dataesr/scientific-paragraphs-categorization huggingface dataset:
This is mostly the case for monoexonic and/or weakly supported transcripts. We then filtered out transcripts located in highly repetitive or low complexity areas (Methods). Although the vast majority of the transcripts had a low content of repeats and low complexity regions, we nevertheless discarded all transcripts that included >90% masked nucleotides (Supplemental Fig. S7B ). As the RNA-seq library preparation protocol does not reliably measure small RNA species  (Ozsolak and Milos 2011) , we removed all isoforms shorter than 200 nt (Supplemental Fig. S7A ). DNA contamination is an important consideration when detecting transcripts from sequencing data. Even with DNase treatment, it is possible for such contamination to produce short spurious monoexonic transcripts. Hence, we applied a filter to detect the presence of DNA contamination artifacts in our data.
2015). Next, we used gffread tool of the Cufflinks package to verify annotations and discard multiexon transcripts that have any intron with a noncanonical splice site (i. e. , not GT-AG, GC-AG, or AT-AC). Finally, we collapsed highly redundant transcript isoforms differing just by a few nucleotides (Methods). A summary of the number of genes and transcripts discarded and retained at each filtering step is shown (Table  1 ). The final HQ assembly comprises 17,885 genes and 27,183 novel transcripts not present in GENCODE. These annotations include both novel transcripts from known GENCODE genes and novel loci (Supplemental Fig. S9 ). In this set, 18,597 transcripts are spliced, whereas the remaining 8586 are expressed as single exons. This filtered set of high quality novel transcripts is provided (Supplemental Data S4) together with relevant genomic and expression features (Supplemental Data S5).
Next, we sought to establish any protein-coding potential of the HQ transcript set utilizing the CPAT algorithm for the detection of coding potential (Supplemental Fig. S10A ;  Wang et al. 2013). These results suggest that 11,756 transcripts have at least some significant coding potential, and 15,427 are true lncRNAs. This prediction is also supported by BLASTx  (Altschul et al. 1990 ) and rpstBlastN  (Marchler-Bauer et al. 2002)  analyses (Supplemental Fig. S10B ). The coordinates of coding and noncoding transcripts, respectively, are provided (Supplemental Data S6, S7). The population of HQ exons originating from spliced transcripts and not overlapping GENCODE (M4) exon annotations in any strand comprises both coding and noncoding elements (Methods; Fig. 2A, B ; Supplemental Fig. S11 ). These exons display three distinctive conservation patterns (Fig.
S13 ). This fraction of intronic transcripts is mainly caused by probes designed to target GenBank  (Benson et al. 2014 ) mRNA regions (Methods). We found 8684 genes (16,708 transcripts) overlapping known GENCODE exons on the same strand, comprising refined and expanded annotations achieved using CaptureSeq. These include totally novel unreported exons (8807), different isoforms of known exons (8981) and novel splice junctions  (11, 933). The compendium originating from the combination of these 16,708 transcripts and GENCODE (M4) is significantly more complex than GENCODE alone (Fig. 3 ; Supplemental Fig. S14 ). Overall both coding and noncoding genes show an increased number of exons or isoforms per gene (Fig. 3 ). In this context, some of the HQ isoforms extend GENCODE genes by adding alternative starts and ends.
The gene Tspan7 encodes for Tetraspanin 7, a transmembrane protein associated to X-linked mental retardation (Supplemental Fig. S19A ;  Holinski-Feder et al. 1999; Zemni et al. 2000; Abidi et al. 2002; Maranduba et al. 2004). Another example is represented by TCONS_00134637 and TCONS_00134636, a brain-specific HQ transcript expressed on the promoter of the protocadherin gene Pcdh11x in antisense orientation. Pcdh11x is crucial for the activity of the central nervous system, and its mutation is linked to female epilepsy and cognitive impairment (Supplemental Fig. S19B ;  Dibbens et al. 2008). The expanded HQ annotations led to an increase in the ORF sizes of 594 known lncRNAs, allowing CPAT to reclassify a number as protein coding. Thirty GENCODE lncRNAs without any substantial coding potential are now predicted to form 30 proteincoding transcripts in the HQ transcript set (Fig.
5 ; Supplemental Data S9). Similarly, the inclusion of exons can result in the disruption of previous ORFs, thus supporting the lack of proteincoding capability of the annotated lncRNA (965 cases) (Supplemental Fig. S20 ). A summary of the relevant statistics of genes and transcripts in the HQ set is shown (Supplemental Table  S2 ). The overall conservation (phastCons) of the refined assembly is lower than when compared to previous annotations (Wilcoxon P-value 9. 7 × 10 -134 ) (Supplemental Fig. S21 ). This suggests that with CaptureSeq, we have succeeded in identifying weakly expressed or fast evolving isoforms of GENCODE genes otherwise undetected with standard RNA-seq and conservation approaches. Similarly, the nucleotide sequence of the HQ intergenic monoexonic transcripts shows low levels of evolutionary conservation (Supplemental Fig.
