dataesr/scientific-paragraphs-categorization huggingface dataset:
It is not uncommon in practice to filter short alignments. We repeated the analysis by keeping only those alignments predicted by the Dammit pipeline that covered at least 50% of a contig. The precision-recall curves for this cases is shown in Figure  3 , which shows a significant drop in recall of the assembly-based approach. With real data too, our method outperforms the assembly-based approach We applied our pipeline and the assembly-based approach to a recently published real RNA-seq dataset ArrayExpress E-MTAB-8090 ERR3393437-42. The dataset contains RNA-seq reads of the hemocyte tissue of D. melanogaster samples with and without injury, with 3 replicates for each condition. After cleaning and trimming low-quality reads using fastp  [46] , there were roughly 110 million pairs of reads.
Continuing with the assumption that no reference sequences are available for D. melanogaster, we applied our pipeline and the assembly-based pipeline as in the previous section using the mosquito and two Drosophila reference proteomes. Since we do not know the ground truth for this dataset, to serve as baseline, we additionally ran the Bowtie2-RSEM-DESeq2 pipeline using D. melanogaster reference transcriptome. To be able to compare the DE call sets, we mapped our predicted DE genes to the D. melanogaster gene names using the same Inparanoid orthology maps as before. Intersection of the sets of DE genes obtained from the two approaches and the baseline are shown in Fig-  ure 4. At FDR threshold of 0. 01, there were 104 genes identified as DE by the baseline method. Based on the observation from Figure  1  that the Bowtie2-RSEM-DESeq2 pipeline has high precision at FDR 0.
Avoiding assembly dramatically reduces running time and memory usage All the experiments above were carried out on a system with Intel Xeon Silver 4114 Processor with 10 cores and 20 threads. For the real dataset E-MTAB-8090 which contains roughly 110 million pairs of cleaned reads, de-novo assembly alone took more than 24 hours. In contrast, DNA-protein alignment, which is the most compute-intensive part of our pipeline, takes less than 20 minutes per sample containing roughly 20 million pairs of reads, using 20 threads. While the denovo assembly had a massive peak memory usage of ∼65 Gbytes, the memory requirement of our method is dominated by the size of the proteome index, which was just ∼33 Mbytes for the mosquito proteome. In general, the index size is roughly 5 × n bytes, where n is the length of the proteome.
Choice of reference Our results suggest -not surprisingly -that the choice of reference has a huge influence on the outcome of differential expression analysis, since a distant reference means fewer reads are aligned (correctly). One source to find a closest possible proteome is the UniProt Reference Proteome database. This database currently contains almost 20,000 proteomes of organisms which are relatively well-studied and "provide broad coverage of the tree of life"  [47]. Apart from single-species reference proteomes, it is also common to use cross-species proteins sets such as Swiss-Prot for annotating transcriptome assemblies. In theory, our method can also use Swiss-Prot as reference. However, Swiss-Prot is extremely redundant due to presence of orthologous proteins, which can needlessly aggravate the issue of multi-mapping.
Long reads This paper is focused on short-read datasets, since from our cursory literature search in the Introduction section, it appears that long-read technologies are currently not widespread as in the applied fields that deal with non-model organisms. Theoretically, the core idea of DNA-protein alignment carries over just as well to long reads. Longer sequences can potentially improve accuracy as it would be easier to disambiguate counts among paralogous genes. However, application to long reads warrants a separate benchmarking process as one needs to account for error profiles and error rates characteristic to long-read technologies.
Conclusions With RNA-seq becoming accessible to even labs with limited resources, the time, labor, and infrastructure cost of bioinformatics analysis has grown. Transcriptome assembly is one such resource-hungry process, which takes several tens of hours on typical datasets, even on high-performance computing systems. For many labs, such requirements can impose a serious bottleneck. By avoiding assembly, our pipeline allows for quick and easy, yet more sensitive and precise, analysis of differential gene expression in non-model organisms. 4 : For the three reference proteomes, Venn diagrams showing the intersections among the Baseline set consisting of DE genes called by the baseline approach of Bowtie2-RSEM-DESeq2 using the D. melanogaster reference transcriptome, Our set consisting of D.
