dataesr/scientific-paragraphs-categorization huggingface dataset:
The assembled contigs were blasted against the CARD antibiotic gene database  [38]  to identify ARGs. Results were filtered at an amino acid identity percentage of 60%, 33 amino acid length and an e-value of 10 -5. The best hit was used. Finally, contigs were binned based on their differential coverage across samples using anvi'o  [37] , and the bins were refined based on differential coverage and sequence composition. Bins with <50% completion and ≥10% redundancy were discarded, since they were considered low-quality metagenome assembled genome (MAG) drafts according to Bowers et al. [39]. The taxonomy of the bins estimated from the ORFs using kaiju  [40]  (as part of the anvi'o workflow) was confirmed by "blasting" all contigs included in each bin against the RDP database to identify ribosomal sequences.
3A ) and after a two-day exposure (Fig. 3B ). The Limnohabitans bins obtained from both assemblies contained a sequence that matched a gene coding for an aminoglycoside acetyltransferase involved in gentamicin resistance, aac(6')-Ib8, with an identity of 67. 6% over 34 amino acids. No matches were found for genes coding for aadA aminoglycoside nucleotidyltransferases. The bin reconstructed Fig. 3  Limnohabitans MAGs containing a gentamicin resistance gene obtained from the hybrid co-assembly short and long of metagenomic sequences from water microcosms polluted at 50 ng/ml of gentamicin. A Samples exposed to gentamicin for 0, 1 and 2 days; B Samples exposed to gentamicin for 2 days. Taxonomy shown is based on Open Reading Frames (ORFs) on contigs search against the protein reference database kaiju.
from sequences from microcosms with gentamicin at 50 ng/ml after 0, 1 two-day exposures had a completion of 63. 4%, a redundancy of 2. 8%, 107 contigs, a length of 1. 87 Mbp and a mean coverage across samples of 2. 2x (Fig. 3A ), whereas the one reconstructed from sequences after two-day exposure had a completion of 81. 7%, a redundancy of 9. 9%, 300 contigs, a length of 2. 99 Mbp and a mean coverage across samples of 5. 68x (Fig. 3B ). All contigs from each of the two bins were blasted against the RDP database to identify ribosomal sequences. In both cases, only one contig matched the RDP database. It was identified in both bins as Limnohabitans, with 99. 58% identity over 1 411 nucleotides.
Data processing and linkage map construction Sequence data processing and linkage map construction are described in detail in Bainbridge et al. [1]. Briefly, RAD sequences were demultiplexed using the RADpools function in RADtools v1. 2. 4  [3]. FASTQ files were mapped to the H. erato v1 reference genome  [4]  or the H. melpomene v2. 5 reference genome  [5]  using bowtie2 v2. 3. 2  [6]. BAM files were then sorted and indexed with SAMtools v1. 3. 1  [7]  and PCR duplicates were removed using Picard tools MarkDuplicates v1. 102. SAMtools mpileup and Lep-MAP data processing scripts  [8]  were used to call genotype posteriors. Linkage maps were constructed with Lep-MAP3  [8] , using the modules ParentCall2 (with options ZLimit=2 and removeNonInformative=1)  [8] , Filtering2, SeparateChromsomes2 (using sizeLimit = 50 and a LOD score of 12 for H.
erato and 23 for H. melpomene) and JoinSingles2All. Finally, the module OrderMarkers2 was used to order the markers along the linkage groups, taking into account the genomic positions of the markers. Female recombination was set to 0 and male recombination set to 0. 05 (following Morris et al. [9] ). We used LMPlot to visualise the maps and check for errors in marker order. Any erroneous markers that caused long gaps at the beginning or ends of the linkage groups were manually removed. Genotypes were phased using Lep-MAP's map2genotypes. awk script, and markers were named using the map. awk script and a list of the SNPs used to provide the scaffold name and position. The final linkage maps contained 5648 markers for H. erato, and 2163 markers for H. melpomene.
