dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we can also predict the amino acid change for the positioned SNPs, and intersect these results with condition-specific SNPs using our package KISSPLICE2REFTRANSCRIPTOME (K2RT). SNPs but divergences. On this system also, we were able to validate experimentally that the loci we identify were truly divergent. We outline that, even though the case studies presented in this paper include two replicates, the method can be applied to any number of replicates. Larger cohorts can be helpful to narrow down the list of SNPs likely to be really causal for the phenotype. Our key contribution is that we are able to produce a list of SNPs stratified by their impact on the protein sequence, and ranked by difference of expressed allele frequency across conditions. This list can be further mined for candidates to follow up experimentally.
Overview We present here a collection of methods which can be used together to produce, from RNA-seq data alone, a list of condition-specific SNPs, stratified by their predicted impact on the protein. Figure  1  summarises the different steps. TRINITY, TRANSDECODER and BLAT are third-party software. KISSPLICE was published recently  (11) , KISSDE and KISSPLICE2REFTRANSCRIPTOME (K2RT) are methods we introduce in this paper. 11 ) is a software initially designed to find alternative splicing events (AS) from RNA-seq data, but which ) is that a SNP corresponds to a recognisable pattern, called a bubble, in a de Bruijn graph (DBG) built from the reads. De Bruijn graphs are widely used data structures in de novo assembly  (13) (14) (15) , as they are well tailored for large amounts of short reads.
Indeed, most repeats do not have expression levels that are conditionspecific. The ones that are not filtered out at this step correspond to paralogous genes, where one copy is more expressed in the first condition and the second copy is more expressed in the other condition. Although these are not SNPs, we can argue that they are still relevant candidates for an association study aiming at proposing causes for the difference of phenotype. Predicting the impact of SNPs on the protein sequence KISSPLICE predicts SNPs, but outputs only a very local context around the SNP. In order to predict the amino acid change it causes, if any, we need to place the SNP in a larger genomic context. For this, we relied on a widely used global transcriptome assembler: TRINITY  (15) , which takes as input RNA-seq reads and outputs contigs that correspond to either full-length transcripts (if the expression level of the transcript is sufficient) or to fragments of transcripts.
The results of KISSPLICE were aligned onto the transcripts predicted by TRINITY using BLAT  (20). Concurrently, we Fdsearched for coding potential in the transcripts using TRANSDECODER. Once we had the location of the SNP within the transcript and the location of the open reading frame (ORF), we could assess if the SNP was located within the CDS or not, and if so, if it was a synonymous or non synonymous SNP. In the case where no ORF was predicted for the transcript, we concluded that the SNP was within a non coding region. In practice, this can correspond to a non coding RNA, a UTR or an intron. Prediction of the amino acid change of a SNP was included in a Python package, called KISSPLICE2REFTRANSCRIPTOME (K2RT), which takes as input a set of predicted ORFs (bed format), the output of KISSPLICE (fasta format), and a mapping of the results of KISSPLICE to the transcripts (psl format).
Importantly, TRINITY, TRANSDECODER and BLAT are third party software which can be replaced by others, provided the exchange formats are respected (bed and psl). In the case where a SNP mapped to several TRINITY transcripts, we reported the amino acid change of the SNP in each transcript. This happened in particular when a SNP was located in a constitutive exon of a gene that gave rise to multiple alternative transcripts through alternative splicing. We further show in the Results Section that our ability to call SNPs both in constitutive exons and alternative exons is a strong advantage of our method against others that first map the reads to the assembled transcriptome and then call SNPs using a genotyper. In the case where a SNP mapped to no transcript, then it could not be treated by K2RT and it was filtered out.
The same SNP may therefore be present in multiple bubbles. When mapping the bubbles to a reference transcriptome, it is possible to remove this redundancy and count the true number of SNPs. Indeed, if two bubbles map to the same transcript at the same location, then it means that they refer to the same SNP, and we count it only once. The software versions that we used were: TRINITY r20140717, TRANSDECODER v2. 0. 1, BLATSUITE36, KISSPLICE v2. 4, KISSPLICE2REFTRANSCRIPTOME v1. 0. All were used with default parameters. We set the minimum query coverage to 90% in K2RT. Changing this from 70% to 90% only marginally affected our results. A critical parameter in de novo assembly is the k-mer size. In TRINITY, this value is set to 25 and cannot be modified. In KISSPLICE the default value is 41 as we found it is a good compromise between recall and precision.