To this end, we sequenced 16 RNA samples from five mouse tissues and three brain subregions with external RNA spike con-trols. These data together with GENCODE (M4) annotations were used to generate a novel and comprehensive transcriptome assembly. The linear relationship between known concentrations of RNA controls and measured quantities suggests that we have reliably measured transcripts whose expression lies in a range between 0. 06 and 117. 19 attomole/µL. This remarkable sensitivity is due to the use of CaptureSeq technology and has expanded considerably the number of exons and isoforms associated to known genes (Fig. 3 ). Additionally, we describe a comprehensive computational pipeline, containing extra safeguards and filtering steps. This system can detect previously unannotated transcripts and reduce their number to a small high quality set suitable for further validation.
The filtering pipeline also ruled out the possibility that the monoexonic intergenic transcripts in the high-quality set represent DNA contamination, low complexity/repeat artifacts, or short spurious read mappings. One possibility is that elements in this set represent species-specific lncRNA or RNAs whose sequence is not required for their function. With CaptureSeq, we succeeded in the detection of transcripts with concentrations as low as 0. 02 attomole/µL (Supplemental Fig. S5 ); deeper sequencing would still be needed to fully capture the lowest end of the transcriptome expression range. For example, the HQ transcript TCONS_00004285 improves on the annotation of Gm42415 by adding novel conserved exons (Fig. 5A ). Yet this annotation is likely to be incomplete. The adjacent gene is the heavy chain 14 of dynein (Dnah14, 17 exons), which is annotated in GENCODE as representing just a fragment of a bigger dynein sequence for which there is insufficient evidence.
We compared the much longer human DNAH14 homolog (136 exons) with a combination of mouse TCONS_00004285 and Dnah14 (Fig. 5B ). On one hand, this analysis shows that TCONS_00004285 aligns with the human dynein and constitutes an additional fragment of Dnah14. On the other hand, there is still a gap between the two parts, suggesting that some exons are still missing. Although the CaptureSeq methodology represents a new paradigm for transcript annotation and detection, many challenges still lie ahead. In particular, the problem of RNAs expressed in very specific cell types or developmental stages remain difficult to quantitate and characterize. However, we propose that other model organism annotations could benefit greatly by the CaptureSeq strategy as described herein and as shown previously for human transcripts  (Clark et al.
2015). These novel transcripts together with revised and improved annotations can shed more light onto the multifaceted transcriptional landscape of the murine genome and our understanding of transcriptional regulation in general. For example, 1193 of these high-quality novel transcript models are associated with Ensembl genes linked to disease  (Yates et al. 2016). We believe the methods, approaches, data, and annotations generated by this study will be of significant benefit to the mouse community. From top to the bottom: the GENCODE (M4) annotations, lncRNA and splice junction CaptureSeq probes, the predicted ORF, all the assembled transcripts, GenBank mRNAs, phyloP conservation, and RepeatMasker  (Smit et al. 2013 (Smit et al. -2015) )  tracks. The "ORF Track" reports the longest ORF predicted with Pinstripe  (Gascoigne et al.
LncRNA Capture design Oligonucleotide-probe target regions were designed to capture all mouse lncRNAs. Mouse lncRNAs were identified by updating and extending the method outlined in  Clark et al. (2012). Mouse RNAs were obtained from the following sources: the UCSC mm9 mRNA track (alignments between GenBank RNAs and the genome), UCSC genes (predictions based on RefSeq, GenBank, and the tRNA Genes track)  (Kent et al. 2002) , and RefSeq genes  (Pruitt et al. 2012 ) (downloaded April 10, 2012) ; also included were lincRNAs identified in  Belgard et al. (2011) , transcripts overlapping novel lincRNA loci identified in  Guttman et al. (2010) , and transcripts overlapping lincRNA loci investigated by  Guttman et al. (2011). In the initial classification, transcripts with <5% overlap with protein-coding exons from RefSeq and UCSC genes and an ORF less than 100 amino acids were putatively defined as noncoding.