Samples were imaged on a JEOL JSM-6010LA microscope and we used the InTouchScope software to take images of the regions of interest at a magnification level of 900X. Images included both cover and ground scales. At the midpoint of the scale length, we drew a linear transect perpendicular to the orientation of the ridges (Figure  S9 ). Ridge spacing was calculated using the PeakFinder tool  [10] ; a plugin for the Fiji image analysis package  [11] , using a tolerance level of 60 and a minimum distance threshold of 10 pixels between peaks. PeakFinder detects spikes of intensity along the linear transect, each peak corresponding to a single ridge. It then yields the distances, in pixels, between subsequent peaks (Figure  S9 ). Using the scale bar of SEM slides we determined the variable rate of conversion from pixels to nm to be 25 x 10 -3 / M where M is Brien et al.
Analysis of USAXS data to extract ridge spacing values We first did a qualitative joint assessment of our USAXS and SEM data to get an idea of the scale structures we captured in our experiment and how to best get their information. Our interpretation of scale structures in a USAXS scattering pattern is shown in Figure  S10. Two important observations come from this joint assessment. Firstly, it can be observed that blobs of scattered radiation distribute along linear paths from the centre of the picture with equal spacing between them (Figure  S10 A ). This periodic scattering can be understood as the Brien et al. The Genetic basis of structural colour variation signature of the interaction of the beam with the array of parallel ridges on the surface of the scale (Figure  S10  B)  [12]. Secondly, the beam goes through the dorsal and ventral sides of the wing, projecting onto the detector, with the scattering due to structures of as many as four scales: cover and ground scales on both dorsal and ventral sides of the wing, in the case that all of them overlap.
This can be observed in Figure  S10  A, where at least three overlapping scales were picked up by the beam. Our SEM analysis revealed that scales with smaller ridge spacing are found on the dorsal side compared to the ventral side of the wing regardless of the structural colour of the butterfly analysed (Figure  S11 ). These differences in ridge spacing between dorsal and ventral scales should be discernible in the USAXS data. We used these two observations to implement a script that extracts relevant scale structure information from the USAXS data. The script scans the 2D scattering pattern in nonoverlapping regions of 5°. If the region contains periodic scattering it is assumed to correspond to the ridges of the scale and is kept for comparison with other sections that also show periodic scattering.
The script keeps the section with the highest magnitude of the scattering vector q based on the observation that q should be larger for dorsal scales because they have tighter ridge spacing. The chosen region is recorded and the scanning section is rotated 85° anti-clockwise to capture the scattering due to cross-ribs, which are perpendicular to ridges in the scale. Azimuthal integration is then performed on the two regions of interest, reducing them to a one-dimensional representation of scattered intensity I as a function of the Brien et al. The Genetic basis of structural colour variation magnitude of the vector q in a similar way to previous studies  [2, 13] , applying corrections for transmitted flux and masking the pixels corresponding to the beam stop. The script uses the pyFAI  [14]  and fabio  [15]  libraries to do the azimuthal integration and USAXS data handling.
The retained 1D scattering patterns were fitted to a Lorentzian curve (Figure  S12 ) using the FitManager module of the silx package  [16]. The "center" parameter of the fitting procedure gives the magnitude of the scattering vector q. This magnitude contains information on the distance between peaks of intensity in reciprocal space. To get estimates of structure sizes in real space, we use the expression d = 2π/q where d is the distance between ridges or crossribs in real space. We averaged estimates of each structure on a per-individual basis to get estimates of average ridge spacing and average cross-rib spacing per butterfly. Brien et al. The Genetic basis of structural colour variation Figure  S12 : Example of peak fitting to find the magnitude of the scattering vector (q). 2D patterns are previously scanned for Bragg peaks and integrated over a small (5°) arc to produce the data of the blue curve.
RNA sequencing data generation and processing RNA was extracted using QIAgen RNeasy. Paired-end sequenced data was generated from Illumina TruSeq RNA libraries (H. melpomene libraries were made using a TruSeq Stranded mRNA-seq kit and H. erato libraries were made using a TruSeq v2 kit) on 6 HiSeq v4 lanes. Low quality reads (PHRED <33) were removed and Illumina adapters clipped (using the ILLUMINACLIP option) using Trimmomatic v. 0. 38  [18]. Read quality was then inspected Brien et al. The Genetic basis of structural colour variation using FastQC v. 0. 11. 8  [7, 19]  and the results aggregated and viewed using multiQC v. 1. 5  [18, 20]. Trimmed reads were aligned to the respective Heliconius reference genomes using HISAT2 v. 2. 1. 0 [21, 22]. HISAT2 was run with the '--known-splicesite-infile' option to enable alignment of reads with small anchors.
