dataesr/scientific-paragraphs-categorization huggingface dataset:
This mutation was present in the SNP database at a frequency of 0. 4-0. 7% in Han Chinese, though Han Chinese was not included in their control samples  [17]. Our studies identified p. N17K variant which was observed in 18 patients (all heterozygote) and in 31 controls (30 heterozygotes and one homozygote with genotype AA). The frequencies of allele and genotype in patients were not significantly different from those in healthy controls. As the sample size of our study is relatively large, it is indicated that p. N17K mutation is not associated with autism in Chinese Han population. Biochemical analyses indicated that the p. N17K variant might disrupt the ASMT activity (,4% of the wild type ASMT activity)  [40]. It is not clear whether a heterozygous deleterious mutation of ASMT can dramatically reduce the melatonin synthesis in vivo.
032, two-sided). Among these variants, three variants (p. R115W, p. V179G, and p. W257X) were not present in natural or autism-free populations by using database from the National Heart, Lung, and Blood Institute (NHLBI) 1000 genomes project. These variants were not reported by recent exome sequencing researches in ASD  [42] [43] [44]. Moreover, results of functional prediction showed that p. R115W might affect protein function. Further functional experiments are needed to confirm it. Our research has several limitations. First one is the sample size. Although our sample size is relatively large, it is necessary to expand the sample size to detect rare variants. Indeed, none of the large scale genome wide association studies reported in ASD could detect strong reproducible susceptibility genes for the disorder.
Tolerated EA:A = 2/G = 8588 AA:A = 9/G = 4393 All:A = 11/G = 12981 0. 0233/0. 2045/0Inheritance status only for cases; b , The possible functional impact of amino acid changes was predicted by the PolyPhen 2 program HumDiv model; c , Functional prediction performed by SIFT; d , Functional prediction performed by SIFT indel; e , Mutations found in database of Exome Variant Server; f , The observed allele counts for the listed alleles (delimited by/); g , The minor-allele frequency in percent listed in the order of European American (EA), African American(AA) and all populations (All) (delimited by/). N. A, data was not available. doi:10. 1371/journal. pone. 0053727. t003.
, Tolerated EA:A = 2/G = 8588 AA:A = 9/G = 4393 All:A = 11/G = 12981 0. 0233/0. 2045/0Inheritance status only for cases; b , The possible functional impact of amino acid changes was predicted by the PolyPhen 2 program HumDiv model; c , Functional prediction performed by SIFT; d , Functional prediction performed by SIFT indel; e , Mutations found in database of Exome Variant Server; f , The observed allele counts for the listed alleles (delimited by/); g , The minor-allele frequency in percent listed in the order of European American (EA), African American(AA) and all populations (All) (delimited by/). N. A, data was not available. doi:10. 1371/journal. pone. 0053727. t003.
The microarray data were analysed using the statistical package R, version 2. 14. 0 (R Development Core  Team, 2005)  with various Bioconductor packages (http://www. bioconductor. org, Gentleman et al. , 2004). Microarray quality controls were performed using the arrayQualityMetrics package  (Kauffmann et al. , 2009). Expression intensities were background corrected, quantile-normalized, and summarized using the rma function of the oligo package  (Carvalho and Irizarry, 2010). The raw and normalized microarray data are available on http://www. ebi. ac. uk/arrayexpress; the accession number is E-MEXP-3803. Differentially expressed genes were identified using the limma package  (Smyth, 2005) ; genes with absolute log2 fold changes >1 and Holm  (Holm, 1979)  corrected P-values <0. 05 were considered significant.
Results: Using %G + C content to compare metagenomes, we find that (i) metagenomes are highly replicable, (ii) some treatment effects are minimal, e. g. , sequencing technology choice has 6-fold less impact than varying input DNA amount, and (iii) when restricted to a limited DNA concentration (<1μg), changing the amount of amplification produces little variation. These trends were also observed when examining the metagenomes for gene function and assembly performance, although the latter more closely aligned to sequencing effort and read length than preparation steps tested. Among Illumina library preparation options, transposon-based libraries diverged from all others and adaptor ligation was a critical step for optimizing sequencing yields. Conclusions: These data guide researchers in generating systematic, comparative datasets to understand complex ecosystems, and suggest that neither varied amplification nor sequencing platforms will deter such efforts.
