dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, in contrast to current practice, the sequencing depth applied for generating NGS profiles is a tunable parameter to generate profiles of similar quality. As illustrated in Figures  3  and 5  for similar TMR levels, H4K20me1 or H3K36me3 profiles display in general poorer quality than those of H3K27ac or H3K4me3. However, increasing the sequencing depth will improve their quality descriptors to attain comparable levels, such that, for example, only 'triple A' data sets can be compared (Figure  5 ). In this respect, we believe that the QCi database will become an important reference to perform a priori predictions of the minimal sequencing depth required for a given target to reach a predefined quality. The NGS-QCis in the context of previously described working standards and guidelines for ChIP-seq assays Multidimensional comparative analyses of ChIP-seq profiles require prior quality assessment.
SCC is thus applicable mainly, if not exclusively,    4 ) present the worst IDR, while data sets (  5 ) and (  6 ) present the best IDR pattern. to 'sharp' patterns like those observed for transcription factor ChIP-seq data sets. It is rather evident that SCC cannot be used for quality assessment of broad patterns, as significantly enriched reads of such profiles cover large areas. Thus, from the conceptual point of view in addition to the present QCi system, signal distribution skewness appears to constitute the only other universal quality measurement method. To compare signal distribution skewness and our NGS-QC we have evaluated the degree of skewness in four publicly available CTCF ChIP-seq data sets (each of them represented by two biological replicates) and compared it with QCi-STAMP (Figure  6A  and B ).
Both methods provide similar quality predictions, with the important exception that the difference in quality of one pair of the evaluated replicates (GSM646372 and GSM646373 data sets) was predicted by the QCi-STAMP but not by the skewness analysis (Figure  6B ). To understand the origin of this discrepancy, we assessed the number of common and unique sites for each pair of replicate data sets [peak caller MACS  (27) ; default P-value threshold conditions: 1 Â 10 À5 ), followed by IDR analysis for the predicted binding sites (Figure  6C  and D, respectively ). Interestingly, this complementary analysis revealed a lower number of significant common sites for replicate GSM646372 ('triple C') and GSM646373 ('triple B') than for the other replicate data sets. This IDR-defined differential quality of the two pairs of replicates was equally well detected by the QCi-STAMP (but not the skewness) approach.
Overall, these comparisons show that QCi-STAMP provides a more versatile and reliable quality discrimination of NGS-generated profile than the skewness approach. Moreover, in contrast to IDR, QCi-STAMP reveals which of the replicates should be repeated to increase the overall quality without the necessity of using peak caller approaches. An additional limitation of the IDR analysis, namely the dependence on peak caller performance, becomes apparent from analysing CTCF (Figure  6 ; sharp peaks) and H3K4me3 data sets (Supplementary Figure  S6 ; broad peaks). While IDR analysis of CTCF can be done with 40 nt summit distance overlaps (i. e. the maximal distance between predicted summits to consider two binding events as reproduced), such conditions are noninformative for the H3K4me3 data set. To overcome this limitation, larger summit distance thresholds (e.
DISCUSSION The assessment of the quality of ChIP-seq data sets has been mostly performed by visual inspection in a genome browser and/or by the capacity of peak/island/pattern caller algorithms to predict locally enriched sequence counts. In both cases, it is a rather subjective analysis relying on user-defined criteria, such as the choice of 'representative' regions or thresholds for peak detection, and the statistical models and/or parameters used for assessment of enriched patterns. Only recently, methods are being developed that aim at providing a quantitative measure for the quality of ChIP-seq assays but so far there is no tool that provides a universal quality assessment for past and present NGS-generated profiles. The present NGS-QC approach provides quantitative QCis generated from the evaluation of a feature common to all NGS-generated profiles, namely the profile construction from sequenced read overlaps.
Conceptually, the QC Generator interrogates the robustness of such a profile when fewer sequenced reads are available, irrespective of the underlying experimental approach; simplistically this can be described as a numerical analysis similar to the visual inspection of Figure  2A , which displays RCIs at different TMRs but for the entire genome-aligned profile and not only for a selected region. This concept has an inherent universal dimension, which is essential for comparative purposes and considering that the public GEO repository represents a powerful source for performing in silico data set comparisons, we have established a database of QCis for >5600 profiles. Our ultimate goal is to cover all publicly available ChIPseq and enrichment-related NGS data sets to provide a comprehensive QCi library to the scientific community.
