dataesr/scientific-paragraphs-categorization huggingface dataset:
We want to infer the posterior probability distribution for α, β, μ, s, σ, Hill i and pIC50 i , for i = 1, …, N e , giving a total of 5 + 2N e parameters. Using Bayes' Theorem, the posterior distribution in Equation (  6 ) is now given by ( ) We use the same adaptive Metropolis-Hastings MCMC algorithm as in Section 3. 1 to infer a posterior distribution from the experimental data. Since we have many more parameters than in the non-hierarchical case, we expect to have to run our MCMC algorithm for more iterations to adequately approximate the posterior distribution. For most cases in this dataset, we have N e = 3 or N e = 4, which is not too demanding since our mathematical model is a simple analytic expression, and does not require solving differential equations as our previous work did  (Johnstone et al.
Implementation Our tool PyHillFit  (Johnstone et al. , 2016b)  takes a CSV file of dose-response points as its input. The file should be comma separated values (. CSV) in the following format for each line: compound name, channel name, experiment number, dose (μM), response (% inhibition) The Monte Carlo algorithms were written in Python using NumPy 1. 11. 0 for numerical linear algebra (van der  Walt et al. , 2011) , and functions from the SciPy 0. 15. 1 library  (Jones et al. , 2001). Pandas 0. 17. 1 was used to read the input data csv files published by Crumb et al. Ion channel screening is generally performed at concentrations that range to well-above therapeutic concentrations, and so we do not want to infer how a compound will behave at even higher concentrations. These gamma prior distributions were tuned to cover values provided by  Elkins et al.
That is, what are p(Hill N e+1 |data) and p(pIC50 N e+1 |data)? Since the Hill i and pIC50 i are modelled as being drawn from log-logistic and logistic distributions, respectively, we sum the log-logistic and logistic The number of (synthetic) experimental datasets, N e , being fitted was increased. As we fit to more experiments, the prior distributions have a smaller effect on the posterior distributions. Where the black line for the prior distributions looks as if it lies along the x-axis (for μ and σ, the prior distribution was much wider than the marginal posterior distribution; there is a lot of information on these parameters with just one experiment). distributions generated from the 'top-level' parameters at every iteration of our MCMC algorithm output, then normalise them to obtain two new probability distributions.
A comparison of single-level and hierarchical models There are advantages and disadvantages to choosing either the single-level statistical model, or the hierarchical statistical model. The main benefit of the single-level model is that we are only fitting three parameters, meaning that the parameter space of interest is relatively easy to explore. This means that we need to run our MCMC algorithm for fewer iterations to obtain an acceptable approximation of the posterior distribution than if we had a larger number of parameters, reducing overall computation time. However, a model with an analytic solution such as the one dose-response curves plotted using the 'mid-level' pIC50 i and Hill i samples from our MCMC algorithm output from the amiodarone and hERG dataset. Middle & bottom: superimposed normalised histograms for pIC50 i and Hill i , after fitting our hierarchical model to the amiodarone and hERG dataset using the MCMC algorithm.
This allows us to construct a probability distribution for predicted action potential durations based on the original ion channel screening data (uncertainty propagation). To illustrate the proposed uncertainty propagation we use our tool to fit hierarchical dose-response parameters to thirty drug compounds for seven ion currents each using the  Crumb et al. (2016)  dataset supplied with the code associated with this article. We then take 500 samples from the MCMC output for each drug and channel combination, based on the 'underlying effects' curves from hierarchical fits (see Figure  12 , Figure  13  & Figure  14 ), and simulate action potential durations after applying seven ion channel block using the approach outlined in  Mirams et al. (2011). We plot the resulting predicted action potential duration distributions as violin plots in Figure  15.