Due to numerical instabilities associated with the estimation of Negative Binomial parameters, we adopted a model selection approach to determine which model was best suited to handle the over-dispersion parameter. Our strategy was first to estimate a model without over-dispersion using the GLMNET package (model M(φ = 0)). We then considered two different estimation methods for the parameter , namely a global estimation approach using the package AOD (model M(φ = φ global )), and a SNP-specific parameter using the DSS package (model M(φ = φ i DSS ). We used a BIC to choose the best model out of the three. Before comparing the allele read counts from different libraries, the count data were normalised by library sizes as proposed in the DESEQ package  (23). This software has been shown to be the most efficient according to a recent normalisation comparison study  (24).
Methodology for testing and validating our approach We first evaluated our method in human, because it is a species for which a reference genome is available and SNPs are well annotated. We then used our method on a nonmodel species: Asobara tabida, an hymenoptera that exhibits contrasted phenotypes and for which no reference genome is available. Finally, we applied our method on a different evolutionary timescale, working on two recently diverged Drosophila species, D. mojavensis and D. arizonae, for which a draft reference genome is available only for D. mojavensis. The Geuvadis dataset. Our method enables to find SNPs from RNA-seq data. In order to assess if the SNPs we find are correct, and if the list we output is exhaustive, we chose to test our method on RNA-seq data from the Geuvadis project.
Comparison of genome-based and transcriptome-based approaches. In order to compare the SNPs predicted by KISSPLICE with our set of true SNPs, we needed to obtain a genomic position for each of our predictions. To this purpose, we aligned each variant of each bubble to the reference genome using STAR (v2. 3. 0). In the case where a variant mapped to several locations, we used the default behaviour of STAR, which is to assign the variant to the location with the fewer number of mismatches. In case of ties, we kept all equally good locations, and if at least one of the possible locations corresponded to an annotated SNP, we considered that the prediction of KISSPLICE was correct. For MPILEUP, we aligned the transcripts assembled by TRINITY on the reference genome with BLAT. Asobara tabida lines, RNA sequencing and SNP verification.
The experimental design for RNA-seq sequencing aimed at describing the transcriptomic changes associated with the presence / absence of Wolbachia, and the variations observed in the two A. tabida lineages exhibiting an extreme phenotype. To this purpose, cDNA libraries were constructed from infected and non-infected ovaries in these two lineages. Because these RNA-seq data were issued from two distinct lineages from a non-model species, we exploited this dataset to validate the method developed here and to discover biologically relevant SNPs, using libraries obtained from infected ovaries. The samples used for RNA extraction were young female (0-1 day old) ovaries dissected in a drop of A-buffer (two replicates of 30 ovaries per lineage). RNA was extracted as described in Kremer et al. (35).
These RNA extracts were used to generate corresponding cDNA libraries, following the recommendations given by the manufacturer of the SMARTer PCR cDNA synthesis and BD Advantage two PCR kits (Clontech). These cDNA libraries were then purified with the Qiaquick kit (Qiagen) and their quality checked. Sequencing of cDNA was performed by the Genoscope (Evry), on an Illumina GA-IIx instrument, to obtain 1x75bp reads. These data were trimmed using the ShortRead package with default parameters and then used as input of the pipeline defined in Figure  1. Based on these results, 34 SNPS were chosen for verification. For each SNP, primers were designed on the corresponding transcript to amplify the surrounding genomic region. PCRs were performed from an aliquot of the purified cDNA libraries. The reaction was performed in a total volume of 25 l, and the mixture consisted in 2.
The RNA was extracted from a pool of 30 ovaries of 10-days-old flies for each line. The extractions were performed using the RNeasy kit (Qiagen) and samples were then treated with DNase (DNA-free Kit, Ambion) and stored at -80 • C. The samples were quantified by fluorescence in the Bioanalyser 2100 (Agilent), according to pre-established criteria by the sequencing platform. For each line, the extracted RNA was divided into two parts in order to generate two cDNA libraries (two replicates per condition). RNA was sequenced by Illumina Technology, in the IlluminaHiseq 2000. We sequenced 2 × 51 bp pairedend reads and the medium size of the inserts was 300 bp. We used URQT  (38)  with the default parameters to remove the low quality bases and the polyA tail from the dataset before running the pipeline described in Figure  1.
