dataesr/scientific-paragraphs-categorization huggingface dataset:
We performed all the potential contrasts between treated and untreated samples for every cell line. This resulted in 352 contrasts, with an average number of samples of 47 and 2 for untreated and treated samples, respectively (Supplementary Table  2 ). A er applying FLOP, we conducted an analysis of the output from modules 4 and 5. It is important to note that while in ReHeaT, each Spearman correlation data point between pipelines represented an independent study, in PANACEA and CCLE, each data point corresponding to one contrast within a specific dataset. In the context of module IV, we observed varying results depending on the dataset being evaluated. For CCLE, we found that most Spearman correlation values were well conserved, as supported by the data. However, in PANACEA, we observed a pattern similar to what was previously observed in ReHeaT, where pipelines utilising unfiltered counts displayed lower and less stable correlation values (Figure  5 ).
We also observed a similar trend in the output of module V, although similarity values were in general much lower, even between filtered pipelines, (Supplementary Figure  5 ). We attribute this difference to the varying number of samples per group in each dataset and the type of contrasts being conducted, which may result in higher statistical power in CCLE compared to PANACEA. Overall, these findings emphasise the potential impact of selecting specific preprocessing methods on downstream functional results, which may not be apparent when only considering the differential expression (DE) space. It further highlights the utility of FLOP in scenarios with limited statistical power, enabling researchers to make more informed decisions regarding pipeline selection and functional analysis.
Discussion In this study, we present FLOP, a workflow designed to assess the impact of different transcriptomic data analysis pipelines on downstream enrichment analysis results. We applied FLOP to five studies sourced from ReHeaT, a meta-resource of heart failure transcriptomics data developed by our group, which represents a common clinical application scenario of transcriptomic data analysis. We showcased its capabilities to detect instances of low agreement between pipelines in the gene set space, which might not be present in the differential expression analysis space. Through the analysis of FLOP's module 5 output, we found that, while agreements between entire gene set lists are generally high, they can be notably lower when focusing on the extremely ranked features. We also applied FLOP to cancer cell line data from two distinct studies, namely, CCLE  [41]  and the PANACEA DREAM challenge  [42].
These datasets were chosen for their anticipated differences in statistical power due to variations in biological contrasts explored and sample size. As expected, the results obtained in the CCLE dataset exhibited higher robustness across different pipeline choices. Conversely, the results derived from the PANACEA dataset displayed much greater variability, both when considering the entire gene set list and when focusing on its extremes. Numerous benchmarks have investigated the impact of method choice on differential expression (DE) analysis results  [14, 15, [26] [27] [28] [29] [30] [31] [32] [33]. However, none of these studies evaluated the impact of method selection on downstream gene set enrichment results. Additionally, various pipelines have been developed to handle both DE analysis and functional analysis tasks.
It offers researchers the ability to assess the impact of different pipelines on downstream enrichment analysis results, thus enabling a more comprehensive understanding of the implications of method selection. In addition, it is essential to recognize that most transcriptomic studies are o en conducted in settings with limited statistical power. The results in the PANACEA dataset illustrate the value of FLOP to guide pipeline selection, assess its impact, and prevent the pursuit of potentially misleading enrichment analysis results. By using FLOP to evaluate pipeline performance in datasets with varying statistical power, researchers can gain a more comprehensive understanding of the reliability and consistency of functional analyses. This empowers researchers to make informed decisions in scenarios with limited statistical power, ultimately enhancing the credibility of enrichment analysis and facilitating more accurate biological insights from transcriptomic studies.
This study has some limitations: First, FLOP per se cannot determine which pipeline performs better; it is designed to quantify differences between pipelines. We considered using simulated data, but we faced challenges in creating accurate count-level data simulations of the downstream effects for hallmarks, TFs, or signalling pathways. We also examined predefining gene sets for specific datasets, but we were concerned about the generalizability of the conclusions drawn that way. Thus, we opted to remain unbiased, focusing on quantifying inconsistencies among pipelines instead of attempting to rank them. Second, for our applications, we arbitrarily defined certain parameters that can influence the comparison outcomes. Some examples include the minimum number of counts per sample required for the filtering module or the number of top deregulated terms considered when evaluating similarities.
