dataesr/scientific-paragraphs-categorization huggingface dataset:
Only the horse-parasite sharedsequence primers amplified in both organisms (Fig. 6 , Supplementary Note 6 and Supplementary Fig. 8 ). We sequenced ETSTY7 amplicons from multiple parasite samples and developmental stages and showed 90-95% similarity between horse and Parascaris. Multiple sequence alignment of horse ETSTY7 and ETSTY7-like sequences from donkey, Przewalski's horse and Parascaris were used to reconstruct a phylogenetic tree (Fig. 6 ). The most divergent ETSTY7-like sequences were from the horse X chromosome and donkey unplaced scaffolds. The ETSTY7 sequences of known equine Y origin formed a clade (97% bootstrap value), which also included 3 X copies, 7 Przewalski's horse sequences, and 6 Parascaris sequences. Of the latter, three were from GenBank and three were our sequences derived from whole worms, dissected gonads and body wall, and eggs and larvae (Supplementary Data 7).
Methods Ethics statement. Procurement of blood and tissue samples followed the United States Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research and Training. The protocols were approved by Institutional Animal Care and Use Committee as AUP 2012-076 and CRRC09-47. BAC tiling path map: (Supplementary Note 1, Supplementary Figs. 1 2 3 and Supplementary Data 4-5). We constructed a BAC contig map of eMSY by sequence tagged site (STS)-content analysis, chromosome walking, and fluorescence in situ hybridization (FISH) using methods described elsewhere  18, 51, 52. Briefly, we designed primers for known and new eMSY markers with Primer3 software  53  and screened the CHORI-241 male horse BAC library (Thoroughbred "Bravo") (http://bacpacresources. org/) by PCR.
We analyzed a minimum of 10 metaphases and 30 interphase or DNA fiber-FISH images per experiment using a Zeiss Axioplan2 fluorescent microscope and Isis v 5. 2 (MetaSystems GmbH) software. We arranged the BACs into a tiling path by STS content mapping (PCR with all STS primers on all BAC clones) and determined the order and orientation of contigs and the size of gaps by interphase and/or fiber-FISH. The BAC contig map formed the basis for eMSY sequencing. Horse MSY sequencing and assembly: (Fig. 1 , Table  1 ; Supplementary Note 2, Supplementary Fig. 1  and Supplementary Data 4-5). We sequenced 94 BACs from the BAC tiling path map using a multi-platform strategy which included 454 GS-FLX Titanium (Life Sciences) and MiSeq (Illumina) approaches. Single-end (SE) 454 GS-FLX was applied to 45 single-copy BACs from contigs Ia, Ic, II, III, IV with approximately 1.
Longread PacBio was conducted in 2 SMRT cells-one containing a pool of 18 single and multi-copy BACs from contigs Ia and Ib, another with 36 clones from contigs Ic, II, III, and IV. Sequencing depth per BAC was 300X and 150X, respectively. We used Newbler (454 Life Sciences) for de novo assembly of the 454 data based on respective BAC pools. Assembly parameters were optimized to yield the longest contigs and scaffolds. All scaffolds and contigs were aligned with eMSY STS markers (Supplementary Data 4) by BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi) to further orient and assemble scaffolds/contigs based on the physical map. We used Velvet 55 for de novo assembly of the Illumina data and selected parameters yielding the longest contigs and scaffolds. For BACs sequenced on 454 and Illumina platforms, we conducted both Newbler and Velvet assemblies for comparison.
We aligned and compared multi-copy BACs in Mauve  56. Due to the complexity of multi-copy sequences, it was difficult to create scaffolds with high confidence, and we concatenated all representative BACs into one large scaffold. Therefore, the multi-copy portion of the eMSY assembly remained tentative. Attempts to incorporate the PacBio data with Illumina data using Celera Assembler (discontinued and replaced by Canu: http://canu. readthedocs. io/en/stable/) did not significantly improve the assembly and we dropped this strategy. Finally, we experimentally validated by PCR the assembly of selected gene containing regions (Supplementary Data 11) and considered amplification of the expected size products as proof of assembly correctness. Testis RNAseq. We extracted high quality (RIN > 9. 6) RNA from the testis of two normal adult stallions, two normal adult donkeys and two normal adult mules using PureLink RNA Mini Kit (Ambion).
