dataesr/scientific-paragraphs-categorization huggingface dataset:
iMOKA identify DE genes associated with DLBCL chemoresistance In the last dataset, we tested iMOKA in a frequent scenario where differential representation of transcripts is assessed in a very small cohort. To this end, we considered 17 DLBCL patients  [36] , 10 responsive to an anthracycline-based regimen R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and 7 nonresponsive. The RNA-seq used for this dataset is targeted, making it impossible to evaluate the PSI values, so only the abundance of the genes and transcripts were considered in the benchmark (Fig. 5  and Fig. S7 ). iMOKA identified 1928 k-mers having an individual accuracy over 80% and five with 100% accuracy. They corresponded to the genes AKT1, BTBD9, ZBTB45, ZBTB17, and BHLHE40. Amongst those, AKT1 is known to play a role in DLBCL chemosensitivity  [37]  but was not detected as differentially expressed in the original publication  [36].
This study highlights another advantage of using k-mers; they are agnostic to transcript annotation. For example, the k-mer overlapping ZBTB17, a gene involved in B cell development and differentiation  [38] , is located on the splicing site at position chr1:15,947,123-15,948,295 and is part of Refseq transcript NM_001242884. However, this transcript was not annotated in the GENCODE annotation (Fig. 5b ) and thus not detected by salmon. iMOKA runtimes and disk space iMOKA was designed to be scalable; the user can control the number of threads used and the dedicated RAM, allowing the software to run not only on HPC clusters, but also on a laptop. In Fig. 6 , we report the times to analyze three experiments described in the previous sections on a computer with 8-cores and 32 GiB of RAM. Importantly, the higher the number of samples in the cohort, the bigger iMOKA's gains are.
iMOKA's most intensive task is the generation of informative k-mers, where a large amount of data is filtered and aggregated, while the other benchmarked approaches handle data that are already filtered (reads are already mapped to annotated regions). Finally, most methods that calculate differential expression are designed for relatively small cohorts and do not scale well in memory with large cohorts: DESeq2 and edgeR for example required additional RAM in order to analyze the differential expressed transcripts in the TCGA BRCA (TCGA_BC) analysis (61 GiB and 46 GiB, respectively) (Fig. 6 ).
We designed iMOKA with the aim of analyzing HTS data in the reverse manner; we wished to first discover all sequences that were informative, group them according to how well they could classify the input samples, and then break them down into the different components of gene expression, isoform representation, and SNV presence. In doing so, we created a classifier that could explore HTS data without a reference genome or transcriptome and without the need of dedicated bioinformatics pipelines for each type of transcriptional event. Using k-mer counts removes the requirement of a mapping step and allows iMOKA to explore and combine multiple transcriptional events to make more accurate predictions and to explore all these events simultaneously without having to apply multiple pipelines. k-mers can measure changes in transcription, isoform abundance, and sequence simultaneously and were thus able to create better predictive models than other metrics such as transcripts per million (TPM), read counts, or splice site usage.
Preprocessing The input data can be given as SRR identifier, BAM, FASTA, or FASTQ files. In the first and second cases, the corresponding FASTQ files are automatically generated using sra-tools' fastq-dump  [39]  and SAMtools  [40] , respectively. If the data is stranded paired end sequencing, the user can reverse complement one or both the files using SeqKit  [41]. In order to assert the quality of the FASTQ files, the user can use FAST QC  [42]  by adding the flag "-q". For each sample, KMC3  [9]  is used to count the k-mers of the length chosen by the user (default k = 31). Its output is converted into a sorted binary file optimized for the following steps of iMOKA and a JSON file containing the metadata information. The binary file is divided into two parts: a suffix portion, containing the nucleotidic sequence and the relative count, and a prefix portion, which contains the prefixes and the positions of the respective suffixes.
The first assignment is always: H thr ¼ H min -H min Â a 1 Â 2 ð Þ Subsequently: IFðH thr > H min -ðH min Â a 1 ÞÞ : H thr ¼ H min -H min Â a 1 ð Þ ELSE : H thr ¼ H min þ H min Â a 2 ð Þ The adjustment parameters a 1 » a 2 ensure that the new threshold is not set too close to the minimum H min. The number of k-mers required to update the threshold (E up ) increases by 30 at each update in order to reduce the number of computations and reduce the fluctuations of the threshold. Figure  S1  shows the entropy in function of the BF estimated accuracy of a sample of k-mers from the previously defined datasets showing that the number of k-mer would have been rejected by the entropy filter but would have had an accuracy higher than 60% are rare and that the adaptive threshold is able to find a mild cutoff that can save more than 50% of the computation, like in TCGA BC, or can let the BF evaluate most of the k-mers in case of difficult datasets, like in BEAUTY.
