dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparative Genomic Analysis of Association of Secreted and Effector Proteins with Repeat-Rich, Gene Sparse Genomic Regions and RIP-Affected Sequence To identify secreted and effector-like proteins, the same secreted protein and effector discovery pipelines were run on the genomes of the three fungi S. sclerotiorum, B. graminis and L. maculans, and the oomycete P. infestans. The three additional species were chosen as their genomes have been well characterized and exhibit typical features described in the "two-speed" genome hypothesis  (Rouxel et al. 2011; Haas et al. 2009; Amselem, Lebrun, et al. 2015). Genomes used in this analysis, apart from S. sclerotiorum, were downloaded from GenBank. Before predicting secreted proteins, all gene sequences were filtered based on homology to TEs through blasting against the RepBase database version 20.
05  (Bao et al. 2015). Those sequences with more than 30% amino acid identity and alignment coverage and an e-value of <1e À10 with subjects from RepBase were discarded. This was to ensure that TE genes were not included in downstream analysis, as this would affect CDS content and proportion of secreted proteins in repeat-rich regions. The secreted protein prediction pipeline followed the same procedure as detailed in the "Effector prediction in the version two annotations" above but used Pfam instead of GPI-som to predict GPI-anchoring domains. To identify a subset of the secreted proteins that were potential effectors, EffectorP was run. The whole pipeline for secreted protein discovery is available at: https://github. com/markcharder/GeneralBioinformaticsScripts/ blob/master/effector_pipeline.
sh. Repeats in all of these genomes were identified for the purpose of this analysis using RepeatMasker; simple sequence repeats were discarded. To identify potentially RIP-affected sequence, RipCal version 1. 0 was run on whole genomes with default settings to identify regions with a high RIP index. To determine whether there was an association between the presence of secreted and effector-like proteins and repeats, RIPaffected sequence and gene-sparse sequence in the four genomes, two approaches were taken. First, for each set of secreted proteins, each set of effector-like proteins and each total gene set, the "closest" module of Bedtools was used to determine the distance from nearest repeats and RIP-affected sequence. Then, from the distances generated for the total set of genes, random sets the same size as the secreted protein sets and the effector-like sets were generated.
The secreted protein sets and effector-like sets were compared against the random sets using Wilcoxon's test in R version 3. 3. 0 beta. The script used for this analysis is available at: https://github. com/markcharder/ GeneralBioinformaticsScripts/blob/master/testingAssociations. r. Second, percentage of bases covered by CDSs, GC content, and number of genes and number of secreted proteins were determined across a sliding window of 100 kb for each genome, incrementing by 1 kb. The R scripts used for this analysis are available at: Https://Github. Com/Markcharder/ GeneralBioinformaticsScripts/Blob/Master/Test_Two-Speed. r. Spearman's rank was used to assess correlation between proportion of secreted protein content (defined as number of secreted proteins/total number of genes) per sliding window with CDS and GC content.
1 ; supplementary fig. 1a  and table 2 , Supplementary Material online). In the version two assembly, a single gap of unknown size was present within the nucleolus organizing region (identified through homology of repeating sequence to ribosomal DNA of other fungi; data not shown). This gap was given an arbitrary size of 100 Ns; this is contrasted to the 328,761 N-bases of the version one assembly (supplementary table 3 and fig. 1a , Supplementary Material online; fig. 1 ). The new version of the S. sclerotiorum genome and its annotations are deposited in GenBank under bioproject number PRJNA348385. Mapping of Illumina reads to the version two assembly scaffolds revealed 653 insertions (In), 222 deletions (Del), and 8 substitutions (supplementary table 3, Supplementary Material online); all 875 InDels were subsequently corrected.
Gene Predictions in the Version Two Genome Assembly Are More Accurate than Version One To assess the accuracy of the new gene models, both the version one predictions and the version two predictions were 1) tested against the NCBI nr database, 2) scanned for protein domains using InterProScan, 3) assessed for congruence with RNASeq data by comparison to Cufflinks transcripts and determining RNASeq depth for all CDS regions, and 4) manually reinspected randomly to determine which version's models were best supported by accompanying evidence. Based on the BLAST analysis against the NCBI nr database, the version two predictions exhibited a higher mean percent identity (86. 7%) with their best BLAST hits than the version one predictions (81. 78%). Furthermore, the version two sequences exhibited a higher mean subject coverage per alignment with their best BLAST hits (90.