The RNA was converted into cDNA, prepared into 2 × 100 bp PE TruSeq libraries (Illumina), and sequenced on HiSeq (Illumina) platform. We obtained, on average, 80 million PE reads per sample and used Trinity 57 to assemble transcriptomes of the horse, donkey and mule testis by combining data from two individuals per species. Sequence annotation: (Supplementary Data 2 and Supplementary Note 3). The eMSY sequence was analyzed for GC content and the content of interspersed repeats in RepeatMasker (http://www. repeatmasker. org/) (Supplementary Table  2 ). Sequences with 100% intra-chromosomal identity were revealed with a custom Perl code (similar to that applied to human MSY  5  ) and BLAST (http://blast. wustl. edu/) (Fig. 1c ; Supplementary Note 3 and Supplementary Fig. 4 ). Annotation of eMSY genes and transcripts was a combination of rigorous bioinformatics analysis against: (i) known horse MSY genes  18  and STSs (Supplementary Data 4), (ii) available animal cDNA and EST databases, and horse, donkey and mule testis transcriptomes.
First, we downloaded and analyzed by BLAST against eMSY assembly all available sequences (genomic fragments, mRNAs, ESTs) of mammalian MSY genes from Ensembl (http://www. ensembl. org/) and NCBI (https://www. ncbi. nlm. nih. gov/). Annotation of genes followed rigorous criteria: there had to be at least 80% sequence similarity, presence of at least 75% of the exons, and conservation of exon order and exon-intron boundaries. We excluded highly repetitive and fragmentary alignments. Next, we combined the annotated eMSY to the horse reference genome EquCab2 (https://www. ncbi. nlm. nih. gov/ genome/?term=horse) and mapped horse testis transcriptome to it (Supplementary Data 8). This confirmed and refined the gene models obtained by BLAST, and identified novel genes and transcripts. Finally, we used in silico annotation with Maker 58 combining all horse EST, mammalian MSY cDNAs and horse testis  transcriptome (RNAseq assemblies) data.
ncbi. nlm. nih. gov/genome/?term=horse). We used STAR two-stage aligner  59  and non-stringent mapping parameters (SI) to allow divergent read mapping across species. Read counting was performed with the Python framework HTSeq (10. 1093/bioinformatics/btu638). Per-individual read coverage per gene is presented in Supplementary Data 8, inter-species comparison in Supplementary Data 12, Supplementary Fig. 6  and Supplementary Table  4 , and genome-wide comparison in Supplementary Data 1. Validation of horizontal transfer (HT). Analysis by BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi/) aligned ETSTY7 transcript sequence with Parascaris equorum (Parascaris spp. ) genome (GenBank LM462759). To validate putative horizontal transfer and check for possible contamination, we conducted a rigorous series of experiments.
Using established protocols 30 , Parascaris spp. eggs, larval stage 4 (L4) and adult specimens were collected from affected horses. We dissected adult gonads, body wall and intestine, and isolated genomic DNA from all developmental stages and organs using standard phenol-chloroform protocol. We designed primers from 3 types of sequences: (i) Parascaris-specific sequences; (ii) horse-specific MSY, autosomal, and mitochondrial DNA sequences, and (iii) horse-parasite shared sequences (see Fig. 6 , Supplementary Note 6, Supplementary Data 11 and Supplementary Fig. 8 ). We sequenced ETSTY7-3 ex 3 amplification products from adult worms, their gonads, body wall, eggs, and L4 larvae by BigDye chemistry, and estimated the rate of parasite and eMSY sequence homology by BLAST. Phylogenetic trees and estimation of gene acquisition.
Gene sequences for X and autosomal paralogs and Y homologs were obtained from the mRNA RefSeq database (https://www. ncbi. nlm. nih. gov/) for representative eutherian mammals, opossum and platypus. Outgroup sequences from the chicken were also obtained if a clear homolog was present. Przewalski's horse, donkey, and onager Y sequences were obtained by mapping whole genome shotgun sequences from males to the eMSYv3 and extracting the sequences based on our annotation. Sequences were aligned using MUSCLE  60  and phylogenies reconstructed in RAxML 61 with the Maximum Likelihood algorithm using the General Time Reversible + Gamma + Invariant sites and significance evaluated with 1000 bootstrap replicates. Nodes in the best scoring ML tree for each gene were constrained using the 95% CI interval estimates (Supplementary Data 13 and Supplementary Table  5 ) from (http://www.
