dataesr/scientific-paragraphs-categorization huggingface dataset:
Further investigation into taxonomic composition between LSC and nLSC revealed broad changes already at the phylum level. Notably, Bacteroidetes and Verrucomicrobia were enriched within LSC-prepared samples, whereas Firmicutes, Actinobacteria and Deferribacteres phyla were over-represented in nLSC samples. Such depletion or enrichment of several major bacterial phyla have previously been reported by Tanca and colleagues  [24]. While Verrucomicrobia was found enriched by LSC in ours as well as Tanca's study, Bacteroidetes, Firmicutes and Actinobacteria were enriched by opposite methods. Several reasons may explain these discrepancies, such as the host organism under study (i. e. Mus musculus versus Homo sapiens), different protein sequence database construction (i. e. mouse microbiome catalogue versus UniProtKB custom microbiome) and minimal biological variability (i.
Single-step database search allows optimal control of false discovery rate Currently, many metaproteomic studies use two-step database searches as a way to boost identification rates  [40] [41] [42]. However, we demonstrate that this type of search dramatically underrepresents the number of false positives, due to the use of a decoy search strategy that is unsuitable in this context. Our results elaborate on a previous study by Muth and co-workers, who also emphasised the drawbacks of using a two-step search together with decoy strategy  [70]. Using a single human LC-MS/MS measurement, our findings were so extreme that the number of false positives was equal or greater to the number of false negatives, with FDR outside of any accepted range (i. e. factual FDR > 0. 1). Using metaproteome samples of known composition, we expanded our investigation of search strategies by including "two-step taxa" and "two-step two sections".
Unipept software Previous studies have shown that it is possible to derive taxonomic representation from MSidentified peptides of known taxonomic origin  [32, 33, 71]. However, to the best of our knowledge, there has not been a comparison of software for the taxonomic annotation of peptides with unknown origin. Here, we compared three software packages, namely Kraken2  [46] , Diamond  [48]  and Unipept  [28] , which use different algorithms to perform such taxonomic annotation. Using metaproteome samples of known composition, as well as metaproteome samples from 38 murine faeces, we determined that the Unipept software provided superior performance (i. e. precision, sensitivity). Notably, Unipept is very userfriendly, fast and was designed to work on MS-identified peptides  [28]. Whereas, Diamond and Kraken2 have both been designed to work on full protein/gene sequences or nucleotide sequencing reads (as opposed to peptides), which may have contributed to their lower performance  [46, 48].
Our assessments (i. e. sensitivity, specificity) were based on exact taxonomic identity and ignored hits from closely related taxon, which may have negatively affected the performance estimates of Kraken2  [46]. While, Unipept was clearly the optimal taxonomic annotation software for MS-identified peptides, it is currently limited to UniProt proteins, NCBI taxonomic hierarchy and trypsin cleavage. The metaproteome shows an enrichment in functionally-active pathways compared to the matching metagenomic potential Here, we observed an overall positive correlation between gene and protein abundances derived from metaproteome and matching-metagenome analysis. This was previously reported in a longitudinal study of metaproteome/metagenome fluctuations from one individual with Crohn's Disease  [52].
Conclusion To conclude, in this study we present an integrated analytical and bioinformatic workflow to improve protein identification, taxonomic and functional coverage of the murine faecal metaproteome. LSC combined with in-solution digestion provided the highest identification rates, although leading to a potential enrichment in specific taxa. We also show that fast and accurate MS data processing can be achieved using a single-step database search. Taxonomic annotation can be generated directly from MS-based peptide identification using the Unipept software. While protein and gene abundances displayed an overall positive correlation, the metaproteome showed a significant functional enrichment compared to its metagenomic potential; thus, emphasizing the need for more metaproteomic studies for adequate functional characterisation of the microbiome.