Graph mapping and annotation The sequences generated from the graphs can be aligned to a reference genome. Currently, iMOKA supports any aligner that provides an output in SAM or pslx format and uses the information given in the JSON configuration file "mapper-config" (-m argument) to align and to retrieve the annotation file, in GTF format. In this manuscript, we used gmap v. 2019-05-12 with the human genome GRCh38 and the GENCODE annotation v29, excluding from the file the entries with the transcript type "retained_intron". Once the k-mer graphs are aligned, iMOKA identifies the following "alignment derived features" (ADF): -Mutations, insertions, deletions, and clipping are identified by the letters "M", "I", "D" and "S," respectively, in the alignment's CIGAR string. -Alternative splice sites are identified when a k-mer graph is split across exons.
-Differential expression (DE) is identified if 50% (set by parameter d) of an annotated transcript is covered by the k-mer graphs. Since regions with sequence variations not associated with the classes generate holes in the graphs reducing the portion of the transcripts that generate useful k-mers, a higher threshold might result in classifying DE event as general "gene" event, that is, the best k-mer in a gene. -Alternative intronic events are identified if 50% (set by parameter d) of an annotated intron is covered by the k-mer graphs. -Intergenic events are identified if the k-mer graph maps to the genome but not to any annotated transcript. -Unmapped or multimapped events are created for those k-mer graphs that have no mapping or map to multiple sites. iMOKA will preserve one k-mer per event, the one with the highest accuracy score.
iMOKA implementation The feature reduction component of iMOKA is implemented in C++ using the following libraries: MLpack  [45] , armadillo  [48] , cephes  [49] , cxxopts  [50] , and nlohmann/ json  [51]. The self-organizing map and the random forest are implemented in python 3 using the following libraries: numpy  [52] , pandas  [53] , sklearn  [54] , and SimpSOM  [55]. The whole software is included in a ready-to-use Docker and Singularity  [56]  image and is released under the Open Source CeCILL license.
Benchmark Transcript abundance was computed using Salmon  [57]  version 1. 1. 0 using the index built on the reference transcriptome GENCODE v29 (hg38). The PSI values were computed using Whippet  [15]  version v0. 10. 4. We processed the samples in parallel in 4 processes allowing 2 threads and a maximum of 8 GiB of RAM each. The differential expression analysis was performed between each pair of classes in R v3. 6. 3 using the parameters and functions described in a recent benchmark  [58]  for the methods DESeq2  [12] , edgeR  [13] , and limmaVoom  [14]. Significantly different PSI values between two subsets were detected using whippet-delta. jl, included in the Whippet package.
Random Forest classifier feature selection and oob score comparison In order to compare the same number of features extracted by each pipeline, we used the sklearn method SelectFromModel to select 20 features using a decision tree classifier (DTC) trained with all the samples and all the features in order to identify twenty features that, in combination, can be good classifiers. Using an increasing number of features, from 2 to 20, we trained multiple RandomForestClassifier to retrieve the out of the box scores. We also performed a 5-fold cross validation of the largest and better characterized dataset, TCGA BRCA, to evaluate the accuracy of a model on unseen data. For each fold, we performed the feature reduction using only the training in each method. The final list of features is reduced similarly as for the oob score determination and the balanced accuracy score is estimated for the test set.
The first two attempts to sub-divide hg R1a1  [22, 23]  focused on the relationships  [23]  between European and Asian sub-haplogroups. A more recent study resequenced almost 500 hg R1a1 chromosomes, but concentrated on the history of sub-lineages among Ashkenazi Levite Jews  [24]. None of these studies has addressed the question of the possible Viking origin of R1a1 sub-haplogroups. Previously, we generated extensive MSY sequence data in each of 448 human males  [25]. These included samples from Norway, Orkney, England and Denmark, giving a total of 27 hg R1a1 Y chromosomes, in which many novel sequence variants were ascertained. Here we exploit this resource to further investigate R1a1 sub-haplogroups in Scandinavia and western Europe. We compare the frequency of R1a1 and its sub-lineages with regions showing lower and higher Scandinavian autosomal contribution estimated in the PoBI cohort  [8]  and with the Danelaw, and investigate the expansion histories of sub-lineages using multiple shorttandem repeats (STRs).
