dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of AT/GC content and density map The AT content defines the presence of A or T in a particular sequence. Similarly, the GC content defines the presence of G or C in a given sequence. The heatmaps of Figure  3 -figure supplement 1A (AT and GC content) were built as follow: The corresponding fasta sequences of genes used in Figure  3  were retrieved with the R package ChIPpeakAnno  (Zhu et al. , 2010)  in association with the Bioconductor package BSgenome. Hsapiens. UCSC. hg19 (http://www. bioconductor. org/packages/2. 12/data/annotation/html/BSgenome. Hsapiens. UCSC. hg19. html). The command line RSAT tools  (Turatsinze et al. , 2008)  enabled to retrieve the motifs W (A or T) and S (G or C) from those sequences. With an in-house script (Source code 1), the positions of motifs were converted to GFF formatted files taking into account the positions of Refseq hg19 annotations.
Selection of enhancers and promoters using Pol II For the analyses shown in Figure  4  and Figure  4 -figure supplement 1 (scripts and procedure to detect enhancers and promoters can be found in Source code 1), wiggle files of ChIP-seq signals of Pol II, H3K4me1, H3K4me3, and H3K36me3 were analyzed to extract enriched regions at control promoters and putative intergenic enhancers. The peak-calling was performed using an in-house script (Source code 1) fixing a threshold based on the peak height and the gap between two adjacent signals. Enriched regions separated by a distance less than a fixed max gap were merged (chosen values of thresholds and max gap are summarized in Table  1 ). Regions showing a combined enrichment of Pol II, H3K4me1, and H3K4me3 signal were further identified as regions of interest to build control promoters and putative intergenic enhancer sets (Figure  4 -figure supplement 1A-E1P1).
Gene expression analysis Microarrays data of expression in Human Raji cell line was retrieved on Array Express database (E-GEOD-46873). Corresponding symbols between array chip and human hg19 Refseq annotations were downloaded from Ensembl Biomart (Release 73,  Flicek et al. , 2013). Replicates were merged by taking the mean of expression. When several probes indicated expression of a single Refseq gene, the median of expression was attributed to the corresponding Refseq ID. Refseq genes were then ordered by expression removing genes at less than 2 kb from another and particular categories of genes were removed as described in 'Materials and methods-Gene selection and average binding profiles'. Corresponding ChIP-seq and short ssRNA-seq signals were retrieved on the ordered genes and represented with heatmaps at 1 kb around TSS (Figure  2.
Acknowledgements Members of the  Jean Christophe Andrau's lab were supported by grants for ANR (Muhammad Ahmad Maqbool, Lionel Spinelli ),  CNRS  ( Romain Fenouil ),  FRM (Charlène Innocenti), Ministère de la Recherche (Nicolas Descostes)  and  Ligue Nationale contre le Cancer (Nicolas Descostes ). Work in the Dirk Eick's lab was supported by  Deutsche Forschungsgemeinschaft  Grants  SFB1064  and  SFB684. We thank  Salvatore Spicuglia  for suggestion in the manuscript. We thank  Jim Manley  and colleagues for communicating their manuscript prior submission. We thank also  Pierre Ferrier  for his advice and support. Data accession Data reported in this paper can be found at Gene Expression Omnibus database under the accession number  GSE52914.
Adult neurogenesis, the lifelong production of new neurons in the adult brain, is under complex genetic control but many of the genes involved remain to be identified. In this study, we have integrated publicly available gene expression data from the BXD and CXB recombinant inbred mouse lines to discover genes co-expressed in the adult hippocampus with Nestin, a common marker of the neural precursor cell population. In addition, we incorporated spatial expression information to restrict candidates to genes with high differential gene expression in the hippocampal dentate gyrus. Incorporating data from curated protein-protein interaction databases revealed interactions between our candidate genes and those already known to be involved in adult neurogenesis. Enrichment analysis suggested a link to the Wnt/β-catenin pathway, known to be involved in adult neurogenesis.
, 2009). The intersection of all probe sets was calculated, using a Pearson product-moment correlation coefficient (r) value greater than 0. 8 in both data sets. A correlation coefficient cutoff was used rather than a significance threshold due to the large difference in the number of lines between the two datasets (71 BXD lines, 15 CXB lines). The correlation threshold of 0. 8 had a p-value of < 1 × 10 -16 in BXD and ∼0. 00014 in CXB. Use of a significance value threshold would have resulted in many BXD genes being included which had a very small, but still significant, correlation. The threshold of 0. 8 was chosen empirically as a strong correlation, with the intention of selecting for genes highly associated with Nestin. We determined the significance of the intersecting genes by permuting the Nes-PC1 meta-trait data and re-running the analysis 1000 times.
