dataesr/scientific-paragraphs-categorization huggingface dataset:
ENSEMBL-based proteogenomic databases To enable the generation of ENSEMBL-based proteogenomic databases, we have also built the Proteomics-Genomics DataBase  (pgdb-https://nf-co. re/pgdb) workflow in Nextflow using bioconda and BioContainers. The pipeline integrates the various commands of pypgatk allowing the user to generate protein databases by simple parameter selection without any additional input required from the user. Also, the pipeline can be used to generate protein databases for any ENSEMBL species, except for the processes that are dependent on data that are only available for Homo sapiens.
Identification of non-canonical peptides We applied pgdb to generate cell-type specific databases for 64 human cell lines (Fig. 1b  and Supplementary Note S1 and S2). Mutations from the COSMIC Cell Line project and the Broad CCLE project through cBioPortal were downloaded for each cell line to generate the respective set of variant protein sequences. Additionally, a database of non-canonical proteins was generated from the latest human genome assembly. The variant protein database from each cell line was appended to the non-canonical and canonical protein databases and the decoy sequences were generated to search MS/MS proteomics datasets from the corresponding cell lines. The proteomics data were obtained through the PRIDE database (PXD005946, PXD019263, PXD004452 and PXD014145). proteomicsLFQ (https://nf-co.
re/proteomicslfq) was used to identify the novel peptides (Supplementary Note S3). Overall, 402 512 target peptide sequences were identified, including 43 501 non-canonical peptides and 786 variant peptide sequences (Table  1  and Supplementary Note S4 and S5). The majority of the non-canonical peptides were novel coding sequences in their entirety whereas only 16% matched canonical protein sequences with one amino acid mismatch. Additionally, we reanalyzed two mice datasets (PXD018891 and PXD006439) obtained from the B16 melanoma cell line. A proteogenomic database was generated using mice germline variants from the ENSEMBL variation database (release 104) and somatic mutations detected in mouse melanoma tumors. Overall, 374 variant peptides and 166 mutated peptides were identified. The identified peptides with the corresponding mass spectra and metadata annotations can be accessed via ProteomeXchange (PXD029360 and PXD029362).
Conclusions The developed tools facilitate the creation of proteogenomics databases based on ENSEMBL genomes and other relevant sources of genome variation information. The pgdb is the first Nextflow workflow for proteogenomics database generation and its development within the nf-core community will ensure its stability, continued development and community support. pypgatk (https://pgatk. read thedocs. io/en/latest/pypgatk. html) and pgdb (https://nf-co. re/pgdb/ 1. 0. 0/usage) include extensive documentation to help researchers create their custom proteogenomics databases.
Funding: This work is supported by the  Malaysia Ministry of Higher Education (MOHE)  under the  Fundamental Research Grant Scheme  (FRGS)  FRGS17-030-0596. This work benefited from French government support managed by the  National Research Agency  under the  Investments for the Future program  with the reference  ANR-10-LABX-0074-01 Sigma-LIM. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Atmospheric Cherenkov light simulations Atmospheric shower simulations. We performed air shower simulations using the CORSIKA package (version 7. 4387)  [4] , as described in  [5] , but with added atmospheric Cherenkov light emission according to the Bernlöhr model  [6]. With already existing simulations for the ALTO array, we had to ensure that it would be possible to include the new CLiC related information without repeating the time-consuming GEANT4  [7]  simulations. Since CLiC stations are triggered by their corresponding ALTO clusters, we made sure that each simulated shower was exactly the same on the particle side, but with added atmospheric Cherenkov light information. This led to a number of CORSIKA modifications, including larger step sizes during Cherenkov light emission, treatment of particles below their energy cut-off and implementation of a separate random number generator to keep all original random numbers unchanged.
These modifications affected the number of photo-electrons seen by the CLiCs, with the largest differences observed close to the shower core. On average, we tend to get additional photons at low primary energies and a photon deficiency at high energies. For -rays the maximum difference is < 6%, while for protons it is < 12% over the energy range of CoMET. This mismatch caused by CORSIKA modifications will be studied in greater detail in a future study, and subsequently corrected during the CLiC detector response step. CLiC detectors response. After the CORSIKA shower simulation, the emitted Cherenkov photons have to be followed from their emission point until they produce a signal in one of the CLiC PMTs. For this treatment, we produced a detector simulation code, which is based on the sim_telarray simulation package  [6].