Indeed, not modeling explicitly the spatial autocorrelation of unmeasured ecological variables affecting fitness can lead to detect false-positive effects of traits on fitness, as on any other response  (Guillot & Rousset, 2013). To address this problem, we fitted mixed-effect models with spatially autocorrelated random-effects, using the spaMM package  (Rousset & Ferdy, 2014). To our knowledge, it is the first time that spatial effects are taken into account in the estimation of selection gradients. Moreover, spatial variation in plant density and the local sex ratio may generate spatial variation in competition for resources, which can be studied by analyzing their fixed effects on plant fitness. In conclusion, our MEMMseedlings model controls for spatial confounding effects on estimations of effective fecunditity relative to spatial sampling biases and to the spatial heterogeneity of plant distribution (potentially impacting competition among males), while our spaMM procedure controls for spatial environmental heterogeneity.
e. 86 females and 88 males) were mapped (see Supplementary Methods S1), sampled for DNA analyses and measured for several traits in 2004. In summer 2006, a fire burnt the population, killing all adults. A total of 1,265 seedlings were mapped, and their leaves were sampled in the following Fall (February 2007), four to five months after the fire. The spatial distribution of adults and seedlings is shown in Fig. S2. In one part of the study site, a ditch had been dug for construction after seeds had dispersed, so we were unable to determine the undisturbed positions of 172 seedlings located in that area which we therefore eliminated from the dataset. However, the effective fecundity of adults in this area can still be estimated without bias induced by the disturbance, thanks to the use of MEMMseedlings (see below).
Measurements of adult traits in the field For adult shrubs, we measured in 2004 three traits describing plant architecture and three traits describing leaf morphology (available at doi:10. 5061/dryad. ngf1vhhst). All six traits are known to be sexually dimorphic in this species  (Harris & Pannell, 2010; Welsford et al. , 2014 Welsford et al. , , 2016 ; Fig. S4 ). The three traits describing plant architecture were (1) plant height, (2) the first diameter defined as the greatest horizontal diameter of the plant (hereafter, canopy diameter), and (3) the second horizontal diameter defined as the diameter perpendicular to the first diameter (hereafter, second diameter). Several leaves were collected randomly along branches of each adult, dried and photographed. Pictures were analyzed to measure the three traits describing leaf morphology: (1) leaf area, (2) length and (3) width using ImageJ  (Schneider et al.
Microsatellite genotyping We genotyped both adults and their progeny in our focal population (available at doi:10. 5061/dryad. ngf1vhhst). For both adults and seedlings, sampled leaves were preserved in silica gel prior to DNA extraction using a modified version of the CTAB protocol  (Justy et al. , 2009). We designed two polymerase chain reaction (PCR) multiplexes for amplifying DNA at 4 microsatellite loci, each involving primers with different fluorescent labels (Multiplex 1: 4F8, 3C9, 1C7, 1C3; Multiplex 2: 3B11, 2B2, 1D7, 1B8; markers developed by  Justy et al. , 2009). PCRs were performed using the Qiagen Multiplex PCR Kit (Qiagen, Hilden, Germany); each PCR reaction was performed in a final volume of 10μM compo M composed of 5μM compo L of Multiplex Master mix (2X), 1μM compo L of primer sequences (1μM compo M), 1μM compo L of DNA extracts and 3μM compo L of sterile water.
PCR reactions were performed on a Mastercycler pro thermocycler (Eppendorf, vapo. protect) with an initial denaturation step of 15min at 95 °C, 35 cycles of 30s at 94 °C, 1min30s at the T m temperature (M1: 54°C, M2: 53°C) and 1min at 72 °C, and a final step of 30min at 60°C. Genotyping was performed on an ABI3500XL sequencer. The genotypes of all adults and offspring were scored using GeneMapper at the eight microsatellite loci, which exhibited between five and 23 alleles (Table  S1  for information per marker). After excluding individuals that did not amplify, our dataset contained 82 females, 85 males and 869 juveniles, corresponding to an amplification failure of roughly 8% for both adults and juveniles. For each microsatellite marker, we used CERVUS (version 3. 0. 7; Kalinowski et al.
To describe the quality of parentage assignments associated with the dataset, MEMMseedlings computes the posterior probabilities for all seedlings for which (i) both parents are known among the genotyped parents, (ii) only the mother, or (iii) the father is known, and (iv) none of the parents is known. Estimates of dispersal kernels are based on dispersal events within the population, even if the parametric model implies dispersal in unbounded space (beyond the maximal male-female and female-seed distances found in our study population, respectively 106. 4 and 139 meters for pollen and seeds dispersal). To describe dispersal within the study population, we computed, from the estimated kernels, the predicted proportion of seeds and pollen that dispersed within the population range and the predicted proportion that dispersed within an arbitrary short distance of 20m.