DNA extraction and whole-genome sequencing Whole genome analysis was performed on the mouse cohort used for data analysis assessment. In brief, DNA was extracted from faecal samples using the FastDNA SPIN Kit (MP Biochemicals) and following manufacturer's instructions. DNA concentration was measured using a Qubit fluorometer (Invitrogen) and samples were shipped frozen to the Quantitative Biology Centre (QBiC) at the University of Tuebingen for whole genome sequencing. Sequence data were generated on an Illumina HiSeq 2500 instrument (chemistry SBS v3 plus ClusterKit cBot HS) and processed as described previously  [75]  but with minor modifications that follow. Supplied sequence data were checked using fastQC v0. 11. 5  [76]. Data were trimmed with Trim Galore! (--clip_R1 10 --clip_R2 10 --three_prime_clip_R1 10 --three_prime_clip_R2 10 --length 50; Babraham Bioinformatics).
Mouse DNA within samples was detected by mapping reads against the mouse genome (GRCm38). Mouse-filtered read files (with an average of 3. 58 ± 0. 08 Gb sequence data per sample) were used for all subsequent analyses. Kraken2 2. 0. 8-beta [46]  with the pre-compiled Genome Taxonomy Database  [77]  Functional annotation was achieved by mapping centroid protein sequences generated as described before  [46, 75]  using the eggNOG-mapper software (v. 1. 0. 3)  [78]  and associated database (v. 4. 5).
LC-MS/MS measurements Samples were measured on an EASY-nLC 1200 (Thermo Fisher Scientific) coupled to a Q Exactive HF mass spectrometer (Thermo Fisher Scientific). The samples prepared for the sample purification and protein extraction methodologies assessment were all measured in duplicates to assess instrument reproducibility. Peptides were chromatographically separated using 75 μm (ID), 20 cm packed in-house with reversed-phase ReproSil-Pur 120 C18-AQ 1. 9 μm resin (Dr. Maisch GmbH). Peptide samples generated as part of the laboratory method optimisation (LSC vs. nLSC, FASP vs. in-solution) were eluted over 43 min using a 10 to 33 % gradient of solvent B (80 % ACN in 0. 1 % formic acid) followed by a washout procedure. Peptide samples generated as part of the data analysis assessment (metaproteome vs.
LC-MS/MS data processing Raw data obtained from the instrument were processed using MaxQuant (version 1. 5. 2. 8)  [36]. The protein sequence databases used for database search consisted of the complete Mus musculus Uniprot database (54,506 sequences) and frequently observed contaminants (248 entries), as well as the mouse microbiome catalogue (~2. 6 million proteins)  [51]  for the raw data from laboratory method optimisation samples or the matching metagenome gene translation (~1. 5 million proteins) for the raw data from data analysis assessment samples. A FDR of 1 % was required at the peptide and protein levels. A maximum of two missed cleavages was allowed and full tryptic enzyme specificity was required. Carbamidomethylation of cysteines was defined as fixed modification, while methionine oxidation and N-terminal acetylation were set as variable modifications.
Comparison of sample preparation methods Unless stated otherwise, the analyses described below were performed in the R environment  [83]. To compare the different centrifugation, digestion and lysis methods, we counted for each sample the number of peptide and protein groups with intensities and LFQ intensities superior to zero, respectively. We tested for significant differences between methods using unpaired ttests via the ggplot2 package  [84]. Quantified peptides and protein groups were checked for overlap between the centrifugation methods using the VennDiagram package. The proportion of host (Mus musculus) proteins was computed by summing up all host proteins iBAQ values and then dividing by the total iBAQ per sample. The centrifugation methods were evaluated using an unpaired t-test.
The taxonomy representation, for the centrifugation methods, was done via the Unipept online software (v. 4. 5. 1) [37]. The quantified peptides (intensity superior to zero) were imported into Unipept with I-L not equal. The Unipept result were used to count the number of non-redundant peptides assigned to each taxonomic node. For the differential protein abundance analysis (between LSC and nLSC), the MSnBase package was used as organisational framework for the protein groups LFQ data  [85]. Host proteins, reverse hit and potential contaminant proteins were filtered out. Protein groups were retained for further analysis only if more than 90 % of samples within either LSC or nLSC group had an LFQ superior to the first quartile overall LFQ. Significantly changing proteins were identified using paired t-test.