In a supervised learning approach,  Hocking et al. [2017]  introduced seven labeled histone mark datasets that are composed of samples from two different ChIP-seq experiments directed at histone modifications H3K36me3 and H3K36me3 (see Section 4). In a recent study, after training different peak callers using these datasets,  Hocking et al. [2020]  compared them and showed that the constrained segmentation model with coverage data following a Poisson distribution outperforms standard bioinformatics heuristics and the unconstrained segmentation model on these datasets. Modeling question. From a modeling perspective the constrained segmentation model and the Poisson noise are certainly the most natural assumptions to detect peaks in count data. However, it is not clear that they are realistic: • By looking at the shapes of the peaks in coverage profiles (see for instance in figure  1 ), we can see that the background noise and the top of them are sometimes separated by one or more subtle changes.
In contrast to the constrained segmentation model, the unconstrained one should be able to capture these subtle changes. One major issue is that the output segmentation of the unconstrained model does not have a straightforward interpretation in terms of peaks. • Parametric models such as the negative binomial  [Robinson et al. , 2010 , Love et al. , 2014]  or the Gaussian, following a proper transformation of the count data for the latter  [Francis, 1948 , Law et al. , 2014] , are preferred over the Poisson one for the analysis of many high-througput sequencing datasets. Indeed, count data often exhibit more variability than the Poisson model expects which changes the interpretation of the model and makes it difficult to estimate its parameters. These alternative parametric models are well known to reduce this phenomenon, also called over-dispersion.
Contribution. 1. We show that the distribution of counts from H3K36me3 and H3K4me3 datasets exhibits over-dispersion which invalidates the Poisson assumption. The two alternative noise models we propose (negative binomial with constant dispersion parameter & Gaussian after Anscombe transformation) effectively reduce the over-dispersion on these datasets (see Figure  4 ). 2. We propose a new and rather natural post-processing rule to predict the start and end of peaks in an estimated unconstrained segmentation (see Section 3). Indeed, in the unconstrained segmentation we can observe several up (respectively down) changes and it is not obvious which one should be considered as the start or end of the peak. We show that this new post-processing rule improves the accuracy of this segmentation model in both H3K36me3 and H3K4me3 datasets compared to the same model with previous rules described by  Hocking et al.
[2020]  (see Figure  5 ). 3. Hocking et al. [2018]  described a procedure to extract all optimal constrained segmentations for a range of peaks. It is an essential internal step in the supervised approach for learning the penalty parameter of segmentation models. In this work we generalize this procedure so that it works with the unconstrained segmentation model and the post-processing rule mentioned in the previous point (see Algorithm 1). 4. We describe a method to learn jointly both the penalty and dispersion parameters of segmentation models with a negative binomial noise (see Section 5). We then compare the accuracy of unconstrained and contrained segmention models with different noise distributions on the labeled H3K36me3 and H3K4me3 datasets (see Figure  6 ). 2 Segmentation models for ChIP-seq data Unconstrained segmentation model.
It implements several loss functions including the Gaussian, Poisson and negative binomial that allowed us to compare different noise models for the count data. The number of changepoints in a coverage profile being unknown, gfpop takes a non-negative penalty λ ∈ R + parameter that controls the complexity of the output segmentation. Larger penalty λ values result in models with fewer changepoints. The extreme penalty values are λ = 0 which yields n -1 changepoints, and λ = ∞ which yields 0 changepoint. The time complexity of gfpop is empirically O(V n log(n)). Intuitively, V stands for the number states you will need to encode your priors about the form of the output segmentation, e. g. with the Up-Down model at each time the signal can be a part of the background noise (Down) or a peak (Up).