Sex differences in morphology, spatial distribution and analysis of number of cones We tested for sex differences in morphology using Linear Mixed-effects Models (LMM) with spatially autocorrelated random effects. We analyzed all measured adult traits describing either plant architecture or leaf morphology. Random individual effects can be spatially autocorrelated, for instance, due to spatial variation in some ecological variables not included in the model. We fitted models with morphology as the response variable and sex as a fixed effect using the R package spaMM version 3. 3. 0 (Rousset & Ferdy, 2014)  in R 4. 0. 2 (R Core Team, 2020). We fitted models with distinct residual variances for each sex. We updated the code of the spaMM package to allow for different spatial distributions of random effects between sexes (publicly available in spaMM since version 2.
7 with gaussian with a link identity: cAIC=139; or gaussian with a log link: cAIC=126 -cAIC is a metric similar to the traditional AIC, except that it measures prediction performance conditionally on the realization of the random effects,  Vaida & Blanchard, 2005). We followed the same procedure as described for the analysis of sexual dimorphism (see previous section) to fit the models using the R package spaMM and to identify the best fitting random spatial structure (i. e. comparing a sexspecific spatial structure, a common spatial structure and a lack of it). We were thus able to compare the patterns of spatial variation of residual effective fecundity between males and females, once the effects of local density and variation in morphology were included as fixed effects in our global model.
, 2018  for a review). We therefore retained only two morphological traits in our selection analysis (but similar results were obtained for other combinations of traits). We standardized the two focal traits using z-scores based on the mean and variance values of the traits calculated separately for each sex. We confirmed that leaf area significantly differed between individuals by comparing, within each sex, models predicting leaf area with vs. without an individual-level random effect using an exact restricted likelihood ratio test  (Crainiceanu and Ruppert, 2004 ) implemented in package RLRsim 3. 1-6  (Scheipl et al. , 2008) : for females, likelihood ratio (LR)=1573. 9, p<0. 001; for males, LR=625. 1, p<0. 001. We compared the performance of local plant density measured at different scales in explaining variation in male and female effective fecundity (see Supplementary Method S5).
Acknowledgments We dedicate this manuscript to IO who passed away before the completion of this study, which she initiated. She inspired and is still inspiring our research in many ways. We are grateful to  Nicolas Bierne  and  Pierre-Alexandre Gagnaire  for their helpful advice on population genetic structure analysis, to  John Pannell  for helpful discussions and English editing, to  Jeremy Midgley  for help in the field and attracting our interest in this system. Elodie Flaven-Noguier ,  Fabienne Justy  and  Clémence Hatt  helped with the DNA extractions, genotyping and molecular biology protocols. This work was supported by a grant from the  FRB  to IO and AM, from the  ANR  " Evorange ",  ANR-09-PEXT-011  to OR and from the  ANR  " MeCC ",  ANR-13-ADAP-006  to OR, EKK and SOM. JT was supported by a grant to  John Pannell  from the  FNS ,  31003A_163384.
Data accessibility All genotype and morphological trait data are available in Dryad at doi:10. 5061/dryad. ngf1vhhst. The new MEMMseedlings source code for dioecious species is available at https://gitlab. paca. inrae. fr/pub/tonnabel_mol_ecol. The new version of spaMM is publicly available in spaMM since version 2. 6. 0 at https://CRAN. R-project. org/package=spaMM.
Pango lineages were classified using the Pangolin  (24)  software tool (http://pangolin. cog-uk. io/) and standard phylogenetic analysis using complete reference genomes. Our early data indicated the presence of a novel SARS-CoV-2 lineage in Manaus containing 17 amino acid changes (including 10 in the spike protein), 3 deletions, 4 synonymous mutations and a 4 nucleotide insertion compared to the most closely related available sequence (GISAID ID: EPI_ISL_722052) (25) (Fig. 1B ). This lineage was given a new designation, P. 1, on the basis that (i) it is phylogenetically and genetically distinct from ancestral viruses, (ii) associated with rapid spread in a new area, and (iii) carries a constellation of mutations that may have phenotypic relevance  (24). Phylogenetic analysis indicated that P.
Infection with P. 1 and sample viral loads We analysed all SARS-CoV-2 RT-qPCR positive results from a laboratory providing testing in Manaus since May 2020 (Fig. 1A , Data S2) with the aim of exploring trends in sample RT-qPCR cycle threshold (Ct) values, which are inversely related to sample virus loads and transmissibility  (32). By focusing on data from a single laboratory, we reduce instrument and process variation that can affect Ct measurements. We analysed a set of RT-qPCR positive cases for which virus genome sequencing and lineage classification had been undertaken (n = 147). Using a logistic function (Fig. 3A ) we find that the fraction of samples classified as P. 1 increased from 0% to 87% in around 7 weeks (table  S1 ), quantifying the trend shown in Fig 1C. We found a small but statistically significant association between P.