Significance was set at an adjusted p-value of 0. 01 following Benjamini-Hochberg multiple correction testing, as well as a minimum LSC/nLSC fold-change of ±1. 5. The over-representation and GSEA testing of KEGG pathways were done for the significantly up-and down-regulated proteins as well as for the proteins uniquely identified per group via the clusterProfiler package based on hypergeometric distribution (p-adj. ≤ 0. 05)  [39]. Single-versus two-step search assessment using HeLa cell line sample HeLa cells were prepared for LC-MS/MS measurements using published method  [86]. Briefly, cells were grown in DMEM medium and harvested at 80 % confluence. Proteins were precipitated using acetone and methanol. Proteins were reduced with DTT and digested with Lys-C and trypsin. Peptides were purified on Sep-Pak C18 Cartridge.
Sample was measured as described in the LC-MS/MS measurements section but for a few changes. Peptide sample was eluted over 213 min using a 7 % (0 min), 15 % (140 min) and 33 % (213 min) gradient of solvent B (80 % ACN in 0. 1 % formic acid) followed by a washout procedure. The top 10 most intense ions were selected for HCD fragmentation. Raw data were processed as described in the LC-MS/MS data processing section with a few alterations. Match between runs was disabled. The protein sequence databases used for database search consisted of the complete Homo sapiens Uniprot database (93,799 sequences), frequently observed contaminants (248 entries), as well as the mouse microbiome catalogue (~2. 6 million proteins)  [51]. Several processings were performed differing in the number of microbiome catalogue entries included, which led to an increase in database size of 0×, 1×, 2×, 5×, 10× and 20× compared to the H.
sapiens database alone. These processings also differed in the database search strategies used, namely single-or two-step search  [40]. Identified MS/MS, peptides and protein groups were assigned to kingdom of origin (conflicts were resolved to Eukaryota by default). To compare the different database search strategies, we counted the number of identified MS/MS, non-redundant peptides and protein groups associated to each kingdom (as well as reverse hits and potential contaminants). We also calculated the FDR based solely on reverse hits or together with bacterial hits (factual FDR) in order to investigate the true number of false positives.
Database search strategies assessment using known microbiome samples We used the samples generated by Kleiner and colleagues, specifically the uneven organisms preparation described in the earlier publication  [32]. This dataset contained LC-MS/MS measurements (N = 8) that we processed as described in the LC-MS/MS data processing section with a few alterations. Match between runs was disabled. The protein sequence databases used for database search consisted of the proteome of all 32 organisms present in the synthetic samples ("uneven database" = 122,972 sequences), frequently observed contaminants (248 entries), as well as the mouse microbiome catalogue (~2. 6 million proteins)  [51]. Several processings were performed differing in the number of microbiome catalogue entries included, which led to an increase in database size of 0×, 0.
5×, 1×, 2×, 5×, 10× and 20× compared to the "uneven database" alone. These processings also differed in the database search strategies used, namely single-step search, "two-step protein" search to keep identified proteins  [40] , "two-step taxa" search to keep identified taxa  [30] , and "two-step two sections" search to keep identified proteins after sectioned search  [44]. Identified protein groups were assigned to database of origin, namely "uneven database" or mouse microbiome catalogue database. For each sample, this allowed computation of the number of (1) true positive hits, must be hits from the "uneven database"; (2) false positive hits, must be hits from the mouse microbiome catalogue; (3) false negative hits, the total identified protein count in the "uneven database" (total from 8 samples) minus the true positives; and (4) true negative hits, the total protein count in the mouse microbiome catalogue minus the false positives.
This allowed calculation of the accuracy, precision and sensitivity for each increase in the database size. We also calculated the factual FDR based on reverse hits together with mouse microbiome catalogue hits in order to investigate the true number of false positives. Using only the processings against the largest database (20×), we filtered our data for protein groups with a minimum of one or two unique peptides. The true positive count and factual FDR were calculated (and compared) for each combination of search strategy and filtering, as described in the previous paragraph.
