dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Accurate predictions of the responses of organisms to climate change using SDMs require knowledge of microclimates at spatial and temporal scales relevant for studied organisms  [13, 34, 35]. To our knowledge, our study is the first to quantify the thermal heterogeneity among a set of agricultural habitats at fine spatial and temporal scales and to compare those thermal microhabitats to the most widely used global climatic dataset in SDMs. By documenting the mosaic of thermal habitats found in tropical agricultural landscapes, our study confirms previous evidence that microclimates strongly differ from nearby macroclimates due to the variability of air motion and solar radiation patterns created by complex topographies with heterogeneous elevation, slope angle, exposure or roughness  [1, 7, 18, 36].
Fine scale variations in temperature vs. climatic units Our findings show that the complex agricultural mosaic resulting from habitat types and LAI classes at the landscape scale was a major modifier of the thermal patterns in the studied tropical highlands. More importantly, our findings revealed that, at best, 55% of landscape habitats had real mean air temperatures that were well estimated by WorldClim predictions while in average less than 20% of these areas had minimum and maximum air temperatures well estimated. Additional thermal discrepancies between large and fine-scale temperatures resulted from heterogeneity in crop types and phenologies. This strongly supports the view that the common use of the WorldClim database arrayed into 1-km 2 grids may not adequately capture the reality of the climatic environment experienced by living organisms, in particular tiny ectothermic species  [2, 3, 13, 18].
Microclimates and species distribution models From tiny insects to mega-herbivores, it is well recognized that species ecology is strongly influenced by micro-climatic features of the landscape  [2, 10, 11, 12, 13]  yet quantitative information on how thermal landscape heterogeneity may affect species performance is scarce. Short-scale differences in temperatures may provide opportunities for individual organisms, even with limited dispersal capabilities, to escape unfavourable microclimates or to maximize physiological performances by selecting preferred microclimates  [38, 39]. Our analysis showed that predictions on P. operculella growth rates strongly differed between Wordclim-based and locally-measured temperatures, suggesting that global species distribution models using global coarse-scale climatic datasets without further microclimate modelling could be strongly limited to accurately predict species occurrence and performance, in particular that of ectotherms living in habitats such as mountain slopes.
This work was partly conducted within the project '' Adaptive management in insect pest control in thermally heterogeneous agricultural landscapes''  ( ANR-12-JSV7-0013-01 ) funded by the  Agence Nationale pour la Recherche (ANR , http://www. agence-nationale-recherche. fr/). A financial support of the  McKnight Foundation  (http://www. mcknight. org/)to EF during the fieldwork of this study is greatly acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
0, is available on the Samuel Roberts Noble Foundation website (http://mtgea. noble. org/v3/ index. php). Although an extensive transcript dataset is available from anatomical, growth-related, biological, physical, nutritional, and chemical treatments, a transcriptome analysis during S deficiency in non-mycorrhized and mycorrhized plants is still missing. In a previous work we elucidated the role of Medicago truncatula sulfate transporter when plants respond to S starvation and how the AM interaction might help prevent stresses due to reduced sulfate availability  (Casieri et al. , 2012). In the present work we aim to get a first overview on the transcriptional responses of this model leguminous plant during the interaction with the AM fungus Rhizophagus irregularis, in normal (+S) or deficient (-S) sulfur availability conditions, by using a customdesigned Nimblegen microarray based on the latest annotation version (Mt3.
DATA ANALYSIS Slides were scanned at 5 µm/pixel resolution using an Axon GenePix 4000 B scanner (Molecular Devices Corporation, Sunnyvale, CA, USA) piloted by GenePix Pro 6. 0 software (Axon). Scanned images (TIFF format) were then imported into NimbleScan software (Nimblegen Systems, Inc. , Madison, WI, USA) for grid alignment and expression data analyses. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. Gene expression analysis was performed using the Array Star© software package from DNA Star (2014 DNASTAR, Inc. ). A Student test was performed on the data with a false discovery rate (FDR) below 5%. Differentially expressed genes (DEGs) over different conditions were identified using a log2 (ratio) ≥1 and ≤1 filtering profile; the genes with a fold change ≤ -2 and ≥2 compared to the relative control were retained.
At the time of our analysis, no database for direct association between gene IDs and gene ontology codes (GO) was available. For this reason, we used Blast2GO® software  (Conesa et al. , 2005; Götz et al. , 2008)  to create a database for the latest Medicago truncatula genome annotation (Mt3. 5) including: annotation, GO IDs, KEGG and InterPro data (available upon request to the authors). To assess the relative enrichment of DEGs from each experimental condition compared to the whole genome, we created and tested individual files using Fisher's Exact test with Multiple Testing Correction of FDR (Benjamini and Hochberg). In addition, to visualize the gene expression profiles in different metabolic pathways we used MapMan software  (Thimm et al. , 2004)  and KEGG Maps option in Blast2GO® software.
1 -2. 41 -2. 86 Hypothetical IPR006747 (PFAM); PTHR31881 (PANTHER); PTHR31881:SF2 (PANTHER); protein CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TMhelix (TMHMM) Medtr3g027090. 1 -2. 51 -2. 26 Thioredoxin-like IPR010357 (PFAM); IPR012336 (G3DSA:3. 40. 30. GENE3D); IPR010357 (PANTHER); protein clot IPR012336 (SUPERFAMILY) Medtr4g087000. 1 -2. 90 -2. 10 gibberellin 20 no IPS match oxidase Medtr4g086970. 1 -3. 06 -2. 48 SENESCENCE- IPR002283 (PRINTS); IPR005123 (PFAM); IPR027443 (G3DSA:2. 60. 120. GENE3D); associated IPR026992 (PFAM); PTHR10209 (PANTHER); IPR005123 (PROSITE_PROFILES); nodulin 1a SSF51197 (SUPERFAMILY) Medtr1g015000.
1 -3. 40 -4. 00 Copper transporter IPR007274 (PFAM); IPR007274 (PANTHER); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); TMhelix (TMHMM) Medtr4g086990. 1 -3. 51 -2. 83 Gibberellin 20 IPR002283 (PRINTS); IPR005123 (PFAM); IPR026992 (PFAM); IPR027443 oxidase (G3DSA:2. 60. 120. GENE3D); PTHR10209 (PANTHER); IPR005123 (PROSITE_PROFILES); SSF51197 (SUPERFAMILY) Medtr4g015080. 1 -3. 94 -2. 25 Peptide no IPS match transporter ptr1 (Continued) (PHOBIUS); TRANSMEMBRANE (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); TRANSMEMBRANE (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); NON_CYTOPLASMIC_DOMAIN (PHOBIUS); TRANSMEMBRANE (PHOBIUS); CYTOPLASMIC_DOMAIN (PHOBIUS); IPR002645 (PROSITE_PROFILES); IPR002645 (SUPERFAMILY); TMhelix (TMHMM) Gene IDs, fold changes compared to +S conditions, putative annotations and associated InterProScan IDs are reported.
30. 950. GENE3D); IPR014777 (G3DSA:3. 40. 1010. GENE3D); PTHR21091 (PANTHER), PTHR21091:SF16 (PANTHER) Medtr3g026820. 1 2. 62 3. 34 Hypothetical protein no IPS match Medtr5g013530. 2 2. 49 -4. 21 Protein tify IPR010399 (PFAM); IPR018467 (PFAM) Medtr4g015420. 1 2. 36 2. 13 Glycoside hydrolase IPR001360 (PRINTS); IPR013781 (G3DSA:3. 20. 20. GENE3D); IPR017853 (SUPERFAMILY); IPR018120 (PROSITE) Medtr2g037790. 1 2. 28 3. 38 Oxysterol-binding IPR000648 (PANTHER); IPR018494 (PROSITE); SSF144000 (SUPERFAMILY) protein Medtr7g118330. 1 2. 27 3. 00 Protein late elongated IPR001005 (PFAM); IPR006447 (TIGRFAMs); IPR009057 (G3DSA:1. 10. 10. GENE3D); hypocotyl IPR017930 (PROFILE); PTHR12802 (PANTHER), PTHR12802:SF23 (PANTHER) Medtr5g013520. 1 2. 25 -2. 73 Protein tify 10a IPR010399 (PFAM); IPR018467 (PFAM) AC2255195.
1 2. 16 2. 20 Mlo-related protein IPR004326 (PFAM); PTHR31942 (PANTHER), PTHR31942:SF0 (PANTHER) Medtr3g083570. 1 2. 10 2. 52 Calmodulin binding PTHR31250 (PANTHER), PTHR31250:SF0 (PANTHER) protein Medtr3g086830. 1 2. 02 -2. 04 Hypothetical protein no IPS match Medtr7g014570. 2 2. 02 -2. 00 Isoflavone-7-o- IPR001077 (PFAM); G3DSA:3. 40. 50. 150 (GENE3D), PTHR11746 (PANTHER), methyltransferase PTHR11746:SF53 (PANTHER), SSF53335 (SUPERFAMILY) Medtr4g070120. 1 -2. 00 -3. 99 nadh dehydrogenase IPR000164 (PRINTS); IPR001450 (PFAM); IPR009072 (G3DSA:1. 10. 20. GENE3D); subunit i IPR012285 (G3DSA:1. 10. 1060. GENE3D); IPR017896 (PROFILE); IPR017900 (PROSITE); SSF54862 (SUPERFAMILY) Medtr5g098780. 1 -2. 02 -2. 15 Chlorophyll A-B binding IPR001344 (PANTHER); IPR022796 (PFAM); IPR023329 protein (G3DSA:1.
10. 3460. GENE3D); PTHR21649:SF0 (PANTHER) Medtr7g055910. 1 -2. 03 -2. 50 Ribosomal protein s12 IPR005679 (PRINTS); IPR006032 (PANTHER); IPR012340 (G3DSA:2. 40. 50. GENE3D); IPR016027 (SUPERFAMILY) Medtr4g072590. 1 -2. 03 -3. 22 Auxin-induced protein IPR003676 (PFAM); PTHR31929 (PANTHER) Medtr8g020640. 1 -2. 04 -2. 44 Serine threonine kinase IPR000719 (PROFILE); IPR001245 (PFAM); IPR008271 (PROSITE); IPR011009 (SUPERFAMILY); IPR017441 (PROSITE); G3DSA:1. 10. 510. 10 (GENE3D), G3DSA:3. 30. 200. 20 (GENE3D), PTHR24420 (PANTHER), PTHR24420:SF191 (PANTHER) Medtr4g106580. 1 -2. 08 -2. 03 Nadh-plastoquinone PTHR22773 (PANTHER), PTHR22773:SF61 (PANTHER) oxidoreductase subunit 4 (Continued).
1 -2. 27 -2. 82 nadh dehydrogenase IPR001750 (PFAM); PTHR22773 (PANTHER), PTHR22773:SF61 (PANTHER) subunit 4 Medtr7g118030. 1 -2. 32 -2. 04 Hypothetical protein no IPS match Medtr8g073850. 1 -2. 44 -2. 25 Disease resistance IPR004265 (PFAM) response protein Medtr7g017540. 1 -2. 45 -2. 18 Photosystem ii protein IPR000484 (G3DSA:1. 20. 85. GENE3D) d1 Medtr7g013040. 1 -2. 48 -2. 83 Photosystem i IPR001280 (G3DSA:1. 20. 1130. GENE3D); SSF81558 (SUPERFAMILY) apoprotein a1 Medtr8g106380. 1 -2. 54 -2. 69 Hypothetical protein no IPS match Medtr3g007900. 1 -2. 57 -2. 10 Gibberellin induced IPR003854 (PFAM) protein Gene IDs, fold changes compared to NM plants, putative annotations and associated InterProScan IDs are reported.
1 2. 39 2. 57 Glycoside hydrolase IPR000322 (PANTHER); IPR011013 (SUPERFAMILY); PTHR22762:SF9 (PANTHER), SignalP-NN(euk) (SIGNALP), tmhmm (TMHMM) Medtr3g010780. 1 2. 35 2. 26 Pectinesterase IPR000070 (PFAM); IPR006501 (G3DSA:1. 20. 140. GENE3D); IPR011050 inhibitor (SUPERFAMILY); IPR012334 (G3DSA:2. 160. 20. GENE3D); IPR018040 (PROSITE); PTHR31707 (PANTHER), PTHR31707:SF0 (PANTHER) Medtr5g006710. 1 -3. 32 -2. 79 12-oxophytodienoate IPR001155 (PFAM); IPR013785 (G3DSA:3. 20. 20. GENE3D); PTHR22893 (PANTHER), reductase PTHR22893:SF13 (PANTHER), SSF51395 (SUPERFAMILY) Medtr3g073470. 1 -3. 43 -2. 33 Serine hydroxymethyl- IPR001085 (PANTHER); IPR015421 (G3DSA:3. 40. 640. GENE3D); IPR015422 transferase (G3DSA:3. 90. 1150. GENE3D); IPR015424 (SUPERFAMILY); IPR019798 (PROSITE); PTHR11680:SF0 (PANTHER) Medtr8g075420.
1 -3. 52 -2. 39 Thiosulfate IPR001763 (G3DSA:3. 40. 250. GENE3D); PTHR13253 (PANTHER), PTHR13253:SF25 sulfurtransferase (PANTHER) Medtr7g058930. 1 -3. 64 -2. 37 Tetratricopeptide TPR1 IPR001440 (PFAM); IPR011990 (G3DSA:1. 25. 40. GENE3D); IPR019734 (PROFILE); PTHR23083 (PANTHER), PTHR23083:SF190 (PANTHER), SSF48452 (SUPERFAMILY) Medtr7g100320. 1 -3. 79 -2. 13 Glutathione IPR004045 (PROFILE); IPR010987 (G3DSA:1. 20. 1050. GENE3D); IPR012336 S-transferase (G3DSA:3. 40. 30. GENE3D); IPR017933 (PROFILE); PTHR11260 (PANTHER), PTHR11260:SF14 (PANTHER), PF13410 (PFAM), PF13417 (PFAM) Medtr5g054730. 1 -4. 15 -2. 41 Lysine histidine IPR013057 (PFAM); PTHR22950 (PANTHER), PTHR22950:SF9 (PANTHER) transporter Medtr4g086980. 1 -4. 73 -2. 60 gibberellin 20 oxidase IPR002283 (PRINTS); IPR005123 (PFAM); IPR026992 (PFAM); G3DSA:2.
60. 120. 330 (GENE3D), PTHR10209 (PANTHER), PTHR10209:SF55 (PANTHER), SSF51197 (SUPERFAMILY) Medtr5g010410. 1 -4. 93 -2. 75 Heavy metal p-type IPR001757 (PRINTS); IPR005834 (PFAM); IPR023214 (G3DSA:3. 40. 50. GENE3D); atpase IPR023299 (G3DSA:3. 40. 1110. GENE3D); PTHR24093 (PANTHER), PTHR24093:SF42 (PANTHER) Medtr6g012140. 1 -5. 65 -2. 52 Quinone IPR002085 (PANTHER); IPR016040 (G3DSA:3. 40. 50. GENE3D); PF13602 (PFAM), oxidoreductase-like SSF51735 (SUPERFAMILY) protein Medtr1g079520. 1 -5. 92 -3. 05 Protein kinase IPR000719 (PFAM); IPR001611 (PFAM); IPR002885 (PFAM); IPR003034 (G3DSA:1. 10. 720. GENE3D); IPR008271 (PROSITE); IPR011009 (SUPERFAMILY); IPR013210 (PFAM); IPR017441 (PROSITE); G3DSA:1. 10. 510. 10 (GENE3D), G3DSA:3. 80. 10. 10 (GENE3D), PTHR24420 (PANTHER), PTHR24420:SF134 (PANTHER), PF13516 (PFAM), PF13855 (PFAM), SSF52047 (SUPERFAMILY), SSF52058 (SUPERFAMILY), SSF68906 (SUPERFAMILY) (Continued).
1 -8. 91 -4. 29 Gibberellin 20 oxidase IPR002283 (PRINTS); IPR005123 (PFAM); IPR026992 (PFAM); G3DSA:2. 60. 120. 330 (GENE3D), PTHR10209 (PANTHER), PTHR10209:SF55 (PANTHER), SSF51197 (SUPERFAMILY) Medtr7g092240. 1 -9. 36 -4. 20 Oligopeptide IPR004648 (TIGRFAMs); IPR004813 (PFAM); PTHR22601 (PANTHER) transporter opt family Medtr5g054300. 1 -11. 19 -2. 15 SANT/Myb domain IPR001005 (PFAM); IPR006447 (TIGRFAMs); IPR009057 (G3DSA:1. 10. 10. GENE3D); IPR017930 (PROFILE); PTHR31003 (PANTHER) Medtr3g109280. 1 -11. 35 -3. 65 Unknown protein IPR006747 (PFAM); PTHR31881 (PANTHER) Medtr6g012100. 1 -11. 55 -3. 08 Quinone PF13602 (PFAM) oxidoreductase-like protein Medtr4g086990. 1 -17. 83 -13. 41 Gibberellin 20 oxidase IPR002283 (PRINTS); IPR005123 (PFAM); IPR026992 (PFAM); G3DSA:2. 60. 120. 330 (GENE3D), PTHR10209 (PANTHER), PTHR10209:SF55 (PANTHER), SSF51197 (SUPERFAMILY) Medtr4g132970.
1 -19. 54 -3. 32 Unknown protein IPR006502 (TIGRFAM); IPR006502 (PFAM); PTHR31579 (PANTHER) Medtr1g015000. 1 -19. 88 -4. 12 Copper transporter IPR007274 (PANTHER); PTHR12483:SF1 (PANTHER) Medtr4g086970. 1 -20. 28 -7. 03 Senescence- IPR002283 (PRINTS); IPR005123 (PFAM); IPR026992 (PFAM); G3DSA:2. 60. 120. 330 associated nodulin (GENE3D), PTHR10209 (PANTHER), PTHR10209:SF55 (PANTHER), SSF51197 1a (SUPERFAMILY) Medtr6g012170. 1 -32. 25 -5. 25 Quinone IPR002085 (PANTHER); IPR002364 (PROSITE); IPR011032 (SUPERFAMILY); oxidoreductase-like IPR013154 (PFAM); IPR016040 (G3DSA:3. 40. 50. GENE3D); G3DSA:3. 90. 180. 10 protein (GENE3D), PTHR11695:SF34 (PANTHER), PF13602 (PFAM), SSF51735 (SUPERFAMILY) Medtr6g012160. 1 -38. 03 -6. 31 Quinone IPR002085 (PANTHER); IPR002364 (PROSITE); IPR011032 (SUPERFAMILY); oxidoreductase-like IPR013149 (PFAM); IPR013154 (PFAM); IPR016040 (G3DSA:3.
40. 50. GENE3D); protein G3DSA:3. 90. 180. 10 (GENE3D), PTHR11695:SF34 (PANTHER), SSF51735 (SUPERFAMILY) Medtr5g083770. 1 -40. 53 -10. 71 Sodium-coupled IPR013057 (PFAM); PTHR22950 (PANTHER) neutral amino acid transporter Medtr4g132940. 1 -41. 06 -4. 51 Protein IPR006502 (TIGRFAM); IPR006502 (PFAM); PTHR31579 (PANTHER) Medtr7g093010. 1 -43. 15 -25. 14 myb family IPR001005 (PFAM); IPR006447 (TIGRFAMs); IPR009057 (G3DSA:1. 10. 10. GENE3D); transcription factor apl PTHR31314 (PANTHER) (Continued).
Two datasets of RNA sequences were obtained in this study. The first dataset was obtained from Family A. The experimental design for Family A contained two treatments (mock and Fpinfection), two time points (3 and 5 dpi) and six biological replicates (Table  S1 ). The second one was generated from Family B, which were used only to validate those expressed genes with SNPs (SNP-EGs) detected from Family A. A single trial with three biological replications was conducted for these four sets of NILs. Mocks were not used and samples were collected at 5 dpi only. Before RNA isolation, samples from the three biological replications for each of the eight isolines were pooled. Thus, a total of 8 samples (each for a different line of the four sets of NILs) were used for RNA extraction.
Data quality control For Family A, RNA-seq Illumina fastq sequence datasets were pooled by replicate (6 replicates per time point) at time points 3 dpi and 5 dpi. For Family B RNA sequences were pooled by response to FCR inoculation only (+/2) due to the fact that there was no biological replication used. FastQC (version 0. 10. 1) was used as a preliminary check that the Phred scores were acceptable. BioKanga (version 2. 76. 2; developed by bioinformatics team, unpublished) filtering was then employed and reads containing polymorphic variation but not supported by at least two other overlapping reads were removed from the datasets. Additionally, all but one instance of any duplicated reads were removed to reduce the effect of PCR artefacts. All retained reads for alignment were unique.
Exploratory analysis of variance factors in gene expression patterns A total of 152 Gb sequences were obtained from Family A and 77 Gb sequences from Family B. Prior to fitting models, some basic exploratory analysis was conducted looking at the variance components for both Family A and B based on sequence reads. All samples were consistent and no outliers were observed. For Family A, the largest proportion of the variance was driven by time point. Principal component analysis (PCA) demonstrated that the samples may be separated by the first two components according to the time point and the genotype (Figs. S1A  and S1B ). The hierarchical clustering (Fig. S1C ) was consistent with the PCA. For Family B, the experimental run effect was quite strong. However, a large proportion of the variance still contributed to the genotypic effect (data not shown).
Two somatic pseudogenes were novel isoforms of their template genes not recorded in the Ensembl database. We did not observe any base substitutions in the somatic pseudogenes, implying that the reverse transcriptase has reasonable fidelity and the template transcripts had not undergone RNA editing. On the basis of these characteristics, somatic pseudogenes, like their germline counterparts, are probably a product of the reverse transcription and transposition capability of endogenous LINE elements acting on mature mRNA transcripts. Most compelling is the presence of target-site duplications of 8-20 bp, with the point of insertion of the polyA tail almost universally occurring within a TTTTAA or very similar motif (Fig. 2b ), which is the classic signature of LINE retrotransposition  15, 16.
Methods Sequence data. Sequencing data comprised low coverage (2-5 Â genome coverage) paired-end sequencing for genomic rearrangements  [32] [33] [34]  , high-coverage (30-40 Â ) paired-end shotgun sequencing  35, 36  and targeted pull-down and sequencing of the coding exome  37, 38  generated at the Wellcome Trust Sanger Institute as described, both published and unpublished, on the Illumina HiSeq platform (Supplementary Table  1 ). In total, 660 cancer samples (629 primary samples and 31 cell lines) spanning 18 tumour types were analysed (Supplementary Table  2 ). The identity of cell lines was confirmed by STR testing and were obtained from ATCC. The Cambridgeshire Local Research Ethics Committee approved the studies and all patients gave informed consent. Pseudogene detection. Data were aligned to both the reference genome (GRCh37) and the reference Ensembl transcriptome using BWA  39  and the alignment coordinates from the transcriptome mapping were converted back to genome space.
The coloured shapes denote a kernel density plot of the distribution of gene expression levels for all genes. Due to the non-normal distribution, we used Wilcoxon rank-sum tests to test whether expression levels of template genes for somatic pseudogenes were different to that expected. (b) RNA-sequencing data showing expression of the MLL-KRT6A fusion gene. (c) Deletion of the promoter and first exon of MGA during somatic pseudogene insertion, leading to abrogation of expression from that allele. intervening intron(s) aligned with a deletion annotated in the Database of Genomic Variants, as this indicates a potential germline pseudogene. Transcriptome alignments were used to identify reads that split across exon-exon boundaries by visual inspection in IGV (Integrative Genomics Viewer). Due to the large size of the genome, the presence of repetitive elements, which tend to be more prevalent in non-coding sequences, and the presence of multiple genomic structural rearrangements in many tumour types, genome data required a custom pipeline to be developed (Perl scripts available on request).
Total RNA was extracted using Trizol and sequencing libraries prepared by standard Illumina RNA-sequencing of polyA-selected RNA. A 75-bp paired-end sequencing was used and between 24 and 31 Gbp of data were generated per sample. TopHat (v 1. 3. 3) was used to map reads to the genome. The quality of the RNA-Seq data was assessed using a number of metrics, including absence of 3 0 bias and low amounts of ribosomal RNA. We confirmed the positions, insertion sites and expression of somatic pseudogenes in RNA-Seq using two algorithms (Shlien A. , unpublished). First, we looked for discordantly mapped read pairs where one end mapped to the pseudogene and the other near the integration site. We then evaluated grouping of pairs by the consistency of the orientation in which the reads mapped, their position and their overlap with regions of high homology (multi-mapping).
Second, we looked for reads that could be partially mapped to the pseudogene and partially mapped to the insertion site (split reads). To do so, we mapped all the RNA-seq data to a transcriptome database containing all known exon-exon junctions. We then shattered and remapped all of the remaining reads into k-mers (13 bp) to an indexed version of the human genome. Mapped fragments were extended one base at a time so long as they maintained a single mapping position. In this way we resolved the breakpoints of the expressed somatic pseudogenes. Note that in the absence of fusion transcript formation, re-expression of the pseudogene from its insertion site was indistinguishable from expression from the template copy.
Acknowledgements This work was supported by the  Health Innovation Challenge Fund , the  Wellcome Trust  (grant reference  077012/Z/05/Z ), and the  Kay Kendall Leukaemia Fund. P. J. C. and S. M. J. are personally funded through  Wellcome Trust Senior Clinical Research Fellowships. A. S. is funded by the H. L. Holmes Award  from the  National Research Council Canada  and an  EMBO  fellowship. UAM is personally supported by a  Cancer Research UK   Clinician Scientist Fellowship. NB was supported by a starter grant from the  Academy of Medical Sciences  and from  Lady Tata Memorial Trust. The breast cancer sequencing was supported by the  BASIS  project. The  BASIS  project is a European research project funded by the  European Community 's  Seventh Framework Programme  ( FP7/2010-2014 ) under the grant agreement number  242006.