timetree. org)  62 and the previous estimate of 4. 0-4. 5 for MRCA of Equus 22. The divergence times and 95% CIs of the horse Y gene from its nearest autosomal or X homolog were estimated using independent rates model and soft constraints in MCMCTREE. 63 (Supplementary Data 7 and Supplementary Figs. 9-10). For 5 genes the program did not produce 95% CI intervals and so they were approximated from the mean of 95% CI of other genes in their age range. The yn00 program in PAML  63  was used to estimate the K s between the horse Y and X gene-pair using the method by Li et al. 1993  64. Test for gene conversion was performed in the program GENCONV  65  (Supplementary Data 3). Horse MSY comparison in equids and ancient and modern horses. Published short-read (Illumina) sequence data for 18 equids  22, 46, 47, 50  (Supplementary Data 9) were mapped to the eMSY sequence using previously described procedures  46  for mapping reads, and for mapping to Y chromosome fragments, specifically.
We analyzed the data in the context of MSY sequence coverage, mismatch rate, haplotypes, and phylogeny. Note that the ancient horse CGG10023 had a low coverage (max 7. 4X; min < 4X) and poor quality sequence (average mismatch rate 3-fold higher than Batagai), and we excluded this sample from plots that are sensitive to the coverage, i. e. , everything but the average coverage plot; Fig. 7a. Average coverage for each sample was calculated in blocks of 50 kbp, considering only sites called in the eMSY reference sequence. Consequently, each physical block could cover more than 50 kbp of the eMSY reference sequence. The resulting averages were normalized using the mean coverage of high-mappability blocks. Mappability was calculated by running the GEM Mappability v1. 315 tool on the eMSY sequence using 100 bp k-mers (http://algorithms.
cnag. cat/wiki/ The_GEM_library). Scores were converted to WIG format, and the average mappability of each 50 kbp genomic block was calculated, considering only sites covered in the eMSY sequence (see Supplementary Data 14). The full eMSY sequence of each sample was genotyped as described in Schubert et al. 2014  47. Thereafter, mismatch rates were calculated for each sample as the fraction of called sites containing non-reference nucleotides, grouped into 50 kbp blocks (see Coverage), considering only high-mappability blocks. The results were averaged across groups, to produce the final mismatch rates. Genomic blocks were demarcated as described for Coverage statistics, and 1 kbp blocks with an average mappability greater than or equal to 0. 9 were selected. The resulting genomic blocks were genotyped and phylogenetic trees were calculated from a super-matrix of these sequences as described by Schubert et al.
Acknowledgements This work was supported by  USDA  (grant  2012-67015-19632 ,  BPC, TR, JEJ ),  LINK Endowment Equine Foundation (BPC, TR) ,  Danish National Research Foundation  (grant  DNRF94 , LO, MS),  Initiative d′Excellence Chaires d′attractivité, Université de Toulouse (OURASI, LO) , the  European Research Council  (grant agreement  ERC -CoG-2015-681605, LO, MS). We thank  Dr. Loren Skow  and  Dr. François Piumi  for providing BAC clones from  TAMU  and  INRA BAC libraries , respectively;  Dr. Doug Antzack  and  Dr. Donald Miller  for providing blood samples and fibroblast cell lines from the  Thoroughbred stallion Bravo ;  Elaine Owens  for assisting with cell cultures;  Dr. Vaishali Katju  for providing DNA from C. elegans;  Dr. Rytis Juras  for the photos of a Parascaris specimen.
Data availability. Sequences and metadata generated in this work are publicly available. BioProject Accession PRJNA420505 includes information about project, samples, and SRA accessions including SRR636826-SRR636831 for RNAseq reads of 6 samples from 2 horses, 2 donkeys, 2 mules) and SRR6361136-SRR6361138 (for 54 single-end reads and mate-pair reads for tiling path BACs, and Illumina paired-end reads of individually barcoded multi-copy BACs. The eMSYv3. 1 assembly has been deposited in GenBank under accession number MH341179. In the v3. 1 accession, two vector sequences (18,381-bp in position 2,505,950 and 1396-bp in position 8,066,811) have been trimmed from assembly with respect to v3. 0. The GTF file for v3. 0 available as Supplementary Data 2 with information on how to modify v3. 1 assembly to match the v3.