Moreover, we invite all our colleagues to use the QC Generator for evaluation of their own profiles and suggest that all newly reported IP-based NGS profiles (which show the largest variability) are provided with the corresponding global QCis. We also invite the community to import all newly defined QCis into the global QCi database. Collectively, this database will be a highly valuable source of information about the quality that can be achieved, for example, for ChIP-seq of a certain target with a given (batch of) antibodies. We believe that the universality, together with its simplicity and broad accessibility, makes the present system an attractive tool for QC analysis of profiles before engaging peak detection algorithms. Once a profile has been QCed, the QC descriptors provide objective numerical criteria to any NGS-generated profile that is provided to the community.
ACKNOWLEDGEMENTS We would like to thank the members of our laboratory and the computational IT support services at the IGBMC, especially  Jean-Luc Toussaint , for support in generating the Web site interface. Furthermore, we would like to PAGE 11 OF 13 Nucleic Acids Research, 2013, Vol. 41, No. 21 e196 thank  Malgorzata Nowicka  for her support on statistical issues related to this study and  Pierre-Etienne Cholley  for the current maintenance and content curation of the NGS-QC database, and for the implementation of an executable version of the NGS-QC Generator. M. A. M. P developed the concept of Quality Control indicators and generated all data set comparisons; W. v. G implemented the NGS-QC-Generator, the  NGS-QC database  and its web interface together with M. A. M. P; D. C provided statistical support during data sets processing.
Single nucleotide polymorphisms (SNP) and short 'indels' (insertions and/or deletions) were called using the default parameters for SAMtools mpileup utility, followed by bcftools and the vcfutils. pl varFilter script (samtools. sourceforge. net/mpileup. shtml) [31, 32]. SNPs and InDels were annotated by an inhouse Perlscript using USA300-FPR3757 genome as the reference  [29]. The SNPs and indels were classified as coding region, intergenic region and RNA sequences according to the positions. SNPs in the coding sequences were annotated as synonymous or non-synonymous amino acid substitutions. The sequences reported in this study have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read and Archive (SRA) database (accession no. SRP025984).
Acknowledgments We thank  Drs. Wenqi Yu  and  Friedrich Go  ¨tz for the sfGFP-containing plasmid vector. We also thank  Kuan-Tsen Chen ,  Steven N. Ellison , and  Danya Alvarez  for excellent technical assistance and  Apurva Narechania  ( Sackler Institute for Comparative Genetics, American Museum of Natural History, New York ) for sequencing analyses.
PCR conditions were: 95°C for 10 minutes, followed by 45 cycles of pre-incubation at 95°C for 20 seconds, annealing for 20 seconds at the indicated temperature in Additional file 1: Table  S1  and extension at 72°C for 20 seconds. After amplification, PCR products were denatured at 95°C for 1 minute and cooled down to 40°C to allow hetero-duplex formation. The final HRM step was performed from 40°C to 95°C with an increase of 1°C/s with 25 acquisitions/°C. The HRM curve analysis was performed using the LightCycler 480 Gene Scanning Software (Roche). Melting curves were normalized, temperature-adjusted and finally, a difference plot was generated. For methodological optimization, HRM analysis was further applied on both control samples and mutated patients carrying previously identified CRB1 genetic variants by Sanger sequencing.
ncbi. nlm. nih. gov/ sutils/blink. cgi?pid=6014482) and Jalview Alignment Editor program (http://www. jalview. org/) were used to analyse the multiple sequence alignments. If a residue did not change throughout the species it was considered "highly conserved", if a residue was present in less than 3 species it was considered "moderately conserved", if it was present in 3-5 ortholog proteins, it was considered "weakly conserved", otherwise it was classified as "non conserved". Deleterious effects of amino acid substitutions on protein function were assessed using different bioinformatic programs, including Align-GVGD (http://agvgd. iarc. fr/agvg-d_input. php), PolyPhen-2 (Polymorphism Phenotyping v2) (http://genetics. bwh. harvard. edu/pph2/), SIFT (http:// sift. jcvi. org/) and Protein Variation Effect Analyzer (PROVEAN) (http://provean.
Similarly, other mutations such as p. Asp564Tyr, p. Ile1001Asn and p. Glu1330* were present in more than one family (Additional file 1: Table  S3 ). A total of 15 novel variants were identified during the current screening of CRB1: 4 missense variants, 4 nonsense mutations, 3 frameshift indels and 2 splicing-site mutations (Table  3 , Additional file 1: Table  S2 ). Pathogenicity of the novel variants was assessed by co-segregation with a second disease allele on the CRB1 gene whenever family members were available (Additional file 2: Figures  S2, S3  and S4 ) and by absence in 330 healthy Spanish control chromosomes. None of the novel variants were described as polymorphic changes in the dbSNP database or the literature. All mutations described herein were submitted to the existing Locus Specific Databases (LSDB) on CRB1.