Taxonomic representation of known microbiome samples We also used the uneven samples generated by Kleiner and colleagues  [32]  to investigate the taxonomic representation derived from MS-identified peptides. The gold-standard processing was used, with single-step database search against the proteome of all 32 organisms present in the synthetic samples ("uneven database" = 122,972 sequences). MS-identified peptides were submitted to (1) Kraken2 (v. 2. 1. 1) [46] , (2) Diamond (v. 2. 0. 9) [48] , or (3) Unipept online (v.
Taxonomic representation of faecal microbiome samples All subsequent sections use the faecal samples from a 38 mice cohort. These were prepared via LSC and in-solution protein digestion, as described above. The resulting peptide mixtures were measured on a Q Exactive HF mass spectrometer and processed against the matching metagenome gene translation, as described above. The MS-identified peptides in this dataset were taxonomically annotated with Kraken2, Diamond and Unipept, as described above. Taxa were quantified as described above (sum of peptide intensities). The Spearman's rank correlation in taxon representation was calculated for each pairwise combination of samples within software. For each sample, we determined and computed the number of species that are (1) true positive hits, must be an identified species reported in the mouse microbiome catalogue; (2) false positive hits, must be an identified species not reported in the mouse microbiome catalogue; and (3) false negative hits, the total number of species reported in the mouse microbiome catalogue minus the true positives.
Acknowledgments MED, BM, XA and LD are grateful to the European Community 7 th Framework Program under Coordinated  Action NEURON-ERANET  (grant agreement  291840 ). BM was supported by grants from the  Deutsche Forschungsgemeinschaft (German Research Foundation Cluster of Excellence   EXC 2124  and  TRR261 , project  ID   398967434 ). BM and NN acknowledge support by the  High Performance and Cloud Computing Group at the Center for Data Processing of the University of Tübingen , the state of Baden-Wuerttemberg through bwHPC. The metagenomic work detailed herein used the computing resources of the UK MEDical BIOinformatics partnershipaggregation, integration, visualization and analysis of large, complex data ( UK Med-Bio )which was supported by the  Medical Research Council  (grant number  MR/L01632X/1 ).
Islands of runs of homozygosity indicate selection signatures in Ovis aries 6 (OAR6) of French dairy sheep S. T. Rodríguez-Ramilo, 1 * A. Reverter, 2 and A. Legarra 1 respectively. These are AI rams representing several years of birth (10-15) and overlapping generations, and they illustrate the genetic variability of each breed. The SNP quality control included the absence of parent-offspring Mendelian segregation incompatibilities (<3%), SNP call rate >97%, and large deviations from Hardy-Weinberg equilibrium (SNP with P < 10 -6 were discarded). The final data set included 8,700 genotyped individuals and 38,287 autosomal SNP. Markers were positioned using the sheep (Ovis aries) genome assembly 3. 1 (OAR 3. 1; https: / / www. ncbi. nlm. nih. gov/ assembly/ GCF _000298735. 1/ ). See  Legarra et al.
(2014)  for more details. Plink software version 1. 9  (Chang et al. , 2015)  was used to calculate the genetic differentiation coefficient F ST  (Weir and Cockerham, 1984)  between subpopulations and breeds with the option -fst. Significance level was evaluated using  Lewontin and Krakauer (1973)  statistical test  (Bonhomme et al. , 2010)  and applying a Bonferroni correction using the total number of markers. The rehh R package  (Gautier and Vitalis, 2012)  with the default values was used to calculate iHS, where an extreme score (>2 or <-2) provides a strong signal of selection  (Voight et al. , 2006) , indicating the most extreme 5% of iHS values. The software detec-tRUNS  (Biscarini et al. , 2018)  was used to detect ROH with a sliding window-based method and the following criteria: a minimum length of 250 kb, a minimum of 20 homozygous SNP, a maximum distance of 1 Mb allowed between 2 consecutive homozygous SNP in a run, a minimum density of 1 SNP per 10 kb, and no missing or heterozygous genotypes allowed.
