dataesr/scientific-paragraphs-categorization huggingface dataset:
In accordance with human data  [27] , we found that X-linked CGI methylation is not restricted to promoters but occurs at all CGIs in promoters, gene bodies and intergenic regions (Figure  S10A  in Additional file 1). In contrast, CGI methylation is not found at some of the genes known to escape Xinactivation in the mouse (Figure  S10B  in Additional file 1). Out of 13 described escapee genes in the mouse  [28] , 10 have their promoter covered in our dataset and 5 show no signs of promoter methylation (Figure  S10C  in Additional file 1). The other five genes might be misannotated escapees or could have alternative promoters. We then investigated the contribution of DNMT3 enzymes to X-linked methylation by examining RRBS methylomes generated in Dnmt3a-/-and Dnmt3b-/female E8. 5 embryos. Strikingly, X-linked CGI methylation is unaffected in Dnmt3a-/-embryos but completely absent in Dnmt3b-/-embryos (Figure  7D, E ).
RRBS data analysis RefSeq genes, transposons and CGI annotations were retrieved from the UCSC mm10 annotation. We filtered transposons to have a minimum size of 200 bp. HCPs (high CpG promoters), ICPs (intermediate CpG promoters) and LCPs (low CpG promoters) were annotated as previously described  [19]. The genomic coordinates of the imprinted DMRs were retrieved from the Wamidex imprinting database  [61]. The genomic coordinates of canyons were retrieved from a published dataset  [15]. For oocyte methylation, we retrieved a published RRBS dataset (GSE34864)  [6]  and averaged the methylation scores from all the oocyte replicates in the dataset. To annotate CGIs, we measured the distance between the middle of the CGI and the closest RefSeq TSS and overlapped CGIs with RefSeq exons and introns.
0. 12)  [65]. For data visualization, we generated BigWig files of normalized read counts per base with bam2wig. py in the RSeQC package (v2. 4)  [66]  using only reads that map uniquely in the genome. We calculated raw read counts in mouse RefSeq exons from the BAM files with HTseqcount (v0. 6. 0) [67]  and used these counts to identify differentially expressed genes with DESeq2 (v1. 4. 5)  [68]. Genes were called differentially expressed if the false discovery rate-adjusted P-value between WT and knockout was lower than 0. 05 and the fold change greater than 2. Normalized counts and Fragments Per Kilobase of exon per Million fragments mapped (FPKM) scores were calculated with the 'counts' and 'fpkm' functions of DESeq2.
Acknowledgements We thank  Jesus del Mazo  and  Florence Cammas  for contributing samples. This research has received funding from the  ATIP-AVENIR program , the  Association pour la Recherche contre le Cancer  ( ARC SFI20121205729 ), the  Ligue Contre le Cancer , the  MEDDTL  ( 11-MRES-PNRPE-9-CVS-072 ), the  EpiGeneSys European Network of Excellence , and the  European Research Council  under the European Union's  Seventh Framework Programme  (ERC consolidator grant number  615371 ).
A cross-substrate comparison using transcriptomics In an earlier study  [5]  we delineated the transcriptional profile of A. niger under conditions identical to those used here, with the exception that wheat straw was provided as the only lignocellulosic substrate. The composition  of the wheat straw (as % of total solids and using the same compositional analysis methods as used for willow) was 37. 4% (±2. 96) glucose, 16. 8% xylose (±2. 48) and 2. 7% arabinose (±0. 29) and the lignin content was 21. 6% (±0. 73) (the values in parentheses are standard deviations). Re-calculation of the expression data using the updated genome model and method outlined in this manuscript, allows the direct comparison of the two datasets. A timedependent transcriptome profile on the two different substrates was not feasible but we accept that the transcript profiles may change with time differently on wheat straw or willow.