g. experimental protocols, sequencing machines, alignment software, auto-correlation. These factors results in poor accuracy for the detection of peaks  [Hocking et al. , 2017]. Therefore, we will consider the supervised peak detection problem in which the value of tuning parameters can be learned using manually determined labels that indicate a presence or absence of peaks. Benchmark datasets. Introduced by  Hocking et al. [2017] , these seven labeled histone mark datasets are composed of samples from two different ChIP-seq experiments directed at modifications found on the histone 3 N-terminal tails. The first experiment is directed at histone H3 lysine 4 tri-methylation (H3K4me3), a modification localized in promoters. The second one is directed at histone H3 lysine 36 tri-methylation (H3K36me3), a modification localized in transcribed regions.
, φ 16 = 10000). Initial results suggest that this grid of values is a good set of candidates to test in order to calibrate the dispersion parameter φ (see Supplementary Figure  1 ). The exact computation of the error rate as a function of λ (φ remains constant), a piecewise constant function, requires to retrieve all optimal segmentations up to 9 peaks. This way, on the advice of the biologists who annotated the benchmark datasets, we ensure that for each problem there is a segmentation with at least one false positive / false negative label. A procedure that retrieves one optimal segmentation for each targeted number of peaks P * has already been described by  Hocking et al. [2018]. It can be used with the Up-Down model for which there is at most one optimal segmentation that results in P * peaks but not with the PDPA model for which there can be several ones.
• CROPS is a procedure described by  Haynes et al. [2014]  that takes as input a problem w m , as well as two penalty bounds λ & λ and outputs all the optimal segmentations between these two bounds. We slightly modified the original implementation of both SequentialSearch and CROPS in such way that they can work with any penalized changepoint detection solver S provided by the user. The source code of CROCS is available here: https://github. com/ aLiehrmann/chip_seq_segmentation_paper/tree/master/scripts/CROCS. r Algorithm 1 CROCS (Changepoints for a Range of ComplexitieS): extract all optimal segmentations between P and P using a changepoint penalized solver S Learning jointly φ and λ. Once the error function E m (φ ∈ Φ, λ) is computed for each problem of the training set, a natural way to learn the dispersion and penalty parameters is to select the pair of values (φ ∈ Φ, λ) that achieves the global minimum error.
Empirical results Cross-validation setup & evaluation metric. In the following section, for each model compared, a 10-fold or 4-fold 1  cross-validation was performed on each of the seven datasets. Here, the results are shown by type of experiments (H3K36me3 & H3K4me3). Formally, the accuracy can be written 1 - m∈ test set E m / m∈ test set |H m |. Learning of tuning parameters. In section 5 we described two methods for learning the tuning parameters of segmentation models. Based on results shown in supplementary, for the rest of this section, the parameters of the models compared on H3k36me3 datasets are learned through the constant λ method. the parameters of the models compared on H3k4me3 datasets are them learned through the linear λ method. The over-dispersion exhibited by count data under a Poisson noise model can be effectively reduced using a negative binomial or a Gaussian transformed noise model.
The segmentation were computed using CROCS with gfpop + the PDPA model as solver. For each of the selected segments we estimated the empirical and theoretical variances. In the case of the Poisson noise model, the estimated theoretical variance is formally written σ2 = μ, where μ stands for the estimation of the mean of count data in this segment. For the negative binomial one it is formally written σ2 = μ + φ -1 μ2 , where φ stands for the dispersion parameter learned during the cross-validation procedure. For the Gaussian one, the theoretical variance is assumed constant across the segments. We estimated it using the mean squared error computed over all segments. In figure  4  we can see that most of the count data under the Poisson noise model exhibit over-dispersion in both H3k36me3 and H3K4me3.
Indeed, when looking at the segments with on average more than one count per base (log 2 (mean) > 0), the loess curve computed on them is always above the red line which stands for the equality of the two variances. It is also in constant growth which suggests that the over-dispersion becomes larger as the mean of segments increases. partially correct this over-dispersion phenomenon although the reduction seems to be greater with the Gaussian transformed noise model on H3K4me3 datasets. We also note that a small part of segments with count data under Gaussian transformed noise exhibit under-dispersion. From a modeling perspective these count data are a part of the background noise, regions where a lot of genomic positions have a null associated count. Max jump is the more accurate rule for inferring the peaks in segmentations obtained through the PDPA model.
