Supplementary Fig. 1 Composition of small RNA populations during 2 MEF reprogramming. Continued on next page.

Transcription

1 Supplementary Fig. 1 Composition of small RNA populations during 2 MEF reprogramming. Continued on next page.

2 Supplementary Fig. 1 Composition of small RNA populations during 2 MEF reprogramming. (a), Proportion of tags mapping to mirna loci across the 13 samples of the high/low OSKM trajectories and controls. (b), Length distribution of tags mapped to the nuclear genome outside mirna loci in selected libraries. (c), Length distribution of tags mapped to mitochondrial DNA in selected libraries. (d), Length distribution of tags mapped to known mirna loci (mirbase v18) in selected libraries. (e), Tags were grouped by genome feature and groups then clustered by expression across the 13 samples (thresholded to groups within the top 99 th percentile of expression in at least one sample). Tag length distribution and highest proportion of library reached is given on the right for each genome feature, which are further colour-coded as follows: annotated mirna loci, orange; mtdna loci, green; other loci, black. A subset of nuclear encoded loci gave rise to tags of 22 nt modal length (dust and trf simple repeats, LTR/ERVK, LINE/L1, LTR/ERVL-MaLR, SINE/MIR and non-assigned tags), potentially representing un-annotated mirnas in some cases, and showed little change in bulk expression. Relative to other small RNA species, expression of mirna (and mirna-sized small RNAs) is relatively stable during reprogramming.

3 Supplementary Fig. 2 Small RNAs mapped to mitochondrial DNA. Continued on next page.

4 Supplementary Fig. 2 Small RNAs derived from mitochondrial DNA. (a), Tag density distribution across the mitochondrial genome with schematic of mtdna features shown above. Two differently scaled views are shown to highlight distinct tag abundances in selected samples as indicated on the right. (b), Detailed view of tag density around specific trna loci (left to right, trnas: MT-TF, MT-TV and MT-TI, MT-TQ, MT-TM) and within the D-loop region (LSP, light strand promoter, HSP, heavy strand promoter, CSBs, conserved sequence block, Ori H, Origin of DNA replication). Genomic co-ordinates of features are shown below (mm9 assembly). (Colour code for tag density: blue, heavy strand; orange, light strand) (c), Normalised expression of short RNA (sml RNA) at selected mtdna loci across the 13 samples of the high/low OSKM trajectories and controls. All tags beginning at the nucleotide position indicated in brackets were included. Normalised expression of overlapping transcripts and/or surrounding trnas derived from long RNA sequencing 2 is also included (long read). The function of mt-dna encoded small RNAs is currently not known 3, although lack of correlation with long RNA from overlapping and surrounding loci for the trna-associated small RNAs suggests they may have independent functions 4, and good correlation of LSP small RNAs with surrounding long RNA reads suggests these are light strand promoter primers 5.

5 Supplementary Fig. 3 Detailed view of mirna processing variants during reprogramming. Contribution of non-canonical processing variants to mirna expression across 13 samples represented in Fig. 1a as red, blue and black circles. (a), 5 arm bias for each mirna hairpin precursor. (b), 5 isomir expression for each mature mirna. (c), 3 isomir expression. (d), Nontemplated additions in the last 2nt of tags (NTA). Stacked bar graph shows the observed base frequency for all tags with NTA. (e), Incidence of internal editing. mir-5099, mir-107-3p and mir- 92a-3p (grey circles) were excluded due to sequence discrepancies between genome and mirbase sequences (suggesting these observations are not true editing events). NOTE: mir-5117 has a SNP consistent with the observed base substitution (rs , dbsnp). All graphs display data for 424 mirnas with an average expression of >25 counts per million (CPM) per sample. Median variant proportion of total tags across all samples is shown, bars represent 25 th and 75 th percentiles; plotted against average tag abundance). mirnas with variation of isoform proportion between samples exceeding 20% are labelled in red, although visual inspection of the individual examples did not reveal any consistent trends along reprogramming trajectories. Furthermore, the relatively constant levels of NTA for each mirna despite major changes in abundance suggest that NTA might be a mirna feature with a function other than regulating mirna turnover 6. Shaded areas indicate mirnas with a proportion of 20% isoform variant expression.

6 Supplementary Fig. 4 Contribution of processing isoforms to mirna expression at the mir- 290/95 locus and targeting of Clock mrna by mir-292-3p. Continued on next page.