Y-STR typing Twenty-three Y-STRs (DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385ab, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635, YGATAH4, DYS481, DYS533, DYS549, DYS570, DYS576 and DYS643) were amplified in all 619 samples using the PowerPlex Y23 system (PPY23, Promega Corporation, Madison, WI), following the manufacturer's instructions, separated on an ABI3130xL and analysed using GeneMapper v 4. 0 software (both Applied Biosystems).
Results Previously  [25] , we generated 3. 7 Mb of MSY sequence data in each of 448 human males. These included 334 randomly sampled individuals from 17 populations from Europe and the Middle East, plus individuals chosen because their Y chromosomes belonged to specific lineages. The resulting phylogeny included 27 MSY sequences within haplogroup R1a1 (Fig. 2 ). To place these within the context of previous analyses of R1a1 sub-haplogroups, we compared the phylogeny with published data  [23, 34]. We chose six published SNPs not included in our sequenced MSY regions  [23]  and typed them in the 27 samples sequenced in our study  [25]. In addition, our dataset contained the male CEU-NA12155, allowing us to use 1000 Genomes Project data to infer the position in our phylogeny of the variant CTS4385 (ref.
Geographical distribution of hg R1a1 sub-lineages The SNP multiplex was typed on 619 DNA samples from our hg R1a1 collection (Fig. 3b ; Tables  S1, S4 ; Supplementary text). The results can be visualised in interactive form online via the Microreact tool  [35]  at https://microreact. org/project/ P2bq1LnPR. Based on the phylogeny (Fig. 2 ), the typed SNPs define a maximum of 11 sub-haplogroups; of these, we find 10 (one deep-rooting paragroup, R1a1-GML1*, is not observed). The frequencies of the observed sub-haplogroups vary greatly in different populations (Fig. 3b ; Table  S5 ). As expected, given the European bias governing our SNP choices, most of the Asian samples in our dataset belong to the paragroup R1a1-GML3*-equivalent to the clade defined  [23]  by the SNP Z93 (Fig. 2 ). We do not address these any further here.
Acknowledgements We thank all DNA donors,  Mark Thomas  for supplying DNA samples,  Garrett Hellenthal  for discussions about European contributions to the  British Isles  as presented in the PoBI study, and  Judith Jesch  for insights into Viking history. GML, CB, PH, DZ and MAJ were supported by a  Wellcome Trust   Senior Fellowship  grant, no. 087576 ; TEK by  Wellcome Trust  grant no. 084060 ; WFB and BW by  Wellcome Trust  grants  072974  and  088262 ; JHW, PV and TEK by  Leverhulme Trust  grant no. F/00 212/AM  to JS; PB by  Wellcome Trust  grant no. 057559 ; TIH by a  BBSRC iCASE studentship , grant ref. BB/M016706/1; MT and MAJ by  British Heart Foundation  grant  PG/16/49/32176 ; and HD and PMD by PhD studentships from the  University of Leicester. MHDL was supported by  FWO-Vlaanderen (Research Foundation-Flanders)  and the  KU Leuven   BOF-C1-grant C12/15/013.
coli CC-828, can often confound these methods because, for some lineages, strains associated with one host source can be found in another (22)  (30). The adoption of whole genome sequencing techniques and availability of curated genome databases  (31)  have allowed the incorporation of a broader number of host-segregating epidemiological markers in source attribution methods  (32)   (33). This additional genome information has increased the resolution allowing attribution of invasive/non-invasive strains from poultry  (34)  as well as geographical attribution of UK/USA isolates  (19). However, almost all studies focussed exclusively on C. jejuni  (35) , and no study aimed to specifically identify host-segregating markers in C. coli genomes. on October 9, 2020 at UNIV OF BATH http://aem. asm.
Downloaded from In this study, we analyzed 450 C. coli genomes from public databases with defined sampling sources including chickens, cattle and pigs. Using comparative genomics approaches we: (i) tested the ability of traditional MLST-based methods to determine the source of C. coli with isolates from known source reservoirs; (ii) identified host-segregating SNPs in C. coli genomes; (iii) determined the relative contribution of different C. coli infection sources in France. MLST was found to be a good proxy for more complex whole genome SNP-based analysis, showing similar power for segregating isolates from cattle host. However, additional discrimination of isolates from chicken and pig hosts was achieved by identifying genome-wide host-segregating SNPs. In the final probabilistic model, using 259 host-segregating SNPs, chicken was found to be the most common source of C.