Exon and splice site information was extracted from the respective species gene annotation files downloaded from Lepbase (v4)  [23]. Gffread was used to convert the downloaded '. gff' file format into the required '. gtf format'. The python script 'hisat2_extract_splice_sites. py' was used to create the list of known splice sites to pass to HISAT2. In addition, HISAT2 was run with the -dta option to adapt the alignments for downstream programs, including StringTie. The H. melpomene reads were run with the -rna-strandedness RF option to account for the strandedness of the reads. The output SAM files produced from HISAT2 were sorted and converted into BAM files using samtools v. 1. 9  [7]. The average number of reads per sample was 18. 2 million for H. erato and 15. 3 million for H. melpomene (Figure  S13 A ).
The average overall alignment to the respective species reference genomes was 76% for H. erato and 78% for H. melpomene (Figure S13 B). Transcript assembly and read quantification was performed using StringTie (version 1. 3. 5) [24]. The respective H. melpomene and H. erato gene annotations were provided as an input to guide assembly. StringTie was then run in 'merge mode' to create a uniform set of transcripts. This non-redundant transcript set was then used to re-estimate abundance of the output alignment files from HISAT2 using the '-e' option. Gene count matrices tailored to EdgeR were created using the python script 'prepDE. py' provided by StringTie.
Differential Expression analysis Differential expression analysis was performed using the R/Bioconductor package EdgeR v. 3. 28. 1  [25]. Genes with low expression levels were filtered out from subsequent analyses using the 'filterByExpr' function  [26]. Normalisation of library size was then performed using the trimmed mean of M-values (TMM) method  [27]. Trended estimates of dispersion were calculated and negative binomial generalised linear models fitted using the 'glmQLFit' function.
Gene Set Enrichment Analysis (GSEA) GO (gene ontology) enrichment analysis was performed in R v. 3. 6. 2 using the R/Bioconductor package topGO v. 2. 38. 1  [29]. Custom annotations were constructed using GO terms extracted from the InterproScan files downloaded from Lepbase. The gene universe comprised of all the genes expressed in the wings at 50% and 70% development taken from the respective species gene count matrix produced by StringTie. GO analyses were performed on the differentially expressed genes between the iridescent and noniridescent which had an FDR < 0. 05. A Fisher exact test was used to conduct the test using the 'weight01' algorithm. GO terms were considered significant when P < 0. 01. GO enrichment circle plots were constructed using the package GOplot v. 1. 0. 2.
Identification of convergent differentially expressed genes BLAST v. 2. 9. 0  [30]  was used to identify orthologous genes between H. erato and H. melpomene. A blast database was created using reference protein sequences for H. melpomene downloaded from Lepbase (v4)  [23, 30]  using the 'makeblastdb' function. Then, using blastp, protein sequences for H. erato were aligned against the H. melpomene reference to create a table of orthologous genes between the two species. At both 50% and 70% development, genes which were differentially expressed (FDR < 0. 2) between iridescent and non-iridescent races in both H. erato and melpomene were identified. Genes were further filtered based on concordant expression patterns, i. e. only genes which were upregulated or downregulated in both H. erato and H.
Methods The pipeline was constructed using the Snakemake workflow manager  [4]. Then, it was isolated in a Singularity container for autonomy and integrity  [5]. Snakemake splits the workflow into clearly defined individual steps (rules), which are either logical pieces of the procedure (i. e. assembly step, polishing step) or parts with different dependencies (i. e. different python versions). For each rule, the Conda package manager [6] creates unique.
