dataesr/scientific-paragraphs-categorization huggingface dataset:
WGS noise scenario In addition to noiseless scenarios, we simulated 20 replicates of a WGS scenario with noise: 10 of the replicates were based on scenario 11 and another 10 replicates were based on scenario 12. In each case, white Gaussian noise was added to each replicate in order to study the performance of the tools at different amounts of noise, emulating differences in the sequencing quality of real datasets. Specifically, random noise was introduced for different noise levels (0%, 1%, 2. 5%, 5%, or 10%) by resampling every data point in the mutational matrix (i. e. , reflecting the number of mutations of a specific mutation type in a cancer sample) using a Gaussian distribution where the mean corresponds to the value of the data point, and the standard deviation is the value of the data point multiplied by the specific noise level.
Subsequently, decimal values were truncated, and negative values were replaced with zeros. Overall, 5 distinct levels of noise were generated, each repeated 20 times, with an average noise level corresponding to 0%, 1%, 2. 5%, 5%, and 10% of all mutations observed in the replicate. WES noise scenario A WES-based scenario was generated by downsampling the WGS-based noise scenario corresponding to a 5% noise level, reflecting high-quality genomic datasets. The downsampling of synthetic cancer genomes and randomly generated ground-truth mutational signatures was done in a two-step process. Firstly, WGS-based SBS-96 mutational matrices were simulated with SigProfilerSimulator  48  to obtain VCF files with simulated synthetic mutations spanning across the whole genome. Subsequently, exome-specific SBS-96 mutational matrices were created with SigProfilerMatrixGenerator 15 including exclusively the synthetic mutations affecting the exome portion of the human genome based on the SureSelect Human All Exon v7 protocol (Agilent, Santa Clara, CA, USA).
This two-step process allows considering the differences in trinucleotide frequencies between the exome and the whole human genome, which would not be captured by direct downsampling of the original WGS mutational matrices based on the fact that the exome constitutes $2% of the human exome but has a different trinucleotide context. Benchmarking bioinformatic tools for de novo extraction of mutational signatures The hitherto described synthetic scenarios were used to compare SigProfilerExtractor and thirteen other tools for de novo extraction of mutational signatures. The method and parameters used to extract signatures from the simulated datasets using each tool are described below. With the exception of EMu and SignatureAnalyzer, which support only detection of the total number of mutational signatures without a prespecified range, all other tools required specifying the range for the total number of operative mutational signatures.
The ranges for benchmarking with suggested model selection, which most closely matches the analysis of a real dataset with unknown number of signatures, are provided in Table  S9  for each of the scenarios. Benchmarking with forced model selection, where tools were required to extract the known number of ground-truth mutational signatures, performed de novo extraction based on the ground-truth number of total mutational signatures (Table  S9 ). For the WGS noise scenario, the same ranges used for noiseless scenarios 11 and 12 were applied. In the case of the WES noise scenario, a reduced range of signatures was used to optimize running time, based on the low sensitivity observed for all tools on WES data compared to WGS. This WES-specific range was used to extract de novo mutational signatures in all tools except for SigneR, whose signature selection method depends on the maximum number of signatures tested (original WGS range was applied).
SigProfiler_PCAWG The default settings of SigProExtractor 12 (version 0. 0. 5. 48) were used to extract signatures from the benchmark scenarios with exception of ''totaliteration'' = 100. SignatureAnalyzer For the scenarios with and without noise, we used the default parameters for SignatureAnalyzer  32, 34  described in https://github. com/ broadinstitute/SignatureAnalyzer-GPU. For the extended scenarios, the CPU version of SignatureAnalyzer was used with 20 runs and default parameters. The mode number of signatures counts was selected for further evaluation.
MutationalPatterns We downloaded MutationalPatterns 24 version 3. 0. 1 according to the instructions at https://bioconductor. org/packages/release/ bioc/html/MutationalPatterns. html. To extract signatures, we used the NMF method with default parameters with the exception of the ''nrun'' parameter. The ''nrun'' parameter was set to 200 in order to increase the reliability of the extraction. To select the optimum number of signatures, as suggested by the developers of the tool, we used the RSS plot that is generated in the NMF rank survey plot. SignatureToolsLib We downloaded SignaturesToolsLib 35 from https://github. com/Nik-Zainal-Group/signature. tools. lib and the tool was used using the parameters recommended by the authors. Specifically, we utilized ''nboots'' = 20, ''nrepeats'' = 200 and ''filterBest_RTOL'' = 0.