com-Intelince SL, Spain) fixed above the samples diagonally opposite. After each photograph session, pictures were cropped around the portion of the image showing the kernels. The cropping process reduced the number of pixels to be examined and decreased computational time. Pictures were analysed with an own script written in Python (Python Software Foundation, Inc. Python Language Reference, version 3. 4. 1. available at http://www. python. org). The function "getpixel" from the Pillow library (Python Imaging Library Fork, version 2. 5. 3. , available at https://pillow. readthedocs. io/en/latest/in dex. html) was used to acquire the red, green and blue-levels of each pixel (RGB levels) within a picture. These levels display values in the range of 0 to 255, which allow calculating the pixel hue (Table  1 ).
Comparisons between correlation coefficients were performed with Hotelling-Williams tests through the "multilevel" package  (Bliese, 2016). Additionally, six linear regression models were fitted for each kernel set in order to relate DON to either WKS or FDK. WKS repeatability was evaluated using the "lme4" package for mixed model analysis  (Bates, Maechler, Bolker, & Walker, 2015). The influence of kernels overlap and orientation on the image was examined with the 90 photographs of the first picture set (A) using the linear model: WKS140ij ¼ μ þ Si þ e ij ; where WKS140ij is the WKS value of a single image evaluated for a blue-level-limit of 140, µ the overall mean, Si the effect of the i th sample and e ij the residual variation. The same linear model was used for analysing the photograph session effect with the 90 photographs of the second picture set (B).
In both cases, WKS was calculated for a blue-level-limit of 140 resulted in correlations between WKS-FDK and WKS-DON acceptable in a relaxed sense in more than 95% of the cross-validation scenarios. With 44 FDK measured samples in the calibration set, the correlations between for WKS-FDK and WKS-DON were acceptable in a strict sense in more than 95% of the cases (Figure  8 ). When calibrations were based on DON values, a calibration set size of 24 samples yielded correlations of WKS-DON and WKS-FDK acceptable in a relaxed sense in more than 95% of the cases and 47 DON measured samples led to correlations acceptable in a strict sense in more than 95% of the cross-validation scenarios (Figure  9 ). Thus, calibrations for finding an optimal blue-level-limit for WKS estimation are achievable with a moderate number of around 20 samples for which FDK data have been determined and do not necessarily require DON data.
80 and 0. 72,  Maloney et al. , 2014). Maloney et al. (2014)  based the segregation between healthy and Fusarium-damaged pixels on a saturation-level-limit while in our study we applied a bluelevel-limit. Both segregation processes are closely related as in the hues associated with kernels, variations in saturation are mainly due to variations in the blue-level. WKS is a competitive approach for quantifying FHB severity on infected grain samples. Our method for its automated assessment through RGB analysis requires only basic equipment and the pixel sorting process, as described in this publication, can easily be implemented into a computer algorithm in Python or in other programming languages such as JAVA or C++. To perform visual FDK scorings, the assessor has to estimate the proportion of Fusarium-damaged kernels typically by comparing to exactly counted control samples.
We show the systematic superiority of mapping to shiver's constructed reference compared with mapping the same reads to the closest of 3,249 real references: median values of 13 bases called differently and more accurately, 0 bases called differently and less accurately, and 205 bases of missing sequence recovered. We also successfully applied shiver to whole-genome samples of Hepatitis C Virus and Respiratory Syncytial Virus. shiver is publicly available from https://github. com/ChrisHIV/shiver.
Introduction The genetic sequences of pathogens are a rich data source for studying their epidemiology and evolution, and provide information for vaccine and therapeutic design. In the past decade, next-generation sequencing (NGS) has transformed genomics, with decreasing costs and enormous increases in the amount of data available. Despite the success of NGS in other fields, sequencing of human immunodeficiency virus (HIV) is still largely based on the older method of Sanger sequencing. For example, on the comprehensive Los Alamos National Laboratory HIV database (http://www. hiv. lanl. gov/ accessed 11 October 2017), of the 147,751 samples with platform information, 90. 8% were generated by Sanger sequencing, 6. 9% with the Roche 454 platform, 2. 2% with Illumina platforms, and 0. 02% with the IonTorrent platform.