Data access The human data used in this study can be found through the ArrayExpress database (http://www. ebi. ac. uk/ arrayexpress/) under the accession number E-GEOD-29342 and we used the individuals named NA20808, NA20809, NA20810, NA20811, NA20812, NA20813, NA20814, NA20815, NA20819, NA20826, NA06984, NA11840, NA06986, NA06989, NA06994, NA07346, NA07357, NA10851, NA11829 and NA11832. The.
Identification of variants. In order to evaluate the performance of our method, we needed to test it in the case where we knew which SNPs should be found. We thus focused on a dataset from human in which SNPs were already annotated. We selected two populations (Toscans and Central Europeans) from the Geuvadis project  (39) , and downloaded the RNA-seq data of 10 individuals in each population. We sampled 10M reads from each individual and pooled individuals 5 × 5, to obtain two replicates of five pooled individuals per population. We ran KISSPLICE and TRINITY on these four read sets and we aligned the variants of KISSPLICE to the TRINITY transcripts using BLAT (with at least 90% query coverage and 90% identity). Out of the 64824 bubbles initially found by KISSPLICE, 53494 (82%) mapped to TRINITY-assembled transcripts, 8024 partially aligned, and 3306 did not align.
As explained in the Methods Section, SNPs located near other SNPs may be enumerated more than once, but with different contexts (see Supplementary Figure  S2 ). After removing this redundancy, we ended up with 51,235 bubbles. To assess whether these bubbles were true SNPs, we first aligned the sequences of the variants (i. e. each path of the bubble) to the human reference genome and compared their genomic positions to a set of SNPs downloaded from the 1000 genome project webpage. We also benchmarked our method against two software: GATK, a widely used method to call SNPs in the presence of a reference genome and MPILEUP, part of the SAMTOOLS/BCFTOOLS, used here to call SNPs on the transcriptome assembled by TRINITY using the same RNAseq data. GATK was run with parameters recommended from the GATK web page for RNA-seq data.
To obtain the true allelic frequency within a pool, we took advantage of the availability of the genotypes of each individual from the Geuvadis dataset, and we simply summed up the number of alternative alleles over the PAGE 9 OF 13 Nucleic Acids  Research, 2016, Vol. 44, No. 19 e148 • • • • • • • • • • • • • 0 Recall • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • GATK-genome MP-transcriptome MPlong-transcript. MPcdhit-transcript. Kp default Kp C=0. 02 • • • • • • • • • • • • • • 0 • • • • • • • • • • • • • • • • • • • • • • • • • • • • ••• • • • • • • • • • • • • • • • • • • • • • • • • • ••• • • • • • • • • • • • Figure 5. Precision and recall of KISSPLICE, GATK-GENOME, MP-transcriptome and MP-LONG-TRANS as a function of the expression level of the locus.
Prediction of the amino acid change. When no reference genome is available, it is not possible to obtain a genomic location for each SNP and therefore to apply SNPEFF  (41) , or POLYPHEN  (42) , which are widely used software for assessing the impact of a SNP on the protein sequence. In the absence of any reference genome, a reference transcriptome can nevertheless be obtained, using a full-length transcriptome assembler like TRINITY  (15). Based on this transcriptome, it is possible to assess the coding potential of each transcript using TRANSDECODER, to position the predicted SNPs onto the assembled transcripts using BLAT  (20) , and finally to assess the impact of each SNP on its transcript(s). In the end, each positioned SNP is classified as coding or non coding. In the case where the SNP is located in the coding region, it is then classified as synonymous or nonsynonymous (See Methods).