The GH5 family is notable amongst the most highly expressed families for being the only one in which greater expression is seen in response to willow. Whilst the two predominant endoglucanases expressed on willow, TID_20 5580 and TID_209376 (eglC), remain the two most highly expressed members of the family on wheat straw, levels are ~3-fold reduced for each as calculated using DESeq1. 9. The overall responses to these two different substrates, represented by the hardwood and straw feedstocks, retain a core set of highly induced genes that encode enzymes required for the degradation of any lignocellulosic substrate. Regulatory differences at the level of individual The annotations here are for the A. niger CBS513. 88 strain from the AspGD database  [44]  with the exception that the erroneous endoglucanase annotation for the AA9 family member was corrected.
b All of the genes listed here have significantly increased expression on the willow substrate compared to the glucose 48 h control (p < 0. 05, DESeq). The annotations here are for the A. niger CBS513. 88 strain from the AspGD database  [44]. b The fold changes were calculated by the DESeq statistical analysis package using the normalised read counts. For (A), only those families with total RPKM values of 100 or more upon at least one of the substrates are shown. Gene expression was measured following 24 h culturing of A. niger in the presence of willow or wheat straw as the only carbon source. The families and activities are presented in order of decreasing expression from the willow cultures. The error bars represent standard error. Genes were assigned to particular enzyme activities as described in Methods section and Additional file 7.
Comparison of culture supernatant activity using azurine dyed substrates The RNA-seq data suggested transcriptional specificity in the response of A. niger to each substrate. To investigate this biochemically, supernatants harvested from A. niger cultures incubated with either substrate for 24 h (the same conditions used for RNA-seq) were assayed for activity towards two azurine-linked substrates: cellulosea major component of both the wheat straw and willow substrates and arabinoxylana hemicellulose found in much greater abundance in wheat straw than willow. Activities in the culture supernatants were assayed using a predetermined volume of the supernatants that gave a linear response between time and azurine dye release. The activity towards arabinoxylan was 10-fold greater in the straw-induced cocktail compared to the willowinduced cocktail (Table  3 ).
Wheat straw induces a greater and different secretory response than willow The protein concentration estimated within the concentrated supernatant from wheat straw cultures was approximately twice that from equivalent willow cultures. SDS-PAGE of the two sets of supernatants demonstrated highly reproducible protein mixtures across replicate samples. Whilst some major bands were conserved, clear differences were observed in the banding patterns between the two sets of conditions reflecting the general pattern seen in the transcriptomic data (Additional file 3). Comparison of culture supernatant activities using para-nitrophenyl linked substrates Four para-nitrophenyl linked substrates were used to measure specific enzyme activities in the supernatants from the A. niger cultures with straw or willow (Figure  3 ).
Transcripts from three of these genes had higher expression levels in the presence of willow as compared to glucose media, with the exception being the hydrophobin-encoding TID_128530. However, the only member of this category expressed on willow at an RPKM of >10 was TID_188224 at 721 RPKM, further supporting the hypothesis that this protein may play a role in fungal response to lignocellulose. Interestingly, the expression in response to willow was 32-fold greater than that to straw (DESeq, p < 0. 01). Of the carbon and nitrogen metabolising category, only 2 of the 5 genes had transcript levels raised by >20-fold by wheat straw and willow (TID_51997 and TID_52460). This, in a similar manner to the CAZy response, could reflect differences in composition of the substrates and the small metabolites released during their degradation.
Substrate preparation Short rotation coppice willow (Salix sp. ) Tora variety stems were knife-milled using a Fritsch pulverisette 19 knife-mill (Fritsch, Germany) by first passing the stems through a 2 mm screen and then a 0. 5 mm screen for size reduction prior to ball-milling. 5 g of knife-milled willow was ball-milled in 80 mL stainless steel grinding bowls with 25 10-mm-diameter steel balls in a Planetary Mill (Pulverisette 5 classic line, Fritsch, Germany), at 400 rpm for a grinding time of 20 min, resulting in an average particle size of <75 μm. The wheat straw substrate preparation was described previously  [5]. The annotations here are for the A. niger CBS513. 88 strain from the AspGD database  [44]. b The fold changes were calculated by the DESeq statistical analysis package using the normalised read counts.
Read mapping and quantification The Life Technologies LifeScope (v2. 5. 1) Whole Transcriptome (WT) Pipeline was used to filter, then map the SOLiD reads to the reference genome sequence described before  [41]  using the mate-pair read WT pipeline. The RNA-Seq data obtained previously  [5]  of A. niger exposed to wheat straw for 24 h, as well as the data from the corresponding glucose cultures, were reanalysed using the same WT pipeline to allow data comparison. Reads from this dataset were mapped using the single fragment WT pipeline. For both data sets, reads were initially filtered against library adaptor and barcode sequences as well as A. niger rRNA 5. 8S, 16S, 18S & 28S sequences obtained from Genbank. Reads that passed the filter were then mapped to the reference genome sequence, and to a library of exon junction sequences derived from the genome sequence using known exon coordinates.