A violin plot for each of the thirty compounds is coloured according to the Torsade/QT risk categorisation in the CredibleMeds database (https://crediblemeds. org/). We also plot the control action potential duration in the O'Hara model along with a 10% action potential duration prolongation. We see that the vast majority of the compounds have overlapping probability distributions for predicted APD at maximum free therapeutic plasma concentration, suggesting that at least this previously-proposed measure will be insufficient to distinguish compounds in terms of risk based on data such as these. This suggests that either: action potential prolongation is not a good enough marker of pro-arrhythmia; or, there was too much uncertainty associated with the experimental data to constrain these distributions to narrow distinct ranges.
Our hierarchical model is similar to nonlinear mixed effects (NLME) modelling, but we operate in a Bayesian framework. NLME assumes a similar structure to that shown in Figure  7 , but infers best-fit values for the 'top-level' parameters, and a distribution from which the 'mid-level' parameters are sampled. While it does capture inter-experiment variability and would allow us to make predictions about how a future experiment might behave, it only provides a point-estimate for underlying behaviour, rather than different possibilities with relative probabilities. A possible limitation of the hierarchical model is that computation time increases with the number of experimental datasets being fit to at once. This is not a problem for up to 4 or 5 experiments, but the number of parameters quickly becomes intractable for the adaptive-Metropolis MCMC algorithm that we have been using.
In general, with MCMC methods, we want to run our algorithm for as long as possible, to best approximate samples from the posterior distribution. There is therefore no upper limit for how long this method takes, although for these examples we have run our algorithm for 500,000 iterations, which takes approximately 12 minutes for the amiodarone-hERG case which has 3 experimental datasets of 4 concentrations each. Another possible limitation of the hierarchical model is the dependence on the prior distributions for the 'top-level' parameters. As shown in Figure  9 , when there is not much data, the posterior is heavily influenced by the prior. However, we chose our priors based on data published by Elkins et al. which was based on 12,000 ion channel screening experiments, and we therefore have some confidence in their shapes (Figure  8 ).
Data and software availability Latest source code and datasets used in the publication: https:// github. com/mirams/PyHillFit Archived source code and datasets as at the time of publication: https://doi. org/10. 5281/zenodo. 237643 (Johnstone et al. , 2016b)  License: BSD 3-Clause The code contains the experimental input data required to reproduce the examples shown here in comma separated value (CSV) format in the file data/crumb_data. csv. Installation instructions for the tool and its dependencies can be found in the README file, in the main folder at the above links.
Minor Comments It is unclear why the PyHillFit package was developed as these models can be easily fit with existing MCMC software such as JAGS or Stan. Furthermore, using existing MCMC software allows other models to be fit (e. g. 4 parameter logistic models --when the upper and lower asymptote are not rescaled to 100% and 0%), and the sampling may be more efficient (especially with Stan, which handles hierarchical models well). The benefits of PyHillFit over these other tools should be highlighted. The data are analysed with two models but the verbal description of the models is ambiguous. There are three models that could be run: Parameters (IC50 and Hill) are common for all experiments (complete pooling, corresponding to the first model) Parameters are different for all experiments; each experiment has its own parameter estimated independently from the other experiments (no pooling; not used in this paper) Parameters are different for all experiments, but shared across experiments and thus mutually informative (partial pooling; the second model) It is clear from the equations that their second model is number 3 above, but the verbal description is that of number 2.
If the 50% value happened to be a good classifier for these data, we would still not expect this to hold for new compounds, as the ordering could easily reverse when new data become available wherever the distributions overlap to this extent. Nevertheless, we annotated Figure  15  with risk classifications from the CredibleMeds database (a more credible data source than its name suggests!). As one might expect, only the clear shorteners and prolongers correspond well to 'no known risk' or 'known TdP risk'; the majority of compounds have overlapping distributions of moderate APD prolongation, that cannot really be ranked reliably. > It is unclear why the PyHillFit package was developed as these models can be easily fit with existing MCMC software such as JAGS or Stan. Furthermore, using existing MCMC software allows other models to be fit (e.
g. 4 parameter logistic models --when the upper and lower asymptote are not rescaled to 100% and 0%), and the sampling may be more efficient (especially asymptote are not rescaled to 100% and 0%), and the sampling may be more efficient (especially with Stan, which handles hierarchical models well). The benefits of PyHillFit over these other tools should be highlighted. PyHillFit is intended for the specific case of ion channel screening data. We do appreciate that this could have been coded in Stan, but we wanted to have more 'control' over what was happening below the surface, and in how the results were outputted and analysed, so we wrote the algorithms ourselves in Python. Perhaps as much of our 'work' here was in deriving the statistical model and appropriate prior distributions, as it was in writing the software itself.
