth depends mainly on the size of the genome and the number and size of the binding sites of the protein. For mammalian transcription factors (TFs) and chromatin modifications such as enhancer-associated histone marks, which are typically localized at specific, narrow sites and have on the order of thousands of binding sites, 20 million reads may be adequate (4 million reads for worm and fly TFs)  [7]. Proteins with more binding sites (e. g. , RNA Pol II) or broader factors, including most histone marks, will require more reads, up to 60 million for mammalian ChIP-seq  [8]. Importantly, control samples should be sequenced significantly deeper than the ChIP ones in a TF experiment and in experiments involving diffused broad-domain chromatin data.
This is to ensure sufficient coverage of a substantial portion of the genome and non-repetitive autosomal DNA regions. To ensure that the chosen sequencing depth was adequate, a saturation analysis is recommended-the peaks called should be consistent when the next two steps (read mapping and peak calling) are performed on increasing numbers of reads chosen at random from the actual reads. Saturation analysis is built into some peak callers (e. g. , SPP  [9] ). If this shows that the number of reads is not adequate, reads from technical replicate experiments can be combined. To avoid over-sequencing and estimate an optimal sequencing depth, it is important to take into account library complexity. Several tools are available for this purpose. For example, the preseq package allows users to predict the number of redundant reads from a given sequencing depth and how many will be expected from additional sequencing  [10].
g. , if the protein binds frequently in repetitive DNA). The read mappers are designed to allow a (user-settable) number of mismatches in the reads, and it is important to choose this parameter to be appropriate with the NGS platform being used (consult the manufacturer). A final potential cause of high numbers of ''multimapping'' reads is that the protein binds frequently in regions of repeated DNA. In this last case, using paired-end sequencing to reduce the mapping ambiguity may help. It should be kept in mind that multi-mapping reads will be ignored (filtered out) by most peak-calling algorithms (see section ''Peak Calling''), although they can drive the discovery of novel binding sites  [15]. After mapping, the signal-to-noise ratio (SNR) of the ChIP-seq experiment should be assessed, for example via quality metrics such as strand cross-correlation  [7]  or IP enrichment estimation using the software package CHANCE  [16]  (Box 2).
Duplicate reads (same 59 end) can be removed before peak calling to improve specificity (Box 7). Although some peak callers support both single and paired-end reads (e. g. , MACS), others Preprocessing of ChIP-seq data will, in general, be similar to that of any other sequencing data and will assess the quality of the raw reads to identify possible sequencing errors or biases (FastQC can be used for an overview of the data quality). Phred quality scores are used to describe the confidence of each base call in each sequence tag, are logarithmically linked to error probabilities, and can be used to filter low-quality reads. After this filtering step, it may also be necessary to trim the end of reads that are of low quality (see sickle, https://github. com/ najoshi/sickle). Additionally, library complexity is a common quality measure for ChIP-seq libraries (preseq package  [10]  or PCR bottleneck coefficient [PBC] from ENCODE tools, https://code.
Thus it is important to remove the artefact regions with high ChIP signals, such as regions near centromeres, telomeres, satellite repeats, and ENCODE and 1000 Genomes blacklisted regions, before computing the PCC. To measure the reproducibility at the level of peak calling, IDR analysis (Box 8)  [23]  can be applied to the two sets of peaks identified from a pair of replicates. This analysis assesses the rank consistency of identified peaks between replicates, and outputs the number of peaks that pass a user-specified reproducibility threshold (e. g. , IDR = 0. 05). It has been reported that using a reproducibilitybased metric (e. g. , IDR) rather than an enrichment-based metric (e. g. , FDR or pvalue) makes the numbers of peaks declared more comparable across experiments  [7]. In addition, IDR analysis can also be used for comparing and selecting peak callers  [8, 23]  and identifying experiments with low quality  [7].