39%) than the version one sequences (84. 9%) (fig. 2a ). The InterProScan analysis showed that the version two sequences contained more predicted protein domains than the version one sequences (fig. 2b ; supplementary table 5, Supplementary Material online). Comparing CDS sequences from both sets of gene predictions with assembled Cufflinks transcripts demonstrated that 10,523 version two sequences had RNASeq-based transcript support. A total of 7,506 of these were fully supported, that is, gene predictions were fully covered by an assembled Cufflinks transcript which was 100% identical. Furthermore, the version two sequences were more concordant with RNASeq data than the version one sequences. Mean query coverage per high scoring segment pair (HSP) for best BLAST hits was 91. 88% for the version two sequences and 83.
3 ). Seventy Putative Effector Sequences, 61 of Which Have Not Been Reported Before, Were Predicted for S. sclerotiorum To determine whether the new genome contained previously undiscovered effector-like sequences, version two-predicted proteins were filtered based on various criteria, including identification using the EffectorP software  (Sperschneider et al. 2016). Based on these analyses, the version two S. sclerotiorum genome contains 900 predicted proteins with a predicted secretion signal; 695 of these lack a predicted transmembrane domain, of which 523 lack both a predicted transmembrane domain and a predicted GPI-anchoring domain. Of these 523 amino acid sequences, 70 were determined to be likely effectors based on EffectorP analysis; the mean amino acid length of these sequences was 176.
Additionally, similarly sized regions surrounding SsPE models Sscle09g074030 and Sscle16g107730 exhibited a pairwise identity of more than 90% but were divergent from the previously mentioned SsPE regions (fig. 5a ). To determine whether homologous SsPE-containing loci may have arisen through activity of TEs, genomic regions containing these seven SsPEs were inspected for the presence of TE sequences predicted by REPET and assessed for the presence of LTRs and transposition-associated Pfam domains. This showed that all SsPEs were embedded in either a complete or fragmented predicted chimeric LTR retroelement (fig. 5b ). Blasting of the consensus retroelement to the Dfam database showed that it belongs to the Gypsy family of TEs. The model Sscle09g074030 was embedded within the complete retroelement, which contained four upstream open reading frames (ORFs) (within the gene models Sscle09g073990, Sscle09g074000, Sscle09g07410 and Sscle09g07420) that harbored unknown function, zinc knuckle, reverse transcriptase, integrase core, and chromatin organization modifier domains.
Additionally, a 5 0 -LTR was identified 269 bp upstream of the first ORF (Sscle09g073990) and a 3 0 -LTR was identified 2,170 bp downstream of the SsPE. The SsPE model Sscle07g061960 was situated between 5 0 -and 3 0 -LTRs but the region lacked ORFs with predicted transposition-associated domains. The 5 0 -LTR was situated 5,376 bp from the 5 0 -end of the SsPE and the 3 0 -LTR was situated 238 bp from the 3 0 -end of the SsPE (fig. 5c ). The 5 0 -and 3 0 -LTRs surrounding these two SsPEs were adjacent to target site duplications. Other SsPE models were not situated between 5 0 -and 3 0 -LTRs but were flanked by predicted retroelements and DNA TEs containing LTRs and inverted terminal repeats, respectively (data not shown). To determine whether members of this SsPE family were active during infection, data from the previously mentioned RNASeq analysis were analyzed for evidence of expression.
sclerotiorum, the main improvements were in new gene annotations. In the previous genome assembly, 14,522 genes were predicted using ab initio gene finders trained on a manually curated gene set of 542 S. sclerotiorum genes. These 14,522 gene predictions were further evaluated based on predictions from additional softwares, EST and BLAST evidence, homology to TEs, and length. This resulted in a total of 11,860 nondubious gene calls  (Amselem et al. 2011). In the new gene annotation set of S. sclerotiorum, even when considering only the 11,860 "nondubious" genes of the previous genome version, there are still 730 fewer, as the new version contains 11,130 predictions. Furthermore, only 10,528 of these sequences had reciprocal best BLAST hits with version one sequences (supplementary table 4, Supplementary Material online), which suggests that a further 602 sequences were either not present in the previous genome or had changed substantially enough in the version two prediction set for them to be unable to retrieve their corresponding loci as hits through BLAST analysis.