7 Supplementary Fig. 4 Contribution of processing isoforms to mirna expression at the mir- 290/95 locus and targeting of Clock mrna by mir-292-3p. (a) Schematic of the locus organisation with tag density (2 ipsc), incidence of hairpin arm bias, 5 and 3 isomirs, as well as non-templated additions indicated below. (b), Detailed views of tag density (mir-291a-5p) or sequences (mir-292 hairpin) are shown to highlight non-templated additions and isomir variability. Representative data from the 2 o ips sample are shown. (c), mir-292-3p was chosen for further analysis as it is the most highly expressed mirna from this locus exhibiting the ESC-dominant AAGUGC seed hexamer, shared between members of the mir-290/95, mir-302/367 and mir-17/92 clusters 7. Custom targetscan target prediction for the mir-292-3p isomirs were performed using 3 UTRs as observed in the long RNA-seq data generated in parallel 2. Gene ontology and KEGG pathway analysis of targets (performed using DAVID 8 ) suggests involvement of both 5 isomirs in regulating transcription and the cytoskeleton, whereas the canonical form is implicated in regulating protein catabolism and cell cycle. Bold circles indicate statistically significant enrichment (p-value <0.05, Benjamini-Hochberg multiple comparison test). Gene ontology terms were derived from GO:Biological process, unless specified. (d), To shortlist putative new targets of mir-292-3p we required multiple target sites to be present with a degree of anti-correlation between expression of mir-292-3p and the target. Clock, a master regulator of circadian rhythm (which is lost during reprogramming 9 ) and is also a predicted target of other mirnas with the AAGUGC seed, emerged as the best candidate for validation: i, Schematic of Clock mrna indicating two putative mir-292-3p target sites (site 1 and site 2) within its 3 UTR as observed in the long RNA-seq data generated in parallel 2. ii, Minimum free energy (MFE) calculations for both sites are shown with the predicted structure of mir-292-3p and its putative target. The DNA conservation at each site from vertebrate and avian species is shown (light grey shading); the seed region is indicated (black shading); the predicted bases bound by mi-292-3p are underlined. iii, Expression of mir-292-3p and Clock mrna across the 13 samples of the high/low OSKM trajectories and controls. iv, Validation of mir-292-3p targets by dual-luciferase assay showing the luciferase constructs used to test mir-292-3p binding. The ratios of Renilla versus firefly luciferase values were calculated and normalised to wildtype mir-292-3p binding site transfected with control mirna mimic. Each plasmid and mirna mimic pair was tested in six to eight independent experiments, each performed in triplicate. Data represent mean ± s.e.m and statistical significance was determined by Mann-Whitney U-test.

8 Supplementary Fig. 5 Contribution of processing isoforms to mirna expression at the mir- 302/367 locus. (a), Schematic of the locus organisation with tag density (2 ipsc), incidence of hairpin arm bias, 5 and 3 isomirs, as well as non-templated additions indicated below. (b), Detailed views of tag density (mir-302c-3p) or sequences (mir-302d hairpin) are shown to highlight non-templated additions and isomir variability. Representative data from the 2 o ips sample are shown. (c), mir-302d was chosen for further analysis as it is the most highly expressed mirna from this locus exhibiting the ESC-dominant AAGUGC seed hexamer, shared between members of the mir-290/95, mir-302/367 and mir-17/92 clusters 7. Custom targetscan target prediction of three prevalent mir-302d-3p 5 isomirs (seed homology with other mirnas expressed from this locus shown on right) 3 UTR as observed in the long RNA-seq data generated in parallel 2. Gene ontology and KEGG pathway analysis of targets (performed using DAVID 8 ) suggests these isomirs cooperate in regulating many similar reprogramming-related pathways. Bold circles indicate statistically significant enrichment (p-value <0.05, Benjamini-Hochberg multiple comparison test). The term cell cycle is derived from GO: Biological Process.