The software CHANCE  [16]  assesses IP strength by estimating and comparing the IP reads pulled down by the antibody and the background, using a method called signal extraction scaling  [77]. For each sample, it first bins the genome into nonoverlap bins both for the IP and the Input, then partitions the bins into a signal region and a background region by comparing the cumulative distributions of tag counts in the bins of the IP and the Input. It next computes a p-value for significance of enrichment according to the percentage allocation of reads in each type of regions. Based on the empirical p-value distribution computed from a set of ENCODE IP-Input and Input-input experiments on human data, it estimates a q-value by treating the two types of experiments as true positives and false positives, respectively.
The q-value thus is interpreted as the fraction of comparisons with ENCODE data that show differential enrichment at the level of the user's data but turn out to be technical replicates of the Inputs. The software determines the success of the experiment based on the q-values, and also reports some descriptive quality statistics, such as the percentage increase in mean tag density in IP compared to Input and the percentage of the genome classified as signal region. Because the q-values are computed based on human data, users should be aware that the q-values may not be relevant if their data are generated from other organisms. CHANCE also provides a graphical visualization of IP strength with genome coverage, by plotting the empirical cumulative percentage of tags covered by the bins that are sorted in an increasing order of read density for both the IP and the Input.
It is strongly advised to verify that the data fulfill the requirements of the software chosen for the analysis. For instance, DIME  [35]  assumes that a significant proportion of peaks are common to the conditions under comparison, MAnorm assumes that peaks that are common in both conditions do not change significantly, while other methodologies may expect a constant number of peaks across conditions  [25]. Importantly, with some tools only two conditions can be submitted simultaneously for comparison (e. g. , MAnorm), and some may perform better depending on the protein ChIPed (e. g. , ChIPDiff  [36]  for histone marks and POLYPHEMUS  [37]  for RNA Pol II).
g. , TSS), or to identify the genes within a given distance of a peak. The output of such ''location analyses,'' obtained for instance using CEAS  [42]  or the Bioconductor package ChIPpeakAnno  [43] , can be further correlated with expression data (e. g. , to determine if proximity of a gene to a peak is correlated with its expression) or subjected to a gene ontology analysis (e. g. , to Box 3. Peak calling: Punctate-source transcription factors Nowadays, since ChIP-seq data of point-source factors are the most abundant type, most peak callers are designed and fine-tuned for these factors. Existing peak callers differ from each other in terms of signal smoothing and background modeling. Because DNA around interaction sites is more easily sheared, the ends of ChIPed DNA fragments would form footprints on DNA whose size is more related to protein-DNA interaction than to size selection during library preparation.
A modified version of this non-linear normalization is implemented as MAnorm  [34] , assuming that peaks common in the two conditions do not undergo global changes. The R package called POLYPHEMUS  [37]  has also been developed, implementing two normalization methods: (1) the non-linear method described in  [28]  and (2) a Quantile normalization that makes the distribution in different samples the same. Normalization issues are, at present, not fully exploited although they might have a substantial impact on the results  [28, 37, 99]. transcription factors, the transcriptional machinery, or cofactors. The effects of context-dependent regulatory mechanisms can fundamentally differ from the effects of individual binding events  [66]. To address this issue, the Genome Positioning System (GPS) resolves closely spaced peaks using a segmented expectation maximization algorithm  [67].
Biological functions of binding sites are not necessarily indicated by the reproducibility of peaks or FDR/IDR values (Box 8,  [7, 23, 77, 78] ). This issue re-emerged during the ENCODE Project that produced unprecedented regulatory information  [66, 79]  under rigorous quality standards  [7]. DNA-protein binding is dynamic, and the measured strength of a binding event depends (among other things) on the fraction of cells in the (often inhomogeneous) sample where it occurs, as well as the proportion of the time it is occupied in a given cell. Hence, ''weak'' binding sites, regardless of what significance threshold is used, may have strong biological functions  [80] [81] [82]. ChIP-seq will also detect indirect DNA binding by the protein (via another protein or complex), so predicted sites not containing the motif may also be functional.