BMC Research Notes *Correspondence: tereza@hcmr. gr 1 Hellenic Centre for Marine Research (HCMR), Former U. S. Base of Gournes, Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), P. O. Box 2214, 71003 Heraklion, Crete, Greece Full list of author information is available at the end of the article environments that do not interfere with the host system and has pre-installed all required packages and libraries. In such environments, the user has full control while not granted elevated privileges on the host system. This feature makes containers suitable for multi-user domains. The environments, the definition files and the scripts of the pipeline, are all packed in container images that run as a single job in any linux-kernel based system, as long as they have Singularity pre-installed.
Results SnakeCube was first evaluated by reproducing the initial non-automated L. sceleratus analysis  [3]  used as the basis for the establishment of the pipeline. The produced output was generated as expected and SnakeCube scored similarly to the original work (Table  1 ). Then, the tool was benchmarked for its cpu load and memory usage while running serially, and it was optimized for speed and resource handling by parallel rule execution. The benefits of parallel execution and the general performance of SnakeCube were tested using two additional datasets of different organisms, to cover a range of genome sizes, raw data sizes and taxonomic groups: Caddisfly (insect)  [7]  and Crane (bird)  [8].
Benchmarking and optimization The benchmarking, optimization and testing of the pipeline took place in zorba, the HPC system of the Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC, HCMR). Zorba is a high-performance computing cluster consisting of 328 cores and 2. 3 TB memory at benchmarking time  [21]. The cpu load recording was performed with the use of the Linux watch command, which was used to run all relevant commands at regular intervals, alongside loadavg, which gives the number of jobs in the run queue or waiting for input/output (IO), averaged in 15 m intervals. We monitored the whole pipeline in three repeated runs, and reported the average (Fig. 2a ). The memory straining of each individual task was monitored using the benchmark directive of Snakemake, and specifically the max_uss (Unique Set Size) field, which records the actual RAM used by the process without including the shared memory, and thus highlights the true cost of each step.
g. threads: int(config["Available_Cores"]) * 0. 5). Each rule was run using 1 to 10 pairs of cpus (10 runs) (Fig. 2b ). Thus, the cpus that remained available during certain steps were used to run other independent rules in parallel. Nondemanding rules (e. g. summarizing software) were hardcoded to run in a single thread. On the contrary, workload intensive rules, like the main assembly construction, are always executed with full resources. Taking all benchmarks into account, the final optimization was formed using the group directive of Snakemake. Rules that did not directly depend on one another and could share proportions of the overall available resources, were grouped together for parallel execution. This resulted in three different execution groups. According to benchmarking results, the overall run time for each rule of the parallel pipeline was to some extent larger than the one observed in standalone runs, probably due to parallel jobs sharing the same resources (i.
Performance estimation After benchmarking and optimizing SnakeCube, the final performance evaluation was based on the assembly completeness  [9]  and time efficiency for the three datasets (Table  1 ). In all datasets the parameters used were identical, except for the genome size (see Table  1 ) and the BUSCO taxonomy lineages used which were set to actinopterygii, aves and insecta for L. sceleratus, crane and caddisfly respectively.
Conclusion At its core, SnakeCube was designed to meet the needs of non-expert users or anyone who wishes to automate the genome assembly step of a project, saving time, effort and resources for subsequent analyses. The benchmarks and optimization of the software achieved good standardization and highlighted its robustness. We demonstrate SnakeCube is capable of accomplishing equally optimal results as those obtained in the original non-automated analyses used for testing the pipeline, as well as to perform efficiently for a range of inputs. Thus, it is a reliable new genomics software that can promote transparency and reproducibility in genome assembly projects of various fields, in times when genomes are massively produced and used in state-of-the-art research around the world. The general performance of the software varies, as expected, for different types of datasets and organisms, but is notably high, making SnakeCube a competitive, universal candidate for use in future research.
Limitations SnakeCube is restricted to run only in linux-based domains. Moreover, its efficiency highly depends on the resources of the host system and it is proportional to them. When it comes to the applications involved, Snake-Cube uses a combination of specific bioinformatics tools, and although it reflects very good results for various datasets and organisms, may not work for all cases and users, due to the limitations inherited from the underlying software. Thus, although we tried to make the workflow as universal as possible, we acknowledge that this may not always be feasible.