Installation, code availability and reproducibility FLOP can be installed from GitHub (Link) or Zenodo (Link). We packed all the required dependencies in a conda environment which needs to be set up before running the workflow. The exact list of requirements can be viewed in this yaml file (Link). A test dataset can be found in Zenodo (Link). It is a small subset of PANACEA containing three treatments and one cell line. The seven datasets used to perform the analysis detailed in this manuscript were also deposited in Zenodo (Link), along with the files containing the prior knowledge sources as to 31. 08. 2023 (Link). A one-liner option is available to perform an end-to-end analysis using the datasets and prior knowledge sources deposited in Zenodo and obtain the figures present in this manuscript.
Data preprocessing We applied FLOP to 8 different datasets (Supplementary Table  2 ): 5 datasets from the ReHeat resource, the PANACEA DREAM challenge and CCLE. We used 5 datasets from the ReHeaT resource  [40] : Spurrell19  [44] , LiuR  [43] , Pepin19  [45] , Schiano17  [46]  and Yang14  [47]. These datasets contain information about patients who suffered heart failure, along with other metadata such as age, gender, etc. We downloaded the PANACEA gene counts from the NCBI GEO portal (GEO accession number: GSE186341)  [42]. The dataset contains samples from 32 treatments in 11 cell lines. We performed contrasts between each treatment (2 samples) and DMSO (28-60 samples) for each of the 11 cell lines, resulting in 352 comparisons (Supplementary Table  3 ). We translated the gene ID's from ENTREZ IDs to gene symbols.
In multi-mapping situations where several ENTREZ IDs were pointing to the same gene symbol, counts were summed. The CCLE dataset contains RNA-seq data from 1019 cancer cell lines, which are grouped by 26 parental tissue types  [41]. We obtained the raw gene counts from the DepMap portal (Link). We removed tissues with less than 20 cell lines (8 tissues). To account for large differences in cell line number by tissue of origin, we randomly subsetted 20 cell lines per each of the 18 remaining tissues. We then performed all non-redundant pairwise contrasts between all tissue types, resulting in 153 comparisons.
The second step carries out the 6 alternative DE analysis pipelines: tmm-limma, vsn-limma, voom-limma, log quantile-limma, edgeR and DESeq2. The next process merges the 6 pipelines' output files into a long-format table, which serves as input for the next process, decoupleR. A er the functional analysis, several consecutive modules merge the results of each dataset. Lastly, the two evaluation modules perform the analysis detailed in following sections: Rank correlation analysis and Top/bottom features overlap analysis. FLOP output consists of four files: the differential expression results, separated by pipeline and contrast, the activity scores and p-values for all gene sets, and the results for modules 4 and 5. We implemented two profiles to control the parameters for the Nextflow run, depending if the workflow is going to be run on a desktop computer or in a slurm-controlled HPC environment.
Filtering strategies To filter lowly expressed genes, we used the function filterByExpr, from the edgeR package, which eliminates genes that are not sufficiently expressed in a minimum number of samples per group. In other words, it takes into account the experimental design and differentiates between genes lowly expressed across groups (probably not biologically relevant) and genes lowly expressed in only some groups but not in others (potentially relevant)  [10]. We used the default parameters implemented in this function for all the datasets included in this study (Supplementary Table  4 ). To prevent artificial noise in the activity scores of the functional terms, we filtered out contrasts which did not have a sufficient signal, understood as a relevant number of DE genes per contrast, by implementing a cutoff of a minimum of 30 DE genes (this value can be customised).
Normalisation and DE analysis We selected four methods for the normalisation of raw counts (vsn, TMM, log quantile normalisation and voom), and three methods for DE analysis (limma, DESeq2 and edgeR). We applied limma on the normalised values, while we applied DESeq2 and edgeR directly on non-normalized counts, since these methods perform an internal normalisation strategy prior to the differential analysis instead. edgeR did not provide t-statistics, unlike limma or DESeq2 (we treated Wald statistics as t-like statistics). Therefore, we transformed edgeR F statistics into t-statistics via the relation t 2 = F for a valid comparison between methods. Parameter settings and package versions can be found in the Supplementary Table  4.