An R package for computing IDR is given in  [23]. Prototypical examples illustrated using ENCODE data may be found in  [7]. When using IDR, a relatively relaxed peak-calling threshold is advised because the IDR algorithm requires sampling of both signal and noise distributions to assess the reproducibility of peaks. A major advantage of the IDR method is that it is independent of the peak-calling algorithms and can be applied to a variety of significance criteria, across labs and platforms. It has been shown that it produces a stable threshold that is more consistent across laboratories, antibodies, and analysis protocols (e. g. , peak callers) than FDR measures  [7]. assessing reproducibility of ChIP-seq data sets. Panel A shows a scatterplot of the significance scores of peaks identified in two replicate ChIP-seq experiments.
TLB is funded by  National Institutes of Health  grant  R0-1 RR021692-05. PM was supported by the  EU Marie Curie Initial Training Network SYSFLO  (agreement number  237909 ). QL is partially supported by  National Institutes of Health  grants  1UL1 RR033184-01  and  R01 GM109453. IL gratefully acknowledges support from  UNL IANR. The funders had no role in the preparation of the manuscript.
This paper reports recent work on the extension of the multiblock structured solver elsA to deal with hybrid grids. The new hybrid-grid solver is called elsA-H (elsA-Hybrid), is based on the investigation of a new unstructured-grid module has been built within the original elsA CFD (computational §uid dynamics) system. The implementation bene¦ts from the §exibility of the object-oriented design. The aim of elsA-H is to take advantage of the full potential of structured solvers and unstructured mesh generation by allowing any type of grid to be used within the same simulation process. The main challenge lies in the numerical treatment of the hybrid-grid interfaces where blocks of di¨erent type meet. In particular, one must pay attention to the transfer of information across these boundaries, so that the accuracy of the numerical scheme is preserved and §ux conservation is guaranteed.
In the ¦eld of helicopter aerodynamics, Wissink et al. [4]  have applied the hybrid approach by resorting to the external coupling of two existing CFD solvers: a body-¦tted unstructured solver and a high-order block-structured Cartesian solver. The objective of this work is to exploit the §exibility of hybrid-grid generation and associated algorithms based on an extension of the capabilities of the CFD solver elsA  [5]  to deal with unstructured grids. The new hybrid solver is called elsA-Hybrid (elsA-H). The material presented in this paper is organized in the following manner. Firstly, a brief overview of elsA£s general architecture and kernel design is provided. Then the features speci¦c to the implementation of elsA-H, including the general organization of the code (object-oriented design), data structure, and input/output format, are discussed.
Available time-marching strategies include the multistage RungeKutta scheme with implicit smoothing of residuals, and the implicit backward Euler scheme associated with an LU factorization technique. Time-accurate simulations are performed using the implicit dual time-stepping method. A number of turbulence models are available in elsA, including models based on the turbulent viscosity hypothesis and Reynolds-stress models. More advanced models such as the detached-eddy simulation (DES) and the large-eddy simulation (LES) techniques can also be used for the simulation of unsteady turbulent §ows. Laminarturbulent transition prediction models, well-adapted to complex con¦gurations, have also been implemented in elsA. Other signi¦cant features o¨ered by elsA are its aeroelasticity module  [11, 12] , and an optimization module based on adjoint techniques  [13].
The Hybrid-Grid Solver elsA-H The extension of elsA to mixed structured-unstructured con¦gurations is based on the introduction of new generic parent classes from which purely structured and unstructured objects and methods inherit. This approach permitted the development of a new unstructured module within elsA, avoiding the need for external code coupling. Following elsA£s own numerical choices, elsA-H is based on a multiblock cell-centered ¦nite-volume method. The unstructured solver relies on a face-based approach, which involves a preliminary preprocessing stage to convert the element-based connectivity provided by the grid generator into the face-based connectivity required by the solver. A large number of Fortran routines have actually been reused (with minor or no changes) in the development of the new unstructured solver.