Once DNA is extracted, small yields require amplification to obtain enough material for sequencing. While whole genome amplification was an attractive option, it is now documented to result in non-quantitative metagenomes due to both stochastic  [13]  and systematic biases  [14]. In contrast, linker-amplification-based libraries  [15] [16] [17]  provide a nearly quantitative alternative, even from sub-nanogram amounts of DNA  [15]. Together these advances allowed the compilation of the first largescale, systematically prepared comparative metagenomic dataset for quantitative viral ecology  [18]  with new tools and analytical platforms now emerging to handle such datasets  [19, 20]. Beyond viral ecology, these studies provide a roadmap for generating quantitative metagenomic datasets from any low (<100 ng) input DNA samples.
Gene function and read assembly parallel %G + C findings To evaluate variations in gene function, metagenomic reads were compared to an expansive database of marine virus protein sequences (>456K protein clusters derived from over 6M reads from 32 diverse pelagic ocean virus communities  [18] ). As is common for viral metagenomes (reviewed in ref. [18] ) only 3-7% of the reads mapped to protein clusters without self-clustering. However, the resulting gene frequency patterns were well-supported and mirror patterns observed in the above %G + C analyses (Figure  3A ). Replicate metagenomes were most similar (pairwise r-values >0. 95), while the biggest difference was between metagenomes prepared from ≥ 1,000 ng of starting DNA and those prepared from 100ng or less (r-values <0. 8). Within these two large clusters, sequencing technology choice contributed additional, but minor, divergences (r-values 0.
8-0. 9). Notably, these protein cluster pairwise r-values are lower than those for either %G + C or duplicate frequency. This likely reflects increased analytical resolution, as 1,500 protein clusters correlated per metagenome in the function analysis, while only 50 or 10 bins were resolved in the %G + C and duplicate analyses, respectively. Finally, assembly experiments (see Methods, Figure  3B ) revealed that total assembly size positively correlated to the number of reads used in assembly. In turn, the maximum and N50 contig sizes were relatively insensitive to increased read numbers in the larger datasets. This was  In contrast to Experiment 1, all 10 Experiment 2 Illumina libraries (eight 10ng and two 100ng libraries) were successful. Replicate libraries did not cluster together consistently, but this reflected the extremely minimal variance across the replicates rather than poor replication (Figure  4 , note reduced axis scales relative to Figure  2 ).
Transposon-based library preparation slightly impacts %G + C In both Tara Oceans station 41 and 109 datasets, the amount of input DNA (10 or 100 ng) and amplification (12 or 15 cycles) resulted in less variation than was observed in replicate library preparations (Figure  4 ). The only exception was transposon-based libraries, which diverged from the relatively invariant standard Illumina libraries. For all samples, duplicate frequencies varied as much between as within treatments (Figure  4 ) and much less duplication was observed in Experiment 2 than 1. The dendrogram topology observed in pairwise %G + C analyses was recovered in analyses of function (Figure  5A ),.
Protein Clusters Figure  3  Protein cluster functional analysis and assembly statistics for Experiment 1 metagenomes. Metagenomic reads were mapped to POV protein clusters (see text) and hit frequencies were used to produce pairwise correlation heat maps. Details as described in Figure  2 , including bootstrap analysis of statistical support for correlations across metagenomes. Assembly performance of each sample across the dataset was evaluated using metrics of n50 and maximum contig size, as well as the number of reads and base pairs that were assembled. Note that inferior assembly performance was restricted to samples with reduced read yields. Lastly, the Newbler assembler yielded larger contigs and smaller total assemblies when compared to Velvet assembly of the same Ion Torrent dataset.
For samples with ultra-low DNA yields (<1 ng), it is best not to risk failure in standard library preparations and to use instead a sequencing technology optimized linker-amplified protocol. Future research directions include developing a mechanistic understanding of the non-intuitive, but replicable differences in linker-amplified metagenomes, as well as improving understanding of polymerase impacts and developing empirical datasets for a broader range of %G + C samples. th , 2011, respectively. The viral concentrate from the former was purified using both CsCl and DNase, while only DNAse was used for the latter.
Sequencing data All metagenomic sequences are publically available through the CAMERA portal at http://camera. calit2. net/ [CAMERA: CAM_P_00001027]. 454 and Ion Torrent data, provided by UAGC, were delivered in. sff format and converted for downstream processing to FASTA and QUAL formats using sffinfo (roche454 v2. 6) and then to FASTQ format using BioPerl 1. 6. 1. B2 Ocean Illumina data, by Emory Genomics Core, and TARA Oceans Illumina data, by Genoscope, were provided in FASTQ format. Each library was examined for raw quality using FastQC (v0. 9, downloaded Aug 2012 from http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/) and Fastx_Toolkit (v0. 0. 13 downloaded Feb 2010 from http:// hannonlab. cshl. edu/fastx_toolkit/). The FastQC report was the source of duplication data used in the figures.