(C) Ct values for genes E and N in a subsample of 184 infections included in (B) that had their genomes sequenced (dataset A). Using a larger sample of 942 Ct values (including an additional 795 samples for which no lineage information was available) we investigated Ct values across three time periods characterised by increasing P. 1 relative abundance. Average Ct-values for both the E and N genes decline through time, as both case numbers and the fraction of P. 1 infections increased (Fig. 3C ; E gene p = 0. 022 and p<0. 0001 for comparison of time periods 2 and 3 to period 1; N gene p = 0. 025 and p<0. 001, respectively). However, population-level Ct distributions are sensitive to changes in the average time since infection when samples are taken, such that median Ct values can decrease during epidemic growth periods and increase during epidemic decline  (33).
In an attempt to account for this effect, we assessed the association between P. 1 infection and Ct levels whilst controlling for the delay between symptom onset and sample collection. Statistical significance was lost for both data sets (E gene p = 0. 15, n = 42, 22 of which were P. 1; N gene p = 0. 12, n = 42, 22 of which were P. 1). Due to this confounding factor we conclude that we cannot yet determine if P. 1 infection is associated with increased viral loads  (34)  or a longer duration of infection  (35).
Investigation: NRF, TAM, CW, IMC, DSC, SM, MAEC, FCSS, IH, MSR, JGJ, LAMF, PSA, TMC, CAMS, ERM, JTM, RHMP, PSP, MUK, RH, NG, WMS, LJTA, CCC, HH, GMF, ECR, LMS, MCP, FSVM, ABL, JPS, DAGZ, ACSF, RPS, DJL, PGTW, HS, ALPS, MSV, CCC, VSDC, RMFF, HMS, RSA, , BN, JH, MM, XM, HC, RS, AG, MS, TB, SP, CHW, OR, NMF, NJL, PL, AR, NAF, MPSSC, CD, OGP, SF, SB, ECS Visualization: NRF, TAM, CW, DSC, IMC, JT, AR, SP, TB, CW, SB Nanopore sequencing libraries were loaded onto an R9. 4. 1 flow-cell (Oxford Nanopore Technologies, UK) and sequenced using MinKNOW version 20. 10. 3 (Oxford Nanopore Technologies, UK). FAST5 files containing the raw signal data were basecalled, demultiplexed, and trimmed using Guppy v4. 4. 1 (Oxford Nanopore Technologies, UK). The process reads were aligned against the reference genome Wuhan-Hu-1 (GenBank: MN908947.
3) using minimap2 v2. 17. r941 and converted to a sorted BAM file using SAMtools  (52). Length filtering, quality test and primmer trimming was performed for each barcode using artic guppyplex and variant calling and consensus sequences using artic minion with Nanopolish and Medaka versions from ARTIC bioinformatics pipeline ( https://github. com/artic-network/fieldbioinformatics). Genome regions with a depth of <20fold were represented with N characters. The genome statistics were obtained from SAMtools and the Tablet viewer  (53). Any runs suspected to have any level of contamination were discarded. Individual nanopore sequencing statistics for each sequence can be found in Data S2.
Genome Datasets Genome coverage of 184 generated sequences obtained from clinical samples varied from 27 to 99% of the virus SARS-CoV-2 genome. Of these, 35 sequences (average Ct of 21. 78, range 14. 5-29. 2) had a virus genome coverage between 25-75%. However, even partial sequences can provide important information about changes in SARS-CoV-2 lineage structure  (53). We compiled three genome datasets from Manaus from data generated in this study: dataset A included 184 sequences with >25% virus genome coverage (37 from laboratory A and 147 from laboratory B) and was used to estimate virus lineage frequency in Manaus over time; dataset B included 143 near-complete genome sequences with >75% of the virus genome coverage (31 from laboratory A, 112 from laboratory B); and dataset C included 48 sequences with >95% of the virus genome complete (n=17 from laboratory A, n=31 from laboratory B).