Any transcripts of known lncRNAs in lncRNAdb  (Amaral et al. 2011)  were classified as noncoding. All transcripts >200 nt were selected for further filtering. Further filtering steps involved the following: 1. Any transcripts with >95% antisense overlap to a RefSeq coding gene were removed (removes many "mirror" transcripts and 3 ′ UTR transcripts with an incorrectly determined strand). 2. All multimapping or split chromosome mapping RNAs were removed. 3. Cuffcompare  (Trapnell et al. 2010 ) was utilized to create a nonredundant transcript set. Any transcripts classified as "s" or "p" (if they were downstream from a RefSeq coding gene) were removed. 4. The coding potential programs CPAT  (Wang et al. 2013) , CPC  (Kong et al. 2007 ), and PhyloCSF  (Lin et al. 2011)  were run to classify transcripts as coding or noncoding.
Using cutoffs of CPAT: 0. 44, CPC: 0, and PhyloCSF: 20, any transcript called coding by two or more methods was removed. Since not all transcripts have valid CPC and PhyloCSF scores, any transcript with a CPAT score only, and which was above the cutoff, was also removed. 5. The most highly expressed putative lncRNAs were manually examined, and any which appeared to be 3 ′ UTRs were removed. 6. Any transcripts present in lncRNAdb that were removed in Steps 1-5 were reinstated. In total 28,312 transcripts from 23,306 loci were classified as lncRNAs and targeted by the CaptureSeq array (Supplemental Data S1, S2, respectively). Of these, only 28,228 transcripts were successfully converted to the GRCm38 primary assembly using liftOver  (Kuhn et al. 2013 ) and were used for all analysis. As part of the array design, monoexonic lncRNA regions overlapping or within 50 nt of a coding exon in the same orientation were trimmed to reduce off-target capture of coding genes.
Read mapping and transcript assembly We used TopHat2, (version 2. 0. 13; -no-coverage-search; -b2-sensitive)  (Kim et al. 2013)  to align reads against the mouse genome (GRCm38) and a prebuilt transcriptome index generated using GENCODE annotations (M4, released December 3, 2014). We used Cufflinks (v2. 2. 1) to perform a Reference Annotation Based Transcript assembly (RABT) of reads mapped in each of the 16 CaptureSeq samples. We then merged the individual transcriptome assemblies into a comprehensive assembly using Cuffmerge (v2. 2. 1). The resulting assembly comprised 137,562 transcript and 59,206 gene models. We then filtered this assembly to discard transcripts on unplaced contigs. Although the unplaced contigs are valid sequences and the capture design includes probes mapping to them, we excluded these transcripts from the HQ set to allow the comparison with publicly available resources that do not make use of unplaced contigs.
Sample-to-sample distance Gene expression count quantitation was performed using HTSeq count (v0. 6. 1) (Anders et al. 2015)  on genes across the 16 samples. These count data were normalized using DESeq2 (v1. 6. 3) (Love et al. 2014) , and Euclidean distances between the samples were calculated on these regularized log-transformed counts. The distance matrix was used to compute sample-to-sample distances. The BioConductor package FactoMineR (v1. 29) was used to perform principal component analysis on this matrix.
Filtering of redundant transcripts To generate the HQ set, we removed redundant transcript isoforms. The Jaccard distance was used to assess isoform dissimilarity, comparing the union and the intersection of the transcript coordinates. For each gene, we removed all the isoforms with a Jaccard distance score >0. 98 as defined by BEDTools2 (v2. 22. 1) (Quinlan and Hall 2010).
FPKM threshold estimate Transcript assemblies with less than eightfold coverage can be considered unreliable and discarded  (Jiang et al. 2011). To estimate an FPKM threshold corresponding to eightfold coverage, we calculated a second-degree polynomial fit between mean ERCC coverage [log 2 (read counts × read length/ERCC size)] and known concentrations (log 2 ) for each sample; based on this, we calculated the concentration for which the fit value equals 8 (Supplemental Fig. S4 ). We then computed a third-degree polynomial fit between known ERCC concentrations and their FPKM values calculated by Cufflinks; based on this fit, we calculated the fitted FPKM value at the concentration for which the coverage is eightfold (Supplemental Fig. S5 ). The polynomial fits were calculated in R (version 3.
Conservation analysis The UCSC  (Karolchik et al. 2004 ) phastConsElements60way track was used to estimate transcript evolutionary conservation (Supplemental Figs. S4, S20 ). This considers phastCons-conserved elements generated using multiple alignments of 59 vertebrate genomes to the mouse GRCm38 genome. For each transcript, the scores of the conserved elements are multiplied by the number of overlapping bases and added. The total score is normalized by the sum of the exon lengths. The conservation analysis shown in Figure  2  was made with the set of HQ spliced exons not overlapping GENCODE (M4) exons in any strand. The analysis was performed with deepTools (v1. 5. 9. 1)  (Ramírez et al. 2014 ) and the 60-way phyloP  (Pollard et al. 2010 ) conservation bigWig (mm10. 60way. phyloP60way. bw) available from UCSC.