Further inspection revealed that 1,301 version two loci resulted from fusion of previous loci. This is in contrast to the 279 version two gene predictions that resulted from splitting of version one predictions. Thus, it can be surmised that the decrease in filtered gene predictions from the version one assembly to the version two is largely a result of the joining of previous separate loci. Further analyses illustrated that the version two gene predictions were more accurate than the version one predictions. Blasting of amino acid sequences against the NCBI nr database indicated that version two sequences on average covered more of and had a higher identity to their best BLAST hits. This implies that the new gene predictions were able to obtain more BLAST hits from accurate protein predictions that represent truly conserved sequences (fig.
2a ). A similar analysis was performed to compare the recent Parastagonospora nodorum annotation set with a previous version  (Syme et al. 2016 ). Accuracy at the protein level can also be inferred by the increased number of predicted functional domains and GO terms (fig. 2b ; supplementary table 5, Supplementary Material online). Additionally, version two predictions were more similar to their corresponding Cufflinks transcripts based on BLAST analysis. Though median coverage was only improved from 99% to 100% in the version two predictions relative to version one, a high interquartile range of 32% for the version one sequences, as opposed to 1% for the version two sequences, would indicate a decreased degree of variability in correspondence with Cufflinks transcripts in the version two predictions (fig.
Acknowledgements MFS is supported by a Veni grant of the  Research Council for Earth and Life Sciences (ALW)  of the  Netherlands Organization for Scientific Research (NWO). KH-K is supported by the  UK Biotechnology and Biological Sciences Research Council (BBSRC)  through the  Institute Strategic Programme Grant   20 : 20 Wheat (BB/J/00426x/1 ). SH was funded by a  BBSRC Industrial Collaborative Award in Science and Engineering (CASE)  studentship supported by  Syngenta  entitled 'Early sensing of plant pathogenic fungi'. JR received support for this project from the  USDA National Institute of Food and Agriculture ,  Hatch  project  1005726. MM, ON and SR are funded by the  European Research Council  ( ERC-StG 336808  project  VariWhim ) and the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ;  New Frontiers  grant ' ScleRNAi ').
DH and SS are funded by  SaskCanola and the Government of Canada  through the  Developing Innovative Agri-Products programme  (project #  J-000269: P032 ). RPO conducted part of this research in  Wageningen Agricultural University Laboratory of Phytopathology  as a KNAW research fellow. MCD, MD-G and RPO are funded by the  Grains Research and Development Corporation (GRDC)  as part of a bilateral agreement between  Curtin University  under the grant  CUR00023 , within the  Centre for Crop and Disease Management (CCDM). This work was in part supported by resources provided by  The Pawsey Supercomputing Centre  with funding from the  Australian Government and the Government of Western Australia. MCD would like to personally thank  Robert King  and  Keywan Hassani-Pak  ( Rothamsted Research ) for advice and instruction regarding genome assembly during the initial stages of the project.
20. 120. 10jFungal hydrophobin;;j jjj Pfam;SUPERFAMILY;Gene3DjPF00190;SSF51182; G3DSA:2. 60. 120. 10jCupin;;j jjj jjj ProSiteProfiles;Gene3D;SUPERFAMILY; PfamjPS50059;G3DSA:3. 10. 50. 40;SSF54534; PF00254jFKBP-type peptidyl-prolyl cis-trans isomerase domain profile. ;;;FKBP-type peptidyl-prolyl cis-trans isomerasej jjj Gene3D;PfamjG3DSA:3. 20. 120. 10;PF06766j;Fungal hydrophobinj jjj jjj jjj Pfam;Gene3DjPF03966;G3DSA:2. 20. 25. 10j Trm112p-like protein;j Pfam;PIRSF;CoilsjPF05630;PIRSF029958;CoiljNecrosis-inducing protein (NPP1);;j PfamjPF11327jProtein of unknown function (DUF3129)j jjj ProSiteProfilesjPS51257jProkaryotic membrane lipoprotein lipid attachment site profile. j ProSiteProfilesjPS51257jProkaryotic membrane lipoprotein lipid attachment site profile. j jjj Version Two Length/Version One Length 1 1 1.
