dataesr/scientific-paragraphs-categorization huggingface dataset:
This is achieved while also accounting for sequencing base quality. We demonstrate the utility of an additional trimming step in the workflow, which adjusts for the number of reads supporting each insertion site. In addition, we developed a recall procedure to reduce bias associated with proviral integration within low complexity regions of the genome, providing a more accurate estimation of clone abundance. Finally, we recommend the application of a "clean-and-recover" step to clonality datasets generated from large cohorts and longitudinal studies. In summary, we report an optimized bioinformatics workflow for NGS clonality analysis and describe a new set of steps to guide the computational process. We demonstrate that the application of this protocol to the analysis of HTLV-1 and bovine leukemia virus (BLV) clonality datasets improves the quality of data processing and provides a more accurate definition of the clonal landscape in infected individuals.
, 2017; Pérès et al. , 2018; Galli et al. , 2019; Marçais et al. , 2020). This generated an unprecedented volume of raw sequencing data. Here, we report the bioinformatics workflow and describe critical modifications in the pipeline, improving processing speed, increasing both the specificity and the sensitivity of the assay, and reducing bias associated with proviral integration within low complexity regions of the genome. We provide an updated data analysis protocol and recommendations applicable to the majority of bioinformatics pipelines developed to analyze LAM-PCR-based NGS clonality datasets.
Participants and Material NGS clonality datasets were obtained from peripheral blood mononuclear cells collected from ATL patients (HTLV-1, human dataset) and BLV-infected animals (asymptomatic and tumor stages) described in  Artesi et al. (2017)  and  Rosewick et al. (2017). The human studies were approved by the ethics committee CPP Ile de France II, CNIL: number 1692254 and registration number 000001072. Animal experimental procedures were approved by the University of Saskatchewan Animal Care Committee, following the Canada Council on Animal Care Guidelines.
Testing Blast Versus Bowtie2 Bowtie2 v2. 2. 9 index (bowtie2-build) and BLAST v2. 6 (makeblastdb-dbtype nucl) databases were constructed from the BLV_YR2 3'LTR (8,694-8,720 bp, BLV YR2, Genbank: KT122858. 1) or HTLV_ATK (9011-9,055 bp, GenBank: J02029) sequence. Ten million reads containing either the BLV or HTLV-1 3'LTR sequence were randomly selected from our NGS clonality sequencing dataset. Datasets were downsampled to different sets of various read numbers with seqtk. 1  FASTQ files were transformed into FASTA with fastq_to_fasta (-Q33 -r -n) keeping only bases with Phred score > 33. 2  FASTA sequences were compared to the BLAST database using blastn -word_size 7 -evalue 1. FASTQ sequences were aligned with bowtie2 (-L 14 -N 1 -U). Homemade R scripts were used to compare the outputs.
Impact of Read Trimming on Proviral Integration Site Mapping Bowtie2 indexes were constructed with BLV LTRs or HTLV-1 LTRs as separate chromosomes (BLV, LTR5: 1-30 bp, LTR3: 8,694-8,720 bp; HTLV, LTR5: 1-29 bp, LTR3: 9,011-9,055 bp). Reads from BLV-infected sheep (n = 516) or HTLV-1-infected individuals (n = 187) containing the appropriate linker sequence were mapped to their respective index with bowtie2 -L 14 -N 1 -U prior to or after trimming. Trimming was performed in two steps. First, the read section expected to contain the viral LTR was isolated with fastx_trimmer. Next, cutadapt -n 2 -m 10 was used to remove residual sequences, which arise from read overlap (see github for details). Custom R scripts were used to compare the outputs.
Integration Site Recall A recall procedure was performed using custom R scripts [available in the github R package PIC function getISposition (…, RECALL = TRUE)]. Trimmed reads from 184 HTLV-1 infected samples with at least 300 reads after removal of PCR duplicates were mapped with bowtie2 -very-sensitive -N 1 to the combined human genome (hg19) with the HTLV-1 proviral genome as an additional chromosome (HTLV_ATK, GenBank: J02029). Using the R package PIC, the position and abundance of integration sites were first called with high-confidence reads mapped in proper pairs (SAM flags 83, 99, 147, 163, and MAPQ ≥ 33). For each integration site, the RECALL abundance was calculated from the set of reads located in a window spanning 600 bp up-and downstream of the high-confidence integration site.