Quality control Next, procedures for quality control were established to remove suspect sequence data, either by filtering whole reads or trimming reads in accordance with known sequencing technology artifacts. For 454 and Ion Torrent data, whole-read filtering was used, as is common for metagenomics  [11, 15, 45, 46]  (Additional file 1: Figure  S10 ). In contrast, because Illumina errors are localized to particular parts of a read  [47, 48] , these data were trimmed using a threshold predicted quality score to remove suspect regions of the read at both the 3' and 5' ends using DynamicTrim. pl, part of the SolexaQA package  [49]  (Additional file 1: Figure  S11 ). After QC steps, 69-85% of the 454 reads remained, compared to 60% for Ion Torrent and 63-74% for Illumina (Table  1 ). The fastx_toolkit software package was also used to remove Illumina reads under 50bp, while the 454 and Ion Torrent reads were cleaned using a custom pipeline  [18].
%G + C analytics The mean read %G + C was chosen as the focus of our analysis, rather than the %G + C of sequence subsets of a read or the larger genome regions from which the read fragment originated, since mean fragment %G + C the best predictor of GC bias  [50]. QC-ed reads were processed using the BioPerl 1. 6. 1 script bp_gc_calc. pl to obtain average %G + C values for each read. Given the large read length differences across these libraries (90bp to 350bp), only the first 50bp of each read are used in all %G + C distribution analyses to match the shortest QC-ed Illumina data, while normalizing for read length. Reads were truncated to 50bp using fastx_toolkit and processed with bp_gc_calc. pl. Phage metagenomic reads were cut into non-overlapping 50bp fragments using a bash script and also processed with bp_gc_calc.
Protein cluster analyses Functional differences within and between metagenomes were assessed in Experiment 1 by mapping metagenomic reads to the Pacific Ocean Virome database  [18]. The hit frequencies of the 1,500 protein clusters that were most abundant across all metagenomes were then used to obtain pairwise correlation values. A range of 3-7% of the metagenomic reads mapped to these POV PCs, while the 'top 1,500 PCs' subsample represented >99% of the data that mapped. Because the Experiment 1 dataset represented a large diversity of read lengths, greatly impacting inference capacity  [38] , the dataset was normalized to assess sequencing platform biases rather than read length impacts as follows: (i) the longer Ion Torrent and 454 reads were trimmed to 100bp, and (ii) only reads ≥100 bp were used from Illumina data.
Assembly analyses The short reads derived from Illumina and Ion Torrent data were assembled using Velvet v 1. 2. 03  [53]  using default parameters across a range of kmer sizes  (23, 27, 31bp) , but only 31-mer data are reported as kmer size did not impact assemblies. The longer 454 reads were assembled using GS De Novo Assembler v2. 6 (http:// my454. com/products/analysis-software/index. asp) with default parameters.
Acknowledgements We thank  B. Poulos ,  N. Solonenko ,  A. Gregory , and  C. Decker  for technical assistance, as well as TMPL members and two anonymous reviewers for comments on the manuscript. Funding for this particular study was provided by  BIO5 ,  Biosphere 2  and the  Gordon and Betty Moore Foundation  to MBS. We thank the coordinators and members of the  Tara Oceans consortium  (http://www. embl. de/tara_oceans/start/)for organizing sampling and data analysis. We thank the commitment of the following people and sponsors who made this singular expedition possible: CNRS, EMBL, Genoscope/CEA,  VIB ,  Stazione Zoologica Anton Dohrn, UNIMIB ,  ANR  ( projects POSEIDON/ANR-09-BLAN-0348 ,  BIOMARKS/ANR-08-BDVA-003 ,  PROMETHEUS/ANR-09-GENM-031 , and  TARA-GIRUS/ANR-09-PCS-GENM-218 ),  EU   FP7  (MicroB3/No.