As an example, most of the original (structured) classes composing the turbulence module have been readily used in elsA-H, since the unstructured numerical method is formally identical to the structured one. However, dedicated unstructured classes have been introduced for the computation of the gradient terms and the application of the boundary conditions. elsA-H supports multielement grids which may contain hexahedra, tetrahedra, pyramids and prisms. The exchange of data between neighboring blocks is performed through one single layer of ghost cells, requiring adapted numerical schemes at the hybrid-grid interfaces. This point will be addressed in detail in subsection 3. 5. The main input data format supported by elsA-H is the CFD General Notation System (CGNS)  [14] , which provides an easy representation of structured, unstructured and hybrid grids in one single ¦le.
The approach adopted in elsA-H aims at remaining as consistent as possible with the structured method. Note that in the purely structured multiblock algorithm implemented in elsA two ghost layers are employed for a second-order scheme, whereas the purely unstructured and hybrid methods developed in elsA-H consider using one single ghost layer. Using the notation of Fig. 1 , for an interface normal to the i-direction in the structured mesh, one can write: d S 4h = δ 2 U U g -δ 2 U S i ; d U 4h = δ 2 U S g -δ 2 U U i in which the superscripts S and U refer to quantities computed following the structured and unstructured methods, respectively. The second di¨erence terms are then computed as follows: δ 2 U U g = j(g) U gj -U g ; (23) δ 2 U S i = U i+1 -2U i + U g which de¦nes d S 4h in the structured domain.
Two observers on the other, higher platform, known as Tracker, searched from 500 m to the horizon with high-power ''big eye'' (25 Â 100) binoculars (80°sector centred around the transect line) and 7 Â 50 binoculars (120°sector). Animals outside the sector searched by Tracker should not have been able to enter the region searched by Primary. Tracker observers tracked detected animals until they were aft of the vessel. Observers not searching acted as duplicate identifier on Tracker, or data recorder on Primary, or rested. The duplicate identifier assessed whether or not groups of animals detected by Tracker were re-sighted by Primary. Duplicates were classified as Definite (D: at least 90% likely), Probable (P: between 50% and 90% likely), or Remote (R: less than 50% likely). The data recorder recorded all sightings, effort and environmental data into a laptop computer running the LOGGER software, modified specifically for the survey  (Gillespie et al.
Angles and distances were calculated from captured video frames using purpose-written software. Additional data collected from each detected group of animals included: cue, species composition, group size, swimming direction and behaviour. Data validation software was developed for checking all data at the end of each day. Data could also be recorded on pre-prepared paper data sheets in the event of data collection system failure. The effectiveness of the automatic data collection of distance and angle measurements and a comparison of estimated and measured values are described in  Leaper et al. (2010).
Aerial survey There were three scientific members of the crew in each aircraft, which flew at an altitude of 183 m and a speed of 90 knots (167 km h À1 ). One acted as navigator and data recorder for environmental and sightings data, entered real-time into a laptop computer running purpose-written data collection software. Sighting conditions were classified as good, moderate or poor based primarily on sea conditions, water turbidity and glare. Conditions were typically good with Beaufort 62 or moderate with Beaufort 63. The two observers sat at bubble windows on the right and left sides of the aircraft. The times when detected groups came abeam were given verbally over the intercom by the observers and recorded by the navigator. Other data recorded included declination angle to the detected animal or group (from which perpendicular distance was calculated), cue, presence of calves, behaviour, species composition and group size.
Shipboard survey Analysis of the shipboard data followed the double-platform line transect methodology used in the SCANS survey  (Borchers et al. , 1998; Hammond et al. , 2002; Laake and Borchers, 2004). Analyses were undertaken using software DISTANCE  (Thomas et al. , 2010). To estimate the probability of detection, sightings made from the Tracker platform served as a set of binary trials in which success corresponded to detection by observers on the Primary platform. The probability that a group of animals, at given perpendicular distance x and covariates z, was detected from Primary is denoted p 1 (x, z) and modelled as a logistic function (see Eq. (  9 ) in  Borchers et al. , 1998). Although observers on Primary acted independently from those on Tracker, dependence of detection probability on unmodelled covariates may induce correlation in detection probabilities and potentially lead to bias.