DIFFERENTIAL GENE EXPRESSION SEARCH BASED ON THE ALLEN MOUSE BRAIN ATLAS The resulting gene list found to correlate with the Nes-PC1 metatrait for both BXD and CXB families was further constrained FIGURE 1 | The integration of data from two genetic reference populations revealed a set of 144 genes potentially expressed in adult hippocampal neural precursor cells. Principal component analysis was carried out using three probesets for Nes expression (1453997_a_at, 1418289_at, 1449022_at) in both the BXD and CXB genetic reference populations (A). The first principal component of these traits was used to create a meta-trait, Nes-PC1. This meta-trait was then correlated to all probesets in the two hippocampal expression data sets for the two genetic reference populations (B). The intersection of all probesets (r-value greater than 0.
8 in both data sets) revealed 144 probesets in common (C). These genes were then filtered based on enriched expression in the dentate gyrus (highlighted in purple) using the Allen Brain Atlas to yield a subset of 6 candidate genes most likely to be expressed in neural precursor cells (D). Zhang et al. , 2005; Wang et al. , 2013). based on in situ hybridization data registered to a common anatomical atlas, available from the Allen Institute for brain sciences  (Lein et al. , 2007;  Allen Institute for Brain Science, 2014; http://www. brain-map. org). These resources allow one to single out genes enriched in areas of interest that are defined in the anatomical atlas. Using a python script, courtesy of Dr. David Feng, we queried the Allen Brain Atlas database to generate a list of possible candidates by examining the differential gene expression patterns for the dentate gyrus, contrasted against the whole gray matter, thresholding the results at a minimum of 2-fold expression enrichment, a threshold that is comparable with values in the literature  (Tusher et al.
ENRICHMENT ANALYSIS OF CANDIDATE GENES Our candidate genes were investigated using WebGestalt (http:// bioinfo. vanderbilt. edu/webgestalt; Zhang et al. , 2005; Wang et al. , 2013)  for enrichment in GeneOntology (GO), KEGG pathways, wikipathways and transcription factor targets. This allowed us to find commonality between our candidate genes. The whole mouse genome was used as the reference set and the  Benjamini and Hochberg (1995)  method was used to correct for multiple tests. The Mammalian Adult Neurogenesis Gene Ontology, MANGO, is a database of genes known to be involved in adult hippocampal neurogenesis (http://mango. adult-neurogenesis. de;  Overall et al. , 2012). The MANGO API was used to produce a subset of genes which are expressed in type-1, -2a and -2b cells, i. e. , Nestin-positive cells (http://mango.
PREDICTED AND KNOWN INTERACTIONS WITH GENES KNOWN TO BE INVOLVED IN ADULT NEUROGENESIS GeneMANIA (http://genemania. org; Mostafavi et al. , 2008; Warde-Farley et al. , 2010)  was used to investigate known and Enrichment analysis was carried out on the six candidate genes identified (Figure  1 ) and for targets of the transcription factor Lef1. This was further investigated using enrichment of genes known to be expressed in Nestin positive cells, correlation analysis between our candidate genes and Lef1, and the use of interaction databases to find links between our candidates. predicted interactions, including protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. Our candidate genes, genes known to be expressed in Nestin positive cells and our candidate regulator Lef1 were submitted to the website and a summary network created.
CORRELATION ANALYSIS IN THE HIPPOCAMPUS OF RI STRAINS To support links found through protein-protein interactions and enrichment analysis, correlations were carried out between our candidate genes in the BXD and CXB hippocampus microarray data. The probes identified above via the Nes-PC1 meta-trait were correlated against two probes for Lef1 expression (1445568_at and 1454734_at) using the built in functionality of GeneNetwork. All online analyses and database queries were verified on 24 October 2014.
A SUBSET OF NESTIN CORRELATES ARE ENRICHED IN THE HIPPOCAMPAL NEUROGENIC NICHE A differential search was performed using the Allen Brain Atlas Resource which produced a list of 2472 genes with enhanced expression in the hippocampal dentate gyrus, contrasted against the whole gray matter of the brain. This was used to narrow the list of 144 genes obtained through correlation with our Nes-PC1 meta-trait down to six candidate genes (Figure  1D ). These candidates, correlating with Nes and enriched in the dentate gyrus, are therefore hypothesized to be involved in adult hippocampal neurogenesis. The six candidates are: Amer3 (Fam123c),  Eya3, Mtdh, Nr4a3, Polr2a, and Tbkbp1. The ISH data and expression intensity for these 6 genes are presented in Figure  2.