In agreement with our expectation, we observe that for the different models in both H3K36me3 & H3K4me3 datasets, the mean accuracy of the max jump rule is greater than the mean accuracy of the largest peak rule (4. 85% to 12. 36% more accurate on average). Except for the PDPA model with a Poisson noise in H3K4me3 (0. 11% less accurate on average), the mean accuracy of the max jump rule is also greater than the mean accuracy of the thinnest peak ( 0. 38% to 3. 03% more accurate on average). In order to test the statistical significance of the difference in mean accuracy observed between the target and the reference model we performed a paired t-test. The accuracy of each fold were previously pooled by type of experiments as it is suggested in figure  5. After correcting the p-values of the paired t-test with the Benjamini & Hochberg method, 8 differences in mean accuracy were still significant (adjusted p-value < 0.
Because we saw in previous results that a negative binomial or Gaussian transformed noise effectively reduces the over-dispersion exhibited by count data under a Poisson noise, we expected that the PDPA or Up-Down model with these alternative noises will improve the peak detection accuracy on the test set. In figure  6  we look at the difference in mean accuracy between the Up-Down model with a Poisson noise, denoted reference model, against other segmentation models and heuristics, denoted target models. In agreement with our expectation, we can see that the PDPA model with a negative binomial noise has a mean accuracy greater 2 model built on natural assumptions to detect peaks in count data and actual state-of-the-art on H3k36me3 and H3k4me3 datasets than the reference model in both H3K36me3 and H3K4me3 datasets (respectively 2.
0% and 0. 86% more accurate). It has also a greater mean accuracy with a Gaussian transformed noise (respectively 2. 15% and 1. 77% more accurate). As described previously, we performed a paired t-test to test the statistical significance of the difference in mean accuracy between each target and the reference model. After correcting the p-values of the paired t-test, the PDPA model with a Gaussian transformed noise was still significant (adjusted p-value < 0. 05). Note that the PDPA model with a Poisson noise has a mean accuracy similar to reference model (difference in mean accuracy < 0. 5% in both datasets). Thus, the improvement in accuracy cannot be attributed solely to the PDPA model + the max jump rule but also to the distribution chosen for the noise. In disagreement with our expectation, with the Up-Down model the use of alternative noise distributions does not improve the accuracy compared to the Poisson one (differences in mean accuracy < 1% in H3K36me3 and < 0.
Conclusion and perspectives Supervised algorithm for learning segmentation models. We developed the CROCS algorithm that computes, for any segmentation model with constant penalty λ along the changepoints, all optimal segmentations between two peak bounds. This set of segmentations is essential to compute the error rate function, which is in turn used in the supervised approach for learning the tuning parameters of the segmentation models (see Section 5). Modeling of over-dispersed ChIP-Seq count data. We saw in figure  4  that count data under a Poisson noise model exhibit overdispersion in H3k36me3 and H3K4me3 datasets. We showed that this overdispersion can be effectively reduced in these datasets using either a negative binomial or a Gaussian transformed noise model. The use of a negative binomial noise model implies that we must be able to estimate a suitable value for the φ dispersion parameter.
In section 5 we proposed to learn it jointly with the penalty of the segmentation model directly on the labeled coverage profiles. More precisely, a constant φ is selected because it minimizes the label errors of the training set. The negative binomial combined with the constant dispersion parameter allows the phenomenon of over-dispersion to be slightly reduced. With the Gaussian noise model there are no additional parameters than the penalty of the segmentation model to set. This is an advantage compared to the negative binomial one. In this study, in order to satisfy the Gaussian proprieties, we transformed the count data with the Anscombe transformation which is highly appreciated for its variance stabilization properties. Gaussian transformed noise model allowed to reduce the over-dispersion even more efficiently than the negative binomial noise model on the H3K4me3 dataset and as efficiently on the H3K36me3 datasets, while being simpler to implement.
