dataesr/scientific-paragraphs-categorization huggingface dataset:
95%, as shown in the Supplementary Note. For our pipeline comparison though, we decided to subset the PacBio dataset to the same depth of the ONT dataset, so that differences between platform results are driven by platform differences and not different sample sizes. If we selected the PacBio reads randomly until reaching the desired depth, the subset would follow a similar read length distribution of the original dataset, which has a smaller average read length than ONT. This would make it more difficult to interpret the assembly continuity as due to platform read intrinsic features like error rate and distribution, or different read lengths. Because of this, we decided to reduce the number of variables for assembly assessment by selecting a PacBio subsample of the S288C reads with the same depth (31X) but also similar read length distribution as the S288C 2D-Pass ONT reads: the PacBio ONT-Emu sample (Figure  1-(c) ).
More information on how we extracted the subset can be found in the Supplementary Note. To study the dependence of the assembly pipelines on read depth, we also selected two subsets with 10X and 20X depth for the S288C ONT and PacBio data, again with similar read length distributions. For the total PacBio S288C dataset, which consists of 120X read-depth, we performed a more detailed depth study shown in the Supplementary Note for subsets of 10X, 20X, 31X, 61X, 80X and the whole sample at 120X depth. Details about the sequenced datasets presented in this paper are summarized in Table  1  for ONT, and in Table  2  for PacBio samples which 'emulate' the ONT read length profiles (ONT-Emu). The read length distributions for the S288C, N44, CBS432 and SK1 strains of the 2D-Pass ONT datasets are shown in Fig.
1-(a ) and those of the PacBio datasets in Fig. 1-(b). De novo Assembly and Scaffolding Pipelines. It has been shown already  12  that contiguous and accurate yeast assemblies can be generated de novo solely using ONT data. Here, we focus on assessing the various existing pipelines and on comparing the results obtained from ONT and PacBio data with particular attention to the    2. Statistic information for the PacBio datasets for the S288C, N44, CBS432 and SK1 strains. For the S288C strain also the ONT-emulating subsets are shown: 'ONT-Emu' 31X, 20X and 10X subsets, selected to match the 31X, 20X and 10X ONT S288C datasets for depth and read length distribution. similarities and differences between the two platforms. The long reads provided by PacBio and ONT can be used to generate a de novo assembly either by themselves or in conjunction with Illumina (NGS) data.
In this paper, we show examples of assemblies from long reads only, and from'hybrid' pipelines that use the Illumina reads either to correct the long reads or to generate a short-read assembly that is later scaffolded with long reads. We selected eight assembly pipelines for long reads. PBcR 13 (with Self-or MiSeq-based error correction: PBcR-Self, PBcR-MiSeq), Canu  14  and Falcon 15 are based on an Overlap-Layout-Consensus (OLC) algorithm; ABruijn  16  is based on a generalized De-Bruijn graph algorithm. All of them include a base-error correction step on the reads before assembling them. SMARTdenovo (available from https://github. com/ruanjue/smartdenovo) is also based on a OLC algorithm, but does not include a base-error correction step. Miniasm 17 chooses the best path from a string graph created from the overlap of uncorrected reads: it is the only pipeline here that includes neither a base-error correction nor a consensus step.
Racon 18 aligns the raw long reads to a Miniasm assembly and generates a consensus, significantly increasing the initial accuracy. We selected and assessed also three scaffolding pipelines: npScarf  19  , HybridSPAdes  20  and SMIS (available from https://github. com/fg6/smis. git), to scaffold an NGS-based assembly from SPAdes  21. Details about these pipelines and the parameters used for running them can be found in the Supplementary Note. Assemblies from different pipelines and platforms. The de novo assemblies of the whole PacBio datasets for S288C and the other strains are shown and discussed in the Supplementary Note, while here we compare the performances of long-read assemblers when run on a 31X depth ONT or PacBio datasets with similar read length distributions. For this purpose we selected a subsample from the PacBio S288C dataset with the same depth as the ONT 2D-Pass sample (31X) and with similar read length distribution, as described above and shown in Fig.