Mouse ENCODE data processing We use the following RNA polymerase II ChIP-seq data sets downloaded from the Mouse ENCODE portal (http://hgdownload. cse. ucsc. edu/goldenPath/mm9/encodeDCC/wgEncodeLicrTfbs/): cerebellum, cortex, heart, kidney, liver, olfactory bulb, testis, and whole brain. This is combined with EP300 ChIP-seq from heart and the Input samples. DHS data sets were downloaded from the Mouse ENCODE portal (http://hgdownload. cse. ucsc. edu/ goldenPath/mm9/encodeDCC/wgEncodeUwDnase): brain, cerebellum, heart, kidney, and liver. In both cases, reads were remapped to the GRCm38 assembly with Bowtie 2 (-very-sensitive option set)  (Langmead et al. 2009 ). We aligned a total of 76 samples. Alignments from multiple samples corresponding to the same tissue were merged together and transformed to bigWig format.
The ChIP-seq samples were scaled by library size and Input subtracted using bigwigCompare in the deepTools package  (Ramírez et al. 2014). Figure  4  demonstrates the ChIP-seq scaled and Input subtracted scores for the HQ genes starting upstream of the previous annotations. The panels in Figure  4A  represent the mean ChIP-seq scores, in which each panel refers to a different tissue. The light gray slopes indicate all the HQ genes starting upstream of the previous annotations. The dark gray slopes represent tissue-specific subsets, including just the genes expressed with at least 5 FPKM in each specific tissue. In Figure  4B , the curve shows median score. In this case, each gene contributes just with the score of the tissue where it is best expressed (highest FPKM). Similarly, Supplemental Figure  S15  shows the DHS support of the HQ genes starting upstream of the previous annotations.
In  (a) , each panel represents a different tissue. The purple slopes represent mean DHS values for all the HQ genes starting upstream of previous annotations. The orange curves represent tissue-specific subsets, including for each tissue just the genes expressed with at least five FPKMs. The curve shown in (b) represents the median DHS scores, in which each gene contributes with the DHS score of the tissue where it is best expressed (highest FPKM). Two brain RNA-seq replicates (GEO accession GSM1000572) were downloaded from http://hgdownload. cse. ucsc. edu/ goldenPath/mm9/encodeDCC/wgEncodeCshlLongRnaSeq, and the reads were aligned to the mouse GRCm38 genome using TopHat2, (version 2. 0. 13; -no-coverage-search; -b2-sensitive) in combination with a prebuilt transcriptome index generated using GENCODE annotations (M4, released December 3, 2014).
LncRNA expression sensitivity analysis In Supplemental Figure  S24 , the two brain RNA-seq libraries (GEO accession GSM1000572) were used to compare the lncRNA expression sensitivity. To define the lncRNA gene set, the HQ transcript models corresponding to GENCODE M4 lncRNAs were selected using BEDTools2 intersect (version 2. 25. 0) (Quinlan and Hall 2010)  with options "-split -s -F 0. 8" (requiring at least 80% overlap between the GENCODE lncRNA and the HQ model and the same strand). The genes of the selected transcripts were represented as dots in Supplemental Figure  S24. The expression readout was estimated using HTSeq count (version 0. 6. 1) gene counts added with 1 pseudo count, normalized by the total reads mapped by TopHat2, and natural logarithm transformed.
The list of RefSeq identifiers was converted to Entrez-ids and evaluated for GO biological process term enrichment with R 3. 1. 2 using the library GOstats 2. 32 and the R Bioconductor genomewide mouse annotations from package org. Mm. eg. db (version 3. 0. 0) (Falcon and Gentleman 2007; R Core Team 2014). The results were sorted by P-value, and the top 20 terms are shown. Each term is shown in a color scale representing the Benjamini-Hochberg multiple testing adjusted P-value  (Benjamini and Hochberg 1995).
Figure5. UCSC Genome Browser (Kent et al. 2002)  snapshot. (A) Example of a GENCODE lncRNA, whose matching HQ gene shows protein-coding capability. From top to the bottom: the GENCODE (M4) annotations, lncRNA and splice junction CaptureSeq probes, the predicted ORF, all the assembled transcripts, GenBank mRNAs, phyloP conservation, and RepeatMasker(Smit et al. 2013(Smit et al. -2015) )  tracks. The "ORF Track" reports the longest ORF predicted with Pinstripe (Gascoigne et al. 2012). "All assembled transcripts" highlights in gold the HQ transcript (TCONS_00004285) that adds new exons to the known lncRNA. (B) Dot plot alignment of the human DNAH14 gene (x-axis) with a combination of mouse TCONS_00004285 and Dnah14.