For datasets A, B and C, a reference genome sequence Wuhan-Hu-1 (GenBank: MN908947. 3) was appended before multiple sequence alignment using MAFFTv. 7  (54). For dataset A, lineage classification was conducted using manual phylogenetic analysis. Sequences with genome coverage between 25% and 75% were appended to dataset C and assigned to B. 1. 1. 28, P. 2 and P. 1 lineages based on monophyletic clustering of each sequence within each of these lineages (fig. S2 ). Manual   We downloaded all sequences publicly available in GISAID (up to 14-01-2021) and selected for analysis those that were published in PubMed, MedRxiv, BioRxiv or Preprint repositories. Specifically, dataset B and dataset C from Manaus were appended to (i) B1. 1. 28 genome sequences with >95% virus genome coverage from the Brazilian Amazon (55), Minas Gerais  (56) , Pernambuco (57), Rio de Janeiro  (58, 59) , Rio Grande do Sul  (60)  and to data from an early country-wide study of SARS-CoV-2 diversity in Brazil  (17).
Datasets B and C were also appended to (ii) P. 2 genome sequences with >95% virus genome coverage from Rio de Janeiro (59), Rio Grande do Sul  (60, 61). Exceptionally, written permission was obtained from the reference laboratory in São Paulo, Institute Adolfo Lutz, to use P. 1 complete genome sequences shared in GISAID (up to 19-01-2021) as part of their surveillance activities. Duplicate sequences were removed from the alignments, 5' and 3' untranslated regions from each genome were discarded. A table describing GISAID IDs, authors, originating and submitting laboratory for all publicly available data used in dataset B' and dataset C' (with data from this study and publicly available data) can be found in Data S3.
Maximum Likelihood Tree Reconstruction Fast and efficient maximum likelihood (ML) phylogenetic trees were reconstructed using IQTREE 2 (62) for dataset A' (n=999) (used for lineage classification of >25 and <75% virus genome coverage samples from Manaus, Data S2), and for the B. 1. 28, P. 1 and P. 2-specific dataset B' (n=974) and dataset C' (n=883) (ML trees can be found in figs. S2-S4 ). Briefly, a Jukes Cantor (63) DNA substitution model assuming equal substitution rates and equal base frequencies. Near zero branches were collapsed so that the final tree could be multifurcating to account for the many polytomies observed in SARS-CoV-2 phylogenetic trees. To explore temporal structure of dataset B' and dataset C', root-to-tip genetic distances (d) were regressed against sampling dates (yyyy-mm-dd) using TempEst v.
1. 5. 3  (64). One sequence showed incongruent genetic diversity compared with its sampling date (SP-322 EPI-ISL-693197) and was discarded from subsequent analyses. Strong and identical correlations between d and sequence sampling dates were observed for dataset B' (r 2 =0. 80) (fig. S6 ) and dataset C' (r 2 =0. 80) (fig. S7 ). Therefore, we used dataset B' for subsequent phylogenetic analyses. Regressions between root-to-tip distances and sampling dates in dataset B' were also fit separately for B. 1. 1. 28, P. 2, and P. 1 lineages in R v. 3. 6. 2  (54). These showed no obvious difference in evolutionary rates, and suggested that P. 2 and P. 1 show a tendency to fall over the regression line of B. 1. 1. 28 (fig. S8 ). This supports an increased evolutionary rate on the ancestral branches leading to P.
Structural Analysis of P. 1 lineage Lineage defining mutations were mapped onto a previously reported cryoEM structure of the cleaved trimeric SARS-CoV-2 S ectodomain [PDB: 6ZGI, (  62 )] with PyMOL v 2. 4. 0 (63) (fig. S11 ). Substituted residues are indicated as spheres and coloured by type of selection according to MEME support (Table  S2 ). Similarly, SARS-CoV-2 S RBD-hACE2 contact residues were mapped as observed in the RBD-hACE2 complex crystal structure [PDB: 6MOJ, (  11 )] together with RBD-resident substitutions specific to the P. 1 lineage (fig. S11 ). N-linked glycans are omitted for clarity.
Air Travel and Mobile Geolocation Data To better contextualize the spread of P. 1 lineage within Brazil, we investigate two different mobility data sources. First, we analyse monthly air passenger travel data produced by Brazil's Civil Aviation Agency (ANAC) which is publicly available at https://www. anac. gov. br/assuntos/setorregulado/empresas/envio-de-informacoes/base-dedados-estatisticos-do-transporte-aereo. This includes the number of passengers and connections for international flights to and from Brazil, as well as domestic flights within the country. Using this data, we calculated the total number of passengers who travelled from Manaus between November and December 2020 disaggregated by state of origin and destination (Fig. 1D , fig. S12 ). State-level mobility where the origin of the trip was the municipality of Manaus were calculated from approximately 5 million trips aggregated from anonymized cell phone data users in the month of November 2020 (85) (fig.