9 Supplementary Fig. 6 Validation of expression changes for 17 mirnas by qpcr. RNA for qpcr validation was obtained from the same samples used for sequencing and results normalised to total RNA, then to the average expression of 5 mirnas (mir-191-5p, p, -16-5p, p and -30e-5p) chosen because of their invariant expression in NGS data and confirmed to be a relatively stable reference by qpcr (shown in Fig. 1c). Expression measured by qpcr (orange, n=1) and sequencing (blue, n=1) is shown and mirnas are divided into three groups based on relative expression in F-class and ESC-like samples. Boxed mirnas were >4-fold different between ipsc and F-class states, represented in the top 95% of mirnas in at least one ipsc or F-class sample and included as class identifying mirnas in Supplementary Fig. 12. Note, D16L data was removed due to compromised cdna.

10 Supplementary Fig. 7 Hierarchical clustering of mirna expression profiles in reprogramming cell states and related Gene Ontology. Continued on next page.

11 Supplementary Fig. 7 Hierarchical clustering of mirna expression profiles in reprogramming cell states and related Gene Ontology. (a), Expression profiles were normalised to the average expression of each individual mirna across the 13 samples of the high/low OSKM trajectories and controls and log 2 transformed prior to clustering using Pearson correlation with complete linkage and 10,000 rounds of bootstrapping 10. The heatmap contains expression profiles of 217 mirnas that were expressed within the 95 th percentile of expression in at least one sample. Discrete clusters were defined as those with the highest-level node having a greater than 60% likelihood of co-clustering. A subset of clusters were categorised based on expression in the high OSKM trajectory and ESC-like cells. These subsets are shown in Fig. 3a and all clusters are detailed in Supplementary Data 1. (b), GO term Biological Process enrichment analysis of mirna expression clusters shown in a. using experimentally verified vertebrate mirna targets (see Methods). Terms with significant enrichment in at least one cluster are shown with selected parental terms (p<0.01, Benjamini-Hochberg multiple comparison test). Equivalent enrichment patterns were seen with GO Molecular Function and Cellular Compartment (not shown).

12 Supplementary Fig. 8 Early mirna expression changes under high OSKM conditions. Continued on next page.

13 Supplementary Fig. 8 Early mirna expression changes under high OSKM conditions. Samples were taken at 24-hour intervals for four days following addition of 1500 ng/ml of doxycycline to 2 MEF (see green dots in schematic shown in Fig. 1a), followed by small RNA library preparation and NGS analysis to an average depth of ~29.4 million mapped tags (See Methods, Supplementary Methods, Supplementary Data 2 and for details). 134 mirnas that were within the 95 th percentile of expression in at least one sample were included for further analyses. (a), Principal component projection plots for five sample mirna profiles in this set. (b), Percentage of differentially expressed mirnas (> 4-fold) at successive time points (up-regulated, yellow; down-regulated, blue). (c), mirna expression profiles were clustered as detailed for Supplementary Fig. 7, and shown on the right (with key constituent mirnas indicated). (d), Gene ontology (Biological Process) enrichment analysis of mirna expression clusters shown in c using DAVID 8 and experimentally verified vertebrate mirna targets from mirtarbase 11. Terms with significant enrichment in at least one cluster are shown with selected parental terms (p<0.01, Benjamini-Hochberg multiple comparison test). Equivalent enrichment patterns were seen with GO Molecular Function and Cellular Compartment (not shown). mirnas that changed expression early had diverse known functions resulting in no enriched gene ontologies within their targets as a whole. Later mirna changes were associated with targeting of pathways and processes involved in reprogramming such as cell cycle, cell survival, gene expression, development and differentiation.

14 Supplementary Fig. 9 Expression of mature and pri-mirnas, and associated epigenetic modifications at promoters. Continued on next page.

15 Supplementary Fig. 9 Expression of mature and pri-mirnas, and associated epigenetic modifications at promoters. Shown is an analysis of mature mirnas that were expressed within the 95 th percentile of expression in at least one sample, had observable pri-mirna expression in long RNA sequencing and could be unambiguously assigned to one genomic locus (128 mature mirnas and 71 loci). Transcriptional start sites were determined from the 5 end of observed primirna. (a), Expression profiles of mature and pri-mirnas were normalised to the average expression across the 13 samples of the high/low OSKM trajectories and controls. Corresponding levels of H3K4me3, H3K27me3 and DNA methylation at the promoters of these loci are shown. (b), Global median levels of H3K4me3 at mirna promoters. (c), Global median levels of H3K27me3 at mirna promoters. (d), Global median levels of DNA methylation at mirna promoters. b-d, Line shows median and whiskers show 5-95% range. Base data for figure is in Supplementary Data 1. Note: When assigning promoter regions based on observed pri-mirnas the mirnas of the imprinted Dlk1-Dio3 cluster are the main contributors to the group exhibiting high DNA methylation at promoters (shown with asterisk). DNA methylation levels of previously reported regulatory DMRs for the region are shown in Supplementary Fig 12.