1-(c ). The statistic information for the two S288C samples can be compared if looking at the sample S288C '2D-Pass: 31X' in Table  1  for the ONT case, and at the S288C 'ONT-Emu 31X' in Table  2  for the PacBio case emulating the ONT sample. As shown in these tables, the two samples also have similar average error rates (92% average identity for PacBio and 93% for ONT datasets). Using these two similar samples, except for normal fluctuations, differences in assembler's performances can be attributed to the peculiar features of the reads from the two platforms, for instance to error types and distributions. The assembly information for the ONT dataset '2D-Pass 31X' and for the PacBio 'ONT-Emu 31X' can be found in Tables  3  and 4 , respectively. With 31X depth the pipelines generated assemblies with similar features when running on PacBio or ONT data.
The fastest pipeline is Miniasm, which does not include a base error correction nor a consensus step. For both PacBio and ONT datasets it only took 4-5 minutes to run but only achieved 89% accuracy. Because of the high number of indels in the ONT and PacBio data, Miniasm reconstructed only about 95-96% of the genome, significantly less than the other pipelines. Globally, the assembly structure from the Miniasm pipeline is correct, indicating that the missing regions are due to local base errors. Part of these base errors are recovered by the other pipelines either by building a consensus from the raw reads (Racon, SMARTdenovo) or during their base-error correction steps (PBcR-MiSeq, PBcR-Self, Canu, Falcon and ABruijn). The assemblies with the highest accuracy, 99. 94-99. 97% are the ones from the only hybrid assembler, PBcR-MiSeq, that uses MiSeq reads to correct the ONT or PacBio reads.
But PBcR-MiSeq also provided the most fragmented assemblies, with Na50s only 270 kb long, where Na50s are the N50s after breaking the contigs at the misassembly points found by Quast  22. None of the other assemblers uses Illumina reads, and we refer to them as the non-hybrid pipelines. The non-hybrid pipelines reconstructed 98-99% of the genome, with Na50s in the 400-500 kb range long, and accuracy up to 98. 76% for ONT reads and up to 99. 93% for the PacBio reads. The highest accuracy between the non-hybrid assemblies for the ONT data is from Racon, with 98. 76%, immediately followed by SMARTdenovo and ABruijn with about 98. 50%. For the PacBio datasets the Celera-based assemblers (Canu, PBcR-Self) provided the highest accuracy, 99. 92-99. 93%, followed by ABruijn at 99. 87% and Falcon at 99.
78%. To assess the completeness of the newly generated assemblies, we checked for the reconstruction of a list of known S288C genes. From the list of ORF coding sequences reported in the Saccharomyces Genome Database (http://www. yeastgenome. org), we selected 6,615 coding sequences that mapped to the S288C reference for at least 90% of their length with at least 90% mapping identity. We used BWA  23  to align each gene against the new assembly under study, and declared a gene 'reconstructed' if at least 90% of it was assembled with at least 90% accuracy. Tables  3  and 4  for ONT and PacBio data respectively, show a correlation between the assembly continuity and the number of mapped genes, i. e. an assembly from a particular pipeline with longer contigs is likely to have reconstructed more genes.
Missing homomers. Ignoring Miniasm which has no error correction nor a consensus step, and the hybrid pipeline PBcR-MiSeq, which corrects the reads using Illumina data, it is clear that the PacBio assemblies have higher accuracies, from 99. 50% to 99. 93%, while the ONT assemblies reach a maximum accuracy of 98. 76%. Because the average read accuracy, the depth and the read length distribution of the ONT and PacBio datasets used here are very similar, the final higher accuracies for the PacBio assemblies are likely due to platform-intrinsic features of the data. When no base-error correction nor consensus step is performed, like in the Miniasm pipeline, the resulting assemblies are about 89% accurate for both the ONT and PacBio datasets, and present lots of mismatches and indels, with prevalence of indels.