This allowed reads that spanned exon junctions (spliced reads) to be determined. Read counts per gene were determined using the program Htseq-count (http://www-huber. embl. de/users/anders/ HTSeq) using uniquely aligned reads with ≥ MAPQ20. For paired read alignments only the forward read (F3) were counted. The count information was then used to calculate normalized gene expression values as RPKM  [12]. Read counts were also used as the input for calculating differentially expressed (DE) genes using the R package DESeq (version 1. 9)  [42]. An adjusted p-value of ≤0. 05 was the significance threshold (p-value adjusted for multiple testing with the Benjamini-Hochberg procedure for false discovery rate (FDR)). Where fold changes in gene expression are described in the results from our study, the fold changes are those calculated by DESeq using the normalised count data.
The RNA-Seq data obtained previously for 24 h exposure to wheat straw  [5]  is available at the Gene Expression Omnibus (GEO) database  [43]  under accession number GSE33852 and the data obtained in this study for 'willow 24 h' and 'willow 24 h + glucose 5 h' along with the corresponding glucose 48 h control cultures are available under accession number GSE62284. Additional file 1 contains the expression levels for all genes.
Gene annotation datasets used The annotation for A. niger from cazy. org  [15, 16]  was used (last updated July 2013). The annotations for A. niger genes for the CBS513. 88 strain were downloaded from the AspGD database  [44]. A combination of sources was used to assign genes with particular activities including the enzyme commission (EC) number annotations  [10]  and cazy. org annotations  [11, 45]. See Additional file 7 for a summary of which activities were assigned to particular genes based on the sources listed above.
Acknowledgements The authors acknowledge funding from the  Biotechnology and Biological Sciences Research Council (BBSRC) Sustainable Bioenergy Centre (BSBEC)  under the  programme Lignocellulosic Conversion to Ethanol (LACE) (Grant   BB/G01616X/1 ),  Lee Shunburne  for excellent technical support and  Dr. Sarah Purdy  for the willow substrate.
A novel splice acceptor mutation in HEATR2 results in PCD We identified an extended UK-Pakistani family with three affected children presenting with PCD (Figure  1A ). The proband (IV:4) presented at the age of 3 with chronic respiratory infections, middle ear disease and chronic nasal discharge (Table  1 ). She had also experienced neonatal respiratory distress and dextrocardia was apparent on chest X-ray. Nasal ciliary biopsy confirmed the PCD diagnosis by video microscopy, and written reports of transmission electron microscopy revealed absence of both IDA and ODA (data not shown). Her cousins subsequently presented at the age of 2 (IV:1) and 4 (IV:10) with chronic chest infections and nasal discharge (Table  1 ). The diagnosis of PCD was confirmed in both these latter cases by nasal ciliary biopsy, again electron microscopy confirmed absence of both inner and outer dynein arms.
Genotyping, linkage, next generation sequencing and human mutation analysis All patients or their parents gave informed consent and ethical approval was obtained from Leeds (East) Research Ethics Committee (07/H1306/113). DNA from three affected individuals and eleven unaffected individuals from the family was used to generate the genotype data using the Affymetrix 240K SNP arrays. Genotype data was analysed using AutoSNPa  [84]  and IBDfinder software  [85]. A custom Agilent SureSelect pulldown reagent was used to enrich for all coding exons of the 51 UCSCannotated genes in the locus from the genomic DNA of subject Figure  6. CG31320/HEATR2 orthologues share conserved upstream regulatory FOX motifs and X-boxes of a master cilia motility transcriptional programme. (A) Using the human upstream epigenetic markings and conservation to mouse and rat to define conserved predicted regulatory elements, we focused analysis on the 500 bp upstream of the HEATR2 ATG and syntenic regions in other species to identify Xbox sequences, along with the nearest conserved FOX motifs.
After alignment postprocessing and variant calling using standard methods  [86]  only one homozygous potentially pathogenic variant remained, ENST00000297440:c. 2432-1G. C. Presence of the mutation was confirmed by Sanger sequencing of PCR amplified products using BigDye terminator chemistry (Applied Biosystems). Primer sequences used to amplify all HEATR2 exons for sequencing are given in Table  S4. DNA sequence data was analysed using the GeneScreen software  [87]. The 23 additional PCD patients sequenced for HEATR2 mutations had no known mutations at the time, and included two patients with cilia aplasia, two with absent inner dynein arms, three with absent outer dynein arms, 13 with absent inner and outer dynein arms and three with undefined defects by EM.