AbDhn1, AbDhn2 and AbDhn3 Encode Three Dehydrinlike Proteins The camalexin-induced sequence P3E9 (GenBank accession No DY543081) was previously shown to encode a 157-residue protein that exhibits similarities to dehydrin-like proteins from Tuber borchii  [15]. This protein, here referred to as AbDhn1, indeed contained two amino acid repeats (Fig. 1 ) that corresponded to the signature pattern of fungal dehydrins  [6]  recently named DPR domains  [11]. A BLAST search was conducted in the A. brassicicola automatically annotated genome database (http://genome. jgi-psf. org/Altbr1) to check for proteins containing similar motifs. Besides a protein referred to as AB02513. 1 corresponding to AbDhn1, two other hits (AB08993. 1 and AB05365. 1) were found and named AbDhn2 and AbDhn3 (GenBank accession No JX891381 and JX891382), respectively.
2B ). Blast search in the A. brassicicola genome assembly database and Southern hybridization (Fig. S1 ) did not reveal any additional AbDhn2 coding sequence, strongly suggesting that the two transcripts were produced by alternative splicing of a AbDhn2 pre-mRNA. The two deduced AbDhn2 protein isoforms contain 453 amino acids (a isoform) and 400 amino acids (b isoform) and five DPR motifs (Fig. 1 ). A stretch of eleven amino acids repeated seven times was also observed at the N-terminal part of their sequence (Fig. S2 ). The alignment of the DPR domains from the three A. brassicicola dehydrin-like proteins is shown in Fig. 3. Apart from the conserved domains, sequence comparison of these proteins revealed very poor homology, and sequences producing significant alignments (. 50% identity and coverage) were only detected in genomes of closely related Pleosporales species (A.
As shown in Fig. 8 , all the dehydrin-like mutants were characterized by high susceptibility to oxidative stress. Almost complete growth inhibition of the mutant strains was observed in the presence of 10 mM menadione and 5 mM H 2 O 2 while in the same conditions the growth of the wild-type was not significantly affected (Fig. 8 ). Similarly, Al-ITC induced delayed entry into the log phase (ca. 7 h) and brassinin caused a significant reduction in the maximum slope (from 23% for Dabdhn3 to 42% for Dabdhn1) for the three dehydrin-like deficient mutants compared to the wildtype (Fig. 8 ). Similar experiments were performed in the presence of NaCl and sorbitol to assess the susceptibility of the mutant strains to saltand osmotic-stress, respectively. None of the dehydrin-like deficient mutants showed increased susceptibility to any of these stresses compared to the wild-type (data not shown).
Nephelometry, which is based on the measurements of scattered light, was proved to be an accurate indicator of the fungal biomass and can be used as reliable tool for the monitoring of fungal growth  [19]. Data were exported from Nephelostar Galaxy software in ASCII format and further processed in Microsoft Excel. Two variables, i. e. lag time and maximal slope, were calculated from the growth curves using the calculation method reported by Joubert et al  [19]. At least three replicates per treatment were used. To study the susceptibility of the fungal strains to ITC, allyl-ITC (AlITC), purchased from Aldrich Chemical Co. (Milwaukee, WI), was diluted from stock solutions prepared in methanol at the final desired concentrations. The phytoalexin camalexin was synthesized according to Ayer et al  [32]  and brassinin according to Kutschy et al  [33]  and Takasugi et al  [34].
Nomenclature and databases Sequences used for PALB2 nomenclature were obtained from the NCBI RefSeq database (NG_007406. 1 for genomic, NM_024675. 3 for mRNA and NP_078951. 2 for protein) (http://www. ncbi. nlm. nih. gov). Standardized nomenclature was reported considering the A of the ATG initiation codon of the coding DNA Reference Sequence as nucleotide position +1.
Prediction of pathogenicity of splicing and missense variants Splicing predictions were performed with Splicing Sequences Finder (SSF) (http://www. umd. be/searchSpliceSite. html), Max-EntScan (http://genes. mit. edu/burgelab/maxent/Xmaxentscan_ scoreseq. html), NNSplice (http://www. fruitfly. org/seq_tools/ splice. html) and HumanSplicingFinder (HSF) (http://www. umd. be/HSF/) algorithms through the Alamut-Mutation Interpretation Software v. 1. 54 (http://www. interactivebiosoftware. com/ alamut. html). Default thresholds were used for all the analyses. Potential consequences of missense substitutions were obtained using the prediction software PolyPhen-2 (Polymorphism Phenotyping-2, see http://genetics. bwh. harvard. edu/pph2/), SIFT (Sorting Intolerant From Tolerant, see http://sift. jcvi.