Clean and Recover Step Integration sites (704,485) were acquired from longitudinal samples collected from 30 BLV-infected sheep over >5 years (n = 553, unpublished data). From this dataset 377,710 unique integration sites were obtained. Integration sites found in only one animal were retained, while those detected in >1 animal were further explored. Their relative distribution across all serial samples of each individual was assessed by computing the Shannon entropy  (Shannon, 1948)  of their recurrence (= number of times this integration site is detected in each animal; S r ) and highest abundance across all animals and all samples (S a ). Integration sites with a S r or S a <0. 85 were attributed to the animal with the highest recurrence (if S r <0. 85) or the highest abundance (if S a <0.
85) of that particular integration site. Integration sites that did not meet any of these criteria were flagged as "uncertain" and excluded, while the others were retrieved. This category contains integration sites found at a similar occurrence in different animals and at a low abundance in each of them. A detailed description of the protocol is available on github https://github. com/GIGA-AnimalGenomics-BLV/Public/tree/master/PIC/R. The corresponding function is called tagContamination().
Availability of Data and Materials Integration site information and NGS clonality datasets are available from previous publications  (Artesi et al. , 2017; Rosewick et al. , 2017). The datasets used and analyzed during the current study are also available from the corresponding author on request. The full code and a detailed step-by-step description of the bioinformatics pipeline is available at https://github. com/ GIGA-AnimalGenomics-BLV/Public/tree/master/PIC.
We designed a six-step workflow to analyze the NGS datasets (Figure  2 ). The full code of the bioinformatics pipeline is available at https://github. com/GIGA-AnimalGenomics-BLV/Public/tree/ master/PIC. Accelerating Data Processing: Favoring Short-Read Aligners Over BLAST/BLAT Reducing computational time becomes a priority when analyzing large volumes of data. Recently reported pipelines primarily use traditional local alignment tools like BLAST/BLAT  (Altschul et al. , 1990; Kent, 2002)  for at least one step of the NGS clonality workflow. These tools perform poorly in protocols that typically require the alignment of several million short sequences  (Paruzynski et al. , 2010; Firouzi et al. , 2014; Maldarelli et al. , 2014; Cohn et al. , 2015; Aoki et al. , 2016; Farmanbar et al. , 2017).
We applied the short-read aligner bowtie  (Langmead et al. , 2009)  or bowtie2  (Langmead and Steven, 2013)  at every step of the protocol to a set of reads from our HTLV-1 and BLV clonality datasets (Figure  2 , steps 1-2-4, see Materials and Methods section) and demonstrated that this dramatically reduced both processing time and memory footprint (Figure  3 ). In addition, short-read aligners consider base quality, significantly increasing alignment specificity.
DISCUSSION The application of ligation-mediated PCR protocols combined with NGS is having a large impact on our understanding of retroviral pathogenesis. Recently, considerable effort has been expended to optimize NGS clonality methods and explore the clonal architecture of clinically important retroviruses like HTLV-1 and HIV-1. Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential of these methods, we developed an optimized bioinformatics pipeline for analyzing NGS clonality datasets. Here, we report a detailed workflow and describe a set of improved or additional steps in the computational protocol. We demonstrate that application of these steps to HTLV-1 and BLV clonality datasets provides a better estimation of proviral integration site distribution and a more accurate picture of the clonal landscape in infected individuals.