The DNA sequencing data was analysed using Sequencher 4. 10. 1 (Gene Codes Corp. ). For quantitative analysis of splicing variants, sequencing files were analysed using the desktop program QSVanalyzer  [88]. RT-qPCR was carried out on the Roche LightCycler-480 using the Roche Universal Probe Library (UPL) system. Crossing point times were identified, analysed and displayed relative to the control gene (TBP) using the Roche LC480 software. Primer and probe combinations are displayed in Table  S5.
Auditory assay For each assay, five third instar larvae were placed on an agar plate on top of a speaker. Each assay lasted 60-90 seconds, during which a 1000 Hz tone of 1 second duration was played at 30 second intervals. Larval movement was recorded by video camera and videos analysed in Macintosh iMovie software. For each tone, larval behaviour was observed in two 0. 5 seconds time windows: the first starting 1 seconds before the tone, and the second starting from the onset of the tone. Number of larvae retracting (showing clear length shortening) was recorded as a score for each time window.
Chromatin immunoprecipitation ChIP-sequencing experiments were performed as described  [90]  using chromatin from either 10 7 ependymal cultured cells from OF1 wild type mice (Oncins France souche 1, Charles River Laboratory, France)  [37]  or mouse pancreatic cell line MIN6  [90] , full datasets to be published at a later date (A. L, B. D. and W. R). Briefly ChIP was performed as described  [91]  using anti-RFX3 or anti-RFX1  [92]. RNAs were isolated from Rfx3 +/+ or Rfx3 2/2 mouse ependymal cells cultures prepared as previously described  [37].
Finally, immunocomplexes were eluted off the beads and resolved by SDS-PAGE. Resolved immunoprecipitates were subjected to immunoblotting with antibodies to HEATR2 (Proteintech) and DNAI2 (M01, clone IC8; Abnova). Also used for immunoblotting were HSP70 (K-20; SantaCruz), (MAB32861: R&D), DNAAF2/KTU (ab99056; Abcam), DNAAF3 (ab126301; Abcam), ZMYND10/BLU (TA308345; Origene), DNAH5 (HPA037470; Sigma) and DNALI1 (N-13; SantaCruz). All primary antibody dilutions were 1/5000 and secondary antibody dilutions were 1/20000 using SuperSignal West Femto kit (Thermo) for detection. Finch, Heather Barker and Ian Overton for assistance and helpful discussions. The authors thank Peter Budd, Margaret Keighren, Matthew Pearson and Paul Perry for technical assistance and to Richard Mort for help with statistical analyses.
Acknowledgments The authors would like to thank the  NHLBI GO Exome Sequencing Project  and its ongoing studies which produced and provided exome variant calls for comparison: the  Lung GO Sequencing Project  ( HL-102923 ), the  WHI Sequencing Project  ( HL-102924 ), the  Broad GO Sequencing Project  ( HL-102925 ), the  Seattle GO Sequencing Project  ( HL-102926 ) and the  Heart GO Sequencing Project  ( HL-103010 ). The authors are grateful to all patients and their families for participation in this study. The authors thank  Dr. Maria Chiuchiolo  and  Prof. Ron G. Crystal  for sharing reagents. The authors are grateful to  Drs. Emma Hall , Andrew.
After mapping reads, we were able to assemble a large fraction of the annotated transcripts and also novel isoforms for known protein-encoding and noncoding loci. This dataset was compared to All-Exon GeneChip (Affymetrix) data, which were used as an internal control of data quality, which widely confirmed our RNA-seq data. We focused our analysis on systematic identification of long, multi-exonic TUTs that were highly detected during spermatogenesis. A high-confidence set of 1419 TUTs including 435 potential lncRNAs was defined, and subsequent characterizations identified several properties. These unannotated transcripts showed most of the genomic features typically associated with vertebrate lncRNAs (e. g. , short length, low exon number, low abundance, low GC content, low sequence conservation, and restricted expression patterns).