CC-828 isolates segregate by host From all 3 datasets, Dataset S1, S2 and S3 (cf. material and methods), nearly all isolates belonged to the clonal complex -828 (780 isolates out of 900). The second most common clonal complex identified was ST-1150  (26)  with four isolates, sampled from chicken. From the allelic profiles minimum spanning tree, 3 clusters can be identified corresponding to the source of isolation (Figure  1 ). Cattle isolates clustered together, with 162 isolates (64. 8% of all cattle isolates) assigned to ST-1068  (36). Chicken and pig isolates belonged to 78 and 83 sequence types respectively (contrary to cattle with 27 different sequence types), with 24. 2% isolates belonging to STs -828, -829, -825, -854, and -1119. Furthermore, 40. 1% of all clinical isolates belonged to STs -825, -827, -832 and -860.
Genome-wide host-segregating SNPs provide more accurate source attribution than MLST alleles The degree of SNP segregation among isolates from different hosts, and hence the potential as marker for source attribution using STRUCTURE, was quantified. Self-attributions of chicken and pig isolates within the marker-determination dataset were consistently correct (Table  2 ). Using 43 SNPs detected from OR12 alignment as host-segregating markers allowed an average correct selfattribution of 88. 35% (s. d. ± 6. 2%), 63. 75% (s. d. ± 9. 2%) and 96. 2% (s. d. ± 4. 1%) for chickens, cattle and pigs respectively. Using 183 SNPs from HC2-48 alignment correct self-attribution was achieved on October 9, 2020 at UNIV OF BATH http://aem. asm. org/ Downloaded from for chicken, cattle and pig isolates with 91.
Source attribution of cattle C. coli isolates of marker-determination dataset was similar between the two types of markers (genotype or allele) whereas SNPs performed significantly better for chicken and pig populations than the 7 MLST genes. Finally, the discriminatory power of host-segregating SNPs and MLST genes was evaluated performing source re-attribution of 299 C. coli isolates from the validation dataset. SNPs showed correct re-attribution proportions of 96. 2% (s. d. ± 1. 03%), 84% (s. d. ± 0%) and 89% (s. d. ± 0%), and MLST genes scores of 87% (s. d. ± 0%), 81% (s. d. ± 0%) and 65% (s. d. ± 0%) for chicken, cattle and pig populations, respectively (Figure  4 ). Overall, SNPs were able to better reattribute C. coli marker-determination and validation isolates to their source than MLST genes, more specifically for chickens and pig populations.
Chickens are a major source of C. coli infection in France Source attribution of clinical isolates was performed using MLST alleles and all host-segregating SNPs with correct self-attribution >70% (n=259) in the marker-determination and training dataset using STRUCTURE (Figure  5 ). Using MLST genes, 89 clinical isolates (60. 5%) were attributed to chickens, 13 on October 9, 2020 at UNIV OF BATH http://aem. asm. org/ Downloaded from to cattle (9%), 6 to pigs (4%) and 39 clinical isolates (26. 5%) showed attribution scores lower than 70% and were therefore considered as "inconclusive attributions". Inconclusive attributions specifically concern 3 commonly found sequence types: STs -827, -1055 and -1595, representing 48. 7% of inconclusive attributions (n=19). In contrast, using the 259 SNPs, 138 isolates (94%) were attributed to chickens, 9 to pigs (6%) (with an average of source probability equal to 100%) and none to the cattle population.
Campylobacter coli isolate datasets A total of 450 C. coli isolates genomes from two major regions where Campylobacters are a leading cause of foodborne infections, North America and Europe, were selected for the determination of host-segregating markers (Dataset S1). To reduce the detection of regional-specific markers, these genomes were randomly selected from multiple countries within these two regions. That included even numbers (n=150) of chicken, cattle and pig C. coli genomes to avoid bias in the identification of host-specific markers. This first dataset was comprised of 151 isolates from PubMLST databases  (31)  and 299 from the USA National Antimicrobial Resistance Monitoring System (NARMS) project  (53). PubMLST genomes comprised 34% of that first dataset and included 47%, 7% and 47% of all chicken, cattle and pig marker-determination isolates, respectively.