Mitochondrial genome reconstruction. While all of the 16 yeast nuclear chromosomes are well reconstructed by every tested pipeline with respect to continuity, accuracy and completeness, many pipelines failed to reconstruct the mitochondrial genome (Supplementary Tables  S5  and S6 ). This chromosome is the smallest one in the reference assembly, only containing 85,779 bases, but appears to be very challenging to assemble, especially with ONT datasets. Using ONT data, only PBcR-MiSeq was able to reconstruct it almost completely (96%), and Table  3. Statistic information about the de novo assemblies for the S288C ONT datasets for the hybrid pipeline PBcR-MiSeq, the pipeline Miniasm, with no base error correction nor consensus step, and for the non-hybrid pipelines: Racon (on a Miniasm draft assembly), Falcon, SMARTdenovo, ABruijn, PBcR-Self and Canu for, from top to bottom: all the 2D-Pass reads (2D-Pass 31X), the 2D-Pass 20X subset and the 2D-Pass 10X subsets.
For the 2'D-Pass 31X' dataset also the results from Nanopolish on the Canu assembly is shown. In each column the best value is highlighted in bold. For the identity column the best value is always for the hybrid assembly PBcR-MiSeq, but we also highlighted (bold and underlined) the best value for the non-hybrid pipelines, ignoring Nanopolish as it is the only polishing tool. For the 10X datasets, we ignored assemblies with less than 80% reference coverage when choosing the best values. only Falcon and Canu were able to reconstruct at least half of it (67%, 64%, respectively). Using PacBio data there are three full reconstructions from Racon, SMARTdenovo and Canu, while Miniasm manages to reconstruct 77% of it. There are a number of possible reasons for the mitochondrial genome to be more challenging to reconstruct than the other chromosomes.
The GC content in the mitochondrial genome is only 17%, much lower than the average value of 38% for the nuclear chromosomes. There are also a lot of highly repetitive small AT k-mers, which contribute to the difficulty of assembly. We found that while for the ONT dataset the mitochondrial read depth was consistent with that of the other chromosomes, the PacBio mitochondrial depth was about 6 times higher which could help explain why the mitochondrial genome reconstruction is more complete with the PacBio data. To exclude the possibility that this is due to a bias in our subsetting, we looked at the whole PacBio dataset and found that, similarly to the subset, the mitochondrial genome depth is about 7 times higher than the nuclear chromosomes. This is likely due to the high copy number of mitochondria in a cell.
De novo Assembly by Varying the Read Depth. In order to assess the scalability of the assembly pipelines and to determine how the performances of the assemblers vary with read depth, we selected and analyzed subsets of the ONT and the PacBio full datasets. A dedicated study focusing only on the higher depth PacBio dataset is discussed in the Supplementary Note. Here, we compared the assemblies at 31X mentioned in the previous section, with 20X and 10X subsets from the ONT 2D-Pass and similar subsets from PacBio reads emulating the read length distribution of the ONT subsets, as we did for the 31X case. These datasets are presented in Tables  1  and 2  for ONT and PacBio respectively, where the PacBio subsets are labeled as ONT-Emu. The results of the pipelines are summarized for the 31X, 20X and 10X in Table  3  for ONT and Table  4  for PacBio data.
At 20X, all the Celera-based pipelines still performed very well, with Canu and PBcR-Self providing Na50s in the range of 400s kb, as well as SMARTdenovo. The assembly from Racon distinguishes itself on ONT data for the highest accuracy, immediately followed by ABruijn and SMARTdenovo, while for PacBio data Canu, PBcR-Self and ABruijn assemblies have the highest accuracies. Already at 20X, Canu, PBcR-Self, PBcR-MiSeq and SMARTdenovo reconstructed about 99% of the genes. At 31X depth the non-hybrid pipelines increased the Na50s up to 500s kb and slightly improved their final accuracies. Genome scaffolding using long reads. We explored the scaffolding performance of three pipelines that use long ONT/PacBio reads to bridge and merge contigs from a NGS assembly. The NGS assembly used here has been generated from a dataset of 80X of 2 × 150 bp MiSeq paired-reads by SPAdes, as one of the scaffolding pipelines is embedded with it (HybridSPAdes).