List of novel CRB1 likeky pathogenic variants identified in the studied cohort (Continued)Nucleotide numbering is based on RefSeq DNA accession number NM_201253. 1. Lam AG: Laminin AG-like domain, EGF: EGF-like domain, C: cytoplasmatic domain. Conservation of the amino acid substituted or deleted in 17 species was detailed. HC: Highly conserved, MC: Moderately conserved, WC: Weakly conserved, and NC: non-conserved residue. The amino acid substitution is predicted damaging by if the SIFT score is < = 0. 05 and PROVEAN scores is < -2. 5. Polyphen predict a non-synonymous variant as benign, possibly damaging, or probably damaging, if score is < 0. 2, between 0. 2 and 0. 85 or > 0. 85. GV: Grantham Variation; GD: Grantham distribution. Class C65: most likely pathogenic, Class C0: less likely pathogenic.
Acknowledgments The authors thank everyone at the  Genetics and Ophthalmology Services of Fundación Jiménez Díaz , especially all patients who participated in the study. This work was supported by  Centre for Biomedical Network Research on Rare Diseases (CIBERER , grant  INTRA/10/704. 1 ),FIS (grants  PI09/90047  and  PS09/00459 ) and  Retics Bionbank  ( RD09-0076-00101 ) from the  Spanish Ministry of Health, Merck-Serono  and  FUNDALUCE. MC is supported by  CIBERER , SDT by  Fundación Conchita Rábago de   Jiménez Díaz  and AAV by  CIBERER and FIS. ( PI09/90047  grant).
Results and discussion According to Ensembl (www. ensembl. org; ENSOCUG000 00017763), TLR3 is assigned to chromosome 2 of the European rabbit genome, with a total length of 11762 bp. The gene is organised into 4 or 5 exons with the coding sequence (CDS) comprising 2718 bp and 2655 bp, respectively. These transcripts translate into 905 and 884 amino acids, respectively, with the shorter transcript having a 21 amino acid deletion at exon 3 corresponding to amino acids 783-803. The analysis of the TLR3 structure revealed the presence of an ectodomain with a LRR-NT, twenty four LRRs and a LRR-CT, a transmembrane and TIR domains (Figure  2 ). This structure is in agreement to that of other TLRs. Several studies addressing the TLR3 genetic variation showed low levels of diversity in many mammals.
Methods The program LRRfinder was used to determine the European rabbit TLR3 domain structure  [33]  (http:// www. lrrfinder. com/). In this study we analysed 20 individuals of the subspecies Oryctolagus cuniculus cuniculus from Spain (Tarragona (OCCIP_T) and Zaragoza (OCCIP_Z)) and 20 of the subspecies Oryctolagus cuniculus algirus from Portugal (Pancas (OCA_P) and Mertola (OCA_M)). These natural ENSOCUT00000017763 I L M E K V S Q S K L M L S O. c. algirus Pancas I L/S M E K/R V S Q V/A K F M L/F A/S Mertola I L/S M E/D K/R V S Q/H V K F M/I L/F A/S O. c. cuniculus Tarragona I/V L/S M/T E K V S/P Q V K F/L M L/F A/S Zaragoza I L/S M/T E/D K V S Q V K/E F/L M L/F A Cerisay I L/S M E K V/G S Q V K F/L M L/F A Domestic I L M E K V S Q V K F* M L A *only in the English Spot breed: F/L. populations are located within the species' original range.
The electropherogram analysis was performed in 3130xl Genetic Analyzer (Applied Biosystems, Foster City). Sequences were edited, aligned and analysed with CLUSTAL W  [35]  as implemented in the BioEdit software  [36]. Haplotype reconstruction was obtained for each individual using PHASE (Version 2. 1)  [37]  as implemented in DnaSP (Version 5. 10. 01) [38]. Only haplotypes with P > 0. 90 were considered as inferred reliably. Population genetic parameters (S, π), Tajima's D, number of haplotypes, haplotype diversity, variance of haplotype diversity and standard deviation of haplotype diversity were estimated with DnaSP. The sequences obtained were also aligned and compared to the reference sequence available in Ensembl (ENSOCUT00000017763).