Out of 47,243 positioned SNPs (those which aligned to TRINITY transcripts), 14,804 cases (31%) fell in CDSs and the other 32,439% fell in non-coding regions (including UTRs). Among the ones falling in CDSs, we found that 53% (7788) were synonymous, while the other half (7016) were non synonymous. To validate our predictions, we then intersected the genomic positions of our predicted SNPs with the genomic positions of SNPs in dbSNP, for which the impact on the protein sequence is known. Out of the 47,243 SNPs we predict, 39313 could be assigned a genomic position which matched a SNP annotated in dbSNP. Out of those 39313 cases, 2725 have no functional annotation in dbSNP, 35,141 had a correct prediction and 1447 cases wrongly predicted. A thorough examination of the 1447 cases wrongly predicted reveals that in most cases, the transcript predicted by TRINITY was very partial and was overlapping an intron (this happens when pre-mRNA is sampled together with mRNA at the RNA extraction step, despite selection  of polyA+RNAs).
Application of the method using biological data from species without any reference genome From our study on the human dataset, we conclude that our method has a precision and recall similar to methods which require a reference genome. We now turn to the application of our method to non-model species. Application to intraspecific polymorphism: the case of Asobara tabida. We first applied our method to Asobara tabida, for which RNA-seq data from two lineages (SFR2 and SFR3) were available. These lineages come from the same population, but they differ by their phenotype of dependence to their symbiont Wolbachia. In the absence of Wolbachia, SFR2 individuals produce no eggs, while SFR3 produce some. Consequently, we suspect a low but significant genetic differentiation between lineages that could be associated with the phenotypes, or to genetic drift associated with maintenance in the laboratory.
While the experimental design, with a single lineage for each phenotype, does not enable us to separate between these two effects, we think that this dataset is still well tailored for a validation of our method because: (a) no reference genome is available for this species; (b) individuals were pooled for RNA extraction and (c) replicates are available for each lineage. The transcriptomes of two replicates of pools of 30 individuals were sequenced through RNA-seq for each lineage, leading to 15M reads for each replicate. We ran our pipeline and found a total of 18609 positioned SNPs out of which 17,031 are condition-specific. The large proportion of condition-specific SNPs is largely due to the fact that most of them are fixed in at least one lineage. Indeed, 21% of them are fixed in both lineages, 63% are fixed in one lineage and polymorphic in the other, and 7% are polymorphic in both lineages (Supplementary Figure  S6B ).
Another example concerns genes involved in oogenesis, like OTU-domain containing protein or Female sterile, that exhibit non-synonymous SNPs in their CDS regions. These few examples show how the suite we propose in this paper rapidly allows to link the SNPs detected to their impact on the protein sequence, thus permitting to pinpoint candidate genes involved in phenotypic variation. Validation of these genes could involve either genetic studies (e. g. , knockdown experiments) and/or other linkage analyses targeted to these candidates. Application to Interspecific Divergence: the case of Drosophila mojavensis and Drosophila arizonae. Similarly to the Asobara dataset, the drosophila dataset corresponds to non-model species, where individuals had to be pooled prior to RNA sequencing. In this case however, the two modalities of the phenotypes are not two populations of the same species, but two recently diverged species.
ACKNOWLEDGEMENTS This work was performed using the computing facilities of the  CC LBBE/PRABI. The authors would like to thank  Marie-Christine Carpentier  and  Delphine Charif  for help on the analysis of the Asobara dataset;  Gustavo Sacomoto  for help on the analysis of the Geuvadis dataset;  Sebastien Deraison  for help on experimental validation of the candidates for the Drosophila dataset, and  Vincent Miele  and  Alice Julien-Laferrière  for help on developing KISSPLICE and KISSDE.
FUNDING Agence Nationale de la Recherche  [ ANR-12-BS02-0008 ,  ANR-11-BINF-0001-06 ,  ANR-2010-BLAN-170101 ];  São Paulo Research Foundation -FAPESP/Brazil  [ 2010/10731-4  to C. M. C. ]; European Research Council  under the European Community's  Seventh Framework Programme  [ FP7 /2007-2013)/ERC Grant Agreement No. [247073]10 ]. Funding for open access charge: INRIA.