This research was supported by grants from the  Xunta de Galicia  ( 10PXIB 9101297PR ) and  FMM Foundation  given to AV. MH was supported from the  Instituto de Salud Carlos III, Fondo de Investigacion Sanitaria (FIS)   Research Grant   09/00859 , and  Fundacion Mutua Madrilen ˜a (FMM) Research Grant   FMM-08. SGE is supported by a Miguel Servet contract of the  Instituto de Salud Carlos III. EAV was supported in part by grants  BIO39/VA27/10  ( Consejerı ´a de Sanidad ) and  CSI004A10-2  ( Consejerı ´a de Educacion)  from the  Junta de Castilla y Leon. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
8 Mb and 294 kb/214. 2 kb, respectively. Aligning a set of 13 finished fosmid sequences to the genome revealed 187 non-gap adjacent mismatches out of 442,732 bp aligned, yielding an average base-pair accuracy of 99. 96%. We estimated the completeness of the euchromatic portion of the assembly by aligning 74,606 Prunus ESTs obtained from GenBank onto the assembly, and approximately 1% of the ESTs were not found (Supplementary Note, Supplementary Tables  2 3 4 5 and Supplementary Figs. 1 2 3 4 ). An extensive check of the current release revealed a few misassembly and orientation issues that will be dealt with in an upcoming release (Supplementary Note, Supplementary Fig. 5  and Supplementary Tables  6 7 8 9 10 ). We compared the quality of the peach sequence assembly to that of other plant genomes using the standards established in a previous publication  8  and noted a high level of contiguity and fraction of mapped sequences (Supplementary Table  11 ).
8  and 9 ), suggesting that the diversification into families largely predates the divergence of the two genera (Supplementary Note). A total of 27,852 protein-coding genes and 28,689 protein-coding transcripts were predicted; of these, 24,423 have Arabidopsis homologs, 18,822 have Swiss-Prot homologs and 26,731 have TrEMBL homologs (Supplementary Note). The gene content in peach is considerably lower than those observed in apple (57,386;  ref. 9) and poplar (45,654; ref. 12) but is similar to those in grape (30,434; ref. 13) and Arabidopsis (27,416; TAIR 10, ref. 10, http:// www. arabidopsis. org/portals/genAnnotation/gene_structural_annotation/annotation_data. jsp). The gene density in peach (1. 22 genes per 10 kb on average) was higher than that in apple (0. 78; ref. 9) but was lower than that in Arabidopsis (2.
The data were largely sufficient to conclude that in peach the duplications were organized in seven major triplicated subgenomic regions (Fig. 3  and Supplementary Fig. 18a-g ). The dot plot analyses, however, indicated that the pattern of triplication was not as evident as that in grape (Supplementary Fig. 19a, b ). This suggests that several interchromosomal rearrangements occurred during peach genome evolution. Regions without the paralogs corresponded to those with high SNP diversity (Figs. 2  and 3 ). We compared the grape and peach genomes using the Mercator program  40  , which identifies segments with one-to-one orthology relationships across species rather than DNA regions having multiple syntenic partners. Notably, each grape segment, corresponding to part of one of the paralogous triplets of putative ancestor paleochromosomes, showed orthology to a single peach chromosome (Supplementary Figs.
Methods and any associated references are available in the online version of the paper. Accession codes. This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AKXU00000000. The version described in this paper is the first version, AKXU01000000. Illumina Short reads have been deposited into NCBI Short Read Archive under accession number SRA053230. The 13 completely sequenced fosmids have been deposited into GenBank under accessions AC253537 to AC253549. Note: Supplementary information is available in the online version of the paper.
Protein-coding gene annotation. Gene models were derived from weighted consensus prediction using several gene algorithms (FGENESH+ (ref. 45) and GenomeScan  46  ), taking into account transcript assemblies (done with PASA  47  ) and protein homology. Repeats analysis. Both RepeatScout  48  and ReAS  49  were used to perform de novo identification of repeats. LTR retrotransposon structural identification was done using LTR_finder  50. The REPET pipeline was used to generate a primary file of consensus transposable elements. Sequences were further manually curated using both BLASTX and Censor from the RepBase 51 database. Transcriptome analysis. For RNA-Seq analyses, total RNA was extracted from different tissues: fruit at different ripening stages (cv. Imera, endocarp mesocarp and epicarp, S1-S2-S3-S4), roots (open pollinated cv.