dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation and Benchmarking A known mix of commercially available standards was analysed at three different concentrations. Datasets were acquired on two different GC-MS instruments using different (high and low) resolutions using multiple replicates each (Section 4. 3. 1). High quality peak sets were generated using the WiPP implementation, utilising centWave and matchedFilter peak picking algorithms. The full pipeline was run, including a manual classification of training sets to generate SVM peak classifiers, parameter optimisation for both algorithms, and the filtering step, as described by the methods section. The sets of optimal parameters determined for each peak picking algorithm and both datasets are available in Table  S1. For comparison, the parameter optimisation of matchedFilter was also performed on the high resolution data using IPO  [18] , a tool optimising parameters based on isotopologue detection.
For example, in the high concentration, low resolution dataset 1, the total number of peaks detected was 137 for matchedFilter and 238 for centWave, of which 88 (59. 9%) and 144 (60. 5%) unique peaks were respectively classified as high quality. By contrast, in the high concentration, high resolution dataset 2, the total number of peaks detected was 2153 for matchedFilter and 997 for centWave, of which 280 (13. 0%) and 181 (18. 2%) unique peaks were respectively classified as high quality. Figure  1A  shows that, in low resolution data, the number of peaks annotated as high quality and detected by centWave is on average 47% higher than the number of high-quality peaks detected by matchedFilter. However, the output of the two algorithms do not entirely overlap, and there are cases where matchedFilter detects high quality peaks which centWave does not and vice versa.
Therefore, the number of peaks classified as high-quality increases by merging the results of both algorithms. As the compound mix concentration increases, the number of high-quality peaks found by both individual algorithms and in WiPP increases due to a reduced number of peaks near the noise level. The number of peaks filtered out by WiPP (Figure  1B ) in low resolution data for matchedFilter and centWave represents on average of 40% and 43% of the total number of peaks reported by the two algorithms, respectively. Similarly, as for low resolution data, the use of centWave and matchedFilter together increases the number of high-quality peaks detected in high resolution datasets. However, matchedFilter detects 44% more high quality peaks in comparison to centWave (Figure  1C ). The number of filtered peaks is very high in comparison to low resolution data, as they represent an average of 90% of the total number of peaks detected by matchedFilter and 80% of the total number of peaks detected by centWave (Figure  1D ).
Compound detection is not a built-in feature of WiPP, but the WiPP output format enables easy library matching using existing compound libraries. To test the ability of WiPP to report true compound related peaks, peaks detected and classified as high quality were annotated using our internal library, corresponding to the compound mix using reverse matching (Supplementary Materials). The output of the automated annotation implemented in WiPP was separated into two categories: High confidence annotation, requiring both the retention index (RI) to be within a 1. 5 RI window and a spectra similarity score higher than 0. 9 (Supplementary Materials), and low confidence annotation requiring only a spectral match to the internal library within the RI window. Manual annotation of datasets 1 and 2 was performed by an experienced mass spectrometrist and used as a gold standard to assess the ability of WiPP to detect known compounds.
For comparison, a manual annotation was generated according to our labs best practice. It consisted of data pre-processing and peak detection using Chromatof (Leco), followed by manual annotation using the in-house software, Maui-via  [19]. Parameters used in Chromatof for data pre-processing are available in Table  S3. The automated WiPP workflow achieves comparable performances to the manual annotation for medium and high concentration, as summarized in Figure  2  (full details for all compounds and concentration are shown in Figure  S4 ). WiPP delivers 95% of the manually annotated compounds in the high concentration samples and 86% of the compounds in the medium concentration samples. However, WiPP shows some limitation with low concentration data as only 42% of the metabolites are recovered.
Case Study To further validate the results produced by WiPP, we ran the full workflow, including the generation of classifiers, on a publicly available biological dataset, and results were compared to original results reported by the study. The selected study and data are introduced in Section 4. 3. 2. WiPP classifiers were trained using a subset of the biological samples (two samples from each biological condition) as no pooled samples were available, and peak picking algorithms parameters were optimised using a different subset of the dataset (two samples from each biological condition). The final high quality peak set obtained by WiPP was annotated using the same spectral matching similarity score, reference masses, and intensities as the original study  [20]. Nine analytes were confirmed and manually validated in this study through targeted analysis.
Discussion In this study, we present WiPP, a machine learning-based pipeline that enables the optimisation, combination, and comparison of existing peak picking algorithms applied to GC-MS data. WiPP integrates machine learning classifiers to automatically evaluate the performance of peak picking algorithms and their selected parameters. WiPP also offers to the community a new approach to compare the performances of different peak picking algorithms, not only based on quantity but also on quality, and enables an automated parameter optimisation. Our results show that WiPP produces comparable outputs to manually curate data in an automated and, thus, more reproducible and scalable manner. For low concentration peaks, the comparability is less convincing than for medium and high concentrations.
This result indicates either general incapability of embedded algorithms to detect low intensity peaks or algorithm parameters to be less suitable for low intensity peaks. The latter might be the case in our setting as parameters were optimised based on samples from all three concentration ratios, therefore fitting best for the entire dataset. However, specific training and optimisation for detection of low concentration peaks would be feasible with WiPP. We consider this use case to be less relevant because low quality and low intensity peaks are also difficult to quantify and therefore add unwanted noise to statistical downstream modelling. Our main use case for WiPP currently is the automatic generation of a comprehensive set of reliably quantified high-quality peaks that is suitable for immediate downstream analysis, as needed for large scale high sample number studies.
Peak detection in GC-MS data is a challenging and long-lasting problem. New approaches and tools emerge every year, yet there is still no established procedure to evaluate their performances objectively, and simple comparisons, such as the total number of peaks detected, is not a robust metric for benchmarking purposes  [12]. It is also influenced by the selection of algorithm-specific parameters, which leads to a certain subjective component when assessing each algorithm. We have demonstrated that WiPP can objectively assess the performance of multiple peak picking algorithms and is flexible enough that new algorithms can be added by the user, thus enabling future algorithm developers to objectively rate their algorithms against competitors.
Optimising Parameters for Peak Picking Currently, most peak picking algorithms require manual optimisation of parameters for every analysis. This is laborious and if not done can lead to suboptimal parameters being used to process datasets, having a strong effect on the selected peaks  [18]. It is noteworthy that the heatmap figures that illustrate the parameter optimisation strategy (Figure  S1 ) also highlight the fact that the best parameters found for matchedFilter on the considered samples do not correspond to the parameters that find the maximum number of peaks. In this specific case, the parameters displaying the highest number of peaks also find the highest number of high-quality peaks. However, it comes, at the cost of an increased number of poor quality or false positive peaks, compared to the best parameters returned by WiPP.
The combination of both algorithms as implemented in WiPP shows, in both low-and high-resolution data, a significant improvement on the coverage of peaks and compounds detected. These results clearly argue towards the use of several peak picking algorithms over a single one, as previously shown  [21]. While only centWave and matchedFilter were integrated so far, it is possible to integrate any peak picking algorithm to the workflow to further improve the coverage of detected high quality peaks. The modular architecture of WiPP, based on the python workflow framework Snakemake, enables new peak picking algorithms integration with little effort. The more peak picking algorithms used, the longer the workflow runtime will be. Based on dataset 1 and 2 presented here, we estimate a 4 h manual peak labelling process to generate the training data per algorithm, which must only be done once.
The total runtime of the workflow is highly dependent on the computing power available and the range of parameters tested. For example, the full processing for dataset 1 can be completed overnight using four cores. This time can be further reduced by narrowing the parameter search space. For high resolution data, we recommend using a high-performance computing (HPC) cluster as the number of parameters tested increases significantly. The overall results from the benchmarking process on a known mix of commercial standards and the replication of the workflow using a publicly available dataset show that WiPP brings automated data analysis closer to the current gold standard, which is manual curation using exclusively existing peak picking algorithms. In a context where large studies become routinely run in metabolomics laboratories, it is crucial to develop automated tools that can match manually validated standards.
Peak Detection We started the WiPP workflow definition by discussing the problems of peak detection. Myers et al. have shown that, when applied to the same dataset, different peak picking algorithms return two different, yet overlapping, peak sets  [8, 12]. To assess the performance of a peak picking algorithm, we would like to consider the true peak set, which is unknown. The true peak set can be defined as the full set of peaks corresponding to all metabolites or contaminants (including metabolite adducts and fragments) present in a sample. The accurate quantification of a peak requires precise measurement of the peak area, which necessitates knowledge of the peak centre and boundaries. A multitude of effects, including but not limited to neighbouring peaks and technical drifts, can transform and blur the peak shape, making peak quantification problematic.
Class D described noise signal and was considered as a false positive, while the last 3 classes, E, F, and G represented intermediate quality true peaks, which we considered not to be robust enough for downstream analysis. In WiPP, classes E, F, and G were reported to the user for manual attention. The first step of the workflow aimed at generating a training peak set containing a large variety of peaks differing in quality and intensity. For this purpose, we recommend applying the algorithms to pooled or quality control samples using a wide range of parameters, and manually classifying a minimum of 700 peaks (which, in our datasets, equated to a minimum of 7 peaks in the smallest class) to generate the training set (Figure  S2 ), henceforth called the calibration dataset. The parameter search ranges were user-defined and should be set by an experienced user with prior knowledge on both the data produced by the instrument and the algorithm they are applying (see Table  S2  for the parameters used in this study).
A representative set of peaks for the supervised training was generated in step 2 by sampling algorithm parameters and retention time ranges (Supplementary Materials). WiPP provided an efficient peak visualization tool (Figure  S5 ), allowing users to label each detected peak with one of the seven classes described in Figure  4. This important step allowed expert users to imprint their peak classification know-how into the machine learning model. The labelled peaks formed the training dataset that was used in step 3 of the workflow to train SVM classifiers. Every peak was described by an array of intensities within a certain m/z and retention time window. The peaks were baseline corrected, scaled, and flattened to meet the input format required by the classifiers. During training, hyper-parameter optimisation  [23]  was performed using stratified cross-validation to avoid over-fitting.
We called this peak set the optimisation dataset (Figure  5 ). The peaks detected by every single parameter set were classified using the algorithm specific classifier and scored using the scoring function. We applied a simple grid search approach to determine the parameters returning the highest score. Those parameters were considered as optimal. We considered this method preferable to other alternatives; descent methods may lead to suboptimal solutions if the algorithm is trapped in local minima, and annealing methods are potentially computationally costly. As minima were generally shallow and broad, there was very little benefit in using more computationally costly methods. An example of the results for parameter optimisation performed by WiPP for matchedFilter based on optimising the number of peaks per quality class is shown in Figure  S1.
The following step (step 5) consisted of running the peak detection algorithms with their optimal parameters on the full biological dataset. Finally, a high-quality peak set was generated in step 6 through a series of sub-steps. First, the peaks detected by the different algorithms were classified using their respective classifiers. Next, simple filters, such as class-based removal of duplicate peaks or rejection of peaks presenting less than n m/z, were applied (n was set to 3 by default and could be user-defined). The resulting algorithm specific peak sets were then merged, removing duplicate peaks where the peak sets overlapped. The final peak set was composed of only high quality peaks, and the peaks predicted as low and intermediate quality were kept aside for optional further manual inspection.
Implementation and Availability The pipeline was implemented in python 3 using Snakemake  [24] , a reproducible and scalable workflow management system. WiPP offered a modular design to allow the addition of other existing or newly developed peak picking algorithms written in common programming languages (e. g. , Java, python, R). Currently, centWave and matchedFilter peak picking algorithms are available in WiPP. eRah was also considered as an additional algorithm, but, in our hands, had memory issues when processing large datasets and was thus omitted from the current release. The pipeline can be run on local computers as well as on a high-performance cluster. WiPP supports mzML, mzData, and NetCDF input formats. It was tested with Ubuntu 16 and CentOS 7. 6. 1810 (Core). A comprehensive user manual and quick start guide are available on the GitHub repository.
WiPP is released under the permissive MIT open source license and freely available at https://github. com/bihealth/WiPP; contributions and bug reports are welcome. The first two datasets were made of an identical three-point dilution series (designated high, medium, and low concentration) of a compound mix of 69 metabolites in known concentrations  [25]. Nine samples of each dilution (1:1, 1:10, and 1:100) for a total of 27 samples were used to form the first dataset. These samples were prepared in duplicates to be run on two instruments with different resolutions (Pegasus 4D-TOF-MS-System: Resolving Power (FWHM) = 1290 at m/z = 219 and 7200 Q-TOF: Resolving Power (FWHM) = 14,299 at m/z = 271,9867, see Supplementary Materials for details). Sample preparation and data acquisition details are available in the Supplementary Materials.
Dataset 3 The third dataset was collected by Ranjbar et al. and is publicly available in the Metabolights repository  [26]  at https://www. ebi. ac. uk/metabolights/MTBLS105. The study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC) cases vs. patients with liver cirrhosis by analysis of human blood plasma using GC-MS  [20]. Briefly, data was collected using a GC-qMS (Agilent 5975C MSD coupled to an Agilent 7890A GC) equipped with an Agilent J&W DB-5MS column (30 m × 0. 25 mm × 0. 25 µm film, 95% dimethyl/5% diphenyl polysiloxane) with a 10 m Duragard Capillary column with a 10 min analysis, using a temperature gradient from 60 • C to 325 • C. Only 89 files generated in the selected ion monitoring (SIM) mode were used for validation purposes here. Although SIM normally simplifies peak detection, in this dataset, there were often several peaks detected for the same m/z, meaning that there was still a peak detection issue to be addressed.
RNAi Target sequencing (RIT-seq) screen The RIT-seq screen was carried out essentially as described  (41) , except that cells were selected on 100 g/ml G418 for 7 days prior to extraction of genomic DNA. Highthroughput sequencing was on a MiSeq platform (Illumina) at the Beijing Genomics Institute. Data were analysed as previously described  (40). Briefly, reads were mapped to the T. brucei 927 reference genome (v6, tritrypdb. org) with Bowtie 2 (42) using the parameters -very-sensitive-local -phred33. Alignment files were manipulated with SAMtools (43) and a custom-script  (40)  and data were further assessed using the Artemis genome browser  (44).
Flow cytometry Cells were harvested by centrifugation at 600 × g for 10 min at 4 • C and washed with ice-cold PBS. Cell pellets were resuspended in ice-cold 70% (v/v) methanol in PBS and vortexed thoroughly. Cells were blocked in 50% FBS in PBS for 1 h at room temperature with mixing. Antibodies were incubated in 3% BSA in PBS for 1 h at room temperature with mixing. Wash steps and final resuspension were performed with 1% BSA in PBS. Samples were run on an LSR Fortessa (Beckton Dickinson) and data analysed using FlowJo.
Per-gene read counts were derived using the Artemis genome browser; MapQ, 0. Read counts were normalized using edgeR and differential expression was determined with classic edgeR. Reads Per Kilobase of transcript per Million mapped reads (RPKM) values were derived from normalized read counts in edgeR. Violin plots were generated using BoxPlotR (http: //shiny. chemgrid. org/boxplotr/) also including cohorts of mitochondrial  (47)  and non-mitochondrial proteins  (48).
EC 50 assays Cells were seeded at 1 × 10 3 cells/ml in 96-well plates in an oligomycin A (Sigma) 2-fold dilution series. After 72 h growth, 20 l of 0. 49 mM resazurin sodium salt (Sigma) in PBS was added to each well and the plates were incubated for a further 6 h. Fluorescence was determined using an Infinite 200 pro plate reader (Tecan) at an excitation wavelength of 540 nm and an emission wavelength of 590 nm. Data were analysed using Prism (GraphPad).
Meme suite motif searching Annotated 3 -UTRs in the T. brucei TREU927 reference genome sequence were downloaded from TriTrypDB (tritrypdb. org). Redundant UTRs and those shorter than 100 bp were removed from the dataset. The annotated 3 -UTR sequences of the respiratome cohort were used as the Primary sequences and the remaining 3 -UTRs were used as the Control sequences. The Meme Suite (memesuite. org/tools/meme) was used for motif discovery using the parameters: discriminative mode, zero or one occurrence per sequence, search given strand only, minimum width 5, maximum width 15.
A number of proteome-based studies have clarified the composition of the respiratome complexes in T. brucei in recent years (32,47,52,53) and, using these datasets, we compiled a respiratome-set for this study (Supplementary Data File S1, Sheet S4); as in other eukaryotes, most of these components are encoded in the nuclear rather than mitochondrial genome in trypanosomatids. Analysis of this respiratome set and cohorts of 'control' transcripts encoding other mitochondrial or non-mitochondrial proteins (Supplementary Data File S1, Sheet S5) confirmed specific down-regulation of respiratome components following ZC3H40 knockdown (Figure  4B ). Further analysis of the transcriptome revealed increased transcript abundance for many silent expression site-associated VSG genes, again confirming a role in telomeric VSG silencing (Figure  4C , top-left).
We also assessed the expression of the F1␤ subunit of the ATP-synthase (complex V), using protein blotting. Again, consistent with our hypothesis, we observed substantially reduced abundance of this subunit following ZC3H40 knockdown (Supplementary Figure  S5B ). Encouraged by these results, we next used stable isotope labelling in cell culture (SILAC) followed by massspectrometry to assess the abundance of a larger number of respiratome components (Supplementary Data File S1, Sheet S7). Quantitative proteomic analysis, comparing uninduced and induced ZC3H40 knockdown, revealed >8-fold down-regulation of ZC3H40 and, as expected (see Supplementary Figure  S2 ), parallel down-regulation of ZC3H39 (Figure  7B ). We also detected down-regulation of >20 respiratome components from complexes I, II and V (Figure  7B ); while few components of complex III and IV were detected, consistent with relatively lower abundance in bloodstream form T.
brucei  (57) (58) (59). Thus, our quantitative proteomic analysis revealed significant depletion of respiratome proteins from complex I (P = 1. 4 × 10 -13 ), complex II (P = 2. 3 × 10 -3 ) and complex V (P = 1. 4 × 10 -119 ). We used two further approaches to assess the impact of ZC3H39/40 on respiratome protein expression. First, we established strains expressing an ectopic and inducible copy of ZC3H40 myc in a null background (Supplementary Figure  S5C ). Second, we assembled a null strain for both ZC3H39 and ZC3H40 (Supplementary Figure  S5D ). SILAC-based quantitative proteomic analyses, comparing induced and uninduced ectopic ZC3H40 cells or wild-type and double zc3h39/40 null cells, again revealed down-regulation of >20 respiratome components (Figure  7C  and D ; Supplementary Data File S1, Sheets S8 and 9); depletion of respiratome complexes was again highly significant in both experiments; complex I (P = 3.
0 × 10 -12 and P = 6. 3 × 10 -11 , respectively), complex II (P = 7. 9 × 10 -41 and P = 3. 5 × 10 -27 , respectively) and complex V (P = 1. 3 × 10 -89 and P = 1. 2 × 10 -48 , respectively). A comparison of the RNA-seq (Fig-  ure 4C ) and SILAC data (Figure  7B ) for complex V components following ZC3H40 knockdown indicated ∼30% re-duction in mRNA expression and ∼60% reduction in protein expression (Supplementary Figure  S6 ), suggesting that both mRNA stability and translation are positively regulated. Finally, we assessed the impact of ZC3H40 on respiratome protein expression in insect-stage T. brucei expressing an ectopic and inducible copy of ZC3H40 myc in a zc3h40 null background. These cells displayed growth that was unperturbed (Supplementary Figure  S7A ) while ZC3H40 myc was efficiently depleted (Supplementary Figure  S7B ).