16 Supplementary Fig. 10 Posttranscriptional mechanisms and complex transcriptional regulation can explain lack of correlation between mature mirna and pri-mirna expression. (a), Heatmaps of 16 mature mirnas (from 15 genomic loci) whose expression is negatively correlated with pri-mirna expression across the 13 samples of the high/low OSKM trajectories and controls. Mature mirna (top) and pri-mirna (bottom) expression was normalised to the average expression across the 13 samples and grouped into: Novel intragenic promoter, due to an intronic H3K4me3 peak, see panel (b); post-transcriptional, where there is no evidence for complex transcriptional regulation; Multiple pri-mirnas, where multiple promoters/overlapping pri-mirnas are observed (e.g. mir-17-5p, detail shown in Supplementary Fig. 11); complex genomic region, where assignment of relevant promoter and pri-mirna was difficult. Mature mirna name, genic/intergenic location and Pearson s correlation with pri-mirna expression (orange) are indicated on the right. (b), mir-149-5p does not correlate with host gene expression but instead with the activity of a novel internal promoter. Above, Ensembl transcript of Gpc1 (mir-149 host gene) and location of mir-149. Below, Normalised read coverage of long RNA sequencing (pri-mirna), epigenetic marks (ChIP sequencing) and percentage of DNA methylation 2,12. H3K4me3 indicates presence of a novel promoter for mir-149, which is supported by RNA sequencing (not shown). mir-149 could be transcribed from either or both of these transcripts.

17 Supplementary Fig. 11 Complex control of mir-17/92 group of mirnas expression across multiple genomic loci during reprogramming. Continued on next page.

18 Supplementary Fig. 11 Complex control of mir-17/92 group of mirnas expression across multiple genomic loci during reprogramming. Above, Refseq (black) and further Ensembl (orange) annotated transcripts of the genomic region surrounding the pri-mirnas of mir-17/92 (Mir17hg), mir-106b/25 (Mcm7) and mir-106a/363 (Kis2). Below, Normalised read coverage of pri-mirna, epigenetic marks and percentage of DNA methylation 2,12, with transcriptional start sites indicated by black arrows (derived from the 5 ends of observed pri-mirna and supported by promoter H3K4me3). The mir-17/92 cluster is poorly correlated with its pri-mirna, which might result from observed pri-mirna expression being a conglomeration of multiple transcripts of unknown structure. RNA sequencing and multiple H3K4me3 promoter peaks, one of which is prominently expressed only in the F-class state (TSS indicated by arrow in D16H H3K4me3 panel), support this interpretation. (Note, RNA sequencing also suggests expression of 3 extended variants of pri-mir-17/92 than have previously been annotated). The mir-106b/25 cluster is poorly correlated with its pri-mirna, but this is likely due to post-transcriptional mechanisms (no evidence of complex transcriptional regulation). The mir-106a/363 cluster is extremely well correlated with its pri-mirna expression and is repressed in the F-class state by sequential H3K27me3 and DNA methylation (which are both relieved in ipscs/escs).

19 Supplementary Fig. 12 Expression profile of mirnas with distinct expression between F-state and ipsc cells. Continued on next page.