5% for ONT and 99. 7% for PacBio). The non-hybrid Celera-based assemblers, PBcR-Self and Canu, generated continuous and accurate assemblies, and appeared to be the most performing pipelines as the read coverage decreased. In our experience, PacBio and ONT platforms provided reads with similar error rates and lengths in the thousands of bases. The average PacBio lengths were generally smaller than the ONT ones, but the ONT throughput per run was significantly lower than the one from PacBio's runs. This was mainly because we used early MinION flowcells and chemistries: the throughput improved significantly both in size and stability with more recent MinION kits and software. In addition, a fairer throughput comparison should be done between two benchtop devices, e. g. the PacBio RSII and the Oxford Nanopore GridION, or PromethION, both not available at the time of this study.
Illumina adapters were added using the NEBNext Quick Ligation Module. Ligation products were purified with AMPure XP beads (Beckman Coulter Genomics, Danvers, MA, USA). Libraries were quantified by qPCR using the KAPA Library Quantification Kit for Illumina Libraries (Kapa Biosystems, Wilmington, MA, USA) and library profiles were assessed using a DNA High Sensitivity LabChip kit on an AgilentBioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Libraries were sequenced on an Illumina MiSeq instrument (San Diego, CA, USA) using paired 150 base read chemistry. Assembly Assessment and Other Tools. The ONT fastq sequencing reads were extracted from the fast5 files using Poretools  26. For the assembly accuracy and other summary statistics we used dnadiff from MUMmer  27  , while the number of misassemblies and the Na50s, i.
e. the N50s after breaking the contigs at the misassembly points, were calculated using Quast  22. We ran Quast with default parameters except for the Miniasm assemblies. For Miniasm we required the additional parameter: -min-identity 85, to enable Quast to align the low accuracy assemblies to the reference. Because of such low accuracies, Quast seemed to overestimate the presence of misassemblies for Miniasm, and reported Na50s smaller than the expected ones if comparing the Na50s with those of the higher accuracy Racon assemblies, based on Miniasm. We used the R Biostrings package (available from https://bioconductor. org/packages/release/bioc/html/Biostrings. html) to count the number of homomers in some of the assemblies, using the function oligonucleotideFrequency. Statistics and assessment for all the assemblies have been estimated after eliminating contigs shorter than 1 kb.
Oxford Nanopore Datasets Strain Dataset Bases (Mb) Reads Average (b) Longest (b) N50 (b) Identity 2D-Pass: 31X 383 42,325 9,040 56,477 11,693 93. 3% S288C 2D-Pass: 20X 121 13,366 9,054 56,028 11,716 92. 0% 2D-Pass: 10X 242 26,721 9,057 56,477 11,659 92. 8% N44 2D-Pass: 11X 130 15,654 8,292 37,837 9,861 NA CBS432 2D-Pass: 9X 110 12,211 8,952 46,481 11,201 NA SK1 2D-Pass: 4X 51 5,938 8,589 36,791 10,971 NA.
Biosciences Datasets Strain Dataset Bases (Mb) Reads Average (b) Longest (b) N50 (b) Identity 120X 1,463 239,408 6,109 35,196 8,656 92. 5% S288C ONT-Emu: 31X ONT-Emu: 20X 375 242 42,180 26,786 8,893 9,035 35,196 35,196 11,196 11,615 91. 9% 91. 7% ONT-Emu: 10X 121 13,456 8,993 31,627 11,582 91. 2% N44 148X 1,794 371,025 4,834 33,906 6,800 NA CBS432 135X 1,639 324,414 5,053 34,173 7,212 NA SK1 248X 3,019 697,989 4,325 34,080 6,184 NA.