Acknowledgements This research was supported through computational resources provided by  IMBBC (Institute of Marine Biology, Biotechnology and Aquaculture) of the HCMR (Hellenic Centre for Marine Research). Funding for establishing the IMBBC HPC has been received by the  MARBIGEN (EU Regpot ) project,  LifeWatchGreece RI , and the  CMBR  ( Centre for the study and sustainable exploitation of Marine Biological Resources) RI. Finally, we would like to thank  Dr. Vasileios Papadogiannis  for proofreading the manuscript.
Availability of data and materials SnakeCube is Python and bash based, and available via an open source license at GitHub: https:// github. com/ genom enerds/ Snake Cube. The final deliverables (containers) work in any Linux-based system with Singularity preinstalled. The Crane, Caddisfly and Lagocephalus sceleratus datasets analysed during this study are included in their corresponding published articles [and their supplementary information files]. All benchmarking code is available on the GitHub repository of SnakeCube.
All phenotypic results were assigned to a class between 0 and 3, to facilitate the mathematical data comparison between different experimental datasets, as previously described  [30]. In detail, the growth was scored in the following way, both in liquid and solid media: score 0 represents no growth (DO 640 = 0. 1) or no visible growth on solid media; score 3 represents at least 1. 5-fold increase of DO 640 , and extensive growth on solid media; scores 1 and 2 indicate intermediate values. For the change of color in arbutin and BiGGY media: score 0 corresponds to no visual growth after 2 or 8 days; score 3 corresponds to dark brown or brown color colonies, and scores 1 and 2 to color colonies ranging from white to brown. The halo size obtained in the esculin medium was categorized in the following way: score 0 was attributed to colonies that did not show a halo; score 3 to colonies with a halo size higher than 0.
Data Analysis Using Statistical and Data Mining Methods The phenotypic and metabolic inter-strain variability of T. delbrueckii was evaluated by principal component analysis (PCA), available in the Orange data mining suite software v. 3. 25. 0 [40]. A set of standard predictive data-mining methods were used for the inference of prediction models. Data analysis was performed in the Orange software, and validated in python, using the scikit learn package. In particular, naïve Bayes, decision tress, support vector machines (SVM), k-nearest-neighbor (kNN) classifier, random forest, stochastic gradient descent (SGD), CN2 rule inducer and neural networks were used to predict the technological groups of the 40 T. delbrueckii strains, using both phenotypic and metabolic datasets. After 10-fold cross validation of the training data, performance scores were assessed, in particular the area under the receiver operating characteristics (ROC) curve (AUC)  [41] , that estimates the probability that the predictive model would correctly differentiate between distinct technological applications, given the associated pairs of strains.
Bioinformatic Prediction of T. delbrueckii Biotechnological Potential To correlate phenotypic and metabolic data, understanding their differential contribution to the strain heterogeneity of T. delbrueckii, the established dataset was analyzed through the k-means clustering algorithm  [89]. Using silhouette score  [90] , the algorithm identified 2 significantly distinct clusters (Figure  3B ), composed of 8 and 32 strains, respectively. The contribution of all of the metabolic and phenotypic features to the establishment of the two distinct groups was evaluated and weighted (Figure  3A ). Results show an equal contribution of both experimental methodologies (HPLC metabolites quantification and phenotypic screening), revealing that the final concentrations of glucose, ethanol and citric and succinic acids, together with the resistance to SO2, copper and to the freezing process, were the features most relevant to explain this differentiation.
Interestingly, cluster 1 was composed almost entirely by arboreal/soil strains, with the exception of the commercial wine strain T30, that once again showed different patterns from the remaining wine strains. Cluster 2 incorporated the remaining strains, that showed characteristics shared by the remaining wine strains. Our next goal was to construct a computation model robust enough to predict the T. delbrueckii biotechnological potential, based on its phenotypic and metabolic profiles. Eleven predictive models were used, and their performance was evaluated in terms of AUC, classification accuracy, F1 score, precision and recall (Supplementary Materials, Table  S2 ). Neural networks were revealed as the classifier with the best performance metrics, and were selected for further analysis. Table  2  shows the confusion matrix obtained using the neural networks' classification of the dataset, after 10-fold cross validation.