20 Supplementary Fig. 12 Expression profile of mirnas with distinct expression between F-state and ipsc cells. (a), The mirna profile of an established F-class cell line (clone 1 Day 30, green 13 ) and those of the 13 samples of the high/low OSKM trajectories and controls were clustered as detailed for Supplementary Fig. 7. Only the 67 mirnas with >4-fold difference in expression between F-class and ipscs from Fig. 6a were included and raw tag counts from all libraries were TMM normalized prior to clustering (as detailed in Supplementary Data 1). (b), Expression of primirna and epigenetic signatures around their transcriptional start sites (TSS). Expression profiles were normalised to the average expression of each individual pri-mirna across the 13 samples of the high/low OSKM trajectories and controls. For the mirnas in panel (a) where a TSS could be unambiguously assigned is shown the level of H3K4me3 and H3K27me3, as well as % of DNA methylation. The regulatory DMR for the Dlk1-Dio3 locus mirnas was derived from previous reports 14, mm9 coordinates Chr12:110,768, ,768,750 (see Methods). (c), Pearson s correlation of pri-mirna expression with mature mirnas listed in panel (b), over all samples or in samples groups 2 MEFs-D8H (inclusive), D8H-D18H (inclusive) or D8H-iPSC (including D8H, D16L, D21L, D21, 2 IPSCs and 1 IPSCs). Identifier mirnas with high expression in ipscs have a white background and mirnas with high expression in F-class have a blue background. Individual mature mirnas are coloured as in panel (a) and the red line indicates median level of correlation. P- values were determined using a two tailed, heteroscedastic t-test. (d), Pearsons correlation of mature mirnas with their known, experimentally validated protein targets shown in Fig. 6b&c (where protein data was available). A random background for each trajectory was created by randomly assigning mirna-protein pairings (>10,000). Dots represent individual mirna:protein expression correlations and background density plot shows distribution of correlations (as a percentage). As expected, most high ipsc expressing mirnas were negatively correlated with their targets in the ipsc trajectory (as this was a factor in the original discovery of their targeting interaction; samples included 2 MEFs-D8H, 2 IPSCs, 1 IPSCs and ESCs). Interestingly, for the mirnas highly expressed in the F-class mirnas were mostly anti-correlated or strongly correlated with targets in the F-class trajectory (samples included 2 MEFs-D18H). For the latter, ChIP sequencing data from Chen et al., 2008 suggests that mir-186, Cdk2 and Cdk6 are regulated by Klf4, mir-18 and Smad4 are regulated by Myc and Oct4, while mir-138 is proposed to be in a feed forward loop with Myc, regulating many of its target including Ccnd3 15. Transcription factor co-regulation of mirnas with their targets is thought to be a common regulatory method to contain the upper limits of transcriptional stimulation (termed an incoherent feed-forward loop 16, and highlights the complexity of mirna targeting relationships in reprogramming. [target interactions included for ipsc trajectory analysis: mir-302 and Cdk2, Mecp2, TgfBr2, Ccnd2, Ccnd1,Cdkn1a, Cdk4; mir-290/295 and Rb1, Casp2, Ei24, cdkn1a; Mir-106a/363 and Cdkn1a. Target interactions included for F-class trajectory analysis: mir-138 and Ccnd3, Trp53; mir-186 and Cdk6 and Cdk2; mir-342 and Dmnt1; mir-298 and Cdkn1a; mir-296 and Cdkn1a; mir-10b and Cdkn1a; mir-124 and Stat3; mir-18a and Smad4, Tgfbr2; mir-20a and cdkn1a, Tgfbr2, Ccnd1, Ccnd2 smad4; mir-17 and Tgfbr2, Ccnd1, Cdkn1a, Ccnd2, Smad4 11 ].