Acknowledgments We are very grateful to  Zach Harmany  who made available the SPIRALTAP software under an open source license and to  Moran Mordechay  for useful discussions on further optimization of the imaging scheme. We would like to thank the whole  IMOD lab and Darzacq labs  for insightful discussions and suggestions to this work. We thank  Astou Tangara  and the  Betzig Lab (HHMI Janelia Research Campus)  for their help in constructing the LLSM. This work used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris.
Background: Population genomic analysis of transposable elements has greatly benefited from recent advances of sequencing technologies. However, the short size of the reads and the propensity of transposable elements to nest in highly repeated regions of genomes limits the efficiency of bioinformatic tools when Illumina or 454 technologies are used. Fortunately, long read sequencing technologies generating read length that may span the entire length of full transposons are now available. However, existing TE population genomic softwares were not designed to handle long reads and the development of new dedicated tools is needed. Results: LoRTE is the first tool able to use PacBio long read sequences to identify transposon deletions and insertions between a reference genome and genomes of different strains or populations.
The global architecture of these softwares is similar: 1. New insertions are detected by retrieving the reads that do not map on the reference genomes but that align both on a TE consensus sequence and a unique region in the genome. 2. Deletions are detected by identifying reads that align on the two flanking sequences of a given TE present in the reference genome indicating that the locus not contains anymore the sequence of the TE copy. Programs like the Transposon Insertion and Depletion AnaLyzer (TIDAL) also take advantage of the presence of paired end sequences on Illumina reads to identify the deleted locus  [12]. This later approach has been extensively tested and benchmarked on diverse Drosophila datasets leading to mixed results. Indeed, comparison of respective performance of each program indicated that a very small fraction of the TE presence/absence was identified by all programs  [12, 13].
Implementation LoRTE is a Python 2. 7 program composed of two main modules (Fig. 1 ) that only required BLAST+ suite and BioPython as dependencies: 1) The first module is designed to verify the presence/ absence in the PacBio reads of a list of annotated TEs in the reference genome (Fig. 1a ). Briefly, the program acquires the flanking sequences of each TEs and align them on the reference genomes using MEGABLAST  [18]  (not shown in Fig. 1a ). The length of the flanking sequences is specified by the user (default = 200 bp). At this stage, a filter verifies if the TE is correctly annotated and if the flanking sequences map uniquely on the genome. TE wrongly annotated or located in region too much enriched in repeats are categorized as "irresolvable locus" in the final output file. The remaining 3′ and 5′ flanking sequences are aligned on the PacBio read using MEGABLAST (Fig.
This latter case may correspond to partially deleted TEs. 2) The second step aims to identify new TE insertions present in the reads but absent in the reference genome. The program removes from the PacBio reads the segments of sequences corresponding to the TEs identified by the first module. Then, the TE consensi are aligned using BLASTN on the reads to identify all the remaining TEs. The flanking 5′ and 3′ ends of these putative new TE insertions are extracted and aligned using MEGABLAST on the reference genome. All the sequences between a 5′ and 3′ ends, in the same orientation, and in a specified window size are extracted and the program verifies if they match with a TE consensus using BLASTN. If the extracted sequences are <50 nt and do not resemble to a given consensus the program considers these cases as new insertions in the reads.
"New polymorphic TE insertion" corresponds to a situation in which a new previously identified TE insertion in step 1 is "present" in some reads but "absent" in some others. Finally, all the reads testifying for a new insertion for the same locus are clustered together. To assess the performance and accuracy, we have tested LoRTE on two Drosophila melanogaster datasets: (i) Benchmark of the program is monitored by random insertion of 250 TEs and random deletion of 100 TEs in the reference genome (release 5) before its segmentation in pieces of 3 to 30 kb in length. More realistic, errorprone, PacBio reads have also been generated using the PBSIM software with default parameters except -lengthmin = 1000  [19]  (ii) genuine PacBio reads of pooled 1950 adult males of the ISO1 strains (same stock used in the official reference assembly)  [20]  with a sequencing depth of 90× (average read length: 10,040 bp).