With the development of cost-effective NGS clonality methods and the availability of NGS platforms for routine diagnosis applications, there is increasing interest from the clinical community to monitor the clonal architecture of HTLV-1-infected ATL patients. Given the urgent need for novel therapeutic strategies for this poorly controlled T-cell malignancy, patients with aggressive and refractory forms of ATL will be increasingly enrolled in clinical trials requiring systematic monitoring of therapeutic responses. In addition to managing ATL patients, routine NGS clonality analysis of asymptomatic HTLV-1-infected individuals has the potential to identify carriers at greater risk of developing ATL. Such routine clinical applications require highly accurate measures of clone abundance. A short processing time for NGS data is a priority when answering questions regarding clinical outcomes for HTLV-1-infected patients.
With the generation of massive volumes of sequencing data in the context of experimental studies, processing speed has also gained increasing importance in basic research. Here, we demonstrate that in contrast to BLAST/BLAT, short-read aligners specifically designed to manage NGS datasets offer increased speed, reducing processing times by an order of magnitude while accounting for sequencing base quality. In addition, shortaligner usage also lessens the computational burden that accompanies this step. Both BLAST and most short-read aligners depend on seedand-extend strategies  (Li and Homer, 2010). If a sufficient match (or seed) between the sequencing read and the indexed reference genome is found, the alignment algorithm will attempt to extend it, assigning a different weight to a perfect match, a deletion, or a mismatch.
Filtering low-quality NGS reads is highly recommended to avoid false positives; however, applied to low-complexity genomic regions, quality filters will impair the detection of proviral integration sites, inflating the abundance of integration sites assigned to higher-complexity genomic regions. We demonstrate that reads aligning to multiple regions in the genome can be used with a certain degree of confidence when a fraction of them map uniquely to that region. We further demonstrated that the recall procedure improved the accuracy of clone abundance estimates for HTLV-1 integration sites in repetitive regions of the human genome, enabling restoration of the clonal landscape in complex HTLV-1 samples. Finally, we recommend the application of a "clean-andrecover" step especially for datasets generated from large cohorts and longitudinal studies.
Applying this additional step to our dataset recovered about one fourth of the recurrent integration sites, which would otherwise have been eliminated. This set included integration sites corresponding to biologically important clones such as predominant malignant clones and their precursors. A major limitation of this approach is its dependence on empirical thresholds to discriminate between insertion sites that can be eliminated and those that should be retained. Integration sites supported by repeated occurrence within an individual and/or high abundance can be assigned with confidence and recovered; however, other contaminating integration sites should be assessed with caution and discarded.
CONCLUSION We further optimized the bioinformatics pipeline and describe critical steps in the computational processing protocol for NGS clonality datasets generated from HTLV-1 and BLV-infected individuals. We demonstrate that the application of rigorous steps in the bioinformatics process is critical to address some of the biases associated with the nature of LAM-PCR-based sequencing libraries. The application of our optimized bioinformatics protocol to HTLV-1 and BLV NGS clonality datasets decreases total execution time and reduces the bioinformatics burden while taking into account read quality. The workflow significantly improves the accuracy of estimating clone abundance by reducing biases associated with the presence of residual sequences and proviral integration within low-complexity regions of the genome.
PTES SIMULATION A script has been written in MATLAB to model the performance of a PTES system. The main script calls several subroutines for modeling the sub-systems such as the thermal stores, compressors and expanders and heat exchangers. The algorithm shown in Figure  9  is intended to show the underlying logic used by the script and in what order the sub system models are called. It shows the temperature and pressure input and output values for each subsystem. The script is based around a forward time marching semi-implicit numerical method which is used to resolve the thermal store profiles. The stability of the solution has been investigated by checking.
Material Models The argon and nitrogen gases are treated as ideal gases. Data for the temperature dependence of gas properties such as viscosity and thermal conductivity are determined from the NIST database  (Stewart and Jacobsen, 1989; Lemmon et al. , 2018). A linear relationship is used to determine the temperature dependence of the gravel as follows. This is based on a fit to data for quartz from  Hemingway (1987)  and  Anderson (1936). Inspection of Equation (  48 ) with C A = 450 and C B = 0. 695 shows how the heat capacity is expected to drop with reducing temperature (Figure  12 ), T in units of Kelvin and specific heat capacity in units of J/kgK. C ps = C A + C B T (48).
Voice discrimination task We assessed participants' ability to distinguish between vocal and nonvocal stimuli in a two-alternative forcedchoice (2FAC) paradigm. Each participant sat in a quiet, dimly lit room looking at a fixation cross on a computer screen. They were tested with a 1-s intertrial interval and were instructed to respond as accurately as possible, using the left or right control button of the E-prime response box to indicate their answer (voice or nonvoice). The response keys were counterbalanced across participants, and they each underwent a short training session to ensure that they understood the test. The 110 stimuli were presented in two blocks of 55. All the stimuli were taken from a database containing vocal and nonvocal sounds used in previous experiments  [11, 31, 32].
Sounds were played using the PC mouse, by double clicking on each square, and participants created categories by dragging and grouping the squares together on the screen. Once participants had finished placing the squares in categories, they were asked to listen to each sound one last time to verify their choices before ending the experiment. They were then asked to type a brief description of each category using the keyboard. There were no limits on the amount of time taken to complete the test or the number of times participants could listen to a given sound (i. e. , playbacks). Participants were also allowed to create as many or as few categories as they wished, such that one category could contain just a single stimulus or all 16. The TCL-LabX software also recorded performance data and statistics for all participants, including the number of categories they created, the number of playbacks they listened to, and the duration of the experiment.
All the sounds were taken from a database owned by the PETRA group at Toulouse Jean Jaurès University (http://petra. univ-tlse2. fr) and were chosen to cover a broad range of semantic and acoustic information (see  [28] ). We selected sounds that are frequent in everyday life and can be divided into three main types: environmental sounds (alarm clock ringing, car engine starting, door opening, footsteps, glass breaking, helicopter, running water); musical sounds (bells, guitar, oboe, violin, xylophone); or vocal sounds (male voice coughing, female voice speaking, female voice laughing, male voice speaking). Stimuli were presented at the comfortable level of approximately 65 dB SPL and were delivered in stereo through headphones plugged into the computer.
Applying the Bayesian approach to the HDDM can shed light on the cognitive and psychological processes behind decision-making, based solely on RT distribution for the two response choices. With this model, the behavioral data can be categorized according to four parameters (see Fig. 2a ): threshold, drift rate for the accumulation speed, nondecision time associated with stimulus perception and response execution, and initial bias. We used the Monte Carlo and Markov chains (MCMC) method to estimate posteriors based on our data. We performed 20,000 iterations. We discarded 5000 initial burn-in items, and only saved every fifth sample. This method yielded 3000 posterior values that were normally distributed. We confirmed our model using the posterior plots available in Python software (PyMC).
However, by increasing the number of desired categories, the inertia can be reduced, and it was by using this process that we were able to choose the final number of categories: if the number of categories is Q, then the optimum number of categories is found when the change in inertia is greater when moving from Q -1 to Q than from Q to Q + 1  [34]. We applied MCA to a multi-participant categorization table (raw data not included) produced by TCL LabX software. This table represented the results as an array of categorical variables as columns and categorical items (sound stimuli) as rows, with each cell containing a number that defined the category membership of each sound for each participant. MCA used correspondence analysis to represent each sound as a data point in an n-dimensional Euclidean space based on the categorical values (i.
e. , categories created by participants). Each of the dimensions was chosen to account for the greatest amount of variance possible within the dataset, and they were produced in descending order of variance. MCA on the participants showed how strongly individual results coincided with the dimensions  [35]. A total of 15 dimensions were used in the analysis. We focused on the two most significant ones (Dim 1 & Dim 2), as they accounted for the greatest amount of variance in the data and also showed the most significant correlations with the acoustic variables measured for the sounds. As there was no a priori knowledge that could be used to automatically establish these relations, a degree of interpretation was required when commenting on the dimensions  [35]. To characterize the distances between the sounds in the MCA maps, we calculated the corresponding Euclidean distances.
Because of its efficient sampling, nested sampling is also routinely used to study thermodynamic partition functions  [32] [33] [34] [35]  and to explore potential energy landscapes of atomistic systems  [36] [37] [38]. When several maxima of the likelihood function are present, nested sampling algorithm can however encounter problems with converging correctly. The parameter space exploration can become inefficient or exclude entire regions, which introduces systematic errors in the estimation of the evidence. In order to avoid such a problem, several solutions are proposed in the literature. Here we present an original approach based on cluster recognition with the mean shift method, one of the classic clustering algorithm widely used and included in the major machine learning libraries. This method is implemented in the program NestedFit, a code developed by one of the authors and described in details in Refs.
A first solution with the use of a cluster recognition algorithm has been implemented in Multinest code already in 2008  [3, 27]. Here, new live points are randomly selected in an ellipsoid that is defined by the covariance matrix of the present live points. Cluster analysis is used for partition-ing of the parameter spaces in a series of ellipsoids. This is obtained by implementing the k-means clustering algorithm, which is triggered when the estimated volume occupied by the live points is much smaller than the ellipsoid volume estimated from their covariance matrix. A partition in two cluster is initially performed (k = 2) and recursively repeated (always with k = 2) to obtain an efficient partition of the space with many ellipsoids. In the more recent Polycord program  [29] , where the search of new live points is based on the slice sampling  [46] , the cluster recognition is obtained by the k-nearest neighbor algorithm.
Once the different cluster are identified, for each of them a parallel exploration and analysis via slice sampling MCMC is independently performed. In the recent and very complete nested sampling code Dynesty  [31] , different sampling methods are proposed: from random uniform selections in ellipsoids, like Multinest, to a series of MCMC (random walks, slice sampling,. ). Difficult cases with several likelihood maxima are treated by decomposing the parameter space in several ellipsoids via a cluster analysis (using k-means algorithm like Multinest), or spheres or cubes (with same radius/side, one per each live point) with no need of any cluster recognition technique. In the following sections, we present a new alternative method based on a MCMC and where the mean shift algorithm is used for the identification of clusters.
C. The NestedFit program NestedFit is a general-purpose code for the evaluation of Bayesian evidence and parameter probability distributions based on nested sampling algorithm. It is written in Fortran90 with some subroutines in Fortran77, and parallelized via OPEN-MPI. It is mainly developed and implemented for the analysis in the fields of atomic, nuclear and solid-state physics  [16, 39, 40, [47] [48] [49] [50]. It is accompanied by a Python function library for visualization of the results and for automatization of series of analyses. In this publication we present the version 3. 2 that has the cluster analysis of the live points as substantial upgrade with respect to older versions (see Ref. [39]  for v. 0. 7 and Ref. [40]  for v. 2. 2). In addition, in this new version some new improvements in the search of live point are also implemented.
The source code is freely available in the repository https://github. com/martinit18/nested_fit. The code requires two main input files: the main input file (nf_input. dat) where the anal-ysis parameters are selected, and the data file, in the format (channel, counts) or (channel, y value, y uncertainty). Dependent on the data format, a Poisson or Gaussian statistics likelihood function is used. The function name in the input file indicates the model to be used for the calculation of the likelihood function. Several functions are already defined in the function library for different model of spectral lines. Additional functions can be easily defined by the user in a dedicated routine. Non-analytical or simulated profile models can be considered as well. In this case, one or more additional files have to be provided by the user for interpolation by B-splines using FITPACK routines  [51].
A. Preliminary tests and considerations on other cluster recognition algoritms Before the implementation of one particular cluster recognition method in NestedFit, different algorithms from classical machine learning libraries (https://scikit-learn. org as example) have been considered and some of them have been tested with simple Python scripts. For this purpose, we used different ensembles of live points issued from NestedFit runs on real data when convergence problems were encountered. We excluded a priori Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. This method is well adapted for detecting cluster with singular shapes (e. g. arc of circle) without necessary improving the implemented random walk algorithm that is based on the standard deviation of the recognized cluster.