21 Supplementary Methods Mapping protocol for total genome analysis Mapping to the genome: High quality tags were mapped such that one random mapping position was selected for tags mapping to more than 1 locations by invoking the M 1 option in Bowtie command. This set of mapping referred to as genomic alignments was used for counting tag distribution in various genomic features. Bowtie Command, Genomic alignments: bowtie -f -C - Q input.qv.qual --integer-quals -l 20 --nomaqround --maxbts tryhard --chunkmbs M 1 -a --best --strata --snpfrac col-cqual --colkeepends --sam --mapq 20 --offrate 2 --threads 12 --shmem reference_bowtie_colorspace_index input.csfasta >output.sam Assigning tags to genomic features: We used the genomic alignments to determine the proportion of tags mapping to various genomic features. Coordinates for genomic features such as protein coding loci, non-coding RNA loci and genomic repeats (e.g. LINE, SINE, simple repeats) were obtained from Ensembl v67.additionally, mirbase v18 annotations were used for mirna loci, pirna coordinates were obtained from the pirnabank 17 and protrac 18,and 18/28S rrna were obtained from the GenBank. A custom BAM file parser was designed (Supplementary Software 3) by using the BamTools API 19 to identify the number of tags that overlap each genomic feature. Tags were preferentially assigned to mirna loci even if multiple, overlapping genomic features were present for that locus. If the mapped position of the tag overlapped a non-mirna locus with multiple, overlapping genomic features, the tag was randomly assigned to one genomic feature. Mitochondrial genome-focused analysis: We included only uniquely mapping tags in the analysis of mitochondria-derived tags for increased specificity (shown in Supplementary Fig. 2). Normalisation: Tag numbers assigned to each genomic feature (or mitochondrial locus) were normalised to library size and expressed as CPM. Analysis of mitochondrial gene expression in long RNA sequencing libraries: RNA was prepared as described 2, with no depletion of mitochondrial rrna. Sequence mapping was performed using Applied Biosystems LifeScope v2.5 whole transcriptome (paired-end) analysis pipeline against the NCBIM37 (mm9) genome and exon-junction libraries constructed from the Ensembl v64 gene model. This pipeline first removes potential contaminant reads by aligning to a filter set containing nuclear rrna, nuclear trna, adaptor sequences and retrotransposon sequences. Following filtering, LifeScope then aligns all reads to the genome and exon-junction library. Mapping protocol for mirna-focused analysis Mature mirnas derived from multiple genomic loci can be identical in sequence. Therefore randomly assigning tags to all of the identical loci can effectively reduce the number of tags assigned to each individual locus. This random assignment can affect the downstream expression analysis. Therefore, we chose to assign tags to all of the identical loci to retain the true levels of tag counts observed for any mature mirna in our data. To assign the number of tags at each mirna locus, tags were allowed to map to up to 20 locations. If a tag mapped equally to more than 20 locations, one location was randomly chosen. These set of alignments are referred to as mirna alignments from here on. Bowtie Command, mirna alignments:

22 bowtie -f -C - Q input.qv.qual --integer-quals -l 20 --nomaqround --maxbts tryhard --chunkmbs M 20 -a --best --strata --snpfrac col-cqual -- col-keepends --sam --mapq 20 --offrate 2 --threads 12 --shmem reference_bowtie_colorspace_index input.csfasta >output.sam Assigning tags to mirna loci: Tags were assigned to a mature mirna if their 5 mapped position was within +/-3nt of the mirbase annotated 5 start site and were between 20 26nt in length inclusive. These mature mirna tags are referred as mirna tags from here on. Tag count information tables were generated by using Supplementary Software 4. These tables were then loaded into MySQL database. Using Supplementary Software 5, which used the Supplementary Software 6 and MySQL database as input, final counts tables were generated. Normalisation: mirna tag counts for each mature mirna were first normalized against the total mirna mapped tags in each library to correct for library size differences and counts per million (CPM) were obtained. Additionally, tag counts were also normalized for the total RNA output by the trimmed mean of M-value (TMM) scaling method described in ref. 20 to obtain normalized CPM (ncpm), which were used for all expression analysis. Datasets were normalized in three batches for the following analyses: (a) core samples, 2 MEFs, D2H, D5H, D8H, D11H, D16H, D18H high doxycycline treated cells, ESCs, 1 ipscs, 2 ipscs, D16L, D21L and D21 (Fig 1-6, Supplementary Figs. 3-7,9-11, Supplementary Data 1); (b) Day0-4 focused analysis, 2 MEF E, D1H E, D1H E, D2H E, D3H E, D4H E (second sequencing run only) (Supplementary Fig. 8, Supplementary Data 2); (c) clone 1 and core samples, which is (a) plus clone 1, day 30 libraries (Supplementary Fig. 12, Supplementary Data 1: clone 1 sheets). Analysis of isomirs, arm bias, non-templated addition and editing frequencies. This analysis was restricted to mature mirnas or hairpins that had >25 average ncpm in either (a) core samples or (b) Day 0-4 focused analysis. isomirs: mirbase v18 annotated start and end positions for a mature mirna are considered as canonical start and canonical end positions, respectively, for this analysis. mirna tags differing in their 5 start positions compared to the canonical start position were considered as 5 isomirs and those that differed at 3 end positions compared to the canonical end position were considered as 3 isomirs. The percentage of canonical mirna tags for each mature mirna in each sample was calculated with respect to total tags for that mirna, which includes all 5 /3 isomirs, with 1 st and 3 rd quartiles. Arm Bias: The percentage of mirna tags derived from the 5 arm of the hairpin was determined for all mirnas in all samples. The median percentage of 5 arm-derived mirnas for each hairpin was then calculated with 1 st and 3 rd quartiles. Non-templated addition (NTA): mirna tags with a nucleotide mismatch in the last two nucleotides of tags were considered as tags containing NTA. For each mature mirna the number of tags with NTA was recorded as well as the identity of the non-templated base. The percentage of NTA tags was then determined relative to the total mirna tags for a given mature mirna. The median percentage of NTA was then calculated with 1 st and 3 rd quartiles for all mirnas. Note, only NTA distinct from the genomic template can be assessed and thus the true prevalence of NTA is likely