Models were selected using Akaike's Information Criterion (AIC). The extent of responsive movement was investigated by inspection of plots of Tracker and Primary perpendicular distance for duplicate detections (Supplementary Appendix, Fig. A1 ) and of data on direction of travel of detected groups following the methods described in  Palka and Hammond (2001)  (Supplementary Appendix, Table  A1 ). Perpendicular distance data for modelling detection probability were generally truncated at the largest value recorded by observers on Primary but for some species the data were truncated further to allow reliable models of detection probability to be fitted. For harbour porpoise, data obtained while surveying in sea conditions of Beaufort 2 or less were used; for other species data from sea conditions of Beaufort 4 or less were used.
If the group size correction was estimated as <1, it was set to 1. Estimates of mean group size were obtained by dividing abundance of individuals by abundance of groups. Variance was estimated using a transect-based non-parametric bootstrap procedure in which the block structure of the data was preserved. Encounter rate variance was estimated using the method of  Innes et al. (2002). Investigation of the sensitivity of shipboard estimates to uncertainty in duplicate classification is described in Supplementary Appendix, Tables  A2  and A3. Where there were insufficient duplicate sightings to support double-platform methods, conventional line transect methods (assuming certain detection on the transect line) were used to obtain the detection function. For these analyses mean group sizes were estimated from detections made from the Tracker platform (see  Hammond et al.
Aerial survey Analysis of the ''racetrack'' data for harbour porpoise used the methods of  Hiby (1999) , and  Hiby and Lovell (1998)  to estimate total effective strip width (both sides of the transect) in good and moderate sighting conditions lg and lm , respectively), taking account of detection probability less than 1 on the transect line. Abundance was estimated as: b N ¼ A L n g lg þ n m lm s where A is area, L is length of transect searched in good or moderate conditions, n g and n m are number of sightings made in good and moderate conditions, respectively, and s is mean observed group size. Variance was estimated by bootstrapping within blocks. A parametric bootstrap was used to generate effective strip width estimates, which were combined with encounter rates obtained from a non-parametric transect-based bootstrap procedure.
This formulation assumes a logarithmic link function; we assumed the error distribution was an over-dispersed Poisson with mean-variance relationship l = ur 2 where u is a scaling parameter estimated from the data. Available covariates were latitude, longitude, distance from the coast, seabed depth and slope. The best fitting models were parameterised in all cells in a grid (0. 066°latitude Â 0. 033°longitude; similar resolution to segment length) to generate a density surface over the whole study area. These surfaces show broad scale variation in abundance; they should not be used to make inferences at fine spatial scales. Density surface modelling was carried out in R (R Development Core Team, 2010) using package mgcv for modelling with GAMs  (Wood, 2006).
Harbour porpoise For the shipboard analysis, the data were truncated at 1000 m perpendicular distance. The selected final model for detection probability included Beaufort and vessel as covariates, in addition to perpendicular distance (Supplementary Appendix, Fig. A2 ). Estimated detection probability on the transect line, conventionally known as g(0), for Primary was 0. 216 (CV = 0. 16). There was evidence of responsive movement (avoidance -Supplementary Appendix, Fig. A1, Table A1 ). For analysis of aerial survey data, the total effective strip width was estimated to be 187 m (CV = 0. 30) under good conditions and 107 m (CV = 0. 31) under moderate conditions, incorporating g(0) values of 0. 45 and 0. 31, respectively. Visual inspection of the data indicated no dependence of group size on perpendicular distance nor was group size found to be a significant explanatory covariate in the estimation of effective strip width.