NARMS genomes comprised 66% of the dataset, and included 53%, 93% and 53% of all chicken, cattle and pig marker-determination isolates. These datasets were entirely composed of European and North American genomes. European isolates represented 29% of the dataset, including 41%, 1% and 45% of all chicken, cattle and pig isolates respectively, while North American isolates comprised 71% of the dataset including 59%, 99% and 55% of all chicken, cattle and pigs isolates. North American isolates were mostly selected from the USA (n=315). Remaining isolates (n=4) were selected from Canada. A total of 424 isolates (94%) were obtained from 2005 to 2019. A second dataset (validation dataset) comprised 300 supplementary C. coli isolates of known source reservoirs was used in order to test the discriminatory strength the host-segregating SNPs previously obtained (Dataset S2).
This dataset comprised North American C. coli isolates from the NARMS project; 100 for each source. Finally, 150 French clinical isolates comprised a last set of genomes on October 9, 2020 at UNIV OF BATH http://aem. asm. org/ Downloaded from (clinical dataset) and were used to attribute the putative source reservoir of clinical isolates (Dataset  S3 ). This comprised 150 clinical isolates from French laboratories and hospitals surveillance network sampled from stools between 2015 to 2017. Clinical isolates were chosen to represent patients from diverse geographic regions in France, with a sex ratio of 1. 03 and a mean age of 39. HiSeq 4000 technology (Integragen, Evry, France). Additionally, FastQC v0. 11. 8  (55)  was used to run data quality tests. Genomic data was cleaned and genomes were assembled using Sickle v1.
Characterization of genomic variation In silico, MLST was performed for a comparative analysis with host-segregating SNPs. Profiles were obtained for all 900 isolates using 7 housekeeping genes (aspA, glnA, gltA, glyA, pgm, tkt and uncA) determined for Campylobacter species  (27). Sequence types (STs) and clonal complexes ("CC", groups of isolates with a sequence type that share four or more loci (  27 )) were defined using the sequence tag tool of PubMLST  (58). Using this method, two clinical isolates (2016_1990 and 2017_2288) and one validation isolate (FSIS11705596) were miss-identified as C. coli and were actually C. jejuni and removed from the dataset. The updated validation and clinical datasets were then comprised of 299 and 147 isolates, respectively. A phylogenetic tree was constructed according to all sequence types using GrapeTree  (59).
A second tree was built based on every host-segregating markers determined in this study, in order to make a direct comparison with the MLST tree. A multifasta file containing sequences from concatenated SNPs of all isolates (n=896) was created. Sequences were aligned using Muscle v3. 8. 1551  (60)  and a Newick format tree from maximumlikelihood method was generated using Fasttree v2. 1. 11 (61). Microreact online platform was used to visualize the tree (62). To identify candidate SNPs, genome-wide variant calling was primarily performed by aligning all isolates from the marker-determination dataset (n=450) to C. coli reference genomes. Three on October 9, 2020 at UNIV OF BATH http://aem. asm. org/ Downloaded from references from each source were chosen in order to target source-specific genomic regions and capture all potential markers: OR12 strain isolated from a chicken (NZ_CP019977.
1) (63), HC2-48 strain isolated from a cow (NZ_CP013034. 1) (64) and ZV1224 strain isolated from a pig (NZ_CP017875. 1) (65). The bwa v0. 7. 17  (66)  tool developed for mapping sequences against given genomes was used here to align each isolate to OR12, HC2-48 and ZV1224 references. Alignment files were sorted using samtools v1. 9  (67). Genotypes were determined with bcftools v1. 9 "mpileup" variant calling tool  (67) , and 3 variant calling files (vcf) were generated (one for each reference). A script was written in Python (see data availability) to filter all SNP variations found in more than 2 out of 3 isolates. Since a source represents 33% of the total dataset (150 isolates over 450), a proportion greater than 66% means that a same SNP variation is likely to be found in each of the 3 selected sources.
Identification of host-segregating marker In order to identify host-segregating markers, source attribution tests of marker-determination isolates (of known sources) were performed using all previously selected SNPs individually to identify host-segregating markers. A matrix was constructed of all genotypes in the 450 markerdetermination isolate dataset (nucleotides were translated into numbers: "1" for "A", "2" for "T", etc. ). Source attribution tests were performed in triplicate for each SNP using STRUCTURE  (68) , with the no admixture model, 3 putative populations (K = 3), 10,000 iterations, and a burn-in period of 10,000 iterations. For each STRUCTURE test, 60 different random isolates (20 from each population) were set to "unknown source" (POPFLAG = 0) in order to estimate the probability of correct selfattribution, and then to evaluate the SNP host-segregating strength.