Segmentation models for peak detection in ChIP-Seq count data. In figure  6  we saw that when combining the PDPA model with a negative binomial or a Gaussian transformed noise it is possible to improve upon the natural and current state-of-the-art on the peak detection accuracy, the Up-Down model with a Poisson noise, in both H3K36me and H3K4me3 datasets. We argue that this improvement is likely explained by the ability of the negative binomial and Gaussian distribution to model the over-dispersion as illustrated in figure  4. In summary, we believe that the better we model dispersion the better we improve the accuracy of the segmentation model. The PDPA model seems to capture more subtle changes in count data than the Up-Down one which have sometimes a poor fit to the signal (see Figure  1  & 2).
Reproducible Research Statement We have created a dedicated GitHub repository with the code and data necessary to reproduce our figures and empirical results: https://github. com/aLiehrmann/chip_seq_ segmentation_paper/ (Bottom) For each φ i , i. e 16 values evenly placed on the log scale between 1 and 10000, the minimum error of E m (φ i , λ) has been plotted. We can see the errors growing constantly at the left en right side of φ * which suggests that this range of φ is appropriate for learning a suitable dispersion parameter value. differences in mean accuracy were still significant (adjusted p-value < 0. 05). The concerned models are: PDPA gaussian max jump; PDPA gaussian largest; PDPA negative binomial largest; PDPA poisson thinnest; updown gaussian; updown negative binomial. In H3K36me3 datasets, there is no clear trend on which learning method is the best.
Figure2: The linear λ learning method improves the accuracy of the segmentation models upon the constant λ learning method on H3K4me3 datasets. The mean accuracy and its 95% CI computed on the test folds is shown in red ( ). In 46 of the 48 comparisons on the H3K4me3 datasets, the linear λ learning method was better in average than the constant λ learning method. After pooling the folds by type of experiment, we performed a paired t-test on each comparison. After correcting the p-values with the Benjamini & Hochberg method, 6 (/12).
Economic growth-carbon emissions nexus: multicountry studies The relationship between economic growth and CO 2 emissions has been widely used through the EKC by reference to the seminal  Kuznets (1967)  contribution. Traced back to  Grossman et al. (1991) , this hypothesis supports that environmental pollution first rises with economic development. However, after a certain turning point, the trend reverses, and the basic conflict among these two entities becomes solved. When confirmed, the relationship between economic and environmental indicators exhibits a major non-linearity, taking the form of an inverted U-shaped curve. In this case only, it is said that a delinking relationship emerges among these indicators. Often, the authors of these studies included energy consumption data to test whether the validation of the EKC is sensitive to the energy channel.
SGD: construction of the model In SGD, the study has considered just one sample at a time to take a single step. The cost fluctuates over the training samples and does not necessarily decrease. However, in the end, the cost starts to decrease with fluctuations. SGD converges faster when the dataset is larger. Linear regression cost function: J ∅ ð Þ ¼ 1 2m ∑ m i¼1 y *i -y i À Á 2 w h e r e m = t r a i n i n g d a t a → i i n r a n g e ( m ) → ∅ j ¼ ∅ j -α y *i -y i ð Þx i j In this context, the Python codes area as follows: Predictive model building procedure Step 1 In this step, the study collected the data for the model, as the process also involves the ingestion of the data into python software for processing and analysis. Step 2 The research separates the data into two: the input variable (GDP growth) and the output variable (CO 2 emissions).
The algorithms used to perform the analysis, with the same dataset of the GD model, are the following: The previous commands represent a summary the calculation used by us on the MLP function. In other words, we generated neural network training. The goal is, given a set of input vectors x and targets t, to make our network learn the relationship from which inputs and outputs are linked. Therefore, if we introduce (after training) an input to the network, the output will be very close to the target. To understand how the MLP algorithm programmed by us works, we first introduce the weight space, which is the size space of the network parameters. This space has dimension n, where n is the number of weights in the network. On the Cartesian axes, there are the weights (w 1 , w 2 ,. , w n ), and, therefore, each point of the plane corresponds to an exact function of the network.