Breakdowns of these numbers by date and sequence length are in Supplementary Section S1. More broadly, NGS has been hugely successful both for sequencing samples with no within-sample diversity, and at the opposite end of the spectrum, for metagenomic studies. In the first case, any apparent within-sample diversity is attributable to sequencing error; in the latter case, there is no presumption that different fragments of sequence in the same sample have the same origin, and so each fragment is checked against large databases to catalogue these diverse origins  (Kunin et al. 2008; Thomas, Gilbert, and Meyer, 2012). HIV is an intermediate case: the long duration of chronic infection coupled with high rates of replication and mutation mean that a single infection, and hence a single sample, will contain a diverse collection of related viral particles, frequently called a quasispecies.
Indels are known to be very common in HIV  (Wood et al. 2009) , especially in the env gene  (Starcich et al. 1986 ). To quantify this further, we calculated indel size and position distributions in 3,249 whole genomes from the Los Alamos National Laboratory HIV database, shown in Fig. 3. The loss of reads during mapping has been shown to be roughly proportional to the divergence between the true consensus and the reference used  (Archer et al. 2010). The bias in the loss of reads (and the loss of accuracy in their alignment) occurs at different scales. Data are more likely to be lost in (1) those samples in a dataset that differ more greatly from the reference used for their mapping; (2) those parts of the genome, in a single sample, where the sample and reference are most different; and (3) a subset of genotypes, in a single diverse sample, that are more different from the reference than the other genotypes.
A B Figure  3. Quantifying indels in 3,249 whole genomes-those in the 2016 'all genome' group M alignment from the Los Alamos National Laboratory HIV database. We trimmed both 3' and 5' ends of the alignment where sequences align poorly, then considered each of the roughly 5. 3 million possible pairs of references therein. For each pair we calculated the size and position of their relative indels (i. e. taking their relative alignment from the overall alignment, ignoring positions at which both have a gap). We also considered just the subset of 1,019 subtype B sequences, which is less diverse than group M as a whole but shows similar indel patterns. Left panel: the distribution of indel sizes. The striking bias towards frame-preserving indels could be biological (frame-shifting indels will generally have a large fitness cost), artefactual (removal of frame-shifting indels from sequences during analysis before public release, on the assumption that this is sequencing or bioinformatic error), or a combination of both.
This is expected to be a more accurate identification of the closest existing reference than, for example, finding which existing reference most reads match most closely, which gives undue weighting to regions of the genome where more rounds of amplification resulted in an exponentially greater number of reads. shiver creates a reference for mapping by using contig sequence where available, and the closest existing reference to fill in any gaps between contigs (at parts of the genome where assembly failed). Before mapping, reads are trimmed for lowquality bases, adapter and primer sequences using Trimmomatic  (Bolger, Lohse, and Usadel, 2014)  and fastaq (https://github. com/sanger-pathogens/Fastaq); contaminant read pairs are diagnosed as those matching contaminant contigs more closely than the tailored reference, and are removed.
e. no insertion). By default the most common base is called to give the consensus; optionally ambiguity codes can be used more readily, when the frequency of the most common base(s) is below a threshold. A consensus base is only called if the coverage equals or exceeds a minimum threshold specified by the user, to protect against the effect of residual low-coverage contaminant reads in genomic regions lacking genuine HIV reads. By default this is 15, but this is likely to need adjusting for different datasets. A tool contained in shiver helps the user to explore appropriate values (see the discussion of LinkIdentityToCoverage. py in Supplementary Section S3). By default, once the consensus is called, the cleaned reads are re-mapped to it (with any missing coverage in the consensus filled in with the corresponding part of the original tailored reference) for a second iteration of calling the base frequencies and the consensus.
Using the shiver code shiver and its documentation are available at https://github. com/ ChrisHIV/shiver. It was designed to be run in Linux-like environments, including Mac OS. Once dependent packages are installed, shiver itself requires no installation: it is a set of executable scripts. The Genomic Virtual Laboratory  (Afgan et al. 2015) , provided for example on the UK Medical Research Centre's Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB)  (Connor et al. 2016) , contains all dependencies (except smalt, which is loaded on MRC CLIMB with the single command brew install smalt, and otherwise available at http://www. sanger. ac. uk/science/ tools/smalt-0), allowing shiver to be run immediately. The GitHub repository also contains a platform-independent virtual machine containing shiver with all of its dependencies pre-installed.