Importantly, classification of TUTs and known lncRNAs according to their expression pattern during spermatogenesis revealed several specific characteristics, including an exon length of meiosis-induced TUTs and known lncRNAs that was unexpectedly twice as long as that of known lncRNAs with other expression patterns. Transcriptional profiling and subsequent characterization of TUTs dynamically expressed during rat spermatogenesis may lead to identification of novel candidate loci for the regulation of gene expression in either cis or trans in mammalian testis. Our study provides insight into the lncRNA expression program in mammalian testis and significantly improves annotation of the rat genome. It may also ultimately help elucidate molecular events leading to reproductive disorders. A graphic display of this high-quality dataset is conveniently available to the scientific and medical communities through the ReproGenomics Viewer (RGV)  [54].
Surplus PCR primers were then eliminated by purification (Agencourt AMPure XP beads; product no. A63881; Beckman). The resulting DNA libraries were checked for quality and quantified (2100 BioAnalyzer; Agilent). Each library was loaded into two lanes of the Illumina flow cell at a concentration of 5 pM, and clusters were generated using the cluster station and sequenced on the genome Analyzer II (Illumina) as unstranded, paired-end 2 3 60 base reads at depths of ;16. 2-18. 0 million paired-end reads per library (for statistics of read counts see Supplemental Table  S1 ; all supplemental data are available online at www. biolreprod. org). Pipeline version 1. 6 software (Illumina) was used for image analysis and base calling. Mapping Reads, Transcriptome Assembly, and Quantification with the Tuxedo Suite Comprehensive database of known transcripts.
Transcript annotations from public databases (Ensembl  [56] , National Center for Biotechnology Information [NCBI; release RGSC3. 4] [57], and AceView  [58]  and mRNAs from University of California Santa Cruz [UCSC] rn4  [59] ) were merged into a combined set of nonredundant, known transcript annotations using Cuffcompare software  [60, 61]. Mapping reads. RNA-seq-derived reads from each sample replicate were aligned independently with the Rattus norvegicus genome (rn4, downloaded from the UCSC genome browser website  [59] ) with TopHat (version 1. 4. 1) [62]  using published approaches  [29, 61]. The database of known transcripts and expressed sequence tag alignments (from UCSC) was used to define an additional junction set (AJS) for each TopHat run. The junction outputs from individual TopHat runs were pooled and added to the AJS to allow TopHat to use junction information from all samples.
TopHat software was run again for each sample, using the resulting AJS. The output of this second run comprised the final alignment. Finally, individual sample alignments for each testicular cell type were pooled. Ab initio transcriptome assembly. The transcriptome of each individual cell type was assembled with Cufflinks (version 1. 2. 0) by finding a parsimonious allocation of reads to the transcripts within a locus, using default settings  [60, 61]. The Cufflinks assembly step yielded a set of ;23 000-47 000 transcript fragments (transfrags) for each testicular cell type. Merging and classification of transcript fragments. Cuffcompare software  [60, 61]  was used to merge the individual transfrags into a combined set (nonredundant union of all transcript fragments that share all introns and exons) and to classify the 122 262 resulting transcripts according to the known transcript annotation database.
All transcripts that were not automatically annotated as complete match (Cuffcompare class ''¼''), potentially novel isoform (class j), unknown intronic (class i; i. e. , loci falling entirely within a reference intron and without exon-exon overlap with another known locus), and intergenic (class u) isoforms were discarded, yielding 77 490 transfrags that were retained in the analysis. Transcriptome quantification and preprocessing. The isoform abundance (expression) levels were assessed using Cuffdiff  [60, 61]  for each sample with upper quantile normalization. Abundance was measured in fragments per kilobase of exons model per million reads mapped (FPKM). A matrix of FPKM values was then prepared from the results of transcriptome quantification. These expression data were subsequently log2-transformed after adding 0.
GeneChip Hybridization and Preprocessing A parallel expression profiling of the same testicular cells was performed in triplicate (rat exon 1. 0 ST GeneChip; Affymetrix). Total RNA was purified using an RNA cleanup kit (Zymo Research). One microgram of each RNA sample was processed as prescribed by the GeneChip whole-transcript sense target labeling assay (Affymetrix). Briefly, GeneChip wild-type cDNA synthesis kit, cDNA amplification kit, and terminal labeling kit (Affymetrix) were used for target preparation. Fragmented second cycle cDNA were verified with RNA Nano 6000 chips run with the BioAnalyzer (Agilent), and endlabeled cocktail hybridization was applied to GeneChip rat exon 1. 0 ST arrays. Arrays were hybridized for 16 h. A wash-and-stain script (precommercial FS450_0001 script) was applied (Station 450; Fluidics).