001. As suggested by the developers, we selected the optimum number of signatures from the plot illustrating the overall metrics. We mostly used the ''norm. Error'' and ''Ave. SilWid'' with Clustering with Matching (MC) to select the total number of operative mutational signatures. SigneR We used the SigneR 36 version 1. 16. 0 as described in http://bioconductor. org/packages/release/bioc/vignettes/signeR/inst/doc/ signeR-vignette. html. The signatures were extracted with default parameters without using an opportunity matrix. MutSpec We used the command line platform of MutSpec 27 version 2. 0, as described at https://github. com/IARCbioinfo/mutspec. To extract signatures, we used the default parameters. As suggested by the developers, we estimated the optimum number of signatures from the ''NMF rank survey'' plot generated from the ''MutSpec-NMF_Estimate_Signatures'' module of the package.
SigMiner We installed SigMiner 31 version 1. 0. 0 according to the instructions provided at https://shixiangwang. github. io/sigminer-doc/. All the parameters were set as defaults to both estimate as well as extract mutational signatures. To select the optimum number of signatures, as suggested by the developers, we assessed the statistics provided in the NMF rank survey plot. SigFit We used the instructions provided at http://htmlpreview. github. io/?https://github. com/kgori/sigfit/blob/master/doc/sigfit_vignette. html to download and extract signatures from SigFit 28 version 2. 0. 0. Signature extraction was done using default parameters with the exception of the total number of iterations which was set at 100. EMu Benchmarking for EMu 20 was done with version 1. 5. 2 using default parameters for suggested extractions.
Additionally, the optional ''force'' parameter was used for benchmarks done with a specific number of signatures. EMu was the only tool that was unable to complete de novo extractions from a number of synthetic datasets (Tables S1, S2 and S3) with the tool either running out of memory on instances with 256 GiB memory or running for 4+ weeks without producing any results. These scenarios were considered as failed and assigned F 1 scores of zero. MutSignatures MutSignatures 25 version 2. 1. 1 was downloaded and run according to https://cran. r-project. org/web/packages/mutSignatures/ vignettes/get_sarted_with_mutSignatures. html. Signatures were extracted using 500 iterations (''num_totIterations'' = 500). TensorSignatures TensorSignatures 40 version 0. 5. 0 was downloaded and run according to the instructions at https://github.
L. B. A. , S. M. A. I. , and M. D. -G. designed both SigProfilerExtractor's methodology and performed analyses with help from N. P. , J. Z. , D. J. A. , I. M. , B. S. A. , L. H. , D. C. W. , M. T. L. , P. B. , M. R. S. , and S. G. R. S. M. A. I. and M. D. -G. developed SigProfilerExtractor with help from M. B. , R. V. , M. V. , E. N. B. , Y. H. , C. D. S. , and J. W. All synthetic benchmarking datasets were generated by Y. W. and S. G. R. S. M. A. I. and M. D. -G. documented SigProfilerExtractor and performed the benchmarking of all tools on synthetic data with help from M. B. , R. V. , B. O. , A. K. , and A. A. Additional validations, confirmations, and applications of SigProfilerExtractor to real and synthetic datasets were performed by M. D. -G. , S. M. A. I. , M. B. , R. V. , S. M. , S.
Computational implementation of SigProfilerExtractor and its seven modules B Analysis of the genomics data from cancer and normal somatic tissues B Additional approaches for miscellaneous analysis B Creation of scenarios with synthetic datasets B Benchmarking bioinformatic tools for de novo extraction of mutational signatures d QUANTIFICATION AND STATISTICAL ANALYSIS d ADDITIONAL RESOURCES SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi. org/10. 1016/j. xgen. 2022. 100179.