Acknowledgments The authors acknowledge the following funding sources: an  Australian National Health and Medical Research Council (NHMRC) Australia Fellowship  ( 631668  to J. S. M. and  631542  to M. E. D. ); an  NHMRC   Early Career Fellowship  ( APP1072662  to M. B. C. ); an  EMBO   Long Term Fellowship  ( ALTF 864-2013  to M. B. C. ); an  Australian National Health and Medical Research Council (NHMRC)  Project  Grant  ( APP1062106  to T. R. M. ) and  Career Development Fellowship  ( APP1062470  to T. R. M); and an  EMBL Interdisciplinary Postdoc (EIPOD)  under  Marie Curie Actions (COFUND)  (to G. B. ). The contents of the published material are solely the responsibility of the administering institution, a participating institution, or individual authors and do not reflect the views of  NHMRC.
2. 3 Adsorbent characterization Specific surface area, pore volume, and pore size distribution were obtained from the nitrogen adsorptiondesorption isotherm measured using a Micrometrics ASAP 2020 V1. 05H surface area analyzer by the Brunauer, Emmett and Teller (BET), and Barrett, Joyner and Halenda (BJH) methods. X-Ray diffraction (XRD) data were obtained by using a BRUKER D8 ADVANCE with Vario 1 focusing primary monochromator (Cu Kα1 radiation, λ = 1. 54059 Å). Fourier transform infrared (FTIR) spectra were recorded before and after arsenate adsorption at different initial arsenate and arsenite concentrations. The FTIR measurements were performed at room temperature in the transmission mode using a BOMEM (Hartmann & Braun) spectrometer. Determination of the zeta potential of the adsorbents was realized using a zeta potential analyzer (Zetasizer 2000, Malvern, UK).
The pH values at the point of zero charge (pH PZC ) of the samples, i. e. the pH above which the total surface of the samples is negatively charged, were measured using the pH drift method. Scanning electron microscopy with a field emission gun (FEG-SEM) was performed using a TESCAN MIRA3 electron microscope. The diameters of the nanocomposites were determined using MIRA TESCAN in-situ measurement software. Transmission electron microscopy (TEM) and high resolution TEM (HRTEM) analyses were performed on a Jeol 2100F (200 kV, Cs-corrected condenser, GIF Tridiem imaging filter) electron The arsenic concentrations in the solutions after the adsorption and kinetic experiments were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), using an Agilent 7500ce ICP-MS system (Waldbronn, Germany) equipped with an octopole collision/reaction cell, Agilent 7500 ICP-MS ChemStation software, a MicroMist nebulizer and a Peltier cooled (2 °C) quartz Scott-type double pass spray chamber.
Statistical analysis of the experimental data (error analysis) In order to select the most appropriate isotherm and kinetic models, i. e. to confirm the successfulness of the fitting of the experimental data, it was necessary to perform error analysis together with the determination of the values of the correlation coefficient (r), obtained from the regression analysis. The adsorption experiments were performed in triplicate, and only mean values are reported. The kinetic, isotherm and thermodynamic parameters and their standard errors were calculated, applying linear and/or non-linear least-squares methods, using commercial software (Microcal Origin 8. 0). The non-linear isotherm modeling has numerous benefits over linearization models: the involvement of the minimization or maximization of error distribution between the experimental data and the modeled isotherm based on its convergence criteria.
Morphological characterization The morphological characterization of nanostructural deposit of the T-Cu(A-C) samples were studied by using FEG-SEM (Fig. 3 a-c ) and TEM/HRTEM techniques (Fig. 3d  and Supporting Information Fig. S2 ). Selected area diffraction pattern (SAED) and dark field images of T-Cu(A) are given in Supporting Information Fig. S3. The results of the SEM analysis (Fig. 3 ) showed that the particles were sphere-like with corrugated T-Cu grains and prominent non-uniformity on the surface. According to MIRA TESCAN in-situ measurement software, the mean diameter of adsorbent aggregates were evaluated to be 6. 4 ± 2. 2 μm for T-Cu(A), 64. 6 ± 14. 8 μm for T-Cu(B) and 780 ± 187. 6 μm for T-Cu(C). The applied treatment under mild reducing conditions did not cause etching of the tufa surface, but instead led to slight increases in the grain diameters and higher porosities introduced by the formation of the deposit on the tufa surface.