7 68 364 99. 9% 0. 1, 0. 04 99. 97% 5 317 6,583 27 12 31X SMIS 11. 7 89 546 98. 8% 0. 04, 0. 04 99. 97% 40 309 6,399 9 6 Oxford Nanopore Datasets Memory Dataset Assembler Bases (Mb) Contigs N50 (kb) Genes (6,615) CPU Time (h) (GB) SPAdes 11. 6 187 117 5,475 7 13 N44 2D-Pass: 11X npScarf HybridSPAdes 11. 7 11. 7 19 61 898 324 5,538 5,547 1 8 2 13 SMIS 11. 7 58 511 5,474 2 5 SPAdes 11. 6 181 150 5,498 5 12 CBS432 2D-Pass: 9X npScarf HybridSPAdes 11. 4 11. 7 19 49 928 515 5,443 5,611 1 6 1 12 SMIS 11. 7 64 658 5,499 1 5 SPAdes 11. 6 240 118 6,341 6 12 SK1 2D-Pass: 4X npScarf HybridSPAdes 11. 7 11. 7 43 111 507 227 6,435 6,444 0. 3 5 1 12 SMIS 11. 7 142 358 6,341 1 3.
sin increases the binding affinity for the Klumpfuss transcription factor sin is a single base pair insertion within a previously uncharacterized non-coding region of the ss locus located ~7 kb upstream of the transcriptional start (Figure  1B  and Figure  3A ). To identify trans factors whose binding might be affected by sin, we searched for binding motifs affected by sin in SELEX-seq  (Nitta et al. , 2015)  and bacterial one-hybrid datasets (B1H)  (Zhu et al. , 2011; Enuameh et al. , 2013). sin lies in a predicted binding site for the zinc finger transcription factor Klumpfuss (Klu), the fly homolog of Wilms' Tumor Suppressor Protein 1 (WT1) (Figure  3B , Figure  3 figure supplement 1A)  (Klein and Campos-Ortega, 1997; Yang et al. , 1997). This region is perfectly conserved across 21 Drosophila species covering 50 million years of evolution, consistent with a critical regulatory role (Figure  3C , Figure  3 -figure  supplement 1B-C ).
A circular ''region of interest'' was manually placed at the center of each Ss ON R7 (identified by expression of Ss and the R7 marker Prospero) to avoid signal from neighboring photoreceptors. ImageJ software assessed the mean pixel intensity for each region of interest for each Ss ON R7. Levels of Klu expression in R7s were assessed with a Klu antibody in third instar larval animals. Five retinas were scored (N). 65 or more R7s were scored for each retina (n). We used ImageJ software to quantify Klu levels in all R7s (Figure  4 -figure supplement 1). A circular ''region of interest'' was manually placed at the center of each R7 (identified by pm181 >GAL4; UAS > GFP reporter expression) to avoid signal from neighboring photoreceptors. ImageJ software assessed the mean pixel intensity for each region of interest for each R7.
For the remaining regions, normalized intensities from the Rh3 and Rh4 channels were compared in order to assign each region a label, indicating that its R7# is stained with Rh3 or Rh4. A MATLAB (The MathWorks, Inc. ) script that implements our algorithm is available at https://app. assembla. com/spaces/roberts-lab-public/wiki/Fly_Retina_Analysis.
Genome-Wide association studies Genotype data from the DGRP freeze two lifted to the dm6/BDGP6 release of the D. melanogaster genome was obtained from (ftp://ftp. hgsc. bcm. edu/DGRP/). Phenotypes were calculated for the progeny of crosses of DGRP lines and Df(3R)Exel6269 flies. To estimate genotypes of these flies from the DGRP data, we simulated each cross. For each SNP or indel variant in the DGRP genotype data, we assigned a new genotype: (1) homozygous reference remains homozygous reference, (2) homozygous alternate maps to homozygous alternate if in deficiency region, otherwise heterozygous, and (3) all other genotypes mapped to missing or unknown and not included in subsequent analyses. We performed quantitative trait association analysis using plink2 -linear (version 1. 90 beta 25 Mar 2016; PMID:25722852).