ACKNOWLEDGMENTS The authors would like to thank  Allan Balmain  ( UC San Francisco ) for the many useful discussions as well as  Ville Mustonen  ( University of Helsinki ) and  Israel Tojal Da Silva (A. C. Camargo Cancer Center ) for help in configuring EMu and SigneR, respectively. This work was supported by  Cancer Research UK Grand Challenge  Award  C98/A24032  (L. B. A. , P. B. , and M. R. S. ) and  Wellcome  grant reference  206194  (M. R. S. ), as well as  US National Institutes of Health  grants  R01MH116281-01A1  (B. S. A. ),  R01ES030993-01A1  (L. B. A. ),  R01ES032547  (L. B. A. ), and  R01CA269919  (L. B. A. ). This work was also supported by  Singapore National Medical Research Council  grants  NMRC/CIRG/1422/ 2015  and  MOH-000032/MOHCIRG18may-0004  and the  Singapore Ministry of Health  via the Duke- NUS Signature Research Programmes.
L. B. A. is an  Abeloff V scholar , and he is supported by an Alfred P. Sloan Research Fellowship. Research at  UC San Diego  was also supported by a  Packard Fellowship for Science and Engineering  to L. B. A. A. J. G. was funded by a postdoctoral fellowship (grant no. P2BSP3_178591 ). I. M. is funded by  Cancer Research UK  ( C57387/A21777 ) and the  Wellcome Trust. Y. R. L. was supported by the  NCI F32  training grant and  NCI K12  career development award. N. P. receives funding through the  Cancer Research UK   Clinician Scientist Fellowship  scheme and is supported by  University College London Cancer Institute. Research at  Los Alamos National Laboratory  was conducted under contract no. 89233218CNA000001  by the  US Department of Energy's National Nuclear Security Administration  and was supported by  Laboratory Directed Research and Development (LDRD)  grant  20190020DR  (B.
The inference of phylogenetic trees from raw sequencing reads is foundational to biology. However, state-of-the-art phylogenomics requires running complex pipelines, at significant computational and labour costs, with additional constraints in sequencing coverage, assembly and annotation quality. To overcome these challenges, we present Read2tree, which directly processes raw sequencing reads into groups of corresponding genes. In a benchmark encompassing a broad variety of datasets, our assembly-free approach was 10-100x faster than conventional approaches, and in most cases more accurate-the exception being when sequencing coverage was high and reference species very distant. To illustrate the broad applicability of the tool, we reconstructed a yeast tree of life of 435 species spanning 590 million years of evolution.
In addition, Read2Tree is able to also provide accurate trees and species comparisons using only low coverage (0. 1x) data sets as well as RNA vs. genomic sequencing and operates on long or short reads. This makes Read2Tree a highly versatile method to obtain key insight from a single sample scaling up to thousands of samples. To establish this novel approach we assess its performance on a battery of genomic and transcriptomic datasets spanning different kingdoms, divergence time, and sequencing technology. Subsequently we apply Read2Tree to construct a large yeast tree of life and apply it to compare SARS-CoV-2 samples-thus highlighting the accuracy (e. g. compared to NCBI classification) and speed of Read2Tree. Importantly, these sequences in reference orthologous groups are not restricted to singlecopy marker genes, such as the mitochondrial cytochrome c oxidase I (COI) gene or BUSCO genes  20  ; they also include multiple paralogous genes as well as non-universal genes.
Accuracy as a function of distance to closest reference and coverage In general, Read2Tree was able to maintain a high precision in terms of sequence reconstruction (Fig  2B ) and tree reconstruction (Fig  2C ) across all datasets, with varying levels of recall depending on the dataset difficulty. First we assessed the effect of coverage ranging from 0. 2x to 20x of the individual data sets. We observed that increasing the sequencing coverage had little impact on precision, and mainly lowered recall. Thus, remarkably, Read2Tree is able to maintain typically 90-95% precision at the sequence level even with coverages as low as 0. 2x (Fig  2B ). The best low-coverage results were obtained on transcriptomic short read data in mice, where precision reached 98. 5% at 0. 2x coverage. To assess the versatility of Read2Tree we benchmarked it across DNA and RNA data sets.
This did not have a large impact in general, but transcriptomic RNA results (in the mouse dataset) are marginally less impacted by differences in average coverage, perhaps due to the large coverage variance from uneven gene expression levels in these data (see Fig 2B&C ). Next, we assessed if Read2Tree is capable of utilising the range of current sequencing technologies. For this we applied it across traditional short reads, Oxford Nanopore and PacBio long reads. To enable this, Read2Tree has slightly different mapping strategies built in for long vs. short reads (see methods). As Figure  2B&C  shows Read2Tree maintained a high accuracy across each sequencing technology, but we observed the highest accuracy over traditional short reads. We have not assessed more recent sequencing technologies such as PacBio HiFi or Illumina infinity that might change this result.