RNAseq libraries from D. mojavensis and D. arizonae are available through the NCBI Sequence Read Archive (SRA : http://www. ncbi. nlm. nih. gov/sra) under the accession no. SRX1272419 and SRX1277353. e148 Nucleic Acids Research, 2016, Vol. 44, 19 PAGE 8 OF 13 gov/ sra) under the accession no. SRX1701817, SRX1701824, SRX1701826 and SRX1701855. The A. tabida dataset is available through the NCBI Sequence Read Archive (SRA : http://www. ncbi. nlm. nih.
8 to assign individuals to clusters. This value provides a statistical cut-off within the range of suggested values in the literature  (Manel et al. 2002)  and indicates that ≥ 80% of ancestry can be attributed to the respective subpopulation. Finally, using the Alleles in Space (AIS) software  (Miller 2005) , we performed a Genetic Landscape Shape interpolation analysis in order to relate genetic data with the geographic coordinates of individuals.
Five of them showed positive FIS values, but only the value for loci 300_304_832 was significant (Table  1 ). Waples (2015)  conducted an exhaustive study analyzing the possible causes of departures from HWE in natural populations. The possible causes include: overlapping generations, population structure, endogamy, small effective population size, and genotyping errors (i. e. null alleles), among others  (Waples 2015). Departure from HWE in our data set could be due to an overlapping generations effect, taking into account that samples used in our study were taken from 2006 to 2013, and that offspring, juveniles and adults were captured. Another possibility is the presence of null alleles in the data set, which frequencies ranged from 0. 029 to 0. 261 (Table  1 ). However, these values should be taken with caution since null alleles frequencies calculated in FreeNA and related software are obtained assuming panmixia and ascribing heterozygote deficiencies to the presence of null alleles.
Funding This work has been financially supported by the  Centre National de la Recherche Scientifique ,  Consejo Nacional de Investigaciones Científicas y Técnicas of Argentina  (PIP No. 11420100100189 ),  Agencia Nacional de Promoción Científica y Tecnológica of Argentina  ( PICT-2010-1978 ),  Universidad Nacional de Luján, Argentina  (Fondos Finalidad 3. 5), and  Universidad Nacional de La Plata. This study has benefited from an " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). This is contribution ISEM 2016-XXX-SUD of the  Institut des Sciences de l'Evolution de Montpellier.
Since 2009, the social media Twitter provides free access to localization data consisting of geotagged messages sent on the platform. We used this data to highlight their potential contributions to the study of social groups' activity space. Using a sample of luxury shoppers, we analyzed the daily activity and mobility patterns of the upper middle class in Delhi, India. We intersected these data with the results of an ethnographic study carried out in two luxury shopping places in Delhi and found that the lower middle class was overrepresented in the Twitter sample. We argue that the varying motivation to post on social media tend to skew the representativeness of social groups sampled on a spatial criterion. We eventually advocate the complementary use of geotagged social media data and qualitative data based on ethnographic research.
INTRODUCTION The number of single smartphone users in India has increased considerably in the past decade, particularly in urban areas. These devices facilitate the obtaining of data the geographic position of users, which several applications use to provide personalized services. In India, where data on mobility or activity spaces is often difficult to find, the increasing use of smartphones offer new possibilities for research in geography and social sciences; however, this data is often the property of private service providers, and public access to the data is limited. Twitter (since 2009) has been a significant exception, and provides the most comprehensive database of user locations. It thus allows new possibilities for research on activity space, defined in 1971 by Horton and Reynolds as the "the subset of all urban locations with which the individual has direct contact as the result of dayto-day activities"  (Perkins et al.
, 2014). While several studies have already used Twitter datasets, very few of them have dealt with spatiotemporal and human daily mobility analysis  (Steiger et al. , 2015). Nevertheless, some studies have highlighted the feasibility of using geotagged tweets to observe human mobility patterns at a global scale  (Hawelka et al. , 2014)  at a country scale  (Jurdak et al. , 2015; Liu et al. , 2015) , and at a city scale  (Cebeillac et al. , 2017). However, the socio-spatial picture depicted by these studies does not provide an accurate picture of human mobility. Twitter users are never, at the global scale, or at the country scale, representative of socially diverse populations due to several reasons. First of all, Twitter users are unevenly distributed over space. In 2016, Twitter claimed 316 million active users worldwide, with a penetration rate more than ten time higher in South America than in Africa and in India  (Hawelka et al.