SELEX-seq analysis SELEX-seq datasets from  (Nitta et al. , 2015)  were obtained from ENA (ERX606541-ERX606544). For read-level analysis, we counted the number of reads containing the Klu binding site with sin, without sin, and neither site (there were no reads with both sites). We performed McNemar's test to assess significance. We computed the frequency of each 10-mer within each dataset using Jellyfish version 2. 2. 6 (Marcais and Kingsford, 2011). Using these counts, we determined the number of 10-mers with frequency greater than that of the Klu binding site with and without sin. Frequencies reported are for the combination of all four SELEX datasets. Jellyfish 2. 2. 6 was used to canonically count kmers of length 10 with an initial hash of size 100M from fasta files generated from the first and fourth rounds of selection.
johnpool. net/genomes. html). Regions of admixture in African genomes were identified based on analyses by  Lack et al. , 2016. DGN data were mapped to Release 5 of the Drosophila genome and we converted those coordinates to Release six using the lift-over file available from the UCSC Genome browser (http:// hgdownload. soe. ucsc. edu/goldenPath/dm3/liftOver/dm3ToDm6. over. chain. gz). We sought to assess whether the distribution of sin among populations sampled worldwide was significantly different than expected by chance based on other comparable indel polymorphisms. sin was originally identified in the Drosophila Genetic Reference Panel (DGRP), derived from a population in Raleigh, NC, where it segregates at ~25%. We observed that sin segregates at ~10-25% in other North American and European populations but is rare/absent in ancestral African populations (Figure  1 -figure supplement 3A-F).
We calculated the extended haplotype homozogosity (EHH) score and integrated EHH (iEHH) score for haplotypes with and without sin (derived and ancestral haplotypes, respectively) in the DGRP data where sin was originally identified. EHH scores were also calculated at the control set, as described above. EHH scores were calculated using the R package rehh  (Gautier and Vitalis, 2012). The derived sin allele shows an elevated iEHH score compared to the ancestral allele, suggestive of a partial selective sweep (Figure  1 -figure supplement 3I-J). To test this model, we calculated the integrated haplotype statistic as, IHS ¼ log2 ðiEHH derived =iEHH ancestral Þ for sin as well as the control set. IHS for sin is not significantly different than expected by chance relative to other comparable indel polymorphisms (Figure  1 -figure supplement 3I-J).
Acknowledgements We are grateful to  Steve Britt ,  Claude Desplan ,  Lily Jan ,  Yuh-Nung Jan ,  Thomas Klein ,  Chuck Langley ,  John Pool ,  Charles Zuker , and the  Bloomington and Kyoto Stock Centers  for generously providing published fly stocks and antibodies. We thank  Erik Anderson ,  Greg Bashaw ,  John Kim ,  Alex Kolodkin ,  Rejji Kuruvilla ,  Chris Potter ,  Geraldine Seydoux ,  Mark Van Doren  and members of the  Johnston lab  for helpful comments on the manuscript. RJJ was supported by the  Pew Scholar Award 00027373  and  NIH R01EY025598.
Collagen Preservation and Stable Isotopes. Collagen of sixteen previously unanalysed specimens from Geißenklösterle and Hohle Fels (Germany) produced here C:N coll values of between 2. 9-3. 6  14  and had percentages of C coll above 8% and N coll above 5%  15, 16  , representing well preserved and reliable collagen values (Methods and Supplementary Data S2). The corpus of previously published isotopic data from Geißenklösterle and Hohle Fels from the Late Pleistocene was augmented with the new  13  C and  15  N values of mammoth (n = 9), rhino (n = 6) and horse (n = 1) (Supplementary Data S2). Species identification was isotopically inferred through Ward's minimum variance clustering alongside previously identified specimens (See Methods and Supplementary Information). The new isotopic values on mammoth were consistent with those measured previously  7, 13  , with typical ca.