Finally, we assessed the robustness of Read2Tree with respect to the evolutionary distance between the sample at hand and the closest relative in the reference set. This is often critical as one might not know the closest ancestor that is assembled or it is not available  24. Thus we tested Read2Tree across a wide range of evolutionary distances ranging from 7 million years ago to over 1. 1 billion years ago. While these are certainly extreme scenarios, overall Read2Tree was able to cope with them successfully. Figures 2 B&C show that the choice of reference set mainly impacted recall, with closer reference genomes leading to more reconstructed positions. Remarkably, Read2Tree was able to maintain high accuracy even in the datasets with very distant references-e. g. processing mouse RNA-seq data without any vertebrate genome in the reference set.
The conventional assemblies were processed using OMA standalone, including the same exported reference genomes, as OMA standalone was previously shown to identify the most accurate phylogenetic marker genes  25. For the inclusion of orthologous markers in the concatenated alignment used for tree inference, we required a commonly set minimum threshold of 80% taxon presence. As above, we varied the closest remaining species in the dataset by removing species along the reference tree (Fig 2. A ). With different coverages and reference sets, we obtained 42 data points per species. For each of these data points we performed the orthology inference separately and recorded its computation time. The proportion of sequences placed into the respective OGs showed high levels of variation (SFig 5). For each assembly and variation of proteomes, we computed the topological distance between the resulting tree from assembly or Read2Tree with trees obtained using high quality genome assemblies for A.
thaliana and S. cerevisiae. Figure  3  shows the overall results highlighting the performance of Read2Tree. Perhaps unsurprisingly, we observed that coverage levels had a profound impact on the performance of assembly-based approaches rendering them incapable of dealing with coverages below 5-10x. Thus for these data sets we only report Read2Tree results. Where both approaches can be compared, the only cases where the conventional de novo assembly approach outperformed Read2Tree were with high coverage and very distant (>500 Mya) to the closest reference species (Figure  3A , upper right region of each graph). In all other scenarios, Read2Tree outperformed the conventional approach in accuracy. Specifically, on the yeast dataset at higher coverage level, both assembly and Read2Tree performed well overall-we never observed more than two different branches between the obtained and reference trees.
With at least 10x coverage and distant reference species, the conventional assembly approach outperformed Read2Tree (Fig  3A  and SFig 4 ). By contrast, on the more complex A. thaliana and M. musculus datasets, Read2Tree outperformed the assembly approach-with fewer differences to the reference (up to 2 different branches for Read2Tree, versus up to 4 for the conventional approach). On the ONT data-characterised by longer reads but higher error rate, Read2Tree outperformed the conventional approach on both dataset. Finally, in terms of compute time, Read2Tree was generally much faster than the conventional approach, up to 100x faster on the larger genomes (Fig  3 B ). Altogether, these results indicate that Read2Tree is faster in all conditions, and produces reliable trees in low coverage datasets and other datasets where the conventional approach fails entirely (long-read transcriptomics).
At higher coverage levels, the trees inferred by Read2Tree rival in quality with those obtained from assembled reference species with a full pipeline, particularly when applied to more complex genomes, and unless the closest reference species is very distant (> 500 million years). Read2Tree accurately reconstructs a yeast tree of life encompassing 435 genomes   26. The top row shows full trees and the alignment matrix used to compute the tree as outer circles. Red dots indicate nodes with bootstrap below 100. The species Naumovozyma dairenensis, previously misclassified  27, 28  , is highlighted in red. Bottom row shows trees trimmed to an overlapping leaf set. To assess a potential large scale application for Read2Tree, we applied it to reconstruct a large yeast phylogeny from raw reads. Thanks to Read2Tree's ability to process low coverage datasets, we could extend our analysis to all Illumina single and paired-end, ONT, PacBio and 454 Sequencing read datasets available for budding yeast in the NCBI SRA database (November 2018, 404 species) and 31 reference species obtained from the OMA Database (release 2018, 3063 OGs).