Animals were genotyped with the Illumina OvineSNP50 BeadChip. After applying filtering criteria, the genomic data included 38,287 autosomal SNP distributed across 26 chromosomes and 8,700 individuals. One island of ROH was detected on OAR6 in the same genomic position across animals (between 30 and 40 Mb). • R Y SC IEN C E A M E R IC AN D A I A S SO CIA T I O N • ® https: / Global Wright's differentiation coefficients for 2 SNP within this ROH island were high (0. 67-0. 68). The linkage disequilibrium between both SNP was also elevated (0. 98). The divergence in allele frequencies in those SNP grouped Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse breeds in one cluster and Lacaune Confederation and Lacaune Ovitest subpopulations in another cluster. The closest candidate genes are NCAPG and LCORL, which have been reported to be under positive selection and suggested to control weight and height in sheep.
Adverse event rates can reach 17. 1% for thoracic biopsies  [15]. Considering these limitations, less invasive and more between September 2018 and February 2020 were included in this retrospective cohort study. For each sample, medical data were collected through a mandatory prescription sheet attached to each sample and edited by the prescribing physician. As recommended, blood screening was performed either at diagnosis or during progression alongside a regular follow-up computed tomography scan (usually performed quarterly). At diagnosis, all tumors were histologically characterized on formalin-fixed paraffin-embedded specimens and an EGFR-sensitizing mutation detection was performed either on formalin-fixed paraffin-embedded tumor samples or by using cfDNA in the case of tumor tissue genotyping failure as part of the routine practice.
Discussion EGFR assays on liquid biopsy routinely enable physicians in the clinic to detect sensitive and resistant genetic alterations at an early stage that may occur during TKI therapy, and to monitor disease progression as evidenced by increasing levels of ctDNA. This can be achieved by frequent plasma sampling and testing of patients at defined intervals of treatment and recovery, alongside imaging, to incorporate analyses of evolving molecular landscapes during treatment. In this setting, the molecular exploration of ctDNA is highly encouraging, especially in advanced NSCLC, where sequential tissue biopsies and tumor tissue testing are impractical, expensive, and may cause undue harm. This is supported by the results of ADAURA (NCT02511106), a phase III, double-blind randomized study assessing the efficacy and safety Fig.
Availability of data and material The datasets used or analyzed during the current study are available from the corresponding author on reasonable request. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Computational methods System preparation and MD simulations setup. The following systems were considered for MD simulations: WT YB-1 protein alone, WT YB-1:RNA/DNA stem loop complexes, where two types of stem loops were considered ACU-4G/ACT-4G and ACU10/ACT10, WT CSD:RNA/DNA stem loop complexes with both stem loop types as above, R97A/K98A YB-1:RNA/DNA stem loop complexes, with ACU-4G/ACT-4G, And K137A/Y138A YB-1:RNA/DNA stem loop complexes, with ACU-4G/ACT-4G. Protein sequence used for YB-1 is from A45 to Q165 (length 121 a. a. ) and for CSD is from A45 to G129 (length 85 a. a. ). The length of all RNA/DNA stem loops is of 24 nucleotides. Molecular modeling. The starting 3D coordinates of YB-1 stem loop/RNA models were constructed using as a template the X-ray structure of human Lin28A in complex with let-7f-1 micro RNA pre-element (PDB ID 5UDZ, resolution 2 Å)  (47, 48).
YB-1 structure (A45-Q165) bound to polyC RNA was taken from the trimer complex of YB-1:ssRNA (16-nt C), which is a linear nucleoprotein filament recently published by our group (12) that used the human WT YB-1 NMR structure (position 52-129) as a building block for the cold shock domain (PDB ID: 1H95). Models were constructed by extracting the middle YB-1 structure and superimposing it to Lin28 on the alpha carbons. Lin28 was then removed and the RNA was extended in order to form a total of 24 nucleotides (4 nucleotides forming the loop and 10 bp for the stem). Then the nitrogenous bases were substituted with the stem loop sequences studied here (ACU-4G, ACT-4G, ACU10 and ACT10). In a next step, the CSD:RNA/DNA stem loop complexes were modeled from the YB-1:RNA/DNA structures previously generated by truncating the CTD part (residues 130 to 165).