Acknowledgements The  Portuguese Foundation for Science and Technology supported the doctoral fellowship of Helena Areal  ( SFRH/BD/74948/2010 ) and the postdoctoral fellowship of  Joana Abrantes  ( SFRH/BPD/73512/2010 ) as well as the project  PTDC/CVT/108490/2008. We are grateful to the  Fédération Française de Cuniculture, Union des Sélectionneurs Avicoles du Sud Ouest, Société central d'Aviculture de France, Union des Aviculteurs du Haut-Rhin, Groupement Avicole Pyrénées Atlantiques et Landes, Lapin Club du Sud-Ouest ,  J. Ruesche ,  D. Allain , and  G. Saleil  for providing the domestic rabbit samples.
Results: Based on 322,504 and 307,622 reads of 454 sequencing for O. scapulalis and O. nubilalis respectively, we reconstructed 11,231 and 10,773 transcripts, of which 40% were functionally annotated by BLAST analyzes. We determined the level of completeness of both assemblies as well as the recovery level of published Ostrinia genomic resources. Gene ontology (GO) of common and species-specific de novo transcripts did not reveal GO terms significantly enriched in one or the other species. By applying stringent homology searches on transcripts common to O. scapulalis and O. nubilalis, we identified a set of homologous transcripts, with a mean nucleotide identity value of 98. 1%. In this set, the most divergent transcripts revealed candidate genes involved in developmental, sensorial and pathogen defense processes.
ncbi. nlm. nih. gov/sra), dedicated to raw sequencing data from the NGS platforms, 482 NGS data sets for 59 Lepidoptera species were available in October 2012, among which 49 correspond to cDNA sequencing from Bombyx spp. , Chilo suppressalis, Erynnis propertius, Euphydryas aurinia, Galleria mellonella, Heliconius spp. , Lymantria dispar, Manduca sexta, Maruca vitrata, Melitaea cinxia, Papilio spp. , Plutella xylostella, Spodoptera litura, Striacosta albicosta, Zygaena filipendulae, illustrating the great variety of species and families under current screening. In the present study, our main challenge was to enhance the genomic resources of O. scapulalis and O. nubilalis for future comparative researches. We then built a first skeleton of both species transcriptomes from RNA extracted from adult head and thorax tissues sequenced by the 454 technology.
We developed an ad hoc assembly procedure on more than 300,000 reads for each species and obtained 11,231 and 10,773 transcripts for O. scapulalis and O. nubilalis, respectively, 40% of which were annotated based on similarities detected by BLAST  [22]  alignment against the Non-Redundant (NR) NCBI protein database. We also examined the completeness level of both de novo transcriptome assemblies based on various statistics of the transcripts, and on recovery estimations of published Ostrinia genomic resources. Additionally, we present a first comparative analysis between both transcripts sets based on gene ontology categories and on the sequence identity level of transcripts common to O. scapulalis and O. nubilalis. These resources, developed in parallel in both Ostrinia species, pave the way to researches on their divergent genomic history.
Assembly and quality assessment The original data set from the sequencing center contained 322,504 and 307,622 expressed short sequence reads for O. scapulalis and O. nubilalis, respectively. Sequence filtering resulted in 287,429 (O. scapulalis) and 272,490 (O. nubilalis) short reads, respectively. Newbler (version 2. 5. 3, Roche) assembled 11,231 and 10,773 transcripts (Newbler "isotig") for O. scapulalis and O. nubilalis, respectively. Finally, 8,892 unigenes (Newbler term "isogroup") were identified for O. scapulalis and 8,656 for O. nubilalis. Table  1  lists the main features to the two transcriptome raw read data and assemblies. Newbler predicted 1. 26 (O. scapulalis) and 1. 24 (O. nubilalis) alternative transcripts in average per unigene, and 1. 5 exons (Newbler "contig") in average per transcript (for both species).
Assignment of GO-terms We applied Blast2GO  [25]  to assign GO-terms to each assembled transcript. GO terms were associated to 2,774 (20. 3%) of the O. scapulalis transcripts and 2,084 (19. 3%) of the O. nubilalis transcripts with significant hits in NR database. The distributions of GO terms categories for O. nubilalis and O. scapulalis were highly similar (see Additional file 6: Figure  S6 ).