ENRICHMENT ANALYSIS SUGGEST LEF1 AS A COMMON REGULATOR IN NEURAL PRECURSOR CELLS Enrichment analysis was carried out using WebGestalt for our six candidate genes, Amer3, Eya3, Mtdh, Nr4a3, Polr2a, and Tbkbp1. There is significant enrichment for several GeneOntology terms related to transcription (Supplementary Table  2 ). For example "transcription, DNA-dependent," was significantly enriched (Benjamini and Hochberg adjusted p-value; adjP = 0. 0395) due to Nr4a3, Eya3, Mtdh, and Polr2a. In addition, the six candidate genes were enriched (adjP = 0. 0001) for the transcription factor Lef1, which targets Nr4a3, Eya3, Amer3, and Polr2a (Table  1 ). To investigate if this link to Lef1 is common to many adult neurogenesis genes, or just our candidates, a list of genes expressed in Nestin-positive cell stages was retrieved from the MANGO database and was tested for enrichment in transcription factor targets using WebGestalt.
To investigate whether a strong hippocampal in situ signal is a prerequisite for involvement in neurogenesis, a list of 35 genes from the MANGO database which are known to be expressed in Nestin-positive cells (Table  3 ) were examined in the Allen Mouse Brain Atlas for hippocampal expression. This showed that four genes known to be involved in adult hippocampal neurogenesis; Id1  (Nam and Benezra, 2009) , Neurog2  (Ozen et al. , 2007; Roybon et al. , 2009) , Eomes  (Hodge et al. , 2008; Azim et al. , 2013)  and Kdr  (Cao et al. , 2004; Warner-Schmidt and Duman, 2007; Segi-Nishida et al. , 2008; Bernal and Peterson, 2011)  all had low expression levels, similar to Lef1. Indeed Nestin itself appears in the Allen Brain Atlas to be poorly expressed, despite it being the dominant marker of the proliferating cell population.
The differential search option in the Allen Brain Science Institute's database allowed the identification of enhanced gene expression in a specific brain region. However, data for the subgranular zone, which is located in the immediate vicinity of the granule cell layer, and is only 2-3 cells thick, is not available at the present time. Therefore, the entire dentate gyrus was used as a proxy and compared to the whole brain gray matter. Because the subgranular zone (SGZ) is a thin band of cells located at the boundary of granule cell layer, small inaccuracies in boundary definition or spatial normalization could severely affect the analysis results. Therefore, we decided upon using the whole dentate gyrus as our best candidate for differential search. Amer3 has a well-defined enhanced expression in the granule cell layer of the hippocampus, and is a particularly good candidate since it has already been linked to embryonic neurogenesis  (Comai et al.
ACKNOWLEDGMENTS We would like to thank the  International Neuroscience Coordinating Facility (INCF)  and the  Center for Integrative and Translational Genomics of the University of Tennessee Health Science Center, Memphis, TN, USA  for funding the  INCF Short Course on Neuroinformatics, Neurogenomics  and  Brain Disease , which brought us together for this project. We are grateful to  Dr. David Feng  for his insight into querying the  Allen Brain Atlas database , and to  Dr. Richard Nowakowski  for support in the initial stages of the project. Alexandra Badea  acknowledges funding from  NIA , through  K01 AG041211 , and the  Center for In Vivo Microscopy , supported by  NIBIB  through  P41 EB015897.
In addition, these precursors produced at least 10-fold more annotated miRNAs than any other additional smRNA, as commonly observed for miRNA genes. We also used miRDeep-P  [30]  to select and classify miRNAs (Materials and methods; Figure  1 ). Sets of 542 and 71 pre-miRNA genes fall into classes 1 and 2, respectively, and 159 pre-miRNA genes from classes 3, 4 and 5, producing similar mature miRNAs to genes from classes 1 and 2, were added  [31]. In total, these 772 loci encode 416 nr mature smRNAs (Additional file 2). More than 36% (282) of these precursors have been identified thanks to the novel 140 Mbp of genomic sequences disclosed by our study. As expected, most predicted miRNAs had a 5' uridine residue (62%) and were 21 nucleotides in length (92%). However, this percentage is biased due to our selection procedure.
Second, all precursors were reanalyzed using miRDeep-P, a software commonly used for plant miRNA identification, and only smRNAs with at least one precursor accepted by miRDeep-P were considered (Figure  1 ). This allowed the selection of a restricted final set of 52 novel miRNA candidates, which fulfill all criteria for being genuine miRNAs (Additional file 5 and details in Additional file 2: Table  S2b ). As expected, at least one precursor of each novel mtr-miRNA family was located outside an annotated transposable element, a repetitive element or an exon (Additional file 5). Indeed, 15 genes lie in intergenic regions, 3 are antisense to a protein-coding gene and 34 reside in annotated untranslated regions  (7)  or introns  (27)  (Figure  2e, f ). Introns can host small nucleolar RNA, miRNA or even long non-coding RNA genes.
Nevertheless, three of the genuine novel miRNAs have a degradomevalidated target. E4D3Z3Y01BW0TQ has a corresponding cleavage site in a transcript encoding a gibberellin-regulated protein. This hormone is known to play a crucial role during mycorrhization  [59]  and this novel miRNA seems to be up-regulated four-fold in mycorrhized plants. Target prediction using miRanda gave a broader spectrum with an average of 19 candidates per novel miRNA. Furthermore, 35 of our newly identified microRNAs, including 6 genuine ones, exhibit an anti-correlated expression with their identified targets in different samples of the Medicago truncatula Gene Expression Atlas (MtGEA; Additional file 9). For 22 miRNAs, however, no target was predicted in the genome, whatever the software used. The corresponding miRNA may not target any predicted transcript, their target site may overlap two exons, or the genomic locus of the potential target may not be sequenced yet.
miRNA co-expression analysis identifies functional modules driving the M. truncatula root response to biotic interactions and symbiotic signals To identify relevant miRNA-mediated regulatory pathways involved in biotic interactions and symbiotic signals, we investigated the organization of the miRNAomes by applying weighted gene co-expression network analysis (WGCNA)  [69]. This correlation network approach identifies groups (modules) of genes with similar expression patterns and high topological overlap  [70]. The analysis of miRNA expression data from roots treated with Nod and Myc symbiotic factors and corresponding controls revealed three biologically relevant modules (colored in green, cyan and orange in Figure  9a ; another module correlating with experimental replicate variation was not considered for subsequent analyses).
On the other hand, miRNAs of the green module (positively correlated to the response to S. meliloti infection) preferentially target genes related to post-translational protein modifications, vesicle-mediated transport and nodule morphogenesis to a lower extent. These functions are in accordance with previous knowledge for symbiotic organogenesis and pathogenic interactions. Therefore, WGCNA helped us to reveal the structure of miRNA regulation in the M. truncatula root in response to pathogenic and symbiotic interactions and signals. Coordinated miRNAome responses were revealed early on when Nod and Myc symbiotic signals are detected by the plant. This response seems associated with significant differences in GO terms for the corresponding targets. The coexpression network approach revealed new and differentially expressed miRNAs grouped in distinct regulatory modules, with known miRNAs modulating plant responses towards symbiotic or pathogenic interactions.
5' protocol. In brief, a quantity of 5 μg of total RNA per library was used to elute the small RNA fraction (in a size range from 15 nucleotides to 35 nucleotides) from a 12% polyacrylamide-urea gel. Adaptors were ligated to the purified smRNAs at 5' and 3' ends and these assemblies were amplified by PCR (12 cycles). Once purified on 6% polyacrylamide gel, the libraries were analyzed using the Agilent Bioanalyzer High Sensitivity DNA and sequenced by the Genoscope (Evry, France) using a HiSeq Solexa sequencer from Illumina. Raw reads for the smRNA sequencing are available in the Gene Expression Omnibus database  [74].
M. truncatula genome assembly Whole genome identification of miRNAs and their targets is highly dependent on the availability of a robust genome sequence. BAC sequencing (Sanger) of the M. truncatula A17 genome provided the high quality Mt 3. 5. 1 genome assembly covering 262 Mbp of the estimated 500 Mbp of the genome  [25]. To complement this resource we carried out whole genome shotgun assembly of genome using Illumina technology and a combination of paired-end libraries (0. 3 to 0. 5 kb inserts, 200× coverage). Data assembly using the SOAPdenovo software (release 1. 05)  [75]  generated 46,525 scaffolds with N50 = 13 kb covering 350 Mbp, including 140 Mbp not present in Mt3. 5. 1. The combined assembly (260 Mbp Mt3. 5. 1 + 140 Mbp whole genome shotgun) was annotated using EuGene  [76]  as described in Young et al.
Computational analysis of sequencing data Raw reads from smRNA sequencing were filtered using the LeARN Pipeline  [77]  adapted for plant smRNAs. In brief, reads were cleaned (adaptor suppression, 18 to 25 nucleotide size filtering, 'N'-containing or tRNA and rRNA-like reads suppression) and the redundancy was counted and removed. For smRNA identification, sequences were retained if: (i) they matched perfectly at least one but less than 30 times (T. Faraut and E. Courcelle: glint software and source code, http://lipm-bioinfo. toulouse. inra. fr/download/glint/, unpublished) on the M. truncatula genome; and (ii) they were contained in the top 5% of the most expressed smRNAs of corresponding size in at least one library  [28]. Known miRNA sequences (present in miRBase v. 20) were also retained.
Classes 1 and 2 are considered bona fide precursors. For classes 3, 4 and 5, only precursors conserved by miRDeep-P were retained. All these data have been deposited in a dedicated website  [31]. Annotation of mature miRNAs and related precursors was performed with NCBI-BLASTN against release 20 of miRBase (June 2013). The variants of newly identified mature miRNAs were clustered into families using CD-HIT (Cluster Database at High Identity with Tolerance)  [80]  with at least 84. 2% identity. miRNA target prediction miRNA targets were predicted in silico with miRanda 3. 3a  [56]  modified to consider criteria available for plant miRNA targets and the mRNA from the Mt combined assembly annotation as targets. For this, the penalty score had to be lower or equal to 2 (gap cost = 2; mismatch cost = 1; GU pair cost = 0.
5)  [81]  and the alignment length had to be higher or equal to 18 bp (miRanda parameters: Alignment Score Threshold = 140, Energy Threshold = -20 kcal/mol). We also predicted miRNA targets using four available Mt degradomes (mycorrhizal and non-mycorrhizal M. truncatula roots  [36] ; and mercury-treated or nontreated seedlings  [37] ). Degradome data were first cleaned (trimmed of adaptor sequences and sequences containing 'N' removed). Selected degradome sequences were then perfectly mapped to M. truncatula mRNAs with the glint software and then miRNA targets were predicted with Cleaveland 3. 0  [58]  (option -s 20 read size; use of TargetFinder v1. 6  [82] ; P-value = 0. 05). To get additional information, we also used the RNAplex software (v. 0. 2)  [57]. The functional categories of targets were identified using the Gene Ontology Annotation  [83].
The GO term classification was performed with CateGOrizer  [84]  using the Plant_GOslim classification with the 'accumulative all occurrences' count method. Target IDs were also identified by equivalence on the latest version of the M. truncatula genome (Mtv4)  [79]. To evaluate the miRNA conservation in angiosperms, we searched for homologous sequences of candidates in genomes of six other species: three Fabaceae (L. japonicus, P. vulgaris and G. max), three non-legume eudicots (V. vinifera, P. trichocarpa and A. thaliana) and two monocots (O. sativa and Z. mays). smRNA sequences were mapped to the genomes using the glint software (number of mismatches ≤3).
Polymorphism analysis in M. truncatula genotypes and statistical analyses of SNP abundances We used SNP markers from the M. truncatula HapMap project  [26]  identified by aligning Illumina 90-bp sequence reads from 26 M. truncatula accessions to the M. truncatula A17 reference genome assembly v. 3. 5. Those 26 accessions were deeply resequenced up to 30× and represent the overall neutral genetic variability of the species  [51]. SNP positions in putative pre-miRNAs, mature miRNAs and 1. 5 kb upstream and downstream regions were identified based on gff3 files of pre-miRNAs and mature miRNA positions, using custom awk and bash scripts. Statistical analyses for differential SNP abundances between genome regions and conserved or novel miRNAs were performed using the R software package  [85] , using a generalized linear model with negative binomial distribution or zero-inflated models for SNPs when required.
Statistical analyses for differentially expressed miRNAs Mature miRNA counts were subjected to statistical analyses using the DESeq package  [63]  of the R/Bioconductor statistical language and customized scripts. Briefly, after adjustment for library sizes, data were modeled by a negative binomial distribution, which allows overdispersion of counts, and fitted using generalized linear models to test for differential abundances. In the absence of replicates, the 'blind' method was chosen, which conservatively estimates the dispersion for a miRNA by treating measurements between conditions as replicates, based on the observation that most miRNAs are not differentially expressed. In other cases, differential expression is tested in 'maximum' mode, where the dispersion is set to the maximum between the observed dispersion for the miRNA and a fitted curve for the dispersion estimates of all miRNAs.