Quantitative Biology, IMED Discovery Sciences, AstraZeneca, Cambridge, UKSummary of the PaperIn this manuscript Johnstonepropose a new tool (PyHillFit) for fitting dose response curves from et al cardiac ion channels. This tool uses Bayesian inference methods to determine the distributions of the parameters. They investigate two alternative models, one that ignores inter-experimental variability and a second hierarchical model that characterises variability. Using a recently published dataset of inhibition of multiple ion channels they demonstrate the use of the tool and how experimental variability propagates through to the fitted dose response curves. They then extend these to simulations using a cardiac action potential model to show the distribution of model outputs.
PIPE-chipSAD is a pipeline for bacterial transcriptome studies based on high-density microarray experiments. The main algorithm chipSAD, integrates the analysis of the hybridization signal with the genomic position of probes and identifies portions of the genome transcribing for mRNAs. The pipeline includes a procedure, align-chipSAD, to build a multiple alignment of transcripts originating in the same locus in multiple experiments and provides a method to compare mRNA expression across different conditions. Finally, the pipeline includes anno-chipSAD a method to annotate the detected transcripts in comparison to the genome annotation. Overall, our pipeline allows transcriptional profile analysis of both coding and non-coding portions of the chromosome in a single framework. Importantly, due to its versatile characteristics, it will be of wide applicability to analyse, not only microarray signals, but also data from other high throughput technologies such as RNA-sequencing.
Different statistical algorithms have been developed to process high-density array data and to obtain such segmentation. The widest exploited method was introduced by Kampa et al. (Kampa et al. , 2004)  and was successively implemented in the Tiling Array Software (TAS) (http://www. affymetrix. com/estore/partners_programs/programs/developer/TilingArrayTools/index. affx). The method was based on the generation of a transcription map constructed by collecting neighbor expressed probes, i. e. , probes with a smoothed intensity above a given threshold. TAS extended the method of  Kampa et al. by  estimating the significance of differential expression using a Wilcoxon signed-rank test within local windows of a given width, centered on each probe. More recently, the Model-based Analysis of Tiling-arrays (MAT) was introduced by Johnson et al.
To address these needs, we have developed PIPE-chipSAD, a pipeline to conduct high-density array data analysis. The main algorithm is chipSAD (chip Signal Areas Detector), which provides the segmentation of the hybridization signal and defines the boundaries of the detected transcripts. Anno-chipSAD performs an annotation of these regions guiding to the identification of new architectural features as operons, small-RNAs and antisense messenger RNAs. Finally, align-chipSAD identifies the transcriptional units analyzing multiple experiments from different chip layouts at the same time. Herein we provide explications of the main steps of PIPE-chipSAD and details of the algorithms strategies. We also show the application of PIPE-chipSAD on two previously published datasets  (Mellin et al. , 2010; Fagnocchi et al.
MATERIALS AND METHODS PIPE-chipSAD was designed to investigate two color microarray datasets, but it is suitable to analyse data from different sources (one color microarray or sequencing technologies such as RNAseq) with minimal data elaboration. The pipeline is composed by five steps, as indicated in the flow chart shown in Figure  1 , that correspond to three main programs. This module structure provides more flexibility in the data analysis because the user can access to the different programs independently. The pipeline is freely available at: https://github. com/silviamicroarray/chipSAD.