In order to identify false positives, LoRTE predictions are then compared with the genome assembly of the PacBio reads. Reads and the Falcon assembly  [21]  are available at https://github. com/PacificBiosciences/ DevNet/wiki/Drosophila-sequence-and-assembly. To test the impact of the coverage on the performance of LoRTE we have sub-sampled the datasets to lower coverages (from 1× to 40×). For these experiments, we have used a list of 4239 annotated TEs  [22]  and corresponding TE consensi obtained from FlyBase FB2016_04 release (http://flybase. org/) and RepBase version 31/01/2014 (http://www. girinst. org/repbase/). Input and raw output files used in this study are available at http://www. egce. cnrs-gif. fr/?p=6422 LoRTE predictions on the ISO1 PacBio reads have been evaluated using the de novo 90× Falcon assembly.
Results As existing softwares designed to detect TE-induced genomic variations are not able to handle long read sequences, it is virtually impossible to compare the respective performances of LoRTE with these tools. However, LoRTE was carefully benchmarked on two different D. melanogaster PacBio datasets. The first is a synthetic dataset composed of 3 to 30 kb PacBio-like reads generated from the reference genome in which we inserted and deleted respectively 100 and 250 TEs. The second is a real biological dataset with D. melanogaster PacBio reads coming from pooled individuals of the same strain used in the reference genome. We first tested the ability of LoRTE to provide variant calls on a list of 4239 annotated TEs with respect to the read coverage (Fig. 2a ). For both datasets, LoRTE was able to provide a decision for >99% of the TE locus with a coverage of 9×.
Due to the relatively high error rate of the genuine PacBio raw read (around 10%, mainly short insertion/deletion events) leading to MEGABLAST misalignments, synthetic reads performed better at low coverage. Moreover, LoRTE achieved a complete analysis of the data with 10× coverage on a standard computer with 2 cores running at 2. 3 GHz in less than 48 h, using a maximum of 8 Gb of RAM. This result indicate that a low PacBio read coverage, corresponding to a single single-molecule real-time (SMRT) cell generating 500 to 1000 Mb of sequences, is sufficient to make a call on the vast majority of the TE identified in the D. melanogaster genome. We then tested the ability of LoRTE to detect the insertions/deletions made on the synthetic datasets. Figure  2b  displays the percentage of insertions/deletions detected by LoRTE with respect to the read coverage.
LoRTE detected 98% of the deletions and 100% of the insertion from coverage of 9× and did not generated false positive calls, whatever the coverage. We have also tested LoRTE with the synthetic datasets generated by the PBSIM software  [19]  that simulates the size distribution and the high error rate of genuine PacBio reads. With a coverage of 10×, we obtained very similar results using error-free and PBSIM error-prone PacBio reads. The detection of the deletion appears slightly less efficient with error-prone reads, mainly because the alignments of the flanking 5′ and 3′ sequences of each TE locus generate some misalignments. This phenomenon leads to the extraction of some sequences located between these 5′ 3′ that are longer than the threshold of 50 nt. Consequently, these loci appear as «ambiguous negative >50 nt» or «possible polymorphism» rather than «TE absent».
By relaxing the threshold at 100 nt, most of these loci now appear as «TE absent». However, on real PacBio reads, a relaxation of this threshold could generate false positives or an overestimation of the level of polymorphism. Taken together, these results strengthen the reliability of LoRTE, even in a context of low coverage PacBio datasets. We finally analyzed the results obtained by LoRTE on genuine D. melanogaster PacBio reads and compared the predictions with the Falcon 90× PacBio assembly. Figure  2c  shows the number of deletion/insertion found in these reads. The number of deletions was relatively constant whatever the read coverage considered. With a coverage of 40×, we identifed a maximum of seven deletions corresponding mainly to LTR retrotransposons (two roo, two 297, one 412), one LINE (I element) and one hAT DNA transposon (Fig.
Clarify the stakeholders PMRV depends on the collaboration of local communities, NGOs, local governments, the scientific and the international community. Meaningful involvement of local people. The role of local people is currently limited to trainees or resource persons for collecting data on land use changes. Their participation needs to be integrated at higher levels, including the design of PMRV activities, and they need feedback from the national level on how decisions about forest management will impact them. Risks for local livelihoods (e. g. important economic activities being forbidden because of their environmental impact, see Bong et al  [20]  Ã ) also need recognition. Information based on community perceptions of forest cover changes, and indicators of social and environmental safeguards can ensure that MRV systems monitor beyond emission reductions.