Using an automated approach for retrieval and mapping we were able to obtain direct sequences for 404 species (2019, Supplementary files 1). Read2Tree could process these datasets in around a month of computation (adding each species sequentially and performing the mapping on 30 CPUs -one CPU per reference -in parallel), due to its "embarrassingly parallel" architecture-with every sample being processed independently up to phylogenetic inference (10x Illumina: ~20 minutes using 4 threads). A large proportion of these data sets were recently used to construct a phylogeny across 363 budding yeast species  26. This included a dataset of 196 new assemblies and their annotations  26. This large effort provided the first delineation of the yeast tree of life into 13 main clades and highlighted the influence of horizontal gene transfer in the evolution of yeast species  26.
Read2Tree for zoonotic surveillance and human epidemiology To further illustrate the versatility of Read2Tree, we used it to reconstruct a phylogeny encompassing various coronaviruses from the OMA coronavirus database as well as coronavirus sequences deposited to the Short Read Archive. Besides 122 putative SARS-CoV-2 sequence, we also included two samples from bat (SRR11085797 29 and SRR11085736  30  ), and one from mink 31 (SRX9605666). The reconstructed phylogeny was in complete agreement with the lineage classification obtained from the UniProt reference proteomes. In particular, the tree not only recovered the main coronavirus genera (Alpha-, Beta-, Gamma-, and Deltacoronavirus), but also all subgenera with complete consistency (Fig  5 ). The first bat sample corresponds to the reads of RaTG13, which is the closest relative of SARS-CoV-2 identified yet  29.
Reads Whole genome sequencing reads for A. thaliana and S. cerevisiae were obtained from the SRA database for technologies PacBio, Illumina and Oxford Nanopore. mRNA sequencing reads for M. musculus were also obtained for all three technologies from the SRA database. Subsampling of reads was performed in python (see repository). For PacBio and ONT reads subsampling was optimised such that cumulative number of bases fits to the expected coverage. For coverage test reads were subsampled assuming for mouse 38 MB accumulated gene length (transcriptome), thale cress 120 MBp and yeast 12 MBp genome lengths. Reads were sampled to obtain 20X, 10X, 5X, 1X, 0. 5X and 0. 2X coverage levels. Reads for the big yeast tree were obtained from the SRA database (Supplementary File 1). Reads for coronavirus were obtained from the SRA database (Supplementary File 1).
Reference Tree construction Reference trees for the 3 evaluated species were computed using the species as defined in Figure  2  A. Species were selected from OMA  53  as described in the OG selection. Individual OGs were aligned using mafft v7. 310 (--maxiter 1000 --local) and trees were inferred with iqtree v1. 6. 9 ( -m LG -nt 4 -mem 4G -seed 12345 -bb 1000). For reference trees that were used for testing the dependency on the reference dataset, specific species were deleted from existing alignments and trees were computed with iqtree as stated before. All reference trees are available in the Supplementary file 2. To highlight the years of evolution we collected the time using timetree  54  (April 2022).
Assemblies For the three species, the whole genome data was assembled with individual sequencing technology specific assembly programs following best practice or default parameters. For Illumina we first used megahit  55  (v1. 2. 9) with default parameters for assembling the contigs. Subsequently SOAPdenovo 56 (version 2. 04-r241) for scaffolding: First, SOAPdenovo-fusion -D -K 41 -c megahit. contigs. fa -g scaffold_prefix -p 20 followed by SOAPdenovo-63mer map and scaff with recommended parameters over the config file. For ONT reads we assembled the reads using Canu 57 (v2. 0) with a specified genome size (genomeSize) gnuplotTested=true -nanopore-raw and useGrid=false parameters to run it locally on only one node on the cluster. Lastly for PacBio CLR data we also used Canu (v2. 0) with similar parameters, but specifying the -pacbio-raw parameter.
All runtimes were measured using linux time and the wall and CPU Time were recorded. The RNA seq data was assembled differently to the whole genome. For Illumina we used Trinity 58 (v2. 8. 5) with the following parameters: --seqType fq --max_memory 50G --left reads1. fq. gz --right reads2. fq. gz --CPU 6 --trimmomatic --full_cleanup --output prefix. These execute Trimmomatic automatically and follow the recommendations from trinity.