Then, we extended the estimation of the assembly completeness by calculating the Ortholog Hit Ratios (OHRs) as defined by O'Neil et al. [27]  (see Methods). The mean OHR were 0. 61 (O. scapulalis) and 0. 62 (O. nubilalis). Among all functionally annotated transcripts 2,015 O. scapulalis transcripts (45. 3%) and 1,969 O. nubilalis transcripts (47. 6%) had an OHR of at least 0. 7 (see Additional file 7: Table  S1 ). Finally, 488 O. scapulalis transcripts and 469 O. nubilalis transcripts were assembled at full-length (OHR = 1. 0) to the respective homologous sequence of the NR database.
Comparison of de novo assemblies with already published Ostrinia genomic data All Ostrinia expressed sequence tags (ESTs, n = 14,918) available in the NCBI EST database were downloaded in February 2012. They were then aligned with BLAST against the de novo transcriptomes. We found that 32. 3% (33. 8%) of the ESTs had similarities with the O. scapulalis (O. nubilalis) de novo transcript set. For half of these transcripts, the OHR values were ≥ 0. 7 (see Additional file 7: Table  S2 ). When blasting each species-specific A CDS is that part of the exonic region of a gene which is actually coding for a protein (the other exonic regions being 5 0 UTR and 3 0 UTR). The N-terminus is the amino terminus of a protein, and the C-terminus represents the carboxyl terminus. transcript set against the NCBI ESTs we identified 8,249 O.
scapulalis transcripts and 7,958 O. nubilalis transcripts that did not have any hit in the Ostrinia NCBI ESTs. This indicates that our de novo assemblies identified formerly unknown Ostrinia transcripts. In addition, we compared both de novo transcript sets to published data issued from independent Sanger and 454 sequencing on specific tissues of Ostrinia species  [28] [29] [30]. Among the 7,413 assembled contigs of that study, 62. 3% (n = 4,619) could be aligned to our O. scapulalis assembly and 63. 3% (n = 4,691) to the set of O. nubilalis transcripts with mean OHR values of 0. 79 for both species (see Additional file 7: Table  S3 ). Vice versa, we determined that 6,293 and 6,024 transcripts were specific to our de novo O. scapulalis and O. nubilalis assemblies. In total, in the current study we identified more than 5,500 new transcripts to each of both Ostrinia species (n = 5,818 in O.
scapulalis, n = 5,516 in O. nubilalis), which were not contained in the public databases. Of the sets of de novo assembled unigenes, 4,841 O. scapulalis (comprising 5,526 transcripts) and 4,612 O. nubilalis unigenes (comprising 5,253 transcripts) were unique and have not been described at all. Divergence between sibling species 8,907 (79. 3%) and 8,701 (80. 8%) transcripts in O. scapulalis and O. nubilalis could be aligned to the respective sibling species. Among the similar transcripts a high mean OHR value of 0. 72 was observed (see Additional file 7: Table  S4 ). Overall, 2,324 (20. 7%) O. scapulalis transcripts and 2,072 (19. 2%) O. nubilalis transcripts were specific to their respective species assembly. Among these, 1,141 O. scapulalis transcripts and 997 O. nubilalis transcripts had hits in the NR database with respective mean OHR values of 0.
In order to analyze the divergence among the orthologous genes of both sibling species we determined their level of identity. Homologous transcripts between both species were determined by limiting the set of common transcripts to those with the corresponding reciprocal best hit (RBH) determined with stringent criteria (i. e. e-value ≤ 1e-10, sequence overlap ≥ 90%). This conservative procedure aimed at avoiding false positives. These conditions were fulfilled for 846 transcripts. Among them the identity percentage varied between 82. 8% and 100% (mean identity = 98. 1%) (see Figure  1 ), with half of the transcripts showing a high sequence identity of at least 98. 7%. By contrast, 146 homologs had a slightly lower identity (between 82. 8 and 97%). Focusing on these most divergent homologs, we could assign 41 functional via BLAST similarity searches against NR (see Additional file 7: Table  S7 ).
Discussion Lepidoptera constitute the second major group of insects  [32]  and a major source of agricultural pests, which justifies the need of increasing genomic resources for numerous species of this group. Indeed, at the time of this study, only the genomes of four lepidopteran species are completely sequenced and accessible through biological databases (Bombyx mori  [33] , Heliconius melpomene, Danaus plexippus and Plutella xylostella, http:// www. ncbi. nlm. nih. gov/bioproject/71053,72423,73593, 73595,73597,73599, PRJNA78271). The decreasing costs of NGS provide a higher accessibility to small research groups. Hence, the number of lepidopteran species transcriptomes and genomes sequenced by NGS  [34] [35] [36] [37]  is increasing. The present study aimed at reconstructing the set of expressed genes of two moth species, one of which being a major pest of maize.