Identification of Transcriptional Units chipSAD is the first program of the pipeline. The main algorithm defines pairs of contiguous sliding windows along the genomic coordinate x. The width of the windows is iteratively increased to include contiguous probes with a consistent signal. Finally, two correlated probe regions (CPRs) of different sizes are positioned at the two sides of each coordinate on the genome (Figure  1A  in set 1 (red boxes)). Then, it follows the identification of positions where the M value signal is subject to an abrupt change of intensity. A generalized t (x) parameter to compare the right and left CPR was defined as in  (Tusher et al. , 2001)  in order to establish the signal "change points. " To create a signal area (SAS) a strategy based on the intensity value of contiguous CPRs was employed (Figures  1B, C ).
Comparative Analysis of Multiple Experiments In case of multiple experiments, align-chipSAD can be run. In order to avoid that noisy areas may influence the results of this procedure, only the SAS with a minimum value of the M pseudomedian (M) might be selected before running align-chipSAD. Then, graphs based on the overlap of the SAS are built as indicated in Figure  1D  by the orange links. For each connected graph, the consensus boundaries are calculated considering a weighted average of the boundaries of the SAS belonging to the graph. In detail, the weighted position x (start or end) of the consensus SAS is: x = i p i x i i p i where p i = M i i M i with i = 1, 2. , N enumerates each SAS belonging to the graph. Thus, for each connected graph, a consensus SAS is determined, and the conclusion of this step is a unique list of consensus SAS.
Putative Transcripts Annotation The final step of the PIPE-chipSAD is the comparison of the detected SAS with the genome annotation and the classification of putative transcripts when they represent single open reading frames (ORFs), entire operons (polycistronic transcripts), antisense RNAs, untranslated transcribed regions (UTRs) and intergenic RNAs (Figure  1E ). This procedure consists in the comparison of the identified SAS with the gbk file of the organism of interest and is implemented by anno-chipSAD; comparing the identified SAS with the gbk file of the organism of interest. The first step of the classification was the selection of SAS overlapping annotated ORFs: if the SAS overlapped a minimum of 30% of a single ORF length in the same strand then it was classified as ORF (magenta arrow in Figure  1E ), otherwise as antisense (orange arrow in Figure  1E ).
Datasets We analyzed two previously published datasets used to study the transcriptome variation of N. meningitidis hfq mutant strain with respect to wild type. The transcriptome data of the N. meningitidis MC58 hfq null mutant ( hfq) strain grown in GC medium described by Fagnocchi et al. (Fagnocchi et al. , 2015)  and the dataset presented by Mellin et al. (Mellin et al. , 2010) , which examined the N. meningitidis MC58 hfq null mutant and the relative complemented strain, grown in iron depleted (100 µg desferal ml -1 ) or replete conditions (100 µg ferric nitrate ml -1 ), were used in this study, to test the capability of chipSAD to handle data from different experimental designs. The microarrays analyzed are one or two-color hybridizations. In case of two-color experiments  (Fagnocchi et al.
, 2015)  a competitive hybridization between the null mutant and the wild type strains growth under the same in vitro growth condition was performed. In case of one-color experiments  (Mellin et al. , 2010)  an in silico comparison to wild type strains growth was composed during global normalization. Three biological replicates of each experimental condition were available in each dataset. In order to make the data comparable, we merged the replicas of the same condition by averaging the M value of each probe after a global normalization of signals by using limma R package  (Smyth and Speed, 2003).
Comparative Analysis of the Transcriptome of N. meningitidis hfq Mutant in Different Growth Conditions We analyzed two previously published datasets  (Mellin et al. , 2010; Fagnocchi et al. , 2015)  used to study the transcriptome variation of Neisseria meningitidis hfq mutant strain to test the capabilities of chipSAD to manage different chip designs and hybridizations in a single analysis (Figure  2 ). In Table  1  SM is reported the number of differentially expressed transcripts for each genomic category found by PIPE-chipSAD after the alignment of the two datasets. The application of PIPE-chipSAD allowed the detection of differentially expressed transcripts, especially for transcripts located in intergenic regions, in the five samples without affecting the results obtained running chipSAD on a single sample.
In order to see how the alignment procedure, implemented in align-chipSAD, affects the SAS identified by chipSAD, we independently ran the chipSAD algorithm only on the hfq mutant experiment of Fagnocchi's dataset and on the all experiments together and we compared the results. Among the 114 regions classified as single ORFs detected in the single run, 104 are still present after the alignment: 84 of those maintain the same classification, while 20 regions are rearranged in different kind of transcripts mainly as mRNA with UTRs or operons. Ten transcripts are no longer detected as differentially expressed after the application of align-chipSAD. A very similar scenario is observed with operons and mRNAs with UTRs. The 93 mRNAs with UTRs identified in the analysis of a single experiment overlap 115 transcripts (some mRNAs with UTRs comprise two genes), 74 of them are still classified as mRNAs with UTRs after the alignment procedure, while 33 transcripts are classified in a different class, more often in single ORFs than in operons.
Finally, 100 out of 123 transcripts classified as operons belong to the same classification with the single run and upon align-chipSAD application. The intergenic regions are the most affected by the alignment procedure, because these regions usually are short and noisy. One hundred twentysix intergenic regions out of 177 survive to the alignment of which 32 are joined in longer transcripts such as mRNA with UTR or operon. Twenty-seven intergenic regions are no longer present mainly because they had M values close to the threshold. Therefore, the main effect of the alignment procedure is the rearrangement of the regions in different classes respect to the classification obtained applying chipSAD on a single experiment. Anyway, after the alignment, only a small percentage of the transcripts have a class change, meaning that the boundaries of the regions detected by chipSAD, even considering only one experiment, are highly conserved.
Moreover, only few transcripts are no longer detected after the alignment and most of them had very low M value, meaning that the align-chipSAD can improve the detection of significant regions removing noisy regions. In order to compare our results with the panel of 132 genes differentially expressed already published in Mellin et al. (Mellin et al. , 2010) , we first selected the transcripts specifically differentially expressed in the hfq mutants with respect to both the wild type and also the complemented strain. Thus, we set up a threshold on the M value, selecting only those transcripts with a minimal expression fold change of 2 between the hfq or the complemented vs wild type strain, in each growth condition. This selection criterion led to the identification of 90 transcripts, including 35 ORFs, 28 operons, 10 mRNA with UTRs, 17 intergenic regions (Figure  3 ), that are the most reliably and consistently Hfq-modulated and iron-dependent transcripts.
Noteworthy, we found that 42 genes were identified by both approaches, however PIPE-chipSAD was able to determine whether these transcripts were organized in more complex structures such as operons or mRNA with UTRs. Furthermore, we individuated 31 previously unidentified Hfq-modulated and iron-dependent transcripts. Finally, our approach found 17 new intergenic regions to be specifically deregulated under iron starvation/abundance and in absence of Hfq, providing also the specific boundaries of the putative transcripts. Overall our method allowed a better understanding of the Hfq targetome. Finally, the results obtained by the application of align-chipSAD made the comparison of transcripts identified across datasets much easier, allowing a compact view such as a heat map (Figure  3 ). We have presented PIPE-chipSAD, a specifically designed pipeline for bacterial transcriptomic analysis of high-density array data.
Homologous LINC complex and lamin-like protein detection A Perl script was developed and applied to proteomic data to identify KASH domain proteins. The program tests the presence of the trans-membrane (TM) domain and 4 specific amino acids at the C-terminus, which are characteristic for KASH proteins. The position of the TM domain is variable and the script searches this TM domain up to 40 amino acids away from the KASH-specific C-terminal motifs detected in A. thaliana (either VIPT, VVPT, AVPT, PLPT, TVPT, LVPT or PPPS. 41, 18, 42 ). The identification of the TM domain is based on the Kyte-Doolittle method. 25 Only proteins, which possess a TM domain and the 4 KASH specific amino acids in the C-terminus of the protein, were selected. For all proteins of interest a Basic Local Alignment Search Tool protein (BLASTp) was used with default parameters as well as HMMER (Hidden Markov Model-based sequence alignment tool; http//hmmer.