23 under-represented. NTA for individual mirnas can be visualized in associated mirspring 1 documents (Supplementary Data 3-21). Internal Editing: mirna tags with a nucleotide mismatch a) before the last two nucleotides of canonical end position and b) before the last two nucleotides of the tag were considered as tags containing possible editing event. For each mature mirna the number of tags with possible editing event as well as the identity of the base substitution was determined. The median percentage of internally edited micrornas was then calculated with 1 st and 3 rd quartiles. Supplementary References 1. Humphreys, D. T. & Suter, C. M. mirspring: a compact standalone research tool for analyzing mirna-seq data. Nucleic Acids Res. 41, e147, doi: /nar/gkt485 (2013). 2. Hussein, S. M. I. et al. Routes to induced pluripotency: A genome wide, multiple omics characterization. Submitted (2014). 3. Mercer, T. R. et al. The human mitochondrial transcriptome. Cell 146, , doi: /j.cell (2011). 4. Small, I. D., Rackham, O. & Filipovska, A. Organelle transcriptomes: products of a deconstructed genome. Curr. Opin. Microbiol. 16, , doi: /j.mib (2013). 5. Chang, D. D. & Clayton, D. A. Priming of human mitochondrial DNA replication occurs at the light-strand promoter. Proc. Natl. Acad. Sci. U. S. A. 82, (1985). 6. Heo, I. et al. Mono-uridylation of pre-microrna as a key step in the biogenesis of group II let-7 micrornas. Cell 151, , doi: /j.cell (2012). 7. Zheng, G. X. Y. et al. A latent pro-survival function for the mir cluster in mouse embryonic stem cells. PLoS Genet. 7, e , doi: /journal.pgen (2011). 8. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44-57, doi: /nprot (2009). 9. Yagita, K. et al. Development of the circadian oscillator during differentiation of mouse embryonic stem cells in vitro. Proc. Natl. Acad. Sci. U. S. A. 107, , doi: /pnas (2010). 10. Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51, , doi: / (2002). 11. Hsu, S. D. et al. mirtarbase: a database curates experimentally validated microrna-target interactions. Nucleic Acids Res. 39, D , doi: /nar/gkq1107 (2011). 12. Lee, D. et al. DNA methylation as a reprogramming modulator: An epigenomic roadmap to induced pluripotency. Submitted (2014). 13. Tonge, P. D. et al. Divergent reprogramming routes lead to alternative stem cell states. Submitted (2014). 14. Sato, S., Yoshida, W., Soejima, H., Nakabayashi, K. & Hata, K. Methylation dynamics of IG- DMR and Gtl2-DMR during murine embryonic and placental development. Genomics 98, , doi: /j.ygeno (2011). 15. Poos, K. et al. How microrna and transcription factor co-regulatory networks affect osteosarcoma cell proliferation. PLoS Comput. Biol. 9, e , doi: /journal.pcbi (2013). 16. Ebert, M. S. & Sharp, P. A. Roles for micrornas in conferring robustness to biological processes. Cell 149, , doi: /j.cell (2012). 17. Sai Lakshmi, S. & Agrawal, S. pirnabank: a web resource on classified and clustered Piwiinteracting RNAs. Nucleic Acids Res. 36, D , doi: /nar/gkm696 (2008).

24 18. Rosenkranz, D. & Zischler, H. protrac--a software for probabilistic pirna cluster detection, visualization and analysis. BMC Bioinformatics 13, 5, doi: / (2012). 19. Barnett, D. W., Garrison, E. K., Quinlan, A. R., Stromberg, M. P. & Marth, G. T. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics 27, , doi: /bioinformatics/btr174 (2011). 20. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25, doi: /gb r25 (2010).