Functional analysis decoupleR  [25]  implements multiple strategies to carry out the functional analysis of omics data by combining it with prior knowledge in the form of gene sets. Here, we used t-values as input for the analysis. Using the python version of decoupleR, we applied the univariate linear model (ulm) method. We used the default threshold of a minimum of 5 genes per gene set. We used three prior-knowledge resources: PROGENy  [37] , DoRothEA  [38]  and MSigDB hallmarks  [39]. PROGENy is a PK resource that provides a collection of responsive genes for 14 different pathways by analysis of a large set of signalling perturbation experiments. The MSigDB Hallmarks are a collection of 50 independent gene sets that encompass genes which represent a well-defined biological state or process.
Evaluation modules In module 4, we apply Spearman rank correlation to the gene set enrichment results generated by each pair of pipelines and prior-knowledge source. In module 5, we take the top N up-regulated and N down-regulated functional categories to calculate the similarity index: for DoRothEA, N = 15; for MSigDB hallmarks, N = 5; and for PROGENy, N = 3. N was defined according to each gene set collection size. For genes, the top 5% and bottom 5% of the total number of genes were used as N. The similarity index is calculated using the formula , being A∩B the intersection of two lists of elements, and N the total 𝐴∩𝐵 𝑁 number of elements in the lists. A similarity score of one indicates that both pipelines have the same top and bottom terms in their ranked lists, whereas a score of zero indicates no overlap between these sections of the ranked lists.
2. 2 normalizeQuantiles - - limma 3. 54. 0 voom voom - - limma 3. 54. 0 limma lmFit - - limma 3. 54. 0 TopTable number Inf limma 3. 54. 0 sort. by none DESeq2 DESeqDataSetFromMatrix - - DESeq2 1. 38. 0 DESeq - - DESeq2 1. 38. 0 results - - DESeq2 1. 38. 0 DE analysis edgeR DGEList - - edgeR 3. 40. 0 calcNormFactors - - edgeR 3. 40. 0 estimateDisp - - edgeR 3. 40. 0 glmQLFit - - edgeR 3. 40. 0 glmQLFTest - - edgeR 3. 40. 0 topTags n Inf edgeR 3. 40. 0 sort. by none decoupleR decouple weight none*, -decoupleR 1. 3. 4 Functional get_progeny organism human decoupleR 1. 3. 4 analysis get_dorothea organism human decoupleR 1. 3. 4 get_resource name MSigDB decoupleR 1. 3. 4 Rank correlation cor method spearman stats 4. 2. 2 Top/bottom features overlap jaccardSets - - bayesbio 1. 0. 0.
The combination peptide treatment once again significantly enhanced transcription of genes relevant to ECM-rich tissues (ECM organisation, collagen biosynthesis, connective tissue development and wound healing) and lipid/steroid-related processes, but also to peptide response and epithelial processes (Fig. 4e  and f and Fig. S2 ). After 7 days' exposure, the peptide combination induced clear clustering by the proteins induced on the PCA plots, which was distinct from both the control and those treated with ATRA (Fig. 4g ). Over 20 ECM proteins in each donor dataset were found to be upregulated by the peptide combination treatment, with ECM glycoproteins and regulators being in the majority. Notably, fibronectin, vitronectin, fibromodulin, as well as MMPs-1 and -14 were all found to be upregulated in multiple cell donors.
, 2013; Rijken et al. , 2005; Xu et al. , 2014). PROSPER uses publicly available data from the MEROPS database including manually curated and experimentally validated protease cleavage consensus sequences  (Rawlings et al. , 2018). Using a support vector machine learning (SVM) algorithm  (Joachims, 1999)  PROSPER estimates the likelihood of proteolytic cleavages occurring in particular positions on protein primary sequences. To make predictions more robust, the PROSPER classifier integrates structural properties such as disordered regions (regions that are capable of undergoing structural changes while performing their functions  (Uversky, 2013) ) as predicted by DISOPRED3  (Jones and Cozzetto, 2015) , assessments of protein secondary structure (psipred  (Buchan et al. , 2013) ) and solvent accessibility (ACCpro 4.
Following further review of these proteins we identified a sub-cohort of 27 target proteins (Fig. 2a ) with relatively high skin abundance (as assessed by immune staining in the Human Protein Atlas  (Ponten et al. , 2008) , and susceptibility to age-related remodelling (as reviewed in the Manchester Skin Proteome  (Hibbert et al. , 2018) ). In silico peptide prediction: In order to predict the identity of cleavage sites and hence cleavage products for each of the 27 proteins, protein FASTA sequences along with domain information were retrieved from Uniprot and the sequences were digested in silico by the same panel of enzymes as used in target protein selection (MMPs-2, -3, -7 and -9, cathepsins -G and -K, granzyme B and elastase-2). We developed a bespoke algorithm (https://github. com/maxozo/Matrikine_Discovery) which to predict the sequence of liberated peptides.
Unmapped paired-end sequences were tested by FastQC version 0. 11. 3 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Sequence adapters were removed, and reads were quality trimmed using Trimmomatic_0. 39  (Bolger et al. , 2014). The reads were mapped against the reference human genome (hg38) and counts per gene were calculated using annotation from GENCODE 31 (http://www. gencodegenes. org/) using STAR_2. 7. 7a  (Dobin et al. , 2013). Normalisation, Principal Components Analysis, and differential expression was calculated with DESeq2_1. 36. 0  (Love et al. , 2014). In vitro characterisation of cell proteomes in response to peptides 1, 7 and 1+7 in combination: In parallel with the transcriptome analysis, cells were treated for a total 7 days prior to protein extraction. Media and treatments were replenished every other day, with the media being replaced by serum-free media for the final two days of treatment prior to protein extrication and analysis.
Proteomic data analysis: LC-MS/MS was used to analyse all peptide treatment samples. This was performed by the Biological Mass Spectrometry Core Facility, Faculty of Biology, Medicine and Health, University of Manchester according to their protocols. Samples were run through the ThermoFisher Scientific Orbitrap Elite Mass Spectrometer, or through the ThermoFisher Scientific Q Exactive HF Mass Spectrometer. Following LC-MS/MS, peptide and protein identification was performed using either Progenesis QI, or Proteome Discoverer. Progenesis QI (Nonlinear Dynamics, Waters, Newcastle, UK) was used to relatively quantify protein abundance for the initial proteomic screens of eight candidate peptides (Fig. 3 ) and the first proteomic analysis of P1, P7 and P1+P7 (Fig. 4c  and d ). Raw mass spectra files were imported and ion intensity maps were generated.
Ion outlines were automatically aligned to a single reference run using default settings. Ion peaks and their relative abundances were then automatically picked without filtering and normalised to a single reference run using default settings. Data were then exported and searched using Mascot v2. 5. 1. Mascot was set up to search the Swiss-Prot database (selected for Homo sapiens, version 2022-08-03, 207,304 entries) assuming the digestion enzyme was trypsin. Mascot was searched with a fragment ion mass tolerance of 0. 02 Da and a parent ion tolerance of 10. 0 ppm. The Mascot search included peptides of charges 2+ and 3+, and a maximum of one missed cleavage site. Carbamidomethyl of cysteine was specified in Mascot as a fixed modification, while oxidation of methionine, lysine, proline and arginine were specified in Mascot as variable modifications.
This was then re-imported back into Progenesis QI, where identified peptide ions were matched. Normalised abundance for each protein was calculated as the sum of each matched peptide ion abundance. Normalised protein abundances, compared between treatments, were statistically analysed within Progenesis QI using a paired (repeated measured) ANOVA test. Relative quantification of protein abundance for the final in vitro proteome study (Fig. 4g ) was performed using Proteome Discoverer (version 2. 5. 0. 400; ThermoFisher Scientific, Waltham, Massachusetts, United States). Raw mass spectra files were imported, and underwent an initial processing step, using Sequest HT to identify peptides within the spectra, searching the Swiss-Prot database (selected for Homo sapiens, version 2022-08-03, 207,304 entries), and assuming the digestion enzyme was trypsin.
"Prediction, screening and characterisation of novel bioactive, tetra-peptide matrikines -peptide combination, n=3", " Accession: PXD039960, DOI: 10. 6019/PXD039960, Reviewer account: Username: reviewer_pxd039960@ebi. ac. uk, Password: 6FhWHe5r. The three transcriptomics data sets (E-MTAB-12711, E-MTAB-12710 and E-MTAB-12704) can be found at ArrayExpress (https://www. ebi. ac. uk/biostudies/arrayexpress). The peptide prediction code can be found at: https://github. com/maxozo/Matrikine_Discovery. In silico prediction of protease cleavage sites and liberated small peptides. Each of the 27 proteins was screened using a bespoke Python algorithm interacting with the PROSPER protease cleavage server to identify cleavage sites with a probability cleavage scores of >=0. 7. In this hypothetical example a single tetra peptide (red dotted box) would be liberated.
Acknowledgments This work was supported by a programme grant from  No7 Beauty Company ,  Walgreens Boots Alliance  to MJS, and REBW. We thank  Leo Zeef  and  Andy Hayes  of the  Bioinformatics and Genomic Technologies Core Facilities at the University of Manchester  for providing support with regard to RNA-Seq. We would also like to thank the  Biological Mass Spectrometry Core Research Facility in the Faculty of Biology, Medicine , and  Health at The University of Manchester  (RRID: SCR_020987).
Systematic literature review Considering the relative immaturity of the literature on consumer barriers to repair, this literature review aims to provide a systematic overview of existing knowledge on the topic to create a reproducible reference for future benchmark and investigation of the effect of RtR implementation. Scientific contributions selection was performed in July 2023 and based on the intersection of the following keywords: "repair" or "right to repair"; "consumer"; "barrier" or "challeng*", or "motivat*". Scopus was the used database as it is a renowned source of engineering studies. The combined use of keywords generated a total of 380 papers, which were then filtered by relevance based on journals, titles and abstracts. A detailed process chart for the SLR is presented in Fig.
-Identification and sequence analysis of TbDBF To identify potential fusion players, we started our search by careful re-examination of trypanosome genomic and proteomic databases for proteins showing sequence homology to known fusion factors (Opa1/Mfn1/2). We were only able to identify the previously reported dynamin-related protein TbMFNL (Tb927. 7. 2410)  (29) , that displays some similarities with both mammalian fusion factors of the inner (Opa1) or outer membrane (Mfn2) with 21 and 23 % of sequence identity, respectively (Figure  1 , S1 and Table  S1 ). Analysis of its sequence identified several motifs and domains found in other mitochondrial DRPs. Indeed, its N-terminal sequence displays a potential mitochondrial targeting signal (MTS) with potential cleavage sites (Figure  S1 ), followed by a GTPase domain with conserved G1 to G4 motifs and, in its C-terminal region, two consecutive transmembrane domains (TM) (Figure  1  and S1).
-TbDBF is localized in the mitochondrion Interrogation of the TrypTag database (cellular localization of trypanosome proteins  (30) ) with the TbDBF sequence (Tb927. 07. 2410) reveals mitochondrial localization for a C-terminal GFP-tagged version and a cytoplasmic, flagellar and nuclear lumen localization for an N-terminal GFP tagged construct. One potential explanation for this apparent discrepancy is that adding an N-terminal GFP sequence in front of a mitochondrial targeting sequence (MTS) is expected to perturb its addressing and therefore does not allow to conclude on its localization. To confirm the subcellular localization of TbDBF in T. brucei, we added a C-terminal 10xHA tag (to avoid disruption of mitochondrial targeting) to endogenous TbDBF (TbDBF::10HA) in both PCF and BSF using the pPOTV7 vector series (31) (Figure  3A ) and verified that the tag has no impact on cell growth (Figure  S4 ).
Immunofluorescence Cells were washed twice with PBS, then fixed with 2% paraformaldehyde (PFA) for 10 min at room temperature and 0. 1mM Glycine was added 10 min to stop the reaction. The cells were spread on slides and permeabilized with 0. 05% triton X-100. After incubation in PBS containing 4% bovine serum albumin (BSA) 20 min, cells were incubated for 1 hour with primary antibodies diluted in PBS-BSA 4%, washed 4 times with PBS and incubated for 45 min with secondary antibodies diluted in PBS-BSA 4% followed by three washes. Then, kinetoplasts and nuclei were labelled with DAPI (10 µg/mL) for 5 min. Slides were washed 3 times with PBS and mounted with SlowFade Gold (Molecular probes). Images were acquired with MetaMorph software on Zeiss Imager Z1 or Axioplan 2 microscope and processed with ImageJ.
Ultrastructure Expansion Microscopy (UExM) The protocol of UExM realized exactly as described by Casas et al. 2022 (dx. doi. org/10. 17504/protocols. io. bvwqn7dw). An expansion factor was determined using the ratio between the size of the coverslip (12mm) and the size of the gels after the first expansion. Images were acquired on a confocal Leica SP5-MP (Bordeaux Imaging Center